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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 17 Nov 2010 09:04:39 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/17/t12899849862wg0yilynfjlce6.htm/, Retrieved Thu, 20 Feb 2025 19:22:54 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=96541, Retrieved Thu, 20 Feb 2025 19:22:54 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

911

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
-   PD    [Multiple Regression] [W7 month] [2010-11-19 11:08:20] [56d90b683fcd93137645f9226b43c62b] 
-   PD      [Multiple Regression] [W7 Trend zonder m...] [2010-11-21 09:20:47] [56d90b683fcd93137645f9226b43c62b] 
-   P       [Multiple Regression] [W7 month en trend] [2010-11-21 09:24:35] [56d90b683fcd93137645f9226b43c62b] 
-   PD    [Multiple Regression] [run sequence plot...] [2010-11-19 12:01:57] [74deae64b71f9d77c839af86f7c687b5] 
-   PD      [Multiple Regression] [] [2010-11-23 22:02:00] [8ef75e99f9f5061c72c54640f2f1c3e7] 
- R PD      [Multiple Regression] [] [2011-11-21 20:08:18] [46d7ccc24e5d35a2decd922dfb3b3a39] 
- R PD      [Multiple Regression] [] [2011-11-21 20:10:27] [46d7ccc24e5d35a2decd922dfb3b3a39] 
- R  D      [Multiple Regression] [] [2011-11-22 09:13:46] [74be16979710d4c4e7c6647856088456] 
-   PD    [Multiple Regression] [] [2010-11-19 12:02:47] [7789b9488494790f41ddb7f073cada1b] 
-   PD    [Multiple Regression] [maand] [2010-11-19 12:05:06] [8a9a6f7c332640af31ddca253a8ded58] 
-   PD    [Multiple Regression] [run sequence plot...] [2010-11-19 12:05:06] [8a9a6f7c332640af31ddca253a8ded58] 
-   PD    [Multiple Regression] [workshop 7 - tuto...] [2010-11-19 12:52:07] [956e8df26b41c50d9c6c2ec1b6a122a8] 
- R         [Multiple Regression] [workshop 7 month] [2010-11-19 13:04:45] [4eaa304e6a28c475ba490fccf4c01ad3] 
-    D      [Multiple Regression] [WS7 comp 3] [2010-11-23 09:24:05] [dc30d19c3bc2be07fe595ad36c2cf923] 
-             [Multiple Regression] [ws 7] [2010-11-23 18:43:19] [4f85667043e8913570b3eb8f368f82b2] 
-             [Multiple Regression] [] [2010-12-02 15:15:07] [2e1e44f0ae3cb9513dc28781dfdb387b] 
- R  D          [Multiple Regression] [ws7] [2011-11-22 17:17:07] [7e261c986c934df955dd3ac53e9d45c6] 
- R  D          [Multiple Regression] [WS 7 ] [2011-11-24 16:46:00] [65d03b877b3f337979d6af245efa927d] 
-             [Multiple Regression] [] [2010-12-03 17:44:36] [b07cd1964830aab808142229b1166ece] 
-   PD    [Multiple Regression] [Workshop 7 - Incl...] [2010-11-19 12:30:30] [6f0e7a2d1a07390e3505a2db8288f975] 
-   P       [Multiple Regression] [W7] [2010-11-23 16:32:56] [5ddc7dfb25e070b079c4c8fcccc4d42e] 
- R PD    [Multiple Regression] [Workshop 7 - Regr...] [2010-11-19 12:57:20] [8b017ffbf7b0eded54d8efebfb3e4cfa] 
-   P       [Multiple Regression] [Workshop 7 - Regr...] [2010-11-19 13:07:16] [8b017ffbf7b0eded54d8efebfb3e4cfa] 
-   P         [Multiple Regression] [workshop 7 determ...] [2010-11-19 14:10:30] [4eaa304e6a28c475ba490fccf4c01ad3] 
-             [Multiple Regression] [workshop 7 multip...] [2010-11-23 14:08:09] [af8eb90b4bf1bcfcc4325c143dbee260] 
- RM          [Multiple Regression] [fdbvdfb] [2011-11-20 13:58:58] [a9671b130b33f9fcb98554992ce4582f] 
-           [Multiple Regression] [workshop 7 multip...] [2010-11-23 14:03:11] [af8eb90b4bf1bcfcc4325c143dbee260] 
- RM        [Multiple Regression] [fhfhg] [2011-11-20 13:58:17] [a9671b130b33f9fcb98554992ce4582f] 
-   PD    [Multiple Regression] [ws7] [2010-11-20 15:19:19] [c7506ced21a6c0dca45d37c8a93c80e0] 
-           [Multiple Regression] [] [2010-11-23 20:56:56] [427b5b758ea29b39381b0cf845587015] 
-   PD    [Multiple Regression] [WS7 - popularitei...] [2010-11-20 16:26:09] [8ef49741e164ec6343c90c7935194465] 
-   P       [Multiple Regression] [WS7 - Determinist...] [2010-11-20 18:14:50] [8ef49741e164ec6343c90c7935194465] 
-    D        [Multiple Regression] [ws 7] [2010-11-23 20:49:21] [8214fe6d084e5ad7598b249a26cc9f06] 
-   PD    [Multiple Regression] [Ws 7 - Daginvloed] [2010-11-21 15:59:47] [603e2f5305d3a2a4e062624458fa1155] 
-    D      [Multiple Regression] [PAPER - maandinvloed] [2010-12-19 15:48:17] [603e2f5305d3a2a4e062624458fa1155] 
-    D        [Multiple Regression] [bbb] [2010-12-19 16:21:48] [74be16979710d4c4e7c6647856088456] 
-               [Multiple Regression] [Multiple Regressi...] [2010-12-19 16:38:07] [8ef49741e164ec6343c90c7935194465] 
- R PD    [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:55:52] [afe9379cca749d06b3d6872e02cc47ed] 
-           [Multiple Regression] [] [2010-11-23 10:29:39] [3fb95cad3bbcce10c72dbbcc5bec5662] 
-   PD      [Multiple Regression] [WS7 - Multiple re...] [2010-11-23 20:19:24] [1f5baf2b24e732d76900bb8178fc04e7] 
-    D        [Multiple Regression] [WS7 - Multiple re...] [2010-11-23 20:47:17] [1f5baf2b24e732d76900bb8178fc04e7] 
- RM            [Multiple Regression] [] [2011-11-23 00:30:44] [19d77e37efa419fdc040c74a96874aff] 
-   PD      [Multiple Regression] [verbetering ] [2010-11-28 15:38:22] [8214fe6d084e5ad7598b249a26cc9f06] 
-   PD      [Multiple Regression] [] [2010-12-02 19:12:54] [94f4aa1c01e87d8321fffb341ed4df07] 
- R           [Multiple Regression] [] [2011-11-25 00:51:26] [74be16979710d4c4e7c6647856088456] 
- R P           [Multiple Regression] [] [2011-11-27 17:12:25] [3931071255a6f7f4a767409781cc5f7d] 
-   PD      [Multiple Regression] [minitutorial ws 4] [2010-12-03 15:39:39] [e4afca2801c0b93eac84a600ed82fb9c] 
-             [Multiple Regression] [minitutorial ws 4] [2010-12-03 15:46:44] [e4afca2801c0b93eac84a600ed82fb9c] 

[Truncated]

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Dataseries X:

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Boxplots

9	41	38	13	12	14	12	53	32
9	39	32	16	11	18	11	83	51
9	30	35	19	15	11	14	66	42
9	31	33	15	6	12	12	67	41
9	34	37	14	13	16	21	76	46
9	35	29	13	10	18	12	78	47
9	39	31	19	12	14	22	53	37
9	34	36	15	14	14	11	80	49
9	36	35	14	12	15	10	74	45
9	37	38	15	9	15	13	76	47
9	38	31	16	10	17	10	79	49
9	36	34	16	12	19	8	54	33
9	38	35	16	12	10	15	67	42
9	39	38	16	11	16	14	54	33
9	33	37	17	15	18	10	87	53
9	32	33	15	12	14	14	58	36
9	36	32	15	10	14	14	75	45
9	38	38	20	12	17	11	88	54
9	39	38	18	11	14	10	64	41
9	32	32	16	12	16	13	57	36
9	32	33	16	11	18	9.5	66	41
9	31	31	16	12	11	14	68	44
9	39	38	19	13	14	12	54	33
9	37	39	16	11	12	14	56	37
9	39	32	17	12	17	11	86	52
9	41	32	17	13	9	9	80	47
9	36	35	16	10	16	11	76	43
9	33	37	15	14	14	15	69	44
9	33	33	16	12	15	14	78	45
9	34	33	14	10	11	13	67	44
9	31	31	15	12	16	9	80	49
9	27	32	12	8	13	15	54	33
9	37	31	14	10	17	10	71	43
9	34	37	16	12	15	11	84	54
9	34	30	14	12	14	13	74	42
9	32	33	10	7	16	8	71	44
9	29	31	10	9	9	20	63	37
9	36	33	14	12	15	12	71	43
9	29	31	16	10	17	10	76	46
9	35	33	16	10	13	10	69	42
9	37	32	16	10	15	9	74	45
9	34	33	14	12	16	14	75	44
9	38	32	20	15	16	8	54	33
9	35	33	14	10	12	14	52	31
9	38	28	14	10	15	11	69	42
9	37	35	11	12	11	13	68	40
9	38	39	14	13	15	9	65	43
9	33	34	15	11	15	11	75	46
9	36	38	16	11	17	15	74	42
9	38	32	14	12	13	11	75	45
9	32	38	16	14	16	10	72	44
9	32	30	14	10	14	14	67	40
9	32	33	12	12	11	18	63	37
9	34	38	16	13	12	14	62	46
9	32	32	9	5	12	11	63	36
9	37	35	14	6	15	14.5	76	47
9	39	34	16	12	16	13	74	45
9	29	34	16	12	15	9	67	42
9	37	36	15	11	12	10	73	43
9	35	34	16	10	12	15	70	43
9	30	28	12	7	8	20	53	32
9	38	34	16	12	13	12	77	45
9	34	35	16	14	11	12	80	48
9	31	35	14	11	14	14	52	31
9	34	31	16	12	15	13	54	33
10	35	37	17	13	10	11	80	49
10	36	35	18	14	11	17	66	42
10	30	27	18	11	12	12	73	41
10	39	40	12	12	15	13	63	38
10	35	37	16	12	15	14	69	42
10	38	36	10	8	14	13	67	44
10	31	38	14	11	16	15	54	33
10	34	39	18	14	15	13	81	48
10	38	41	18	14	15	10	69	40
10	34	27	16	12	13	11	84	50
10	39	30	17	9	12	19	80	49
10	37	37	16	13	17	13	70	43
10	34	31	16	11	13	17	69	44
10	28	31	13	12	15	13	77	47
10	37	27	16	12	13	9	54	33
10	33	36	16	12	15	11	79	46
10	35	37	16	12	15	9	71	45
10	37	33	15	12	16	12	73	43
10	32	34	15	11	15	12	72	44
10	33	31	16	10	14	13	77	47
10	38	39	14	9	15	13	75	45
10	33	34	16	12	14	12	69	42
10	29	32	16	12	13	15	54	33
10	33	33	15	12	7	22	70	43
10	31	36	12	9	17	13	73	46
10	36	32	17	15	13	15	54	33
10	35	41	16	12	15	13	77	46
10	32	28	15	12	14	15	82	48
10	29	30	13	12	13	12.5	80	47
10	39	36	16	10	16	11	80	47
10	37	35	16	13	12	16	69	43
10	35	31	16	9	14	11	78	46
10	37	34	16	12	17	11	81	48
10	32	36	14	10	15	10	76	46
10	38	36	16	14	17	10	76	45
10	37	35	16	11	12	16	73	45
10	36	37	20	15	16	12	85	52
10	32	28	15	11	11	11	66	42
10	33	39	16	11	15	16	79	47
10	40	32	13	12	9	19	68	41
10	38	35	17	12	16	11	76	47
10	41	39	16	12	15	16	71	43
10	36	35	16	11	10	15	54	33
10	43	42	12	7	10	24	46	30
10	30	34	16	12	15	14	85	52
10	31	33	16	14	11	15	74	44
10	32	41	17	11	13	11	88	55
10	32	33	13	11	14	15	38	11
10	37	34	12	10	18	12	76	47
10	37	32	18	13	16	10	86	53
10	33	40	14	13	14	14	54	33
10	34	40	14	8	14	13	67	44
10	33	35	13	11	14	9	69	42
10	38	36	16	12	14	15	90	55
10	33	37	13	11	12	15	54	33
10	31	27	16	13	14	14	76	46
10	38	39	13	12	15	11	89	54
10	37	38	16	14	15	8	76	47
10	36	31	15	13	15	11	73	45
10	31	33	16	15	13	11	79	47
10	39	32	15	10	17	8	90	55
10	44	39	17	11	17	10	74	44
10	33	36	15	9	19	11	81	53
10	35	33	12	11	15	13	72	44
10	32	33	16	10	13	11	71	42
10	28	32	10	11	9	20	66	40
10	40	37	16	8	15	10	77	46
10	27	30	12	11	15	15	65	40
10	37	38	14	12	15	12	74	46
10	32	29	15	12	16	14	85	53
10	28	22	13	9	11	23	54	33
10	34	35	15	11	14	14	63	42
10	30	35	11	10	11	16	54	35
10	35	34	12	8	15	11	64	40
10	31	35	11	9	13	12	69	41
10	32	34	16	8	15	10	54	33
10	30	37	15	9	16	14	84	51
10	30	35	17	15	14	12	86	53
10	31	23	16	11	15	12	77	46
10	40	31	10	8	16	11	89	55
10	32	27	18	13	16	12	76	47
10	36	36	13	12	11	13	60	38
10	32	31	16	12	12	11	75	46
10	35	32	13	9	9	19	73	46
10	38	39	10	7	16	12	85	53
10	42	37	15	13	13	17	79	47
10	34	38	16	9	16	9	71	41
10	35	39	16	6	12	12	72	44
9	38	34	14	8	9	19	69	43
10	33	31	10	8	13	18	78	51
10	36	32	17	15	13	15	54	33
10	32	37	13	6	14	14	69	43
10	33	36	15	9	19	11	81	53
10	34	32	16	11	13	9	84	51
10	32	38	12	8	12	18	84	50
10	34	36	13	8	13	16	69	46
11	27	26	13	10	10	24	66	43
11	31	26	12	8	14	14	81	47
11	38	33	17	14	16	20	82	50
11	34	39	15	10	10	18	72	43
11	24	30	10	8	11	23	54	33
11	30	33	14	11	14	12	78	48
11	26	25	11	12	12	14	74	44
11	34	38	13	12	9	16	82	50
11	27	37	16	12	9	18	73	41
11	37	31	12	5	11	20	55	34
11	36	37	16	12	16	12	72	44
11	41	35	12	10	9	12	78	47
11	29	25	9	7	13	17	59	35
11	36	28	12	12	16	13	72	44
11	32	35	15	11	13	9	78	44
11	37	33	12	8	9	16	68	43
11	30	30	12	9	12	18	69	41
11	31	31	14	10	16	10	67	41
11	38	37	12	9	11	14	74	42
11	36	36	16	12	14	11	54	33
11	35	30	11	6	13	9	67	41
11	31	36	19	15	15	11	70	44
11	38	32	15	12	14	10	80	48
11	22	28	8	12	16	11	89	55
11	32	36	16	12	13	19	76	44
11	36	34	17	11	14	14	74	43
11	39	31	12	7	15	12	87	52
11	28	28	11	7	13	14	54	30
11	32	36	11	5	11	21	61	39
11	32	36	14	12	11	13	38	11
11	38	40	16	12	14	10	75	44
11	32	33	12	3	15	15	69	42
11	35	37	16	11	11	16	62	41
11	32	32	13	10	15	14	72	44
11	37	38	15	12	12	12	70	44
11	34	31	16	9	14	19	79	48
11	33	37	16	12	14	15	87	53
11	33	33	14	9	8	19	62	37
11	26	32	16	12	13	13	77	44
11	30	30	16	12	9	17	69	44
11	24	30	14	10	15	12	69	40
11	34	31	11	9	17	11	75	42
11	34	32	12	12	13	14	54	35
11	33	34	15	8	15	11	72	43
11	34	36	15	11	15	13	74	45
11	35	37	16	11	14	12	85	55
11	35	36	16	12	16	15	52	31
11	36	33	11	10	13	14	70	44
11	34	33	15	10	16	12	84	50
11	34	33	12	12	9	17	64	40
11	41	44	12	12	16	11	84	53
11	32	39	15	11	11	18	87	54
11	30	32	15	8	10	13	79	49
11	35	35	16	12	11	17	67	40
11	28	25	14	10	15	13	65	41
11	33	35	17	11	17	11	85	52
11	39	34	14	10	14	12	83	52
11	36	35	13	8	8	22	61	36
11	36	39	15	12	15	14	82	52
11	35	33	13	12	11	12	76	46
11	38	36	14	10	16	12	58	31
11	33	32	15	12	10	17	72	44
11	31	32	12	9	15	9	72	44
11	34	36	13	9	9	21	38	11
11	32	36	8	6	16	10	78	46
11	31	32	14	10	19	11	54	33
11	33	34	14	9	12	12	63	34
11	34	33	11	9	8	23	66	42
11	34	35	12	9	11	13	70	43
11	34	30	13	6	14	12	71	43
11	33	38	10	10	9	16	67	44
11	32	34	16	6	15	9	58	36
11	41	33	18	14	13	17	72	46
11	34	32	13	10	16	9	72	44
11	36	31	11	10	11	14	70	43
11	37	30	4	6	12	17	76	50
11	36	27	13	12	13	13	50	33
11	29	31	16	12	10	11	72	43
11	37	30	10	7	11	12	72	44
11	27	32	12	8	12	10	88	53
11	35	35	12	11	8	19	53	34
11	28	28	10	3	12	16	58	35
11	35	33	13	6	12	16	66	40
11	37	31	15	10	15	14	82	53
11	29	35	12	8	11	20	69	42
11	32	35	14	9	13	15	68	43
11	36	32	10	9	14	23	44	29
11	19	21	12	8	10	20	56	36
11	21	20	12	9	12	16	53	30
11	31	34	11	7	15	14	70	42
11	33	32	10	7	13	17	78	47
11	36	34	12	6	13	11	71	44
11	33	32	16	9	13	13	72	45
11	37	33	12	10	12	17	68	44
11	34	33	14	11	12	15	67	43
11	35	37	16	12	9	21	75	43
11	31	32	14	8	9	18	62	40
11	37	34	13	11	15	15	67	41
11	35	30	4	3	10	8	83	52
11	27	30	15	11	14	12	64	38
11	34	38	11	12	15	12	68	41
11	40	36	11	7	7	22	62	39
11	29	32	14	9	14	12	72	43

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	16 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 16 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96541&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]16 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96541&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96541&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	16 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 8.35046999717002 -0.40459931201988month[t] + 0.0347834365471902Connected[t] + 0.0433406498517765Separate[t] + 0.576062196352003Software[t] + 0.0796225833189896Happiness[t] -0.0261020091912720Depression[t] + 0.0282902834264182Belonging[t] -0.0326511809003049Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  8.35046999717002 -0.40459931201988month[t] +  0.0347834365471902Connected[t] +  0.0433406498517765Separate[t] +  0.576062196352003Software[t] +  0.0796225833189896Happiness[t] -0.0261020091912720Depression[t] +  0.0282902834264182Belonging[t] -0.0326511809003049Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96541&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  8.35046999717002 -0.40459931201988month[t] +  0.0347834365471902Connected[t] +  0.0433406498517765Separate[t] +  0.576062196352003Software[t] +  0.0796225833189896Happiness[t] -0.0261020091912720Depression[t] +  0.0282902834264182Belonging[t] -0.0326511809003049Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96541&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96541&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 8.35046999717002 -0.40459931201988month[t] + 0.0347834365471902Connected[t] + 0.0433406498517765Separate[t] + 0.576062196352003Software[t] + 0.0796225833189896Happiness[t] -0.0261020091912720Depression[t] + 0.0282902834264182Belonging[t] -0.0326511809003049Belonging_Final[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	8.35046999717002	2.61698	3.1909	0.001596	0.000798
month	-0.40459931201988	0.159517	-2.5364	0.011797	0.005898
Connected	0.0347834365471902	0.034756	1.0008	0.317879	0.15894
Separate	0.0433406498517765	0.03528	1.2285	0.220402	0.110201
Software	0.576062196352003	0.052771	10.9163	0	0
Happiness	0.0796225833189896	0.057845	1.3765	0.169879	0.08494
Depression	-0.0261020091912720	0.042437	-0.6151	0.53905	0.269525
Belonging	0.0282902834264182	0.037766	0.7491	0.454497	0.227248
Belonging_Final	-0.0326511809003049	0.056098	-0.582	0.561053	0.280527

