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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 03 Nov 2012 09:20:08 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/03/t13519489321kajhnzziln07y6.htm/, Retrieved Sun, 03 Jul 2022 14:29:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185717, Retrieved Sun, 03 Jul 2022 14:29:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact92
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
- R       [Multiple Regression] [] [2012-11-03 13:20:08] [54fc54845b7758f1604e617c5518fca2] [Current]
- R P       [Multiple Regression] [Paper_4.3_Model32] [2012-12-21 19:26:17] [be6dd99035eed41c2358246baf91f928]
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Dataseries X:
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time16 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185717&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185717&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185717&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time16 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 4.00445406989653 + 0.161768138570449month[t] + 0.033972045234042Connected[t] + 0.0426648655196232Separate[t] + 0.55391437408327Software[t] + 0.0693843908517882Happiness[t] -0.0309724601101604Depression[t] + 0.0210589104920537Belonging[t] -0.0218367544310297Belonging_Final[t] -0.00651732044274871t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  4.00445406989653 +  0.161768138570449month[t] +  0.033972045234042Connected[t] +  0.0426648655196232Separate[t] +  0.55391437408327Software[t] +  0.0693843908517882Happiness[t] -0.0309724601101604Depression[t] +  0.0210589104920537Belonging[t] -0.0218367544310297Belonging_Final[t] -0.00651732044274871t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185717&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  4.00445406989653 +  0.161768138570449month[t] +  0.033972045234042Connected[t] +  0.0426648655196232Separate[t] +  0.55391437408327Software[t] +  0.0693843908517882Happiness[t] -0.0309724601101604Depression[t] +  0.0210589104920537Belonging[t] -0.0218367544310297Belonging_Final[t] -0.00651732044274871t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185717&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185717&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] = + 4.00445406989653 + 0.161768138570449month[t] + 0.033972045234042Connected[t] + 0.0426648655196232Separate[t] + 0.55391437408327Software[t] + 0.0693843908517882Happiness[t] -0.0309724601101604Depression[t] + 0.0210589104920537Belonging[t] -0.0218367544310297Belonging_Final[t] -0.00651732044274871t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.004454069896533.8937441.02840.3047250.152362
month0.1617681385704490.4087380.39580.6926040.346302
Connected0.0339720452340420.0346750.97970.3281470.164074
Separate0.04266486551962320.0351961.21220.2265580.113279
Software0.553914374083270.05466110.133700
Happiness0.06938439085178820.0581021.19420.2335220.116761
Depression-0.03097246011016040.042456-0.72950.4663530.233176
Belonging0.02105891049205370.0379790.55450.579730.289865
Belonging_Final-0.02183675443102970.056419-0.3870.6990470.349524
t-0.006517320442748710.004332-1.50430.133740.06687

\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) & 4.00445406989653 & 3.893744 & 1.0284 & 0.304725 & 0.152362 \tabularnewline
month & 0.161768138570449 & 0.408738 & 0.3958 & 0.692604 & 0.346302 \tabularnewline
Connected & 0.033972045234042 & 0.034675 & 0.9797 & 0.328147 & 0.164074 \tabularnewline
Separate & 0.0426648655196232 & 0.035196 & 1.2122 & 0.226558 & 0.113279 \tabularnewline
Software & 0.55391437408327 & 0.054661 & 10.1337 & 0 & 0 \tabularnewline
Happiness & 0.0693843908517882 & 0.058102 & 1.1942 & 0.233522 & 0.116761 \tabularnewline
Depression & -0.0309724601101604 & 0.042456 & -0.7295 & 0.466353 & 0.233176 \tabularnewline
Belonging & 0.0210589104920537 & 0.037979 & 0.5545 & 0.57973 & 0.289865 \tabularnewline
Belonging_Final & -0.0218367544310297 & 0.056419 & -0.387 & 0.699047 & 0.349524 \tabularnewline
t & -0.00651732044274871 & 0.004332 & -1.5043 & 0.13374 & 0.06687 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185717&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]4.00445406989653[/C][C]3.893744[/C][C]1.0284[/C][C]0.304725[/C][C]0.152362[/C][/ROW]
[ROW][C]month[/C][C]0.161768138570449[/C][C]0.408738[/C][C]0.3958[/C][C]0.692604[/C][C]0.346302[/C][/ROW]
[ROW][C]Connected[/C][C]0.033972045234042[/C][C]0.034675[/C][C]0.9797[/C][C]0.328147[/C][C]0.164074[/C][/ROW]
[ROW][C]Separate[/C][C]0.0426648655196232[/C][C]0.035196[/C][C]1.2122[/C][C]0.226558[/C][C]0.113279[/C][/ROW]
[ROW][C]Software[/C][C]0.55391437408327[/C][C]0.054661[/C][C]10.1337[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0693843908517882[/C][C]0.058102[/C][C]1.1942[/C][C]0.233522[/C][C]0.116761[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0309724601101604[/C][C]0.042456[/C][C]-0.7295[/C][C]0.466353[/C][C]0.233176[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0210589104920537[/C][C]0.037979[/C][C]0.5545[/C][C]0.57973[/C][C]0.289865[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0218367544310297[/C][C]0.056419[/C][C]-0.387[/C][C]0.699047[/C][C]0.349524[/C][/ROW]
[ROW][C]t[/C][C]-0.00651732044274871[/C][C]0.004332[/C][C]-1.5043[/C][C]0.13374[/C][C]0.06687[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185717&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185717&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.004454069896533.8937441.02840.3047250.152362
month0.1617681385704490.4087380.39580.6926040.346302
Connected0.0339720452340420.0346750.97970.3281470.164074
Separate0.04266486551962320.0351961.21220.2265580.113279
Software0.553914374083270.05466110.133700
Happiness0.06938439085178820.0581021.19420.2335220.116761
Depression-0.03097246011016040.042456-0.72950.4663530.233176
Belonging0.02105891049205370.0379790.55450.579730.289865
Belonging_Final-0.02183675443102970.056419-0.3870.6990470.349524
t-0.006517320442748710.004332-1.50430.133740.06687







Multiple Linear Regression - Regression Statistics
Multiple R0.667804237836336
R-squared0.44596250007217
Adjusted R-squared0.426331250074727
F-TEST (value)22.7169691247507
F-TEST (DF numerator)9
F-TEST (DF denominator)254
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.86017625008155
Sum Squared Residuals878.904943067331

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.667804237836336 \tabularnewline
R-squared & 0.44596250007217 \tabularnewline
Adjusted R-squared & 0.426331250074727 \tabularnewline
F-TEST (value) & 22.7169691247507 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 254 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.86017625008155 \tabularnewline
Sum Squared Residuals & 878.904943067331 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185717&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.667804237836336[/C][/ROW]
[ROW][C]R-squared[/C][C]0.44596250007217[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.426