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 8.35046999717002 & 2.61698 & 3.1909 & 0.001596 & 0.000798 \tabularnewline
month & -0.40459931201988 & 0.159517 & -2.5364 & 0.011797 & 0.005898 \tabularnewline
Connected & 0.0347834365471902 & 0.034756 & 1.0008 & 0.317879 & 0.15894 \tabularnewline
Separate & 0.0433406498517765 & 0.03528 & 1.2285 & 0.220402 & 0.110201 \tabularnewline
Software & 0.576062196352003 & 0.052771 & 10.9163 & 0 & 0 \tabularnewline
Happiness & 0.0796225833189896 & 0.057845 & 1.3765 & 0.169879 & 0.08494 \tabularnewline
Depression & -0.0261020091912720 & 0.042437 & -0.6151 & 0.53905 & 0.269525 \tabularnewline
Belonging & 0.0282902834264182 & 0.037766 & 0.7491 & 0.454497 & 0.227248 \tabularnewline
Belonging_Final & -0.0326511809003049 & 0.056098 & -0.582 & 0.561053 & 0.280527 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96541&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]8.35046999717002[/C][C]2.61698[/C][C]3.1909[/C][C]0.001596[/C][C]0.000798[/C][/ROW]
[ROW][C]month[/C][C]-0.40459931201988[/C][C]0.159517[/C][C]-2.5364[/C][C]0.011797[/C][C]0.005898[/C][/ROW]
[ROW][C]Connected[/C][C]0.0347834365471902[/C][C]0.034756[/C][C]1.0008[/C][C]0.317879[/C][C]0.15894[/C][/ROW]
[ROW][C]Separate[/C][C]0.0433406498517765[/C][C]0.03528[/C][C]1.2285[/C][C]0.220402[/C][C]0.110201[/C][/ROW]
[ROW][C]Software[/C][C]0.576062196352003[/C][C]0.052771[/C][C]10.9163[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0796225833189896[/C][C]0.057845[/C][C]1.3765[/C][C]0.169879[/C][C]0.08494[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0261020091912720[/C][C]0.042437[/C][C]-0.6151[/C][C]0.53905[/C][C]0.269525[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0282902834264182[/C][C]0.037766[/C][C]0.7491[/C][C]0.454497[/C][C]0.227248[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0326511809003049[/C][C]0.056098[/C][C]-0.582[/C][C]0.561053[/C][C]0.280527[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96541&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96541&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	8.35046999717002	2.61698	3.1909	0.001596	0.000798
month	-0.40459931201988	0.159517	-2.5364	0.011797	0.005898
Connected	0.0347834365471902	0.034756	1.0008	0.317879	0.15894
Separate	0.0433406498517765	0.03528	1.2285	0.220402	0.110201
Software	0.576062196352003	0.052771	10.9163	0	0
Happiness	0.0796225833189896	0.057845	1.3765	0.169879	0.08494
Depression	-0.0261020091912720	0.042437	-0.6151	0.53905	0.269525
Belonging	0.0282902834264182	0.037766	0.7491	0.454497	0.227248
Belonging_Final	-0.0326511809003049	0.056098	-0.582	0.561053	0.280527

Multiple Linear Regression - Regression Statistics
Multiple R	0.664098147499979
R-squared	0.441026349512904
Adjusted R-squared	0.423489921262328
F-TEST (value)	25.1491548456242
F-TEST (DF numerator)	8
F-TEST (DF denominator)	255
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.86477720145105
Sum Squared Residuals	886.735472818163

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.664098147499979 \tabularnewline
R-squared & 0.441026349512904 \tabularnewline
Adjusted R-squared & 0.423489921262328 \tabularnewline
F-TEST (value) & 25.1491548456242 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 255 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.86477720145105 \tabularnewline
Sum Squared Residuals & 886.735472818163 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96541&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.664098147499979[/C][/ROW]
[ROW][C]R-squared[/C][C]0.441026349512904[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.423489921262328[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]25.1491548456242[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]255[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.86477720145105[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]886.735472818163[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96541&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96541&T=3

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R	0.664098147499979
R-squared	0.441026349512904
Adjusted R-squared	0.423489921262328
F-TEST (value)	25.1491548456242
F-TEST (DF numerator)	8
F-TEST (DF denominator)	255
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.86477720145105
Sum Squared Residuals	886.735472818163

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	15.9509274269776	-2.95092742697758
2	16	15.6181828665754	0.381817133424643
3	19	16.9166643716608	2.08333562833916
4	15	11.8729748073647	3.12702519263529
5	14	16.3580519877682	-2.35805198776815
6	13	14.7360162717571	-1.73601627175708
7	19	15.1537000085073	3.84629999149269
8	15	17.0077560505479	-2.00775605054788
9	14	15.9484454966395	-1.94844549663945
10	15	14.2980364711644	0.701963528835628
11	16	14.8626172377916	1.13738276220843
12	16	16.1018077007215	-0.101807700721472
13	16	15.3893109658985	0.61068903410149
14	16	15.4079786083135	0.592021391686455
15	17	18.0043950630554	-1.00439506305541
16	15	15.3798159239431	-0.379815923943112
17	15	14.5105588177225	0.489441182277545
18	20	16.3833808166030	3.61661918339702
19	18	15.3748348655024	2.62516513449760
20	16	15.4935321664942	0.506467833505832
21	16	15.3027694651376	0.697230534862387
22	16	15.0411768247971	0.958823175202857
23	19	16.4530618527621	2.54693814723788
24	16	14.9892378950466	1.01076210495340
25	17	16.1668421489873	0.833157851012718
26	17	16.2212087742073	0.778791225792692
27	16	14.9717246067166	1.02827539328339
28	15	16.7639680138983	-1.7639680138983
29	16	15.7661669842349	0.233833015765089
30	14	14.0778957672031	-0.0778957672031147
31	15	15.7460272839639	-0.746027283963944
32	12	12.7373771224324	-0.737377122432354
33	14	14.7974186192349	-0.797418619234864
34	16	15.9285001202188	0.0714998797812121
35	14	15.6022003060943	-1.60220030609430
36	10	12.9219264013097	-2.92192640130971
37	10	13.0146729900312	-3.01467299003119
38	14	15.7899916900747	-1.78999169007471
39	16	14.5626490012885	1.43735099871148
40	16	14.4721133266155	1.52788667338445
41	16	14.7271846001186	1.27281539988142
42	14	15.8283533347221	-1.82835333472214
43	20	17.5740121132113	2.42598788678866
44	14	14.1663108781857	-0.166310878185714
45	14	14.4929035444449	-0.492903544444942
46	11	15.5799467762799	-4.57994677627988
47	14	16.6042289856471	-2.60422898564706
48	15	15.1942294341289	-0.194229434128941
49	16	15.6290939132253	0.37090608677469
50	14	15.7309335277757	-1.73093352777567
51	16	17.1471512900765	-1.14715129007648
52	14	14.2216774089203	-0.221677408920321
53	12	15.1453403734528	-3.14534037345284
54	16	15.8695524007130	0.130447599286978
55	9	11.3645521776952	-2.36455217769524
56	14	12.4006749187661	1.59932508123387
57	16	16.0107717121744	-0.0107717121744176
58	16	15.5876443588646	0.412355641135398
59	15	15.2486517151036	-0.248651715103637
60	16	14.3009604497181	1.69903955028191
61	12	11.5680405712374	0.431959428762605
62	16	15.8480933851408	0.151906614859215
63	16	16.7320968224482	-0.732096822448167
64	14	14.8491657946905	-0.849165794690488
65	16	15.4532184988394	0.546781501160557
66	17	15.786728295299	1.2132717047010
67	18	16.0663974549983	1.93360254500172
68	18	14.2236008420055	3.7763991579945
69	12	15.7039588645577	-3.70395886455774
70	16	15.4478381365797	0.552161863420335
71	10	13.0291955081803	-3.02919550818028
72	14	14.6990097947249	-0.699009794724867
73	18	16.8215387173465	1.17846128265351
74	18	17.0473858368980	0.952614163101954
75	16	15.0618538666444	0.93814613335562
76	17	13.2686578002251	3.73134219977488
77	16	16.2744534843814	-0.274453484381412
78	16	14.2740950485574	1.72590495144259
79	13	15.0334785537398	-2.03347855373977
80	16	14.9247697671811	1.07523023281887
81	16	15.5655347518703	0.434465248129714
82	16	15.5369752066879	0.463024793312053
83	15	15.5563789647738	-0.556378964773841
84	15	14.7091761878920	0.290823812108049
85	16	13.9756487604527	2.02435123954728
86	14	14.0085733239176	-0.00857332391764704
87	16	15.2208307489935	0.77916925100649
88	16	14.7065934689149	1.29340653108514
89	15	14.3547510265222	0.645248973477802
90	12	13.7050807374168	-1.70508073741682
91	17	16.6782641138012	0.321735886198799
92	16	15.7430202889882	0.256979711011831
93	15	15.0195639849035	-0.0195639849034504
94	13	14.9635980286721	-1.96359802867209
95	16	14.6973726642945	1.30262733570547
96	16	15.6830629570827	0.316937042917323
97	16	13.5822989199044	2.41770108009563
98	16	15.7685105700457	0.231489429954300
99	14	14.3198580815311	-0.319858081531108
100	16	17.0247038337605	-1.02470383376054
101	16	14.5787973362837	1.42120266371627
102	20	17.4687674897040	2.53123251029596
103	15	14.0523046259387	0.947695374061293
104	16	14.9563332782169	1.04366672178305
105	13	14.8011674216603	-1.80116742166032
106	17	15.6582118368939	1.34178816310611
107	16	15.7149454231363	0.285054576863653
108	16	14.2651695279916	1.73483047200845
109	12	12.1445025399444	-0.144502539944429
110	16	15.2700317301080	0.729968269891974
111	16	16.0190228965521	-0.0190228965520536
112	17	14.9728991243270	2.02710087567298
113	13	14.6235262603592	-1.62352626035921
114	12	14.5611065152376	-2.56110651523763
115	18	16.182566405197	1.81743359480300
116	14	15.8742392027801	-1.8742392027801
117	14	13.0634243613986	0.93657563860137
118	13	14.7664152300671	-1.7664152300671
119	16	15.57275380411	0.427246195889985
120	13	14.4067456846915	-1.40674568469151
121	16	15.4391647652899	0.56083523471014
122	13	15.8911672712233	-2.89116727122331
123	16	16.9042581868609	-0.904258186860862
124	15	15.8921534889468	-0.892153488946773
125	16	16.9022361707383	-0.9022361707383
126	15	14.7036320628420	0.296367937158032
127	17	15.6113104275905	1.38868957240952
128	15	13.9838607966409	1.01613920335906
129	12	14.7440838384905	-2.74408383849047
130	16	13.9936422626157	2.00635773738434
131	10	13.7576725915982	-3.75767259159823
132	16	13.5176321144828	2.48236788551718
133	12	14.2161631177914	-2.21616311779137
134	14	15.6237963714392	-1.62379637143924
135	15	15.1698667563622	-0.169866756362208
136	13	12.1421557046257	0.857844295374258
137	15	14.5009469200994	0.499053079900579
138	11	13.4686249246835	-2.46862492468351
139	12	13.0157243738587	-1.01572437385866
140	11	13.4194465342762	-2.41944653427621
141	16	12.8831315054463	3.11686849455368
142	15	13.7558505714002	1.24414942859978
143	17	17.0097795066055	-0.00977950660547738
144	16	14.2737946583067	1.7262053416933
145	10	13.3567315625142	-3.35673156251415
146	18	15.6527462059573	2.34725379404273
147	13	15.0228847719538	-2.02288477195385
148	16	14.9620191824016	1.03798081759843
149	13	12.8772591624989	0.122740837501070
150	10	12.9838669107856	-2.98386691078564
151	15	16.1494801243129	-1.14948012431286
152	16	14.0275731378567	1.97242686214327
153	16	11.9110510150754	4.08894898492458
154	14	12.8816202965072	1.11837970349282
155	10	12.5110772982986	-2.51107729829857
156	17	16.6782641138012	0.321735886198799
157	13	11.7748408846068	1.22515911539322
158	15	13.9838607966409	1.01613920335906
159	16	14.7220477570335	1.2779522429665
160	12	12.9024487088536	-0.902448708853636
161	13	12.7234113561509	0.276588643849055
162	13	12.3594447514215	0.640555248578516
163	12	12.2197140578900	-0.219714057889972
164	17	16.1559256930106	0.844074306989396
165	15	13.4927110110311	1.50728898896895
166	10	11.3690856488793	-1.36908564887931
167	14	14.1511837465642	-0.151183746564210
168	11	14.0473814027883	-3.04738140278826
169	13	14.6284207569089	-1.62842075690888
170	16	14.3286401101092	1.67135988989079
171	12	10.2103695148885	1.78963048511152
172	16	15.2294173512872	0.77058264871285
173	12	13.6789589162402	-1.67895891624021
174	9	11.1422436631215	-2.14224366312147
175	12	14.8132494934299	-2.81324949342989
176	15	14.4367200872182	0.56327991278181
177	12	12.0443133302158	-0.0443133302158407
178	12	12.5271258979836	-0.527125897983635
179	14	13.6520380206879	0.347961979312097
180	12	13.2423636290015	-1.24236362900148
181	16	14.9028714322165	1.09712856778351
182	11	11.2308165908512	-0.230816590851180
183	19	16.6302449667749	2.36975503322512
184	15	15.0709573706773	-0.0709573706773391
185	8	14.5002572284975	-6.50025722849752
186	16	14.7385222746564	1.26147772534359
187	17	14.4011157681124	2.59888423188756
188	12	12.2769350009329	-0.276935000932892
189	11	11.3375926910729	-0.337592691072856
190	11	10.2335393682771	0.766460631722878
191	14	14.7383473626722	-0.738347362672238
192	16	15.4068358759607	0.59316412403932
193	12	9.55486413915181	2.44513586084819
194	16	13.9311014734647	2.06889852653535
195	13	13.5896293614340	-0.589629361433967
196	15	14.9324705375573	0.0675294624426769
197	16	12.8970880194329	3.10291198056707
198	16	15.0180094707273	0.981990529272688
199	14	12.3494785543296	1.65052144567040
200	16	14.5413613945402	1.45863860545978
201	16	13.9445932035730	2.05540679642697
202	14	13.3226184610574	0.677381538942598
203	11	13.4275177946162	-2.42751779461623
204	12	14.4367109870216	-2.43671098702159
205	15	12.6699269134548	2.33007308654517
206	15	14.4586524254313	0.541347574568739
207	16	14.4679372463901	1.53206275360994
208	16	14.9316469204900	1.06835307951003
209	11	13.5562780239837	-2.55627802398369
210	15	13.9779398017968	1.02206019820315
211	12	14.2029022057863	-2.20290220578625
212	12	15.7784438651911	-3.77844386519108
213	15	14.1440201791006	0.855979820899417
214	15	12.0307032677153	2.96929673228469
215	16	14.5684829589542	1.43151704104577
216	14	13.0731346341900	0.92686536580997
217	17	14.6746123754413	2.32538762455872
218	14	13.9423598225196	0.0576401774804326
219	13	11.8905028372228	1.10949716277721
220	15	15.2059654363509	-0.205965436350911
221	13	14.6710171706430	-1.67101717064297
222	14	14.1319205655599	-0.131920565559859
223	15	14.3001182465164	0.699881753483601
224	12	13.1092937744911	-1.10929377449114
225	13	12.7116664065425	0.288333593457546
226	8	11.7472131342750	-3.74721313427504
227	14	13.8015801739644	0.198419826035621
228	14	13.0202674279236	0.97973257207643
229	11	12.2297591833158	-1.22975918331585
230	12	12.8968382776945	-0.896838277694458
231	13	11.2452084819542	1.75479151804577
232	10	13.2130657616632	-3.21306576166324
233	16	11.3677174009185	4.63228259908155
234	18	15.9474161696061	2.05258383039394
235	13	13.8693288638037	-0.869328863803698
236	11	13.3430027385425	-2.34300273854246
237	4	10.9726967298314	-6.97269672983141
238	13	14.2678178481433	-1.26781784814330
239	16	14.3069070865238	1.6930929134762
240	10	11.6823923405169	-1.68239234051694
241	12	12.2879119795221	-0.287911979522077
242	12	13.5011921116947	-1.50119211169469
243	10	8.85142253316746	1.14857746683254
244	13	11.1028627902225	1.89713720977750
245	15	13.7092481004796	1.29075189952042
246	12	11.9685057317415	0.0314942682585147
247	14	12.8777319860027	1.12226801399732
248	10	12.5358000227952	-2.53580002279515
249	12	10.7624130858841	1.23758691411588
250	12	11.7393909440044	0.260609055995635
251	11	11.9220624304821	-0.922062430482096
252	10	11.7304631725709	-1.73046317257095
253	12	11.4019661994277	0.59803380057231
254	16	12.8825562632821	3.11744373671786
255	12	13.3765522827852	-1.37655228278524
256	14	13.9048290853521	0.0951709146478995
257	16	14.5198797799651	1.48012022003486
258	14	11.6682798848408	2.33172011515922
259	13	14.3566901566030	-1.35669015660303
260	4	9.3833458059488	-5.38334580594881
261	15	13.8472593284005	1.15274067159949
262	11	15.1083609537208	-4.1083609537208
263	11	11.3476291943178	-0.347629194317837
264	14	12.9144494714043	1.08555052859575

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 15.9509274269776 & -2.95092742697758 \tabularnewline
2 & 16 & 15.6181828665754 & 0.381817133424643 \tabularnewline
3 & 19 & 16.9166643716608 & 2.08333562833916 \tabularnewline
4 & 15 & 11.8729748073647 & 3.12702519263529 \tabularnewline
5 & 14 & 16.3580519877682 & -2.35805198776815 \tabularnewline
6 & 13 & 14.7360162717571 & -1.73601627175708 \tabularnewline
7 & 19 & 15.1537000085073 & 3.84629999149269 \tabularnewline
8 & 15 & 17.0077560505479 & -2.00775605054788 \tabularnewline
9 & 14 & 15.9484454966395 & -1.94844549663945 \tabularnewline
10 & 15 & 14.2980364711644 & 0.701963528835628 \tabularnewline
11 & 16 & 14.8626172377916 & 1.13738276220843 \tabularnewline
12 & 16 & 16.1018077007215 & -0.101807700721472 \tabularnewline
13 & 16 & 15.3893109658985 & 0.61068903410149 \tabularnewline
14 & 16 & 15.4079786083135 & 0.592021391686455 \tabularnewline
15 & 17 & 18.0043950630554 & -1.00439506305541 \tabularnewline
16 & 15 & 15.3798159239431 & -0.379815923943112 \tabularnewline
17 & 15 & 14.5105588177225 & 0.489441182277545 \tabularnewline
18 & 20 & 16.3833808166030 & 3.61661918339702 \tabularnewline
19 & 18 & 15.3748348655024 & 2.62516513449760 \tabularnewline
20 & 16 & 15.4935321664942 & 0.506467833505832 \tabularnewline
21 & 16 & 15.3027694651376 & 0.697230534862387 \tabularnewline
22 & 16 & 15.0411768247971 & 0.958823175202857 \tabularnewline
23 & 19 & 16.4530618527621 & 2.54693814723788 \tabularnewline
24 & 16 & 14.9892378950466 & 1.01076210495340 \tabularnewline
25 & 17 & 16.1668421489873 & 0.833157851012718 \tabularnewline
26 & 17 & 16.2212087742073 & 0.778791225792692 \tabularnewline
27 & 16 & 14.9717246067166 & 1.02827539328339 \tabularnewline
28 & 15 & 16.7639680138983 & -1.7639680138983 \tabularnewline
29 & 16 & 15.7661669842349 & 0.233833015765089 \tabularnewline
30 & 14 & 14.0778957672031 & -0.0778957672031147 \tabularnewline
31 & 15 & 15.7460272839639 & -0.746027283963944 \tabularnewline
32 & 12 & 12.7373771224324 & -0.737377122432354 \tabularnewline
33 & 14 & 14.7974186192349 & -0.797418619234864 \tabularnewline
34 & 16 & 15.9285001202188 & 0.0714998797812121 \tabularnewline
35 & 14 & 15.6022003060943 & -1.60220030609430 \tabularnewline
36 & 10 & 12.9219264013097 & -2.92192640130971 \tabularnewline
37 & 10 & 13.0146729900312 & -3.01467299003119 \tabularnewline
38 & 14 & 15.7899916900747 & -1.78999169007471 \tabularnewline
39 & 16 & 14.5626490012885 & 1.43735099871148 \tabularnewline
40 & 16 & 14.4721133266155 & 1.52788667338445 \tabularnewline
41 & 16 & 14.7271846001186 & 1.27281539988142 \tabularnewline
42 & 14 & 15.8283533347221 & -1.82835333472214 \tabularnewline
43 & 20 & 17.5740121132113 & 2.42598788678866 \tabularnewline
44 & 14 & 14.1663108781857 & -0.166310878185714 \tabularnewline
45 & 14 & 14.4929035444449 & -0.492903544444942 \tabularnewline
46 & 11 & 15.5799467762799 & -4.57994677627988 \tabularnewline
47 & 14 & 16.6042289856471 & -2.60422898564706 \tabularnewline
48 & 15 & 15.1942294341289 & -0.194229434128941 \tabularnewline
49 & 16 & 15.6290939132253 & 0.37090608677469 \tabularnewline
50 & 14 & 15.7309335277757 & -1.73093352777567 \tabularnewline
51 & 16 & 17.1471512900765 & -1.14715129007648 \tabularnewline
52 & 14 & 14.2216774089203 & -0.221677408920321 \tabularnewline
53 & 12 & 15.1453403734528 & -3.14534037345284 \tabularnewline
54 & 16 & 15.8695524007130 & 0.130447599286978 \tabularnewline
55 & 9 & 11.3645521776952 & -2.36455217769524 \tabularnewline
56 & 14 & 12.4006749187661 & 1.59932508123387 \tabularnewline
57 & 16 & 16.0107717121744 & -0.0107717121744176 \tabularnewline
58 & 16 & 15.5876443588646 & 0.412355641135398 \tabularnewline
59 & 15 & 15.2486517151036 & -0.248651715103637 \tabularnewline
60 & 16 & 14.3009604497181 & 1.69903955028191 \tabularnewline
61 & 12 & 11.5680405712374 & 0.431959428762605 \tabularnewline
62 & 16 & 15.8480933851408 & 0.151906614859215 \tabularnewline
63 & 16 & 16.7320968224482 & -0.732096822448167 \tabularnewline
64 & 14 & 14.8491657946905 & -0.849165794690488 \tabularnewline
65 & 16 & 15.4532184988394 & 0.546781501160557 \tabularnewline
66 & 17 & 15.786728295299 & 1.2132717047010 \tabularnewline
67 & 18 & 16.0663974549983 & 1.93360254500172 \tabularnewline
68 & 18 & 14.2236008420055 & 3.7763991579945 \tabularnewline
69 & 12 & 15.7039588645577 & -3.70395886455774 \tabularnewline
70 & 16 & 15.4478381365797 & 0.552161863420335 \tabularnewline
71 & 10 & 13.0291955081803 & -3.02919550818028 \tabularnewline
72 & 14 & 14.6990097947249 & -0.699009794724867 \tabularnewline
73 & 18 & 16.8215387173465 & 1.17846128265351 \tabularnewline
74 & 18 & 17.0473858368980 & 0.952614163101954 \tabularnewline
75 & 16 & 15.0618538666444 & 0.93814613335562 \tabularnewline
76 & 17 & 13.2686578002251 & 3.73134219977488 \tabularnewline
77 & 16 & 16.2744534843814 & -0.274453484381412 \tabularnewline
78 & 16 & 14.2740950485574 & 1.72590495144259 \tabularnewline
79 & 13 & 15.0334785537398 & -2.03347855373977 \tabularnewline
80 & 16 & 14.9247697671811 & 1.07523023281887 \tabularnewline
81 & 16 & 15.5655347518703 & 0.434465248129714 \tabularnewline
82 & 16 & 15.5369752066879 & 0.463024793312053 \tabularnewline
83 & 15 & 15.5563789647738 & -0.556378964773841 \tabularnewline
84 & 15 & 14.7091761878920 & 0.290823812108049 \tabularnewline
85 & 16 & 13.9756487604527 & 2.02435123954728 \tabularnewline
86 & 14 & 14.0085733239176 & -0.00857332391764704 \tabularnewline
87 & 16 & 15.2208307489935 & 0.77916925100649 \tabularnewline
88 & 16 & 14.7065934689149 & 1.29340653108514 \tabularnewline
89 & 15 & 14.3547510265222 & 0.645248973477802 \tabularnewline
90 & 12 & 13.7050807374168 & -1.70508073741682 \tabularnewline
91 & 17 & 16.6782641138012 & 0.321735886198799 \tabularnewline
92 & 16 & 15.7430202889882 & 0.256979711011831 \tabularnewline
93 & 15 & 15.0195639849035 & -0.0195639849034504 \tabularnewline
94 & 13 & 14.9635980286721 & -1.96359802867209 \tabularnewline
95 & 16 & 14.6973726642945 & 1.30262733570547 \tabularnewline
96 & 16 & 15.6830629570827 & 0.316937042917323 \tabularnewline
97 & 16 & 13.5822989199044 & 2.41770108009563 \tabularnewline
98 & 16 & 15.7685105700457 & 0.231489429954300 \tabularnewline
99 & 14 & 14.3198580815311 & -0.319858081531108 \tabularnewline
100 & 16 & 17.0247038337605 & -1.02470383376054 \tabularnewline
101 & 16 & 14.5787973362837 & 1.42120266371627 \tabularnewline
102 & 20 & 17.4687674897040 & 2.53123251029596 \tabularnewline
103 & 15 & 14.0523046259387 & 0.947695374061293 \tabularnewline
104 & 16 & 14.9563332782169 & 1.04366672178305 \tabularnewline
105 & 13 & 14.8011674216603 & -1.80116742166032 \tabularnewline
106 & 17 & 15.6582118368939 & 1.34178816310611 \tabularnewline
107 & 16 & 15.7149454231363 & 0.285054576863653 \tabularnewline
108 & 16 & 14.2651695279916 & 1.73483047200845 \tabularnewline
109 & 12 & 12.1445025399444 & -0.144502539944429 \tabularnewline
110 & 16 & 15.2700317301080 & 0.729968269891974 \tabularnewline
111 & 16 & 16.0190228965521 & -0.0190228965520536 \tabularnewline
112 & 17 & 14.9728991243270 & 2.02710087567298 \tabularnewline
113 & 13 & 14.6235262603592 & -1.62352626035921 \tabularnewline
114 & 12 & 14.5611065152376 & -2.56110651523763 \tabularnewline
115 & 18 & 16.182566405197 & 1.81743359480300 \tabularnewline
116 & 14 & 15.8742392027801 & -1.8742392027801 \tabularnewline
117 & 14 & 13.0634243613986 & 0.93657563860137 \tabularnewline
118 & 13 & 14.7664152300671 & -1.7664152300671 \tabularnewline
119 & 16 & 15.57275380411 & 0.427246195889985 \tabularnewline
120 & 13 & 14.4067456846915 & -1.40674568469151 \tabularnewline
121 & 16 & 15.4391647652899 & 0.56083523471014 \tabularnewline
122 & 13 & 15.8911672712233 & -2.89116727122331 \tabularnewline
123 & 16 & 16.9042581868609 & -0.904258186860862 \tabularnewline
124 & 15 & 15.8921534889468 & -0.892153488946773 \tabularnewline
125 & 16 & 16.9022361707383 & -0.9022361707383 \tabularnewline
126 & 15 & 14.7036320628420 & 0.296367937158032 \tabularnewline
127 & 17 & 15.6113104275905 & 1.38868957240952 \tabularnewline
128 & 15 & 13.9838607966409 & 1.01613920335906 \tabularnewline
129 & 12 & 14.7440838384905 & -2.74408383849047 \tabularnewline
130 & 16 & 13.9936422626157 & 2.00635773738434 \tabularnewline
131 & 10 & 13.7576725915982 & -3.75767259159823 \tabularnewline
132 & 16 & 13.5176321144828 & 2.48236788551718 \tabularnewline
133 & 12 & 14.2161631177914 & -2.21616311779137 \tabularnewline
134 & 14 & 15.6237963714392 & -1.62379637143924 \tabularnewline
135 & 15 & 15.1698667563622 & -0.169866756362208 \tabularnewline
136 & 13 & 12.1421557046257 & 0.857844295374258 \tabularnewline
137 & 15 & 14.5009469200994 & 0.499053079900579 \tabularnewline
138 & 11 & 13.4686249246835 & -2.46862492468351 \tabularnewline
139 & 12 & 13.0157243738587 & -1.01572437385866 \tabularnewline
140 & 11 & 13.4194465342762 & -2.41944653427621 \tabularnewline
141 & 16 & 12.8831315054463 & 3.11686849455368 \tabularnewline
142 & 15 & 13.7558505714002 & 1.24414942859978 \tabularnewline
143 & 17 & 17.0097795066055 & -0.00977950660547738 \tabularnewline
144 & 16 & 14.2737946583067 & 1.7262053416933 \tabularnewline
145 & 10 & 13.3567315625142 & -3.35673156251415 \tabularnewline
146 & 18 & 15.6527462059573 & 2.34725379404273 \tabularnewline
147 & 13 & 15.0228847719538 & -2.02288477195385 \tabularnewline
148 & 16 & 14.9620191824016 & 1.03798081759843 \tabularnewline
149 & 13 & 12.8772591624989 & 0.122740837501070 \tabularnewline
150 & 10 & 12.9838669107856 & -2.98386691078564 \tabularnewline
151 & 15 & 16.1494801243129 & -1.14948012431286 \tabularnewline
152 & 16 & 14.0275731378567 & 1.97242686214327 \tabularnewline
153 & 16 & 11.9110510150754 & 4.08894898492458 \tabularnewline
154 & 14 & 12.8816202965072 & 1.11837970349282 \tabularnewline
155 & 10 & 12.5110772982986 & -2.51107729829857 \tabularnewline
156 & 17 & 16.6782641138012 & 0.321735886198799 \tabularnewline
157 & 13 & 11.7748408846068 & 1.22515911539322 \tabularnewline
158 & 15 & 13.9838607966409 & 1.01613920335906 \tabularnewline
159 & 16 & 14.7220477570335 & 1.2779522429665 \tabularnewline
160 & 12 & 12.9024487088536 & -0.902448708853636 \tabularnewline
161 & 13 & 12.7234113561509 & 0.276588643849055 \tabularnewline
162 & 13 & 12.3594447514215 & 0.640555248578516 \tabularnewline
163 & 12 & 12.2197140578900 & -0.219714057889972 \tabularnewline
164 & 17 & 16.1559256930106 & 0.844074306989396 \tabularnewline
165 & 15 & 13.4927110110311 & 1.50728898896895 \tabularnewline
166 & 10 & 11.3690856488793 & -1.36908564887931 \tabularnewline
167 & 14 & 14.1511837465642 & -0.151183746564210 \tabularnewline
168 & 11 & 14.0473814027883 & -3.04738140278826 \tabularnewline
169 & 13 & 14.6284207569089 & -1.62842075690888 \tabularnewline
170 & 16 & 14.3286401101092 & 1.67135988989079 \tabularnewline
171 & 12 & 10.2103695148885 & 1.78963048511152 \tabularnewline
172 & 16 & 15.2294173512872 & 0.77058264871285 \tabularnewline
173 & 12 & 13.6789589162402 & -1.67895891624021 \tabularnewline
174 & 9 & 11.1422436631215 & -2.14224366312147 \tabularnewline
175 & 12 & 14.8132494934299 & -2.81324949342989 \tabularnewline
176 & 15 & 14.4367200872182 & 0.56327991278181 \tabularnewline
177 & 12 & 12.0443133302158 & -0.0443133302158407 \tabularnewline
178 & 12 & 12.5271258979836 & -0.527125897983635 \tabularnewline
179 & 14 & 13.6520380206879 & 0.347961979312097 \tabularnewline
180 & 12 & 13.2423636290015 & -1.24236362900148 \tabularnewline
181 & 16 & 14.9028714322165 & 1.09712856778351 \tabularnewline
182 & 11 & 11.2308165908512 & -0.230816590851180 \tabularnewline
183 & 19 & 16.6302449667749 & 2.36975503322512 \tabularnewline
184 & 15 & 15.0709573706773 & -0.0709573706773391 \tabularnewline
185 & 8 & 14.5002572284975 & -6.50025722849752 \tabularnewline
186 & 16 & 14.7385222746564 & 1.26147772534359 \tabularnewline
187 & 17 & 14.4011157681124 & 2.59888423188756 \tabularnewline
188 & 12 & 12.2769350009329 & -0.276935000932892 \tabularnewline
189 & 11 & 11.3375926910729 & -0.337592691072856 \tabularnewline
190 & 11 & 10.2335393682771 & 0.766460631722878 \tabularnewline
191 & 14 & 14.7383473626722 & -0.738347362672238 \tabularnewline
192 & 16 & 15.4068358759607 & 0.59316412403932 \tabularnewline
193 & 12 & 9.55486413915181 & 2.44513586084819 \tabularnewline
194 & 16 & 13.9311014734647 & 2.06889852653535 \tabularnewline
195 & 13 & 13.5896293614340 & -0.589629361433967 \tabularnewline
196 & 15 & 14.9324705375573 & 0.0675294624426769 \tabularnewline
197 & 16 & 12.8970880194329 & 3.10291198056707 \tabularnewline
198 & 16 & 15.0180094707273 & 0.981990529272688 \tabularnewline
199 & 14 & 12.3494785543296 & 1.65052144567040 \tabularnewline
200 & 16 & 14.5413613945402 & 1.45863860545978 \tabularnewline
201 & 16 & 13.9445932035730 & 2.05540679642697 \tabularnewline
202 & 14 & 13.3226184610574 & 0.677381538942598 \tabularnewline
203 & 11 & 13.4275177946162 & -2.42751779461623 \tabularnewline
204 & 12 & 14.4367109870216 & -2.43671098702159 \tabularnewline
205 & 15 & 12.6699269134548 & 2.33007308654517 \tabularnewline
206 & 15 & 14.4586524254313 & 0.541347574568739 \tabularnewline
207 & 16 & 14.4679372463901 & 1.53206275360994 \tabularnewline
208 & 16 & 14.9316469204900 & 1.06835307951003 \tabularnewline
209 & 11 & 13.5562780239837 & -2.55627802398369 \tabularnewline
210 & 15 & 13.9779398017968 & 1.02206019820315 \tabularnewline
211 & 12 & 14.2029022057863 & -2.20290220578625 \tabularnewline
212 & 12 & 15.7784438651911 & -3.77844386519108 \tabularnewline
213 & 15 & 14.1440201791006 & 0.855979820899417 \tabularnewline
214 & 15 & 12.0307032677153 & 2.96929673228469 \tabularnewline
215 & 16 & 14.5684829589542 & 1.43151704104577 \tabularnewline
216 & 14 & 13.0731346341900 & 0.92686536580997 \tabularnewline
217 & 17 & 14.6746123754413 & 2.32538762455872 \tabularnewline
218 & 14 & 13.9423598225196 & 0.0576401774804326 \tabularnewline
219 & 13 & 11.8905028372228 & 1.10949716277721 \tabularnewline
220 & 15 & 15.2059654363509 & -0.205965436350911 \tabularnewline
221 & 13 & 14.6710171706430 & -1.67101717064297 \tabularnewline
222 & 14 & 14.1319205655599 & -0.131920565559859 \tabularnewline
223 & 15 & 14.3001182465164 & 0.699881753483601 \tabularnewline
224 & 12 & 13.1092937744911 & -1.10929377449114 \tabularnewline
225 & 13 & 12.7116664065425 & 0.288333593457546 \tabularnewline
226 & 8 & 11.7472131342750 & -3.74721313427504 \tabularnewline
227 & 14 & 13.8015801739644 & 0.198419826035621 \tabularnewline
228 & 14 & 13.0202674279236 & 0.97973257207643 \tabularnewline
229 & 11 & 12.2297591833158 & -1.22975918331585 \tabularnewline
230 & 12 & 12.8968382776945 & -0.896838277694458 \tabularnewline
231 & 13 & 11.2452084819542 & 1.75479151804577 \tabularnewline
232 & 10 & 13.2130657616632 & -3.21306576166324 \tabularnewline
233 & 16 & 11.3677174009185 & 4.63228259908155 \tabularnewline
234 & 18 & 15.9474161696061 & 2.05258383039394 \tabularnewline
235 & 13 & 13.8693288638037 & -0.869328863803698 \tabularnewline
236 & 11 & 13.3430027385425 & -2.34300273854246 \tabularnewline
237 & 4 & 10.9726967298314 & -6.97269672983141 \tabularnewline
238 & 13 & 14.2678178481433 & -1.26781784814330 \tabularnewline
239 & 16 & 14.3069070865238 & 1.6930929134762 \tabularnewline
240 & 10 & 11.6823923405169 & -1.68239234051694 \tabularnewline
241 & 12 & 12.2879119795221 & -0.287911979522077 \tabularnewline
242 & 12 & 13.5011921116947 & -1.50119211169469 \tabularnewline
243 & 10 & 8.85142253316746 & 1.14857746683254 \tabularnewline
244 & 13 & 11.1028627902225 & 1.89713720977750 \tabularnewline
245 & 15 & 13.7092481004796 & 1.29075189952042 \tabularnewline
246 & 12 & 11.9685057317415 & 0.0314942682585147 \tabularnewline
247 & 14 & 12.8777319860027 & 1.12226801399732 \tabularnewline
248 & 10 & 12.5358000227952 & -2.53580002279515 \tabularnewline
249 & 12 & 10.7624130858841 & 1.23758691411588 \tabularnewline
250 & 12 & 11.7393909440044 & 0.260609055995635 \tabularnewline
251 & 11 & 11.9220624304821 & -0.922062430482096 \tabularnewline
252 & 10 & 11.7304631725709 & -1.73046317257095 \tabularnewline
253 & 12 & 11.4019661994277 & 0.59803380057231 \tabularnewline
254 & 16 & 12.8825562632821 & 3.11744373671786 \tabularnewline
255 & 12 & 13.3765522827852 & -1.37655228278524 \tabularnewline
256 & 14 & 13.9048290853521 & 0.0951709146478995 \tabularnewline
257 & 16 & 14.5198797799651 & 1.48012022003486 \tabularnewline
258 & 14 & 11.6682798848408 & 2.33172011515922 \tabularnewline
259 & 13 & 14.3566901566030 & -1.35669015660303 \tabularnewline
260 & 4 & 9.3833458059488 & -5.38334580594881 \tabularnewline
261 & 15 & 13.8472593284005 & 1.15274067159949 \tabularnewline
262 & 11 & 15.1083609537208 & -4.1083609537208 \tabularnewline
263 & 11 & 11.3476291943178 & -0.347629194317837 \tabularnewline
264 & 14 & 12.9144494714043 & 1.08555052859575 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96541&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]15.9509274269776[/C][C]-2.95092742697758[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.6181828665754[/C][C]0.381817133424643[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.9166643716608[/C][C]2.08333562833916[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.8729748073647[/C][C]3.12702519263529[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]16.3580519877682[/C][C]-2.35805198776815[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.7360162717571[/C][C]-1.73601627175708[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.1537000085073[/C][C]3.84629999149269[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]17.0077560505479[/C][C]-2.00775605054788[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.9484454966395[/C][C]-1.94844549663945[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.2980364711644[/C][C]0.701963528835628[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]14.8626172377916[/C][C]1.13738276220843[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1018077007215[/C][C]-0.101807700721472[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.3893109658985[/C][C]0.61068903410149[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.4079786083135[/C][C]0.592021391686455[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]18.0043950630554[/C][C]-1.00439506305541[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.3798159239431[/C][C]-0.379815923943112[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.5105588177225[/C][C]0.489441182277545[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.3833808166030[/C][C]3.61661918339702[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.3748348655024[/C][C]2.62516513449760[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.4935321664942[/C][C]0.506467833505832[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.3027694651376[/C][C]0.697230534862387[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.0411768247971[/C][C]0.958823175202857[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4530618527621[/C][C]2.54693814723788[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.9892378950466[/C][C]1.01076210495340[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]16.1668421489873[/C][C]0.833157851012718[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.2212087742073[/C][C]0.778791225792692[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9717246067166[/C][C]1.02827539328339[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.7639680138983[/C][C]-1.7639680138983[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.7661669842349[/C][C]0.233833015765089[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.0778957672031[/C][C]-0.0778957672031147[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7460272839639[/C][C]-0.746027283963944[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.7373771224324[/C][C]-0.737377122432354[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.7974186192349[/C][C]-0.797418619234864[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.9285001202188[/C][C]0.0714998797812121[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6022003060943[/C][C]-1.60220030609430[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]12.9219264013097[/C][C]-2.92192640130971[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]13.0146729900312[/C][C]-3.01467299003119[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7899916900747[/C][C]-1.78999169007471[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.5626490012885[/C][C]1.43735099871148[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.4721133266155[/C][C]1.52788667338445[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.7271846001186[/C][C]1.27281539988142[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.8283533347221[/C][C]-1.82835333472214[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.5740121132113[/C][C]2.42598788678866[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.1663108781857[/C][C]-0.166310878185714[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.4929035444449[/C][C]-0.492903544444942[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.5799467762799[/C][C]-4.57994677627988[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.6042289856471[/C][C]-2.60422898564706[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.1942294341289[/C][C]-0.194229434128941[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.6290939132253[/C][C]0.37090608677469[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.7309335277757[/C][C]-1.73093352777567[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]17.1471512900765[/C][C]-1.14715129007648[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2216774089203[/C][C]-0.221677408920321[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]15.1453403734528[/C][C]-3.14534037345284[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.8695524007130[/C][C]0.130447599286978[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.3645521776952[/C][C]-2.36455217769524[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.4006749187661[/C][C]1.59932508123387[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0107717121744[/C][C]-0.0107717121744176[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.5876443588646[/C][C]0.412355641135398[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.2486517151036[/C][C]-0.248651715103637[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.3009604497181[/C][C]1.69903955028191[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5680405712374[/C][C]0.431959428762605[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.8480933851408[/C][C]0.151906614859215[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.7320968224482[/C][C]-0.732096822448167[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.8491657946905[/C][C]-0.849165794690488[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4532184988394[/C][C]0.546781501160557[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.786728295299[/C][C]1.2132717047010[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.0663974549983[/C][C]1.93360254500172[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.2236008420055[/C][C]3.7763991579945[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.7039588645577[/C][C]-3.70395886455774[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4478381365797[/C][C]0.552161863420335[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.0291955081803[/C][C]-3.02919550818028[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.6990097947249[/C][C]-0.699009794724867[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.8215387173465[/C][C]1.17846128265351[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.0473858368980[/C][C]0.952614163101954[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.0618538666444[/C][C]0.93814613335562[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.2686578002251[/C][C]3.73134219977488[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.2744534843814[/C][C]-0.274453484381412[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.2740950485574[/C][C]1.72590495144259[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.0334785537398[/C][C]-2.03347855373977[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]14.9247697671811[/C][C]1.07523023281887[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5655347518703[/C][C]0.434465248129714[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.5369752066879[/C][C]0.463024793312053[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.5563789647738[/C][C]-0.556378964773841[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.7091761878920[/C][C]0.290823812108049[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]13.9756487604527[/C][C]2.02435123954728[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.0085733239176[/C][C]-0.00857332391764704[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.2208307489935[/C][C]0.77916925100649[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.7065934689149[/C][C]1.29340653108514[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.3547510265222[/C][C]0.645248973477802[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.7050807374168[/C][C]-1.70508073741682[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.6782641138012[/C][C]0.321735886198799[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.7430202889882[/C][C]0.256979711011831[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.0195639849035[/C][C]-0.0195639849034504[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]14.9635980286721[/C][C]-1.96359802867209[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.6973726642945[/C][C]1.30262733570547[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.6830629570827[/C][C]0.316937042917323[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.5822989199044[/C][C]2.41770108009563[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.7685105700457[/C][C]0.231489429954300[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.3198580815311[/C][C]-0.319858081531108[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]17.0247038337605[/C][C]-1.02470383376054[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.5787973362837[/C][C]1.42120266371627[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.4687674897040[/C][C]2.53123251029596[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.0523046259387[/C][C]0.947695374061293[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.9563332782169[/C][C]1.04366672178305[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]14.8011674216603[/C][C]-1.80116742166032[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.6582118368939[/C][C]1.34178816310611[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.7149454231363[/C][C]0.285054576863653[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.2651695279916[/C][C]1.73483047200845[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]12.1445025399444[/C][C]-0.144502539944429[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.2700317301080[/C][C]0.729968269891974[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]16.0190228965521[/C][C]-0.0190228965520536[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]14.9728991243270[/C][C]2.02710087567298[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.6235262603592[/C][C]-1.62352626035921[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.5611065152376[/C][C]-2.56110651523763[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.182566405197[/C][C]1.81743359480300[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.8742392027801[/C][C]-1.8742392027801[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.0634243613986[/C][C]0.93657563860137[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.7664152300671[/C][C]-1.7664152300671[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.57275380411[/C][C]0.427246195889985[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.4067456846915[/C][C]-1.40674568469151[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.4391647652899[/C][C]0.56083523471014[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.8911672712233[/C][C]-2.89116727122331[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]16.9042581868609[/C][C]-0.904258186860862[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.8921534889468[/C][C]-0.892153488946773[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.9022361707383[/C][C]-0.9022361707383[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]14.7036320628420[/C][C]0.296367937158032[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.6113104275905[/C][C]1.38868957240952[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]13.9838607966409[/C][C]1.01613920335906[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.7440838384905[/C][C]-2.74408383849047[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]13.9936422626157[/C][C]2.00635773738434[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.7576725915982[/C][C]-3.75767259159823[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.5176321144828[/C][C]2.48236788551718[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]14.2161631177914[/C][C]-2.21616311779137[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.6237963714392[/C][C]-1.62379637143924[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.1698667563622[/C][C]-0.169866756362208[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.1421557046257[/C][C]0.857844295374258[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.5009469200994[/C][C]0.499053079900579[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.4686249246835[/C][C]-2.46862492468351[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.0157243738587[/C][C]-1.01572437385866[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.4194465342762[/C][C]-2.41944653427621[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.8831315054463[/C][C]3.11686849455368[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.7558505714002[/C][C]1.24414942859978[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]17.0097795066055[/C][C]-0.00977950660547738[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.2737946583067[/C][C]1.7262053416933[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]13.3567315625142[/C][C]-3.35673156251415[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.6527462059573[/C][C]2.34725379404273[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]15.0228847719538[/C][C]-2.02288477195385[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]14.9620191824016[/C][C]1.03798081759843[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]12.8772591624989[/C][C]0.122740837501070[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]12.9838669107856[/C][C]-2.98386691078564[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]16.1494801243129[/C][C]-1.14948012431286[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]14.0275731378567[/C][C]1.97242686214327[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]11.9110510150754[/C][C]4.08894898492458[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.8816202965072[/C][C]1.11837970349282[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.5110772982986[/C][C]-2.51107729829857[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.6782641138012[/C][C]0.321735886198799[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.7748408846068[/C][C]1.22515911539322[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]13.9838607966409[/C][C]1.01613920335906[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]14.7220477570335[/C][C]1.2779522429665[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.9024487088536[/C][C]-0.902448708853636[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.7234113561509[/C][C]0.276588643849055[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.3594447514215[/C][C]0.640555248578516[/C][/ROW]
[ROW][C]163[/C][C]12[/C][C]12.2197140578900[/C][C]-0.219714057889972[/C][/ROW]
[ROW][C]164[/C][C]17[/C][C]16.1559256930106[/C][C]0.844074306989396[/C][/ROW]
[ROW][C]165[/C][C]15[/C][C]13.4927110110311[/C][C]1.50728898896895[/C][/ROW]
[ROW][C]166[/C][C]10[/C][C]11.3690856488793[/C][C]-1.36908564887931[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.1511837465642[/C][C]-0.151183746564210[/C][/ROW]
[ROW][C]168[/C][C]11[/C][C]14.0473814027883[/C][C]-3.04738140278826[/C][/ROW]
[ROW][C]169[/C][C]13[/C][C]14.6284207569089[/C][C]-1.62842075690888[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]14.3286401101092[/C][C]1.67135988989079[/C][/ROW]
[ROW][C]171[/C][C]12[/C][C]10.2103695148885[/C][C]1.78963048511152[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]15.2294173512872[/C][C]0.77058264871285[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]13.6789589162402[/C][C]-1.67895891624021[/C][/ROW]
[ROW][C]174[/C][C]9[/C][C]11.1422436631215[/C][C]-2.14224366312147[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]14.8132494934299[/C][C]-2.81324949342989[/C][/ROW]
[ROW][C]176[/C][C]15[/C][C]14.4367200872182[/C][C]0.56327991278181[/C][/ROW]
[ROW][C]177[/C][C]12[/C][C]12.0443133302158[/C][C]-0.0443133302158407[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]12.5271258979836[/C][C]-0.527125897983635[/C][/ROW]
[ROW][C]179[/C][C]14[/C][C]13.6520380206879[/C][C]0.347961979312097[/C][/ROW]
[ROW][C]180[/C][C]12[/C][C]13.2423636290015[/C][C]-1.24236362900148[/C][/ROW]
[ROW][C]181[/C][C]16[/C][C]14.9028714322165[/C][C]1.09712856778351[/C][/ROW]
[ROW][C]182[/C][C]11[/C][C]11.2308165908512[/C][C]-0.230816590851180[/C][/ROW]
[ROW][C]183[/C][C]19[/C][C]16.6302449667749[/C][C]2.36975503322512[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]15.0709573706773[/C][C]-0.0709573706773391[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]14.5002572284975[/C][C]-6.50025722849752[/C][/ROW]
[ROW][C]186[/C][C]16[/C][C]14.7385222746564[/C][C]1.26147772534359[/C][/ROW]
[ROW][C]187[/C][C]17[/C][C]14.4011157681124[/C][C]2.59888423188756[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]12.2769350009329[/C][C]-0.276935000932892[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]11.3375926910729[/C][C]-0.337592691072856[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]10.2335393682771[/C][C]0.766460631722878[/C][/ROW]
[ROW][C]191[/C][C]14[/C][C]14.7383473626722[/C][C]-0.738347362672238[/C][/ROW]
[ROW][C]192[/C][C]16[/C][C]15.4068358759607[/C][C]0.59316412403932[/C][/ROW]
[ROW][C]193[/C][C]12[/C][C]9.55486413915181[/C][C]2.44513586084819[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]13.9311014734647[/C][C]2.06889852653535[/C][/ROW]
[ROW][C]195[/C][C]13[/C][C]13.5896293614340[/C][C]-0.589629361433967[/C][/ROW]
[ROW][C]196[/C][C]15[/C][C]14.9324705375573[/C][C]0.0675294624426769[/C][/ROW]
[ROW][C]197[/C][C]16[/C][C]12.8970880194329[/C][C]3.10291198056707[/C][/ROW]
[ROW][C]198[/C][C]16[/C][C]15.0180094707273[/C][C]0.981990529272688[/C][/ROW]
[ROW][C]199[/C][C]14[/C][C]12.3494785543296[/C][C]1.65052144567040[/C][/ROW]
[ROW][C]200[/C][C]16[/C][C]14.5413613945402[/C][C]1.45863860545978[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]13.9445932035730[/C][C]2.05540679642697[/C][/ROW]
[ROW][C]202[/C][C]14[/C][C]13.3226184610574[/C][C]0.677381538942598[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]13.4275177946162[/C][C]-2.42751779461623[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]14.4367109870216[/C][C]-2.43671098702159[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]12.6699269134548[/C][C]2.33007308654517[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.4586524254313[/C][C]0.541347574568739[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]14.4679372463901[/C][C]1.53206275360994[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]14.9316469204900[/C][C]1.06835307951003[/C][/ROW]
[ROW][C]209[/C][C]11[/C][C]13.5562780239837[/C][C]-2.55627802398369[/C][/ROW]
[ROW][C]210[/C][C]15[/C][C]13.9779398017968[/C][C]1.02206019820315[/C][/ROW]
[ROW][C]211[/C][C]12[/C][C]14.2029022057863[/C][C]-2.20290220578625[/C][/ROW]
[ROW][C]212[/C][C]12[/C][C]15.7784438651911[/C][C]-3.77844386519108[/C][/ROW]
[ROW][C]213[/C][C]15[/C][C]14.1440201791006[/C][C]0.855979820899417[/C][/ROW]
[ROW][C]214[/C][C]15[/C][C]12.0307032677153[/C][C]2.96929673228469[/C][/ROW]
[ROW][C]215[/C][C]16[/C][C]14.5684829589542[/C][C]1.43151704104577[/C][/ROW]
[ROW][C]216[/C][C]14[/C][C]13.0731346341900[/C][C]0.92686536580997[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]14.6746123754413[/C][C]2.32538762455872[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]13.9423598225196[/C][C]0.0576401774804326[/C][/ROW]
[ROW][C]219[/C][C]13[/C][C]11.8905028372228[/C][C]1.10949716277721[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]15.2059654363509[/C][C]-0.205965436350911[/C][/ROW]
[ROW][C]221[/C][C]13[/C][C]14.6710171706430[/C][C]-1.67101717064297[/C][/ROW]
[ROW][C]222[/C][C]14[/C][C]14.1319205655599[/C][C]-0.131920565559859[/C][/ROW]
[ROW][C]223[/C][C]15[/C][C]14.3001182465164[/C][C]0.699881753483601[/C][/ROW]
[ROW][C]224[/C][C]12[/C][C]13.1092937744911[/C][C]-1.10929377449114[/C][/ROW]
[ROW][C]225[/C][C]13[/C][C]12.7116664065425[/C][C]0.288333593457546[/C][/ROW]
[ROW][C]226[/C][C]8[/C][C]11.7472131342750[/C][C]-3.74721313427504[/C][/ROW]
[ROW][C]227[/C][C]14[/C][C]13.8015801739644[/C][C]0.198419826035621[/C][/ROW]
[ROW][C]228[/C][C]14[/C][C]13.0202674279236[/C][C]0.97973257207643[/C][/ROW]
[ROW][C]229[/C][C]11[/C][C]12.2297591833158[/C][C]-1.22975918331585[/C][/ROW]
[ROW][C]230[/C][C]12[/C][C]12.8968382776945[/C][C]-0.896838277694458[/C][/ROW]
[ROW][C]231[/C][C]13[/C][C]11.2452084819542[/C][C]1.75479151804577[/C][/ROW]
[ROW][C]232[/C][C]10[/C][C]13.2130657616632[/C][C]-3.21306576166324[/C][/ROW]
[ROW][C]233[/C][C]16[/C][C]11.3677174009185[/C][C]4.63228259908155[/C][/ROW]
[ROW][C]234[/C][C]18[/C][C]15.9474161696061[/C][C]2.05258383039394[/C][/ROW]
[ROW][C]235[/C][C]13[/C][C]13.8693288638037[/C][C]-0.869328863803698[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]13.3430027385425[/C][C]-2.34300273854246[/C][/ROW]
[ROW][C]237[/C][C]4[/C][C]10.9726967298314[/C][C]-6.97269672983141[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]14.2678178481433[/C][C]-1.26781784814330[/C][/ROW]
[ROW][C]239[/C][C]16[/C][C]14.3069070865238[/C][C]1.6930929134762[/C][/ROW]
[ROW][C]240[/C][C]10[/C][C]11.6823923405169[/C][C]-1.68239234051694[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]12.2879119795221[/C][C]-0.287911979522077[/C][/ROW]
[ROW][C]242[/C][C]12[/C][C]13.5011921116947[/C][C]-1.50119211169469[/C][/ROW]
[ROW][C]243[/C][C]10[/C][C]8.85142253316746[/C][C]1.14857746683254[/C][/ROW]
[ROW][C]244[/C][C]13[/C][C]11.1028627902225[/C][C]1.89713720977750[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]13.7092481004796[/C][C]1.29075189952042[/C][/ROW]
[ROW][C]246[/C][C]12[/C][C]11.9685057317415[/C][C]0.0314942682585147[/C][/ROW]
[ROW][C]247[/C][C]14[/C][C]12.8777319860027[/C][C]1.12226801399732[/C][/ROW]
[ROW][C]248[/C][C]10[/C][C]12.5358000227952[/C][C]-2.53580002279515[/C][/ROW]
[ROW][C]249[/C][C]12[/C][C]10.7624130858841[/C][C]1.23758691411588[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.7393909440044[/C][C]0.260609055995635[/C][/ROW]
[ROW][C]251[/C][C]11[/C][C]11.9220624304821[/C][C]-0.922062430482096[/C][/ROW]
[ROW][C]252[/C][C]10[/C][C]11.7304631725709[/C][C]-1.73046317257095[/C][/ROW]
[ROW][C]253[/C][C]12[/C][C]11.4019661994277[/C][C]0.59803380057231[/C][/ROW]
[ROW][C]254[/C][C]16[/C][C]12.8825562632821[/C][C]3.11744373671786[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]13.3765522827852[/C][C]-1.37655228278524[/C][/ROW]
[ROW][C]256[/C][C]14[/C][C]13.9048290853521[/C][C]0.0951709146478995[/C][/ROW]
[ROW][C]257[/C][C]16[/C][C]14.5198797799651[/C][C]1.48012022003486[/C][/ROW]
[ROW][C]258[/C][C]14[/C][C]11.6682798848408[/C][C]2.33172011515922[/C][/ROW]
[ROW][C]259[/C][C]13[/C][C]14.3566901566030[/C][C]-1.35669015660303[/C][/ROW]
[ROW][C]260[/C][C]4[/C][C]9.3833458059488[/C][C]-5.38334580594881[/C][/ROW]
[ROW][C]261[/C][C]15[/C][C]13.8472593284005[/C][C]1.15274067159949[/C][/ROW]
[ROW][C]262[/C][C]11[/C][C]15.1083609537208[/C][C]-4.1083609537208[/C][/ROW]
[ROW][C]263[/C][C]11[/C][C]11.3476291943178[/C][C]-0.347629194317837[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]12.9144494714043[/C][C]1.08555052859575[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96541&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96541&T=4

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	15.9509274269776	-2.95092742697758
2	16	15.6181828665754	0.381817133424643
3	19	16.9166643716608	2.08333562833916
4	15	11.8729748073647	3.12702519263529
5	14	16.3580519877682	-2.35805198776815
6	13	14.7360162717571	-1.73601627175708
7	19	15.1537000085073	3.84629999149269
8	15	17.0077560505479	-2.00775605054788
9	14	15.9484454966395	-1.94844549663945
10	15	14.2980364711644	0.701963528835628
11	16	14.8626172377916	1.13738276220843
12	16	16.1018077007215	-0.101807700721472
13	16	15.3893109658985	0.61068903410149
14	16	15.4079786083135	0.592021391686455
15	17	18.0043950630554	-1.00439506305541
16	15	15.3798159239431	-0.379815923943112
17	15	14.5105588177225	0.489441182277545
18	20	16.3833808166030	3.61661918339702
19	18	15.3748348655024	2.62516513449760
20	16	15.4935321664942	0.506467833505832
21	16	15.3027694651376	0.697230534862387
22	16	15.0411768247971	0.958823175202857
23	19	16.4530618527621	2.54693814723788
24	16	14.9892378950466	1.01076210495340
25	17	16.1668421489873	0.833157851012718
26	17	16.2212087742073	0.778791225792692
27	16	14.9717246067166	1.02827539328339
28	15	16.7639680138983	-1.7639680138983
29	16	15.7661669842349	0.233833015765089
30	14	14.0778957672031	-0.0778957672031147
31	15	15.7460272839639	-0.746027283963944
32	12	12.7373771224324	-0.737377122432354
33	14	14.7974186192349	-0.797418619234864
34	16	15.9285001202188	0.0714998797812121
35	14	15.6022003060943	-1.60220030609430
36	10	12.9219264013097	-2.92192640130971
37	10	13.0146729900312	-3.01467299003119
38	14	15.7899916900747	-1.78999169007471
39	16	14.5626490012885	1.43735099871148
40	16	14.4721133266155	1.52788667338445
41	16	14.7271846001186	1.27281539988142
42	14	15.8283533347221	-1.82835333472214
43	20	17.5740121132113	2.42598788678866
44	14	14.1663108781857	-0.166310878185714
45	14	14.4929035444449	-0.492903544444942
46	11	15.5799467762799	-4.57994677627988
47	14	16.6042289856471	-2.60422898564706
48	15	15.1942294341289	-0.194229434128941
49	16	15.6290939132253	0.37090608677469
50	14	15.7309335277757	-1.73093352777567
51	16	17.1471512900765	-1.14715129007648
52	14	14.2216774089203	-0.221677408920321
53	12	15.1453403734528	-3.14534037345284
54	16	15.8695524007130	0.130447599286978
55	9	11.3645521776952	-2.36455217769524
56	14	12.4006749187661	1.59932508123387
57	16	16.0107717121744	-0.0107717121744176
58	16	15.5876443588646	0.412355641135398
59	15	15.2486517151036	-0.248651715103637
60	16	14.3009604497181	1.69903955028191
61	12	11.5680405712374	0.431959428762605
62	16	15.8480933851408	0.151906614859215
63	16	16.7320968224482	-0.732096822448167
64	14	14.8491657946905	-0.849165794690488
65	16	15.4532184988394	0.546781501160557
66	17	15.786728295299	1.2132717047010
67	18	16.0663974549983	1.93360254500172
68	18	14.2236008420055	3.7763991579945
69	12	15.7039588645577	-3.70395886455774
70	16	15.4478381365797	0.552161863420335
71	10	13.0291955081803	-3.02919550818028
72	14	14.6990097947249	-0.699009794724867
73	18	16.8215387173465	1.17846128265351
74	18	17.0473858368980	0.952614163101954
75	16	15.0618538666444	0.93814613335562
76	17	13.2686578002251	3.73134219977488
77	16	16.2744534843814	-0.274453484381412
78	16	14.2740950485574	1.72590495144259
79	13	15.0334785537398	-2.03347855373977
80	16	14.9247697671811	1.07523023281887
81	16	15.5655347518703	0.434465248129714
82	16	15.5369752066879	0.463024793312053
83	15	15.5563789647738	-0.556378964773841
84	15	14.7091761878920	0.290823812108049
85	16	13.9756487604527	2.02435123954728
86	14	14.0085733239176	-0.00857332391764704
87	16	15.2208307489935	0.77916925100649
88	16	14.7065934689149	1.29340653108514
89	15	14.3547510265222	0.645248973477802
90	12	13.7050807374168	-1.70508073741682
91	17	16.6782641138012	0.321735886198799
92	16	15.7430202889882	0.256979711011831
93	15	15.0195639849035	-0.0195639849034504
94	13	14.9635980286721	-1.96359802867209
95	16	14.6973726642945	1.30262733570547
96	16	15.6830629570827	0.316937042917323
97	16	13.5822989199044	2.41770108009563
98	16	15.7685105700457	0.231489429954300
99	14	14.3198580815311	-0.319858081531108
100	16	17.0247038337605	-1.02470383376054
101	16	14.5787973362837	1.42120266371627
102	20	17.4687674897040	2.53123251029596
103	15	14.0523046259387	0.947695374061293
104	16	14.9563332782169	1.04366672178305
105	13	14.8011674216603	-1.80116742166032
106	17	15.6582118368939	1.34178816310611
107	16	15.7149454231363	0.285054576863653
108	16	14.2651695279916	1.73483047200845
109	12	12.1445025399444	-0.144502539944429
110	16	15.2700317301080	0.729968269891974
111	16	16.0190228965521	-0.0190228965520536
112	17	14.9728991243270	2.02710087567298
113	13	14.6235262603592	-1.62352626035921
114	12	14.5611065152376	-2.56110651523763
115	18	16.182566405197	1.81743359480300
116	14	15.8742392027801	-1.8742392027801
117	14	13.0634243613986	0.93657563860137
118	13	14.7664152300671	-1.7664152300671
119	16	15.57275380411	0.427246195889985
120	13	14.4067456846915	-1.40674568469151
121	16	15.4391647652899	0.56083523471014
122	13	15.8911672712233	-2.89116727122331
123	16	16.9042581868609	-0.904258186860862
124	15	15.8921534889468	-0.892153488946773
125	16	16.9022361707383	-0.9022361707383
126	15	14.7036320628420	0.296367937158032
127	17	15.6113104275905	1.38868957240952
128	15	13.9838607966409	1.01613920335906
129	12	14.7440838384905	-2.74408383849047
130	16	13.9936422626157	2.00635773738434
131	10	13.7576725915982	-3.75767259159823
132	16	13.5176321144828	2.48236788551718
133	12	14.2161631177914	-2.21616311779137
134	14	15.6237963714392	-1.62379637143924
135	15	15.1698667563622	-0.169866756362208
136	13	12.1421557046257	0.857844295374258
137	15	14.5009469200994	0.499053079900579
138	11	13.4686249246835	-2.46862492468351
139	12	13.0157243738587	-1.01572437385866
140	11	13.4194465342762	-2.41944653427621
141	16	12.8831315054463	3.11686849455368
142	15	13.7558505714002	1.24414942859978
143	17	17.0097795066055	-0.00977950660547738
144	16	14.2737946583067	1.7262053416933
145	10	13.3567315625142	-3.35673156251415
146	18	15.6527462059573	2.34725379404273
147	13	15.0228847719538	-2.02288477195385
148	16	14.9620191824016	1.03798081759843
149	13	12.8772591624989	0.122740837501070
150	10	12.9838669107856	-2.98386691078564
151	15	16.1494801243129	-1.14948012431286
152	16	14.0275731378567	1.97242686214327
153	16	11.9110510150754	4.08894898492458
154	14	12.8816202965072	1.11837970349282
155	10	12.5110772982986	-2.51107729829857
156	17	16.6782641138012	0.321735886198799
157	13	11.7748408846068	1.22515911539322
158	15	13.9838607966409	1.01613920335906
159	16	14.7220477570335	1.2779522429665
160	12	12.9024487088536	-0.902448708853636
161	13	12.7234113561509	0.276588643849055
162	13	12.3594447514215	0.640555248578516
163	12	12.2197140578900	-0.219714057889972
164	17	16.1559256930106	0.844074306989396
165	15	13.4927110110311	1.50728898896895
166	10	11.3690856488793	-1.36908564887931
167	14	14.1511837465642	-0.151183746564210
168	11	14.0473814027883	-3.04738140278826
169	13	14.6284207569089	-1.62842075690888
170	16	14.3286401101092	1.67135988989079
171	12	10.2103695148885	1.78963048511152
172	16	15.2294173512872	0.77058264871285
173	12	13.6789589162402	-1.67895891624021
174	9	11.1422436631215	-2.14224366312147
175	12	14.8132494934299	-2.81324949342989
176	15	14.4367200872182	0.56327991278181
177	12	12.0443133302158	-0.0443133302158407
178	12	12.5271258979836	-0.527125897983635
179	14	13.6520380206879	0.347961979312097
180	12	13.2423636290015	-1.24236362900148
181	16	14.9028714322165	1.09712856778351
182	11	11.2308165908512	-0.230816590851180
183	19	16.6302449667749	2.36975503322512
184	15	15.0709573706773	-0.0709573706773391
185	8	14.5002572284975	-6.50025722849752
186	16	14.7385222746564	1.26147772534359
187	17	14.4011157681124	2.59888423188756
188	12	12.2769350009329	-0.276935000932892
189	11	11.3375926910729	-0.337592691072856
190	11	10.2335393682771	0.766460631722878
191	14	14.7383473626722	-0.738347362672238
192	16	15.4068358759607	0.59316412403932
193	12	9.55486413915181	2.44513586084819
194	16	13.9311014734647	2.06889852653535
195	13	13.5896293614340	-0.589629361433967
196	15	14.9324705375573	0.0675294624426769
197	16	12.8970880194329	3.10291198056707
198	16	15.0180094707273	0.981990529272688
199	14	12.3494785543296	1.65052144567040
200	16	14.5413613945402	1.45863860545978
201	16	13.9445932035730	2.05540679642697
202	14	13.3226184610574	0.677381538942598
203	11	13.4275177946162	-2.42751779461623
204	12	14.4367109870216	-2.43671098702159
205	15	12.6699269134548	2.33007308654517
206	15	14.4586524254313	0.541347574568739
207	16	14.4679372463901	1.53206275360994
208	16	14.9316469204900	1.06835307951003
209	11	13.5562780239837	-2.55627802398369
210	15	13.9779398017968	1.02206019820315
211	12	14.2029022057863	-2.20290220578625
212	12	15.7784438651911	-3.77844386519108
213	15	14.1440201791006	0.855979820899417
214	15	12.0307032677153	2.96929673228469
215	16	14.5684829589542	1.43151704104577
216	14	13.0731346341900	0.92686536580997
217	17	14.6746123754413	2.32538762455872
218	14	13.9423598225196	0.0576401774804326
219	13	11.8905028372228	1.10949716277721
220	15	15.2059654363509	-0.205965436350911
221	13	14.6710171706430	-1.67101717064297
222	14	14.1319205655599	-0.131920565559859
223	15	14.3001182465164	0.699881753483601
224	12	13.1092937744911	-1.10929377449114
225	13	12.7116664065425	0.288333593457546
226	8	11.7472131342750	-3.74721313427504
227	14	13.8015801739644	0.198419826035621
228	14	13.0202674279236	0.97973257207643
229	11	12.2297591833158	-1.22975918331585
230	12	12.8968382776945	-0.896838277694458
231	13	11.2452084819542	1.75479151804577
232	10	13.2130657616632	-3.21306576166324
233	16	11.3677174009185	4.63228259908155
234	18	15.9474161696061	2.05258383039394
235	13	13.8693288638037	-0.869328863803698
236	11	13.3430027385425	-2.34300273854246
237	4	10.9726967298314	-6.97269672983141
238	13	14.2678178481433	-1.26781784814330
239	16	14.3069070865238	1.6930929134762
240	10	11.6823923405169	-1.68239234051694
241	12	12.2879119795221	-0.287911979522077
242	12	13.5011921116947	-1.50119211169469
243	10	8.85142253316746	1.14857746683254
244	13	11.1028627902225	1.89713720977750
245	15	13.7092481004796	1.29075189952042
246	12	11.9685057317415	0.0314942682585147
247	14	12.8777319860027	1.12226801399732
248	10	12.5358000227952	-2.53580002279515
249	12	10.7624130858841	1.23758691411588
250	12	11.7393909440044	0.260609055995635
251	11	11.9220624304821	-0.922062430482096
252	10	11.7304631725709	-1.73046317257095
253	12	11.4019661994277	0.59803380057231
254	16	12.8825562632821	3.11744373671786
255	12	13.3765522827852	-1.37655228278524
256	14	13.9048290853521	0.0951709146478995
257	16	14.5198797799651	1.48012022003486
258	14	11.6682798848408	2.33172011515922
259	13	14.3566901566030	-1.35669015660303
260	4	9.3833458059488	-5.38334580594881
261	15	13.8472593284005	1.15274067159949
262	11	15.1083609537208	-4.1083609537208
263	11	11.3476291943178	-0.347629194317837
264	14	12.9144494714043	1.08555052859575

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.249272273417129	0.498544546834257	0.750727726582871
13	0.168954109350983	0.337908218701965	0.831045890649017
14	0.188652474482696	0.377304948965393	0.811347525517304
15	0.113822199570940	0.227644399141881	0.88617780042906
16	0.0773581785451667	0.154716357090333	0.922641821454833
17	0.111089921688014	0.222179843376027	0.888910078311986
18	0.350297244008135	0.70059448801627	0.649702755991865
19	0.273076148217577	0.546152296435153	0.726923851782423
20	0.20036477979948	0.40072955959896	0.79963522020052
21	0.147329038244943	0.294658076489885	0.852670961755057
22	0.142429822152623	0.284859644305245	0.857570177847377
23	0.329613610691874	0.659227221383749	0.670386389308126
24	0.396108111170374	0.792216222340749	0.603891888829626
25	0.364643782622245	0.729287565244489	0.635356217377755
26	0.343013785262702	0.686027570525403	0.656986214737298
27	0.419522216901141	0.839044433802282	0.580477783098859
28	0.431039269566414	0.862078539132827	0.568960730433586
29	0.411178257665486	0.822356515330972	0.588821742334514
30	0.444562864383298	0.889125728766596	0.555437135616702
31	0.389182053396219	0.778364106792438	0.610817946603781
32	0.346620753254168	0.693241506508337	0.653379246745831
33	0.316677130929610	0.633354261859221	0.68332286907039
34	0.275674898300487	0.551349796600974	0.724325101699513
35	0.233139524871994	0.466279049743987	0.766860475128006
36	0.363623899560609	0.727247799121217	0.636376100439392
37	0.400480018023808	0.800960036047616	0.599519981976192
38	0.392518456881072	0.785036913762145	0.607481543118928
39	0.42064689819391	0.84129379638782	0.57935310180609
40	0.394036668820201	0.788073337640401	0.6059633311798
41	0.352608510695984	0.705217021391968	0.647391489304016
42	0.317815560782687	0.635631121565375	0.682184439217313
43	0.330602998434408	0.661205996868816	0.669397001565592
44	0.284193039977059	0.568386079954117	0.715806960022941
45	0.259307353804643	0.518614707609286	0.740692646195357
46	0.457373136543283	0.914746273086566	0.542626863456717
47	0.613015042694859	0.773969914610283	0.386984957305141
48	0.56479096489433	0.870418070211341	0.435209035105671
49	0.550115831249475	0.89976833750105	0.449884168750525
50	0.538886872116339	0.922226255767323	0.461113127883661
51	0.496029187990601	0.992058375981202	0.503970812009399
52	0.448976551667357	0.897953103334714	0.551023448332643
53	0.469409970867621	0.938819941735242	0.530590029132379
54	0.449833835993105	0.89966767198621	0.550166164006895
55	0.452066183365464	0.904132366730929	0.547933816634536
56	0.420708589827937	0.841417179655875	0.579291410172063
57	0.377122146901933	0.754244293803866	0.622877853098067
58	0.343504288197332	0.687008576394664	0.656495711802668
59	0.305381736916119	0.610763473832238	0.694618263083881
60	0.304715664842222	0.609431329684444	0.695284335157778
61	0.274885364314512	0.549770728629024	0.725114635685488
62	0.243391513321	0.486783026642	0.756608486679
63	0.213205555515497	0.426411111030994	0.786794444484503
64	0.184850255438616	0.369700510877231	0.815149744561384
65	0.159612090313782	0.319224180627564	0.840387909686218
66	0.135701682609398	0.271403365218796	0.864298317390602
67	0.118078554966239	0.236157109932478	0.881921445033761
68	0.147491712033944	0.294983424067889	0.852508287966056
69	0.347255538628955	0.694511077257909	0.652744461371045
70	0.309383520800731	0.618767041601461	0.69061647919927
71	0.466018474195675	0.93203694839135	0.533981525804325
72	0.427694883468798	0.855389766937596	0.572305116531202
73	0.415756314370461	0.831512628740923	0.584243685629539
74	0.394545848970234	0.789091697940468	0.605454151029766
75	0.359444377104171	0.718888754208342	0.640555622895829
76	0.40707380269667	0.81414760539334	0.59292619730333
77	0.374300584690822	0.748601169381644	0.625699415309178
78	0.346591344599645	0.693182689199289	0.653408655400355
79	0.375124096917483	0.750248193834965	0.624875903082518
80	0.344523836755938	0.689047673511876	0.655476163244062
81	0.311892769547432	0.623785539094864	0.688107230452568
82	0.277800972444901	0.555601944889803	0.722199027555099
83	0.254136867114173	0.508273734228345	0.745863132885827
84	0.223381795663675	0.44676359132735	0.776618204336325
85	0.213624303551213	0.427248607102426	0.786375696448787
86	0.186095183511232	0.372190367022464	0.813904816488768
87	0.162784483325637	0.325568966651275	0.837215516674362
88	0.147969314030638	0.295938628061275	0.852030685969362
89	0.127536795735545	0.255073591471089	0.872463204264455
90	0.129326332967500	0.258652665934999	0.870673667032500
91	0.111535331873574	0.223070663747147	0.888464668126426
92	0.0961283982313728	0.192256796462746	0.903871601768627
93	0.0825502956078289	0.165100591215658	0.917449704392171
94	0.0877425387684936	0.175485077536987	0.912257461231506
95	0.0780879857657552	0.156175971531510	0.921912014234245
96	0.0658294056936897	0.131658811387379	0.93417059430631
97	0.068906807489887	0.137813614979774	0.931093192510113
98	0.0573579515440505	0.114715903088101	0.94264204845595
99	0.0475906360332567	0.0951812720665134	0.952409363966743
100	0.0425438584868564	0.0850877169737128	0.957456141513144
101	0.0369859310294397	0.0739718620588795	0.96301406897056
102	0.0420002433756051	0.0840004867512103	0.957999756624395
103	0.0356653898073750	0.0713307796147499	0.964334610192625
104	0.0316665200782306	0.0633330401564613	0.96833347992177
105	0.0388094976881857	0.0776189953763714	0.961190502311814
106	0.0335801030215315	0.0671602060430631	0.966419896978469
107	0.0272903859757292	0.0545807719514584	0.97270961402427
108	0.0260134149836965	0.0520268299673931	0.973986585016303
109	0.0212221299636422	0.0424442599272844	0.978777870036358
110	0.0172038184676234	0.0344076369352469	0.982796181532377
111	0.0136321859824952	0.0272643719649905	0.986367814017505
112	0.0136227566436758	0.0272455132873516	0.986377243356324
113	0.0124872959699899	0.0249745919399798	0.98751270403001
114	0.0191461758204056	0.0382923516408112	0.980853824179594
115	0.0177617143290989	0.0355234286581978	0.982238285670901
116	0.017603646006393	0.035207292012786	0.982396353993607
117	0.0143650816437960	0.0287301632875919	0.985634918356204
118	0.0149333160946058	0.0298666321892116	0.985066683905394
119	0.0120691161964736	0.0241382323929472	0.987930883803526
120	0.0110172150504946	0.0220344301009892	0.988982784949505
121	0.00884927981957504	0.0176985596391501	0.991150720180425
122	0.0140520169939783	0.0281040339879566	0.985947983006022
123	0.0120431244374353	0.0240862488748705	0.987956875562565
124	0.0108230439579138	0.0216460879158275	0.989176956042086
125	0.00903731082126301	0.0180746216425260	0.990962689178737
126	0.00728451036486331	0.0145690207297266	0.992715489635137
127	0.0063991416359108	0.0127982832718216	0.99360085836409
128	0.00510659099347213	0.0102131819869443	0.994893409006528
129	0.00783375065335833	0.0156675013067167	0.992166249346642
130	0.00807963042126135	0.0161592608425227	0.991920369578739
131	0.0180949330914169	0.0361898661828337	0.981905066908583
132	0.0203689644007127	0.0407379288014254	0.979631035599287
133	0.0232617612230514	0.0465235224461027	0.976738238776949
134	0.0232562238225967	0.0465124476451935	0.976743776177403
135	0.0190360379134759	0.0380720758269517	0.980963962086524
136	0.0156372488519367	0.0312744977038734	0.984362751148063
137	0.0124430144348134	0.0248860288696268	0.987556985565187
138	0.0165772691347347	0.0331545382694695	0.983422730865265
139	0.0149240627111017	0.0298481254222034	0.985075937288898
140	0.0190311552513490	0.0380623105026979	0.980968844748651
141	0.0261129296616781	0.0522258593233561	0.973887070338322
142	0.0232966146305604	0.0465932292611207	0.97670338536944
143	0.0190133879411869	0.0380267758823737	0.980986612058813
144	0.0180774558323928	0.0361549116647857	0.981922544167607
145	0.035502615792114	0.071005231584228	0.964497384207886
146	0.0384221966721238	0.0768443933442476	0.961577803327876
147	0.0431987522006693	0.0863975044013385	0.95680124779933
148	0.0369645849533085	0.073929169906617	0.963035415046692
149	0.0303268301811558	0.0606536603623117	0.969673169818844
150	0.0457293372484414	0.0914586744968829	0.954270662751558
151	0.0413811043222066	0.0827622086444131	0.958618895677793
152	0.041087091509391	0.082174183018782	0.95891290849061
153	0.0717081799115365	0.143416359823073	0.928291820088463
154	0.0631597123470018	0.126319424694004	0.936840287652998
155	0.0771128120061032	0.154225624012206	0.922887187993897
156	0.0648148402268047	0.129629680453609	0.935185159773195
157	0.0564212903309111	0.112842580661822	0.943578709669089
158	0.0478502954782773	0.0957005909565547	0.952149704521723
159	0.0454570121941229	0.0909140243882458	0.954542987805877
160	0.0390355339078229	0.0780710678156458	0.960964466092177
161	0.031868811329903	0.063737622659806	0.968131188670097
162	0.0261879326304385	0.0523758652608771	0.973812067369561
163	0.0215813417593278	0.0431626835186556	0.978418658240672
164	0.0182524223098581	0.0365048446197162	0.981747577690142
165	0.0161383303745537	0.0322766607491074	0.983861669625446
166	0.0162516658896448	0.0325033317792897	0.983748334110355
167	0.0129568005570451	0.0259136011140902	0.987043199442955
168	0.0184794173862801	0.0369588347725603	0.98152058261372
169	0.0178942596272692	0.0357885192545384	0.98210574037273
170	0.0170189181653016	0.0340378363306032	0.982981081834698
171	0.0165616674819240	0.0331233349638481	0.983438332518076
172	0.0134360476918214	0.0268720953836428	0.986563952308179
173	0.0129583555085127	0.0259167110170254	0.987041644491487
174	0.0138774468141623	0.0277548936283247	0.986122553185838
175	0.0172407628840277	0.0344815257680554	0.982759237115972
176	0.0138951737672713	0.0277903475345426	0.986104826232729
177	0.0110519988555190	0.0221039977110380	0.98894800114448
178	0.00887074341861827	0.0177414868372365	0.991129256581382
179	0.00688321602168362	0.0137664320433672	0.993116783978316
180	0.00577443562849701	0.0115488712569940	0.994225564371503
181	0.00485393565206548	0.00970787130413096	0.995146064347935
182	0.00378879916375457	0.00757759832750914	0.996211200836245
183	0.00424149949036701	0.00848299898073403	0.995758500509633
184	0.00327054716546522	0.00654109433093044	0.996729452834535
185	0.0728407105468326	0.145681421093665	0.927159289453167
186	0.0638369390091457	0.127673878018291	0.936163060990854
187	0.0755513774202354	0.151102754840471	0.924448622579765
188	0.0640371387060657	0.128074277412131	0.935962861293934
189	0.053654156399932	0.107308312799864	0.946345843600068
190	0.0443083311324279	0.0886166622648558	0.955691668867572
191	0.0387335344688042	0.0774670689376083	0.961266465531196
192	0.0324804456063726	0.0649608912127451	0.967519554393627
193	0.0387770188044993	0.0775540376089986	0.96122298119550
194	0.0425803720705000	0.0851607441409999	0.9574196279295
195	0.0355414797065166	0.0710829594130331	0.964458520293483
196	0.0291430151993401	0.0582860303986801	0.97085698480066
197	0.0393432212360945	0.0786864424721889	0.960656778763906
198	0.0321751746361222	0.0643503492722445	0.967824825363878
199	0.0299691694417933	0.0599383388835865	0.970030830558207
200	0.0254685360251700	0.0509370720503399	0.97453146397483
201	0.024209926192595	0.04841985238519	0.975790073807405
202	0.0201830070835602	0.0403660141671205	0.97981699291644
203	0.0255271301508270	0.0510542603016541	0.974472869849173
204	0.0280598687947857	0.0561197375895714	0.971940131205214
205	0.0312224340495115	0.062444868099023	0.968777565950488
206	0.0245716207548289	0.0491432415096578	0.97542837924517
207	0.0242699128877636	0.0485398257755272	0.975730087112236
208	0.0199692881585589	0.0399385763171178	0.98003071184144
209	0.021672831963285	0.04334566392657	0.978327168036715
210	0.0174563467996671	0.0349126935993342	0.982543653200333
211	0.0189292802277185	0.0378585604554370	0.981070719772281
212	0.0292703191646896	0.0585406383293791	0.97072968083531
213	0.0228693544218276	0.0457387088436552	0.977130645578172
214	0.0330240210408292	0.0660480420816584	0.96697597895917
215	0.028456855033694	0.056913710067388	0.971543144966306
216	0.0220849841128482	0.0441699682256964	0.977915015887152
217	0.0243591123907347	0.0487182247814694	0.975640887609265
218	0.0204895888266626	0.0409791776533253	0.979510411173337
219	0.0183205505059492	0.0366411010118983	0.98167944949405
220	0.0137547982773799	0.0275095965547599	0.98624520172262
221	0.0119272356527656	0.0238544713055312	0.988072764347234
222	0.00858122100533887	0.0171624420106777	0.99141877899466
223	0.00634657417409705	0.0126931483481941	0.993653425825903
224	0.00496471680285489	0.00992943360570977	0.995035283197145
225	0.00429464582214436	0.00858929164428872	0.995705354177856
226	0.00994629167948214	0.0198925833589643	0.990053708320518
227	0.00753438359405254	0.0150687671881051	0.992465616405947
228	0.00537338633076858	0.0107467726615372	0.994626613669231
229	0.00380717833516555	0.0076143566703311	0.996192821664834
230	0.00266471220180094	0.00532942440360187	0.9973352877982
231	0.00267158170978464	0.00534316341956929	0.997328418290215
232	0.00494336392668188	0.00988672785336376	0.995056636073318
233	0.0259467354434979	0.0518934708869958	0.974053264556502
234	0.0384579406778061	0.0769158813556122	0.961542059322194
235	0.0277187884740289	0.0554375769480579	0.97228121152597
236	0.0239618139472759	0.0479236278945519	0.976038186052724
237	0.187771175358450	0.375542350716899	0.812228824641550
238	0.147044861176903	0.294089722353805	0.852955138823097
239	0.130216166245390	0.260432332490781	0.86978383375461
240	0.0988684339233115	0.197736867846623	0.901131566076689
241	0.0766001416875223	0.153200283375045	0.923399858312478
242	0.0614634178432018	0.122926835686404	0.938536582156798
243	0.0562097761470191	0.112419552294038	0.94379022385298
244	0.103562675764338	0.207125351528676	0.896437324235662
245	0.0907837726734566	0.181567545346913	0.909216227326543
246	0.0696382930217311	0.139276586043462	0.930361706978269
247	0.0465296435673761	0.0930592871347521	0.953470356432624
248	0.0378059998785717	0.0756119997571434	0.962194000121428
249	0.0340747449655495	0.068149489931099	0.96592525503445
250	0.0414488690680374	0.0828977381360748	0.958551130931963
251	0.0218224478596724	0.0436448957193448	0.978177552140328
252	0.0547426763653312	0.109485352730662	0.945257323634669

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.249272273417129 & 0.498544546834257 & 0.750727726582871 \tabularnewline
13 & 0.168954109350983 & 0.337908218701965 & 0.831045890649017 \tabularnewline
14 & 0.188652474482696 & 0.377304948965393 & 0.811347525517304 \tabularnewline
15 & 0.113822199570940 & 0.227644399141881 & 0.88617780042906 \tabularnewline
16 & 0.0773581785451667 & 0.154716357090333 & 0.922641821454833 \tabularnewline
17 & 0.111089921688014 & 0.222179843376027 & 0.888910078311986 \tabularnewline
18 & 0.350297244008135 & 0.70059448801627 & 0.649702755991865 \tabularnewline
19 & 0.273076148217577 & 0.546152296435153 & 0.726923851782423 \tabularnewline
20 & 0.20036477979948 & 0.40072955959896 & 0.79963522020052 \tabularnewline
21 & 0.147329038244943 & 0.294658076489885 & 0.852670961755057 \tabularnewline
22 & 0.142429822152623 & 0.284859644305245 & 0.857570177847377 \tabularnewline
23 & 0.329613610691874 & 0.659227221383749 & 0.670386389308126 \tabularnewline
24 & 0.396108111170374 & 0.792216222340749 & 0.603891888829626 \tabularnewline
25 & 0.364643782622245 & 0.729287565244489 & 0.635356217377755 \tabularnewline
26 & 0.343013785262702 & 0.686027570525403 & 0.656986214737298 \tabularnewline
27 & 0.419522216901141 & 0.839044433802282 & 0.580477783098859 \tabularnewline
28 & 0.431039269566414 & 0.862078539132827 & 0.568960730433586 \tabularnewline
29 & 0.411178257665486 & 0.822356515330972 & 0.588821742334514 \tabularnewline
30 & 0.444562864383298 & 0.889125728766596 & 0.555437135616702 \tabularnewline
31 & 0.389182053396219 & 0.778364106792438 & 0.610817946603781 \tabularnewline
32 & 0.346620753254168 & 0.693241506508337 & 0.653379246745831 \tabularnewline
33 & 0.316677130929610 & 0.633354261859221 & 0.68332286907039 \tabularnewline
34 & 0.275674898300487 & 0.551349796600974 & 0.724325101699513 \tabularnewline
35 & 0.233139524871994 & 0.466279049743987 & 0.766860475128006 \tabularnewline
36 & 0.363623899560609 & 0.727247799121217 & 0.636376100439392 \tabularnewline
37 & 0.400480018023808 & 0.800960036047616 & 0.599519981976192 \tabularnewline
38 & 0.392518456881072 & 0.785036913762145 & 0.607481543118928 \tabularnewline
39 & 0.42064689819391 & 0.84129379638782 & 0.57935310180609 \tabularnewline
40 & 0.394036668820201 & 0.788073337640401 & 0.6059633311798 \tabularnewline
41 & 0.352608510695984 & 0.705217021391968 & 0.647391489304016 \tabularnewline
42 & 0.317815560782687 & 0.635631121565375 & 0.682184439217313 \tabularnewline
43 & 0.330602998434408 & 0.661205996868816 & 0.669397001565592 \tabularnewline
44 & 0.284193039977059 & 0.568386079954117 & 0.715806960022941 \tabularnewline
45 & 0.259307353804643 & 0.518614707609286 & 0.740692646195357 \tabularnewline
46 & 0.457373136543283 & 0.914746273086566 & 0.542626863456717 \tabularnewline
47 & 0.613015042694859 & 0.773969914610283 & 0.386984957305141 \tabularnewline
48 & 0.56479096489433 & 0.870418070211341 & 0.435209035105671 \tabularnewline
49 & 0.550115831249475 & 0.89976833750105 & 0.449884168750525 \tabularnewline
50 & 0.538886872116339 & 0.922226255767323 & 0.461113127883661 \tabularnewline
51 & 0.496029187990601 & 0.992058375981202 & 0.503970812009399 \tabularnewline
52 & 0.448976551667357 & 0.897953103334714 & 0.551023448332643 \tabularnewline
53 & 0.469409970867621 & 0.938819941735242 & 0.530590029132379 \tabularnewline
54 & 0.449833835993105 & 0.89966767198621 & 0.550166164006895 \tabularnewline
55 & 0.452066183365464 & 0.904132366730929 & 0.547933816634536 \tabularnewline
56 & 0.420708589827937 & 0.841417179655875 & 0.579291410172063 \tabularnewline
57 & 0.377122146901933 & 0.754244293803866 & 0.622877853098067 \tabularnewline
58 & 0.343504288197332 & 0.687008576394664 & 0.656495711802668 \tabularnewline
59 & 0.305381736916119 & 0.610763473832238 & 0.694618263083881 \tabularnewline
60 & 0.304715664842222 & 0.609431329684444 & 0.695284335157778 \tabularnewline
61 & 0.274885364314512 & 0.549770728629024 & 0.725114635685488 \tabularnewline
62 & 0.243391513321 & 0.486783026642 & 0.756608486679 \tabularnewline
63 & 0.213205555515497 & 0.426411111030994 & 0.786794444484503 \tabularnewline
64 & 0.184850255438616 & 0.369700510877231 & 0.815149744561384 \tabularnewline
65 & 0.159612090313782 & 0.319224180627564 & 0.840387909686218 \tabularnewline
66 & 0.135701682609398 & 0.271403365218796 & 0.864298317390602 \tabularnewline
67 & 0.118078554966239 & 0.236157109932478 & 0.881921445033761 \tabularnewline
68 & 0.147491712033944 & 0.294983424067889 & 0.852508287966056 \tabularnewline
69 & 0.347255538628955 & 0.694511077257909 & 0.652744461371045 \tabularnewline
70 & 0.309383520800731 & 0.618767041601461 & 0.69061647919927 \tabularnewline
71 & 0.466018474195675 & 0.93203694839135 & 0.533981525804325 \tabularnewline
72 & 0.427694883468798 & 0.855389766937596 & 0.572305116531202 \tabularnewline
73 & 0.415756314370461 & 0.831512628740923 & 0.584243685629539 \tabularnewline
74 & 0.394545848970234 & 0.789091697940468 & 0.605454151029766 \tabularnewline
75 & 0.359444377104171 & 0.718888754208342 & 0.640555622895829 \tabularnewline
76 & 0.40707380269667 & 0.81414760539334 & 0.59292619730333 \tabularnewline
77 & 0.374300584690822 & 0.748601169381644 & 0.625699415309178 \tabularnewline
78 & 0.346591344599645 & 0.693182689199289 & 0.653408655400355 \tabularnewline
79 & 0.375124096917483 & 0.750248193834965 & 0.624875903082518 \tabularnewline
80 & 0.344523836755938 & 0.689047673511876 & 0.655476163244062 \tabularnewline
81 & 0.311892769547432 & 0.623785539094864 & 0.688107230452568 \tabularnewline
82 & 0.277800972444901 & 0.555601944889803 & 0.722199027555099 \tabularnewline
83 & 0.254136867114173 & 0.508273734228345 & 0.745863132885827 \tabularnewline
84 & 0.223381795663675 & 0.44676359132735 & 0.776618204336325 \tabularnewline
85 & 0.213624303551213 & 0.427248607102426 & 0.786375696448787 \tabularnewline
86 & 0.186095183511232 & 0.372190367022464 & 0.813904816488768 \tabularnewline
87 & 0.162784483325637 & 0.325568966651275 & 0.837215516674362 \tabularnewline
88 & 0.147969314030638 & 0.295938628061275 & 0.852030685969362 \tabularnewline
89 & 0.127536795735545 & 0.255073591471089 & 0.872463204264455 \tabularnewline
90 & 0.129326332967500 & 0.258652665934999 & 0.870673667032500 \tabularnewline
91 & 0.111535331873574 & 0.223070663747147 & 0.888464668126426 \tabularnewline
92 & 0.0961283982313728 & 0.192256796462746 & 0.903871601768627 \tabularnewline
93 & 0.0825502956078289 & 0.165100591215658 & 0.917449704392171 \tabularnewline
94 & 0.0877425387684936 & 0.175485077536987 & 0.912257461231506 \tabularnewline
95 & 0.0780879857657552 & 0.156175971531510 & 0.921912014234245 \tabularnewline
96 & 0.0658294056936897 & 0.131658811387379 & 0.93417059430631 \tabularnewline
97 & 0.068906807489887 & 0.137813614979774 & 0.931093192510113 \tabularnewline
98 & 0.0573579515440505 & 0.114715903088101 & 0.94264204845595 \tabularnewline
99 & 0.0475906360332567 & 0.0951812720665134 & 0.952409363966743 \tabularnewline
100 & 0.0425438584868564 & 0.0850877169737128 & 0.957456141513144 \tabularnewline
101 & 0.0369859310294397 & 0.0739718620588795 & 0.96301406897056 \tabularnewline
102 & 0.0420002433756051 & 0.0840004867512103 & 0.957999756624395 \tabularnewline
103 & 0.0356653898073750 & 0.0713307796147499 & 0.964334610192625 \tabularnewline
104 & 0.0316665200782306 & 0.0633330401564613 & 0.96833347992177 \tabularnewline
105 & 0.0388094976881857 & 0.0776189953763714 & 0.961190502311814 \tabularnewline
106 & 0.0335801030215315 & 0.0671602060430631 & 0.966419896978469 \tabularnewline
107 & 0.0272903859757292 & 0.0545807719514584 & 0.97270961402427 \tabularnewline
108 & 0.0260134149836965 & 0.0520268299673931 & 0.973986585016303 \tabularnewline
109 & 0.0212221299636422 & 0.0424442599272844 & 0.978777870036358 \tabularnewline
110 & 0.0172038184676234 & 0.0344076369352469 & 0.982796181532377 \tabularnewline
111 & 0.0136321859824952 & 0.0272643719649905 & 0.986367814017505 \tabularnewline
112 & 0.0136227566436758 & 0.0272455132873516 & 0.986377243356324 \tabularnewline
113 & 0.0124872959699899 & 0.0249745919399798 & 0.98751270403001 \tabularnewline
114 & 0.0191461758204056 & 0.0382923516408112 & 0.980853824179594 \tabularnewline
115 & 0.0177617143290989 & 0.0355234286581978 & 0.982238285670901 \tabularnewline
116 & 0.017603646006393 & 0.035207292012786 & 0.982396353993607 \tabularnewline
117 & 0.0143650816437960 & 0.0287301632875919 & 0.985634918356204 \tabularnewline
118 & 0.0149333160946058 & 0.0298666321892116 & 0.985066683905394 \tabularnewline
119 & 0.0120691161964736 & 0.0241382323929472 & 0.987930883803526 \tabularnewline
120 & 0.0110172150504946 & 0.0220344301009892 & 0.988982784949505 \tabularnewline
121 & 0.00884927981957504 & 0.0176985596391501 & 0.991150720180425 \tabularnewline
122 & 0.0140520169939783 & 0.0281040339879566 & 0.985947983006022 \tabularnewline
123 & 0.0120431244374353 & 0.0240862488748705 & 0.987956875562565 \tabularnewline
124 & 0.0108230439579138 & 0.0216460879158275 & 0.989176956042086 \tabularnewline
125 & 0.00903731082126301 & 0.0180746216425260 & 0.990962689178737 \tabularnewline
126 & 0.00728451036486331 & 0.0145690207297266 & 0.992715489635137 \tabularnewline
127 & 0.0063991416359108 & 0.0127982832718216 & 0.99360085836409 \tabularnewline
128 & 0.00510659099347213 & 0.0102131819869443 & 0.994893409006528 \tabularnewline
129 & 0.00783375065335833 & 0.0156675013067167 & 0.992166249346642 \tabularnewline
130 & 0.00807963042126135 & 0.0161592608425227 & 0.991920369578739 \tabularnewline
131 & 0.0180949330914169 & 0.0361898661828337 & 0.981905066908583 \tabularnewline
132 & 0.0203689644007127 & 0.0407379288014254 & 0.979631035599287 \tabularnewline
133 & 0.0232617612230514 & 0.0465235224461027 & 0.976738238776949 \tabularnewline
134 & 0.0232562238225967 & 0.0465124476451935 & 0.976743776177403 \tabularnewline
135 & 0.0190360379134759 & 0.0380720758269517 & 0.980963962086524 \tabularnewline
136 & 0.0156372488519367 & 0.0312744977038734 & 0.984362751148063 \tabularnewline
137 & 0.0124430144348134 & 0.0248860288696268 & 0.987556985565187 \tabularnewline
138 & 0.0165772691347347 & 0.0331545382694695 & 0.983422730865265 \tabularnewline
139 & 0.0149240627111017 & 0.0298481254222034 & 0.985075937288898 \tabularnewline
140 & 0.0190311552513490 & 0.0380623105026979 & 0.980968844748651 \tabularnewline
141 & 0.0261129296616781 & 0.0522258593233561 & 0.973887070338322 \tabularnewline
142 & 0.0232966146305604 & 0.0465932292611207 & 0.97670338536944 \tabularnewline
143 & 0.0190133879411869 & 0.0380267758823737 & 0.980986612058813 \tabularnewline
144 & 0.0180774558323928 & 0.0361549116647857 & 0.981922544167607 \tabularnewline
145 & 0.035502615792114 & 0.071005231584228 & 0.964497384207886 \tabularnewline
146 & 0.0384221966721238 & 0.0768443933442476 & 0.961577803327876 \tabularnewline
147 & 0.0431987522006693 & 0.0863975044013385 & 0.95680124779933 \tabularnewline
148 & 0.0369645849533085 & 0.073929169906617 & 0.963035415046692 \tabularnewline
149 & 0.0303268301811558 & 0.0606536603623117 & 0.969673169818844 \tabularnewline
150 & 0.0457293372484414 & 0.0914586744968829 & 0.954270662751558 \tabularnewline
151 & 0.0413811043222066 & 0.0827622086444131 & 0.958618895677793 \tabularnewline
152 & 0.041087091509391 & 0.082174183018782 & 0.95891290849061 \tabularnewline
153 & 0.0717081799115365 & 0.143416359823073 & 0.928291820088463 \tabularnewline
154 & 0.0631597123470018 & 0.126319424694004 & 0.936840287652998 \tabularnewline
155 & 0.0771128120061032 & 0.154225624012206 & 0.922887187993897 \tabularnewline
156 & 0.0648148402268047 & 0.129629680453609 & 0.935185159773195 \tabularnewline
157 & 0.0564212903309111 & 0.112842580661822 & 0.943578709669089 \tabularnewline
158 & 0.0478502954782773 & 0.0957005909565547 & 0.952149704521723 \tabularnewline
159 & 0.0454570121941229 & 0.0909140243882458 & 0.954542987805877 \tabularnewline
160 & 0.0390355339078229 & 0.0780710678156458 & 0.960964466092177 \tabularnewline
161 & 0.031868811329903 & 0.063737622659806 & 0.968131188670097 \tabularnewline
162 & 0.0261879326304385 & 0.0523758652608771 & 0.973812067369561 \tabularnewline
163 & 0.0215813417593278 & 0.0431626835186556 & 0.978418658240672 \tabularnewline
164 & 0.0182524223098581 & 0.0365048446197162 & 0.981747577690142 \tabularnewline
165 & 0.0161383303745537 & 0.0322766607491074 & 0.983861669625446 \tabularnewline
166 & 0.0162516658896448 & 0.0325033317792897 & 0.983748334110355 \tabularnewline
167 & 0.0129568005570451 & 0.0259136011140902 & 0.987043199442955 \tabularnewline
168 & 0.0184794173862801 & 0.0369588347725603 & 0.98152058261372 \tabularnewline
169 & 0.0178942596272692 & 0.0357885192545384 & 0.98210574037273 \tabularnewline
170 & 0.0170189181653016 & 0.0340378363306032 & 0.982981081834698 \tabularnewline
171 & 0.0165616674819240 & 0.0331233349638481 & 0.983438332518076 \tabularnewline
172 & 0.0134360476918214 & 0.0268720953836428 & 0.986563952308179 \tabularnewline
173 & 0.0129583555085127 & 0.0259167110170254 & 0.987041644491487 \tabularnewline
174 & 0.0138774468141623 & 0.0277548936283247 & 0.986122553185838 \tabularnewline
175 & 0.0172407628840277 & 0.0344815257680554 & 0.982759237115972 \tabularnewline
176 & 0.0138951737672713 & 0.0277903475345426 & 0.986104826232729 \tabularnewline
177 & 0.0110519988555190 & 0.0221039977110380 & 0.98894800114448 \tabularnewline
178 & 0.00887074341861827 & 0.0177414868372365 & 0.991129256581382 \tabularnewline
179 & 0.00688321602168362 & 0.0137664320433672 & 0.993116783978316 \tabularnewline
180 & 0.00577443562849701 & 0.0115488712569940 & 0.994225564371503 \tabularnewline
181 & 0.00485393565206548 & 0.00970787130413096 & 0.995146064347935 \tabularnewline
182 & 0.00378879916375457 & 0.00757759832750914 & 0.996211200836245 \tabularnewline
183 & 0.00424149949036701 & 0.00848299898073403 & 0.995758500509633 \tabularnewline
184 & 0.00327054716546522 & 0.00654109433093044 & 0.996729452834535 \tabularnewline
185 & 0.0728407105468326 & 0.145681421093665 & 0.927159289453167 \tabularnewline
186 & 0.0638369390091457 & 0.127673878018291 & 0.936163060990854 \tabularnewline
187 & 0.0755513774202354 & 0.151102754840471 & 0.924448622579765 \tabularnewline
188 & 0.0640371387060657 & 0.128074277412131 & 0.935962861293934 \tabularnewline
189 & 0.053654156399932 & 0.107308312799864 & 0.946345843600068 \tabularnewline
190 & 0.0443083311324279 & 0.0886166622648558 & 0.955691668867572 \tabularnewline
191 & 0.0387335344688042 & 0.0774670689376083 & 0.961266465531196 \tabularnewline
192 & 0.0324804456063726 & 0.0649608912127451 & 0.967519554393627 \tabularnewline
193 & 0.0387770188044993 & 0.0775540376089986 & 0.96122298119550 \tabularnewline
194 & 0.0425803720705000 & 0.0851607441409999 & 0.9574196279295 \tabularnewline
195 & 0.0355414797065166 & 0.0710829594130331 & 0.964458520293483 \tabularnewline
196 & 0.0291430151993401 & 0.0582860303986801 & 0.97085698480066 \tabularnewline
197 & 0.0393432212360945 & 0.0786864424721889 & 0.960656778763906 \tabularnewline
198 & 0.0321751746361222 & 0.0643503492722445 & 0.967824825363878 \tabularnewline
199 & 0.0299691694417933 & 0.0599383388835865 & 0.970030830558207 \tabularnewline
200 & 0.0254685360251700 & 0.0509370720503399 & 0.97453146397483 \tabularnewline
201 & 0.024209926192595 & 0.04841985238519 & 0.975790073807405 \tabularnewline
202 & 0.0201830070835602 & 0.0403660141671205 & 0.97981699291644 \tabularnewline
203 & 0.0255271301508270 & 0.0510542603016541 & 0.974472869849173 \tabularnewline
204 & 0.0280598687947857 & 0.0561197375895714 & 0.971940131205214 \tabularnewline
205 & 0.0312224340495115 & 0.062444868099023 & 0.968777565950488 \tabularnewline
206 & 0.0245716207548289 & 0.0491432415096578 & 0.97542837924517 \tabularnewline
207 & 0.0242699128877636 & 0.0485398257755272 & 0.975730087112236 \tabularnewline
208 & 0.0199692881585589 & 0.0399385763171178 & 0.98003071184144 \tabularnewline
209 & 0.021672831963285 & 0.04334566392657 & 0.978327168036715 \tabularnewline
210 & 0.0174563467996671 & 0.0349126935993342 & 0.982543653200333 \tabularnewline
211 & 0.0189292802277185 & 0.0378585604554370 & 0.981070719772281 \tabularnewline
212 & 0.0292703191646896 & 0.0585406383293791 & 0.97072968083531 \tabularnewline
213 & 0.0228693544218276 & 0.0457387088436552 & 0.977130645578172 \tabularnewline
214 & 0.0330240210408292 & 0.0660480420816584 & 0.96697597895917 \tabularnewline
215 & 0.028456855033694 & 0.056913710067388 & 0.971543144966306 \tabularnewline
216 & 0.0220849841128482 & 0.0441699682256964 & 0.977915015887152 \tabularnewline
217 & 0.0243591123907347 & 0.0487182247814694 & 0.975640887609265 \tabularnewline
218 & 0.0204895888266626 & 0.0409791776533253 & 0.979510411173337 \tabularnewline
219 & 0.0183205505059492 & 0.0366411010118983 & 0.98167944949405 \tabularnewline
220 & 0.0137547982773799 & 0.0275095965547599 & 0.98624520172262 \tabularnewline
221 & 0.0119272356527656 & 0.0238544713055312 & 0.988072764347234 \tabularnewline
222 & 0.00858122100533887 & 0.0171624420106777 & 0.99141877899466 \tabularnewline
223 & 0.00634657417409705 & 0.0126931483481941 & 0.993653425825903 \tabularnewline
224 & 0.00496471680285489 & 0.00992943360570977 & 0.995035283197145 \tabularnewline
225 & 0.00429464582214436 & 0.00858929164428872 & 0.995705354177856 \tabularnewline
226 & 0.00994629167948214 & 0.0198925833589643 & 0.990053708320518 \tabularnewline
227 & 0.00753438359405254 & 0.0150687671881051 & 0.992465616405947 \tabularnewline
228 & 0.00537338633076858 & 0.0107467726615372 & 0.994626613669231 \tabularnewline
229 & 0.00380717833516555 & 0.0076143566703311 & 0.996192821664834 \tabularnewline
230 & 0.00266471220180094 & 0.00532942440360187 & 0.9973352877982 \tabularnewline
231 & 0.00267158170978464 & 0.00534316341956929 & 0.997328418290215 \tabularnewline
232 & 0.00494336392668188 & 0.00988672785336376 & 0.995056636073318 \tabularnewline
233 & 0.0259467354434979 & 0.0518934708869958 & 0.974053264556502 \tabularnewline
234 & 0.0384579406778061 & 0.0769158813556122 & 0.961542059322194 \tabularnewline
235 & 0.0277187884740289 & 0.0554375769480579 & 0.97228121152597 \tabularnewline
236 & 0.0239618139472759 & 0.0479236278945519 & 0.976038186052724 \tabularnewline
237 & 0.187771175358450 & 0.375542350716899 & 0.812228824641550 \tabularnewline
238 & 0.147044861176903 & 0.294089722353805 & 0.852955138823097 \tabularnewline
239 & 0.130216166245390 & 0.260432332490781 & 0.86978383375461 \tabularnewline
240 & 0.0988684339233115 & 0.197736867846623 & 0.901131566076689 \tabularnewline
241 & 0.0766001416875223 & 0.153200283375045 & 0.923399858312478 \tabularnewline
242 & 0.0614634178432018 & 0.122926835686404 & 0.938536582156798 \tabularnewline
243 & 0.0562097761470191 & 0.112419552294038 & 0.94379022385298 \tabularnewline
244 & 0.103562675764338 & 0.207125351528676 & 0.896437324235662 \tabularnewline
245 & 0.0907837726734566 & 0.181567545346913 & 0.909216227326543 \tabularnewline
246 & 0.0696382930217311 & 0.139276586043462 & 0.930361706978269 \tabularnewline
247 & 0.0465296435673761 & 0.0930592871347521 & 0.953470356432624 \tabularnewline
248 & 0.0378059998785717 & 0.0756119997571434 & 0.962194000121428 \tabularnewline
249 & 0.0340747449655495 & 0.068149489931099 & 0.96592525503445 \tabularnewline
250 & 0.0414488690680374 & 0.0828977381360748 & 0.958551130931963 \tabularnewline
251 & 0.0218224478596724 & 0.0436448957193448 & 0.978177552140328 \tabularnewline
252 & 0.0547426763653312 & 0.109485352730662 & 0.945257323634669 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96541&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C]0.249272273417129[/C][C]0.498544546834257[/C][C]0.750727726582871[/C][/ROW]
[ROW][C]13[/C][C]0.168954109350983[/C][C]0.337908218701965[/C][C]0.831045890649017[/C][/ROW]
[ROW][C]14[/C][C]0.188652474482696[/C][C]0.377304948965393[/C][C]0.811347525517304[/C][/ROW]
[ROW][C]15[/C][C]0.113822199570940[/C][C]0.227644399141881[/C][C]0.88617780042906[/C][/ROW]
[ROW][C]16[/C][C]0.0773581785451667[/C][C]0.154716357090333[/C][C]0.922641821454833[/C][/ROW]
[ROW][C]17[/C][C]0.111089921688014[/C][C]0.222179843376027[/C][C]0.888910078311986[/C][/ROW]
[ROW][C]18[/C][C]0.350297244008135[/C][C]0.70059448801627[/C][C]0.649702755991865[/C][/ROW]
[ROW][C]19[/C][C]0.273076148217577[/C][C]0.546152296435153[/C][C]0.726923851782423[/C][/ROW]
[ROW][C]20[/C][C]0.20036477979948[/C][C]0.40072955959896[/C][C]0.79963522020052[/C][/ROW]
[ROW][C]21[/C][C]0.147329038244943[/C][C]0.294658076489885[/C][C]0.852670961755057[/C][/ROW]
[ROW][C]22[/C][C]0.142429822152623[/C][C]0.284859644305245[/C][C]0.857570177847377[/C][/ROW]
[ROW][C]23[/C][C]0.329613610691874[/C][C]0.659227221383749[/C][C]0.670386389308126[/C][/ROW]
[ROW][C]24[/C][C]0.396108111170374[/C][C]0.792216222340749[/C][C]0.603891888829626[/C][/ROW]
[ROW][C]25[/C][C]0.364643782622245[/C][C]0.729287565244489[/C][C]0.635356217377755[/C][/ROW]
[ROW][C]26[/C][C]0.343013785262702[/C][C]0.686027570525403[/C][C]0.656986214737298[/C][/ROW]
[ROW][C]27[/C][C]0.419522216901141[/C][C]0.839044433802282[/C][C]0.580477783098859[/C][/ROW]
[ROW][C]28[/C][C]0.431039269566414[/C][C]0.862078539132827[/C][C]0.568960730433586[/C][/ROW]
[ROW][C]29[/C][C]0.411178257665486[/C][C]0.822356515330972[/C][C]0.588821742334514[/C][/ROW]
[ROW][C]30[/C][C]0.444562864383298[/C][C]0.889125728766596[/C][C]0.555437135616702[/C][/ROW]
[ROW][C]31[/C][C]0.389182053396219[/C][C]0.778364106792438[/C][C]0.610817946603781[/C][/ROW]
[ROW][C]32[/C][C]0.346620753254168[/C][C]0.693241506508337[/C][C]0.653379246745831[/C][/ROW]
[ROW][C]33[/C][C]0.316677130929610[/C][C]0.633354261859221[/C][C]0.68332286907039[/C][/ROW]
[ROW][C]34[/C][C]0.275674898300487[/C][C]0.551349796600974[/C][C]0.724325101699513[/C][/ROW]
[ROW][C]35[/C][C]0.233139524871994[/C][C]0.466279049743987[/C][C]0.766860475128006[/C][/ROW]
[ROW][C]36[/C][C]0.363623899560609[/C][C]0.727247799121217[/C][C]0.636376100439392[/C][/ROW]
[ROW][C]37[/C][C]0.400480018023808[/C][C]0.800960036047616[/C][C]0.599519981976192[/C][/ROW]
[ROW][C]38[/C][C]0.392518456881072[/C][C]0.785036913762145[/C][C]0.607481543118928[/C][/ROW]
[ROW][C]39[/C][C]0.42064689819391[/C][C]0.84129379638782[/C][C]0.57935310180609[/C][/ROW]
[ROW][C]40[/C][C]0.394036668820201[/C][C]0.788073337640401[/C][C]0.6059633311798[/C][/ROW]
[ROW][C]41[/C][C]0.352608510695984[/C][C]0.705217021391968[/C][C]0.647391489304016[/C][/ROW]
[ROW][C]42[/C][C]0.317815560782687[/C][C]0.635631121565375[/C][C]0.682184439217313[/C][/ROW]
[ROW][C]43[/C][C]0.330602998434408[/C][C]0.661205996868816[/C][C]0.669397001565592[/C][/ROW]
[ROW][C]44[/C][C]0.284193039977059[/C][C]0.568386079954117[/C][C]0.715806960022941[/C][/ROW]
[ROW][C]45[/C][C]0.259307353804643[/C][C]0.518614707609286[/C][C]0.740692646195357[/C][/ROW]
[ROW][C]46[/C][C]0.457373136543283[/C][C]0.914746273086566[/C][C]0.542626863456717[/C][/ROW]
[ROW][C]47[/C][C]0.613015042694859[/C][C]0.773969914610283[/C][C]0.386984957305141[/C][/ROW]
[ROW][C]48[/C][C]0.56479096489433[/C][C]0.870418070211341[/C][C]0.435209035105671[/C][/ROW]
[ROW][C]49[/C][C]0.550115831249475[/C][C]0.89976833750105[/C][C]0.449884168750525[/C][/ROW]
[ROW][C]50[/C][C]0.538886872116339[/C][C]0.922226255767323[/C][C]0.461113127883661[/C][/ROW]
[ROW][C]51[/C][C]0.496029187990601[/C][C]0.992058375981202[/C][C]0.503970812009399[/C][/ROW]
[ROW][C]52[/C][C]0.448976551667357[/C][C]0.897953103334714[/C][C]0.551023448332643[/C][/ROW]
[ROW][C]53[/C][C]0.469409970867621[/C][C]0.938819941735242[/C][C]0.530590029132379[/C][/ROW]
[ROW][C]54[/C][C]0.449833835993105[/C][C]0.89966767198621[/C][C]0.550166164006895[/C][/ROW]
[ROW][C]55[/C][C]0.452066183365464[/C][C]0.904132366730929[/C][C]0.547933816634536[/C][/ROW]
[ROW][C]56[/C][C]0.420708589827937[/C][C]0.841417179655875[/C][C]0.579291410172063[/C][/ROW]
[ROW][C]57[/C][C]0.377122146901933[/C][C]0.754244293803866[/C][C]0.622877853098067[/C][/ROW]
[ROW][C]58[/C][C]0.343504288197332[/C][C]0.687008576394664[/C][C]0.656495711802668[/C][/ROW]
[ROW][C]59[/C][C]0.305381736916119[/C][C]0.610763473832238[/C][C]0.694618263083881[/C][/ROW]
[ROW][C]60[/C][C]0.304715664842222[/C][C]0.609431329684444[/C][C]0.695284335157778[/C][/ROW]
[ROW][C]61[/C][C]0.274885364314512[/C][C]0.549770728629024[/C][C]0.725114635685488[/C][/ROW]
[ROW][C]62[/C][C]0.243391513321[/C][C]0.486783026642[/C][C]0.756608486679[/C][/ROW]
[ROW][C]63[/C][C]0.213205555515497[/C][C]0.426411111030994[/C][C]0.786794444484503[/C][/ROW]
[ROW][C]64[/C][C]0.184850255438616[/C][C]0.369700510877231[/C][C]0.815149744561384[/C][/ROW]
[ROW][C]65[/C][C]0.159612090313782[/C][C]0.319224180627564[/C][C]0.840387909686218[/C][/ROW]
[ROW][C]66[/C][C]0.135701682609398[/C][C]0.271403365218796[/C][C]0.864298317390602[/C][/ROW]
[ROW][C]67[/C][C]0.118078554966239[/C][C]0.236157109932478[/C][C]0.881921445033761[/C][/ROW]
[ROW][C]68[/C][C]0.147491712033944[/C][C]0.294983424067889[/C][C]0.852508287966056[/C][/ROW]
[ROW][C]69[/C][C]0.347255538628955[/C][C]0.694511077257909[/C][C]0.652744461371045[/C][/ROW]
[ROW][C]70[/C][C]0.309383520800731[/C][C]0.618767041601461[/C][C]0.69061647919927[/C][/ROW]
[ROW][C]71[/C][C]0.466018474195675[/C][C]0.93203694839135[/C][C]0.533981525804325[/C][/ROW]
[ROW][C]72[/C][C]0.427694883468798[/C][C]0.855389766937596[/C][C]0.572305116531202[/C][/ROW]
[ROW][C]73[/C][C]0.415756314370461[/C][C]0.831512628740923[/C][C]0.584243685629539[/C][/ROW]
[ROW][C]74[/C][C]0.394545848970234[/C][C]0.789091697940468[/C][C]0.605454151029766[/C][/ROW]
[ROW][C]75[/C][C]0.359444377104171[/C][C]0.718888754208342[/C][C]0.640555622895829[/C][/ROW]
[ROW][C]76[/C][C]0.40707380269667[/C][C]0.81414760539334[/C][C]0.59292619730333[/C][/ROW]
[ROW][C]77[/C][C]0.374300584690822[/C][C]0.748601169381644[/C][C]0.625699415309178[/C][/ROW]
[ROW][C]78[/C][C]0.346591344599645[/C][C]0.693182689199289[/C][C]0.653408655400355[/C][/ROW]
[ROW][C]79[/C][C]0.375124096917483[/C][C]0.750248193834965[/C][C]0.624875903082518[/C][/ROW]
[ROW][C]80[/C][C]0.344523836755938[/C][C]0.689047673511876[/C][C]0.655476163244062[/C][/ROW]
[ROW][C]81[/C][C]0.311892769547432[/C][C]0.623785539094864[/C][C]0.688107230452568[/C][/ROW]
[ROW][C]82[/C][C]0.277800972444901[/C][C]0.555601944889803[/C][C]0.722199027555099[/C][/ROW]
[ROW][C]83[/C][C]0.254136867114173[/C][C]0.508273734228345[/C][C]0.745863132885827[/C][/ROW]
[ROW][C]84[/C][C]0.223381795663675[/C][C]0.44676359132735[/C][C]0.776618204336325[/C][/ROW]
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[ROW][C]228[/C][C]0.00537338633076858[/C][C]0.0107467726615372[/C][C]0.994626613669231[/C][/ROW]
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[ROW][C]230[/C][C]0.00266471220180094[/C][C]0.00532942440360187[/C][C]0.9973352877982[/C][/ROW]
[ROW][C]231[/C][C]0.00267158170978464[/C][C]0.00534316341956929[/C][C]0.997328418290215[/C][/ROW]
[ROW][C]232[/C][C]0.00494336392668188[/C][C]0.00988672785336376[/C][C]0.995056636073318[/C][/ROW]
[ROW][C]233[/C][C]0.0259467354434979[/C][C]0.0518934708869958[/C][C]0.974053264556502[/C][/ROW]
[ROW][C]234[/C][C]0.0384579406778061[/C][C]0.0769158813556122[/C][C]0.961542059322194[/C][/ROW]
[ROW][C]235[/C][C]0.0277187884740289[/C][C]0.0554375769480579[/C][C]0.97228121152597[/C][/ROW]
[ROW][C]236[/C][C]0.0239618139472759[/C][C]0.0479236278945519[/C][C]0.976038186052724[/C][/ROW]
[ROW][C]237[/C][C]0.187771175358450[/C][C]0.375542350716899[/C][C]0.812228824641550[/C][/ROW]
[ROW][C]238[/C][C]0.147044861176903[/C][C]0.294089722353805[/C][C]0.852955138823097[/C][/ROW]
[ROW][C]239[/C][C]0.130216166245390[/C][C]0.260432332490781[/C][C]0.86978383375461[/C][/ROW]
[ROW][C]240[/C][C]0.0988684339233115[/C][C]0.197736867846623[/C][C]0.901131566076689[/C][/ROW]
[ROW][C]241[/C][C]0.0766001416875223[/C][C]0.153200283375045[/C][C]0.923399858312478[/C][/ROW]
[ROW][C]242[/C][C]0.0614634178432018[/C][C]0.122926835686404[/C][C]0.938536582156798[/C][/ROW]
[ROW][C]243[/C][C]0.0562097761470191[/C][C]0.112419552294038[/C][C]0.94379022385298[/C][/ROW]
[ROW][C]244[/C][C]0.103562675764338[/C][C]0.207125351528676[/C][C]0.896437324235662[/C][/ROW]
[ROW][C]245[/C][C]0.0907837726734566[/C][C]0.181567545346913[/C][C]0.909216227326543[/C][/ROW]
[ROW][C]246[/C][C]0.0696382930217311[/C][C]0.139276586043462[/C][C]0.930361706978269[/C][/ROW]
[ROW][C]247[/C][C]0.0465296435673761[/C][C]0.0930592871347521[/C][C]0.953470356432624[/C][/ROW]
[ROW][C]248[/C][C]0.0378059998785717[/C][C]0.0756119997571434[/C][C]0.962194000121428[/C][/ROW]
[ROW][C]249[/C][C]0.0340747449655495[/C][C]0.068149489931099[/C][C]0.96592525503445[/C][/ROW]
[ROW][C]250[/C][C]0.0414488690680374[/C][C]0.0828977381360748[/C][C]0.958551130931963[/C][/ROW]
[ROW][C]251[/C][C]0.0218224478596724[/C][C]0.0436448957193448[/C][C]0.978177552140328[/C][/ROW]
[ROW][C]252[/C][C]0.0547426763653312[/C][C]0.109485352730662[/C][C]0.945257323634669[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96541&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96541&T=5

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.249272273417129	0.498544546834257	0.750727726582871
13	0.168954109350983	0.337908218701965	0.831045890649017
14	0.188652474482696	0.377304948965393	0.811347525517304
15	0.113822199570940	0.227644399141881	0.88617780042906
16	0.0773581785451667	0.154716357090333	0.922641821454833
17	0.111089921688014	0.222179843376027	0.888910078311986
18	0.350297244008135	0.70059448801627	0.649702755991865
19	0.273076148217577	0.546152296435153	0.726923851782423
20	0.20036477979948	0.40072955959896	0.79963522020052
21	0.147329038244943	0.294658076489885	0.852670961755057
22	0.142429822152623	0.284859644305245	0.857570177847377
23	0.329613610691874	0.659227221383749	0.670386389308126
24	0.396108111170374	0.792216222340749	0.603891888829626
25	0.364643782622245	0.729287565244489	0.635356217377755
26	0.343013785262702	0.686027570525403	0.656986214737298
27	0.419522216901141	0.839044433802282	0.580477783098859
28	0.431039269566414	0.862078539132827	0.568960730433586
29	0.411178257665486	0.822356515330972	0.588821742334514
30	0.444562864383298	0.889125728766596	0.555437135616702
31	0.389182053396219	0.778364106792438	0.610817946603781
32	0.346620753254168	0.693241506508337	0.653379246745831
33	0.316677130929610	0.633354261859221	0.68332286907039
34	0.275674898300487	0.551349796600974	0.724325101699513
35	0.233139524871994	0.466279049743987	0.766860475128006
36	0.363623899560609	0.727247799121217	0.636376100439392
37	0.400480018023808	0.800960036047616	0.599519981976192
38	0.392518456881072	0.785036913762145	0.607481543118928
39	0.42064689819391	0.84129379638782	0.57935310180609
40	0.394036668820201	0.788073337640401	0.6059633311798
41	0.352608510695984	0.705217021391968	0.647391489304016
42	0.317815560782687	0.635631121565375	0.682184439217313
43	0.330602998434408	0.661205996868816	0.669397001565592
44	0.284193039977059	0.568386079954117	0.715806960022941
45	0.259307353804643	0.518614707609286	0.740692646195357
46	0.457373136543283	0.914746273086566	0.542626863456717
47	0.613015042694859	0.773969914610283	0.386984957305141
48	0.56479096489433	0.870418070211341	0.435209035105671
49	0.550115831249475	0.89976833750105	0.449884168750525
50	0.538886872116339	0.922226255767323	0.461113127883661
51	0.496029187990601	0.992058375981202	0.503970812009399
52	0.448976551667357	0.897953103334714	0.551023448332643
53	0.469409970867621	0.938819941735242	0.530590029132379
54	0.449833835993105	0.89966767198621	0.550166164006895
55	0.452066183365464	0.904132366730929	0.547933816634536
56	0.420708589827937	0.841417179655875	0.579291410172063
57	0.377122146901933	0.754244293803866	0.622877853098067
58	0.343504288197332	0.687008576394664	0.656495711802668
59	0.305381736916119	0.610763473832238	0.694618263083881
60	0.304715664842222	0.609431329684444	0.695284335157778
61	0.274885364314512	0.549770728629024	0.725114635685488
62	0.243391513321	0.486783026642	0.756608486679
63	0.213205555515497	0.426411111030994	0.786794444484503
64	0.184850255438616	0.369700510877231	0.815149744561384
65	0.159612090313782	0.319224180627564	0.840387909686218
66	0.135701682609398	0.271403365218796	0.864298317390602
67	0.118078554966239	0.236157109932478	0.881921445033761
68	0.147491712033944	0.294983424067889	0.852508287966056
69	0.347255538628955	0.694511077257909	0.652744461371045
70	0.309383520800731	0.618767041601461	0.69061647919927
71	0.466018474195675	0.93203694839135	0.533981525804325
72	0.427694883468798	0.855389766937596	0.572305116531202
73	0.415756314370461	0.831512628740923	0.584243685629539
74	0.394545848970234	0.789091697940468	0.605454151029766
75	0.359444377104171	0.718888754208342	0.640555622895829
76	0.40707380269667	0.81414760539334	0.59292619730333
77	0.374300584690822	0.748601169381644	0.625699415309178
78	0.346591344599645	0.693182689199289	0.653408655400355
79	0.375124096917483	0.750248193834965	0.624875903082518
80	0.344523836755938	0.689047673511876	0.655476163244062
81	0.311892769547432	0.623785539094864	0.688107230452568
82	0.277800972444901	0.555601944889803	0.722199027555099
83	0.254136867114173	0.508273734228345	0.745863132885827
84	0.223381795663675	0.44676359132735	0.776618204336325
85	0.213624303551213	0.427248607102426	0.786375696448787
86	0.186095183511232	0.372190367022464	0.813904816488768
87	0.162784483325637	0.325568966651275	0.837215516674362
88	0.147969314030638	0.295938628061275	0.852030685969362
89	0.127536795735545	0.255073591471089	0.872463204264455
90	0.129326332967500	0.258652665934999	0.870673667032500
91	0.111535331873574	0.223070663747147	0.888464668126426
92	0.0961283982313728	0.192256796462746	0.903871601768627
93	0.0825502956078289	0.165100591215658	0.917449704392171
94	0.0877425387684936	0.175485077536987	0.912257461231506
95	0.0780879857657552	0.156175971531510	0.921912014234245
96	0.0658294056936897	0.131658811387379	0.93417059430631
97	0.068906807489887	0.137813614979774	0.931093192510113
98	0.0573579515440505	0.114715903088101	0.94264204845595
99	0.0475906360332567	0.0951812720665134	0.952409363966743
100	0.0425438584868564	0.0850877169737128	0.957456141513144
101	0.0369859310294397	0.0739718620588795	0.96301406897056
102	0.0420002433756051	0.0840004867512103	0.957999756624395
103	0.0356653898073750	0.0713307796147499	0.964334610192625
104	0.0316665200782306	0.0633330401564613	0.96833347992177
105	0.0388094976881857	0.0776189953763714	0.961190502311814
106	0.0335801030215315	0.0671602060430631	0.966419896978469
107	0.0272903859757292	0.0545807719514584	0.97270961402427
108	0.0260134149836965	0.0520268299673931	0.973986585016303
109	0.0212221299636422	0.0424442599272844	0.978777870036358
110	0.0172038184676234	0.0344076369352469	0.982796181532377
111	0.0136321859824952	0.0272643719649905	0.986367814017505
112	0.0136227566436758	0.0272455132873516	0.986377243356324
113	0.0124872959699899	0.0249745919399798	0.98751270403001
114	0.0191461758204056	0.0382923516408112	0.980853824179594
115	0.0177617143290989	0.0355234286581978	0.982238285670901
116	0.017603646006393	0.035207292012786	0.982396353993607
117	0.0143650816437960	0.0287301632875919	0.985634918356204
118	0.0149333160946058	0.0298666321892116	0.985066683905394
119	0.0120691161964736	0.0241382323929472	0.987930883803526
120	0.0110172150504946	0.0220344301009892	0.988982784949505
121	0.00884927981957504	0.0176985596391501	0.991150720180425
122	0.0140520169939783	0.0281040339879566	0.985947983006022
123	0.0120431244374353	0.0240862488748705	0.987956875562565
124	0.0108230439579138	0.0216460879158275	0.989176956042086
125	0.00903731082126301	0.0180746216425260	0.990962689178737
126	0.00728451036486331	0.0145690207297266	0.992715489635137
127	0.0063991416359108	0.0127982832718216	0.99360085836409
128	0.00510659099347213	0.0102131819869443	0.994893409006528
129	0.00783375065335833	0.0156675013067167	0.992166249346642
130	0.00807963042126135	0.0161592608425227	0.991920369578739
131	0.0180949330914169	0.0361898661828337	0.981905066908583
132	0.0203689644007127	0.0407379288014254	0.979631035599287
133	0.0232617612230514	0.0465235224461027	0.976738238776949
134	0.0232562238225967	0.0465124476451935	0.976743776177403
135	0.0190360379134759	0.0380720758269517	0.980963962086524
136	0.0156372488519367	0.0312744977038734	0.984362751148063
137	0.0124430144348134	0.0248860288696268	0.987556985565187
138	0.0165772691347347	0.0331545382694695	0.983422730865265
139	0.0149240627111017	0.0298481254222034	0.985075937288898
140	0.0190311552513490	0.0380623105026979	0.980968844748651
141	0.0261129296616781	0.0522258593233561	0.973887070338322
142	0.0232966146305604	0.0465932292611207	0.97670338536944
143	0.0190133879411869	0.0380267758823737	0.980986612058813
144	0.0180774558323928	0.0361549116647857	0.981922544167607
145	0.035502615792114	0.071005231584228	0.964497384207886
146	0.0384221966721238	0.0768443933442476	0.961577803327876
147	0.0431987522006693	0.0863975044013385	0.95680124779933
148	0.0369645849533085	0.073929169906617	0.963035415046692
149	0.0303268301811558	0.0606536603623117	0.969673169818844
150	0.0457293372484414	0.0914586744968829	0.954270662751558
151	0.0413811043222066	0.0827622086444131	0.958618895677793
152	0.041087091509391	0.082174183018782	0.95891290849061
153	0.0717081799115365	0.143416359823073	0.928291820088463
154	0.0631597123470018	0.126319424694004	0.936840287652998
155	0.0771128120061032	0.154225624012206	0.922887187993897
156	0.0648148402268047	0.129629680453609	0.935185159773195
157	0.0564212903309111	0.112842580661822	0.943578709669089
158	0.0478502954782773	0.0957005909565547	0.952149704521723
159	0.0454570121941229	0.0909140243882458	0.954542987805877
160	0.0390355339078229	0.0780710678156458	0.960964466092177
161	0.031868811329903	0.063737622659806	0.968131188670097
162	0.0261879326304385	0.0523758652608771	0.973812067369561
163	0.0215813417593278	0.0431626835186556	0.978418658240672
164	0.0182524223098581	0.0365048446197162	0.981747577690142
165	0.0161383303745537	0.0322766607491074	0.983861669625446
166	0.0162516658896448	0.0325033317792897	0.983748334110355
167	0.0129568005570451	0.0259136011140902	0.987043199442955
168	0.0184794173862801	0.0369588347725603	0.98152058261372
169	0.0178942596272692	0.0357885192545384	0.98210574037273
170	0.0170189181653016	0.0340378363306032	0.982981081834698
171	0.0165616674819240	0.0331233349638481	0.983438332518076
172	0.0134360476918214	0.0268720953836428	0.986563952308179
173	0.0129583555085127	0.0259167110170254	0.987041644491487
174	0.0138774468141623	0.0277548936283247	0.986122553185838
175	0.0172407628840277	0.0344815257680554	0.982759237115972
176	0.0138951737672713	0.0277903475345426	0.986104826232729
177	0.0110519988555190	0.0221039977110380	0.98894800114448
178	0.00887074341861827	0.0177414868372365	0.991129256581382
179	0.00688321602168362	0.0137664320433672	0.993116783978316
180	0.00577443562849701	0.0115488712569940	0.994225564371503
181	0.00485393565206548	0.00970787130413096	0.995146064347935
182	0.00378879916375457	0.00757759832750914	0.996211200836245
183	0.00424149949036701	0.00848299898073403	0.995758500509633
184	0.00327054716546522	0.00654109433093044	0.996729452834535
185	0.0728407105468326	0.145681421093665	0.927159289453167
186	0.0638369390091457	0.127673878018291	0.936163060990854
187	0.0755513774202354	0.151102754840471	0.924448622579765
188	0.0640371387060657	0.128074277412131	0.935962861293934
189	0.053654156399932	0.107308312799864	0.946345843600068
190	0.0443083311324279	0.0886166622648558	0.955691668867572
191	0.0387335344688042	0.0774670689376083	0.961266465531196
192	0.0324804456063726	0.0649608912127451	0.967519554393627
193	0.0387770188044993	0.0775540376089986	0.96122298119550
194	0.0425803720705000	0.0851607441409999	0.9574196279295
195	0.0355414797065166	0.0710829594130331	0.964458520293483
196	0.0291430151993401	0.0582860303986801	0.97085698480066
197	0.0393432212360945	0.0786864424721889	0.960656778763906
198	0.0321751746361222	0.0643503492722445	0.967824825363878
199	0.0299691694417933	0.0599383388835865	0.970030830558207
200	0.0254685360251700	0.0509370720503399	0.97453146397483
201	0.024209926192595	0.04841985238519	0.975790073807405
202	0.0201830070835602	0.0403660141671205	0.97981699291644
203	0.0255271301508270	0.0510542603016541	0.974472869849173
204	0.0280598687947857	0.0561197375895714	0.971940131205214
205	0.0312224340495115	0.062444868099023	0.968777565950488
206	0.0245716207548289	0.0491432415096578	0.97542837924517
207	0.0242699128877636	0.0485398257755272	0.975730087112236
208	0.0199692881585589	0.0399385763171178	0.98003071184144
209	0.021672831963285	0.04334566392657	0.978327168036715
210	0.0174563467996671	0.0349126935993342	0.982543653200333
211	0.0189292802277185	0.0378585604554370	0.981070719772281
212	0.0292703191646896	0.0585406383293791	0.97072968083531
213	0.0228693544218276	0.0457387088436552	0.977130645578172
214	0.0330240210408292	0.0660480420816584	0.96697597895917
215	0.028456855033694	0.056913710067388	0.971543144966306
216	0.0220849841128482	0.0441699682256964	0.977915015887152
217	0.0243591123907347	0.0487182247814694	0.975640887609265
218	0.0204895888266626	0.0409791776533253	0.979510411173337
219	0.0183205505059492	0.0366411010118983	0.98167944949405
220	0.0137547982773799	0.0275095965547599	0.98624520172262
221	0.0119272356527656	0.0238544713055312	0.988072764347234
222	0.00858122100533887	0.0171624420106777	0.99141877899466
223	0.00634657417409705	0.0126931483481941	0.993653425825903
224	0.00496471680285489	0.00992943360570977	0.995035283197145
225	0.00429464582214436	0.00858929164428872	0.995705354177856
226	0.00994629167948214	0.0198925833589643	0.990053708320518
227	0.00753438359405254	0.0150687671881051	0.992465616405947
228	0.00537338633076858	0.0107467726615372	0.994626613669231
229	0.00380717833516555	0.0076143566703311	0.996192821664834
230	0.00266471220180094	0.00532942440360187	0.9973352877982
231	0.00267158170978464	0.00534316341956929	0.997328418290215
232	0.00494336392668188	0.00988672785336376	0.995056636073318
233	0.0259467354434979	0.0518934708869958	0.974053264556502
234	0.0384579406778061	0.0769158813556122	0.961542059322194
235	0.0277187884740289	0.0554375769480579	0.97228121152597
236	0.0239618139472759	0.0479236278945519	0.976038186052724
237	0.187771175358450	0.375542350716899	0.812228824641550
238	0.147044861176903	0.294089722353805	0.852955138823097
239	0.130216166245390	0.260432332490781	0.86978383375461
240	0.0988684339233115	0.197736867846623	0.901131566076689
241	0.0766001416875223	0.153200283375045	0.923399858312478
242	0.0614634178432018	0.122926835686404	0.938536582156798
243	0.0562097761470191	0.112419552294038	0.94379022385298
244	0.103562675764338	0.207125351528676	0.896437324235662
245	0.0907837726734566	0.181567545346913	0.909216227326543
246	0.0696382930217311	0.139276586043462	0.930361706978269
247	0.0465296435673761	0.0930592871347521	0.953470356432624
248	0.0378059998785717	0.0756119997571434	0.962194000121428
249	0.0340747449655495	0.068149489931099	0.96592525503445
250	0.0414488690680374	0.0828977381360748	0.958551130931963
251	0.0218224478596724	0.0436448957193448	0.978177552140328
252	0.0547426763653312	0.109485352730662	0.945257323634669

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	10	0.0414937759336100	NOK
5% type I error level	85	0.352697095435685	NOK
10% type I error level	133	0.551867219917012	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 10 & 0.0414937759336100 & NOK \tabularnewline
5% type I error level & 85 & 0.352697095435685 & NOK \tabularnewline
10% type I error level & 133 & 0.551867219917012 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=96541&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]10[/C][C]0.0414937759336100[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]85[/C][C]0.352697095435685[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]133[/C][C]0.551867219917012[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=96541&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=96541&T=6

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	10	0.0414937759336100	NOK
5% type I error level	85	0.352697095435685	NOK
10% type I error level	133	0.551867219917012	NOK

Figure 1

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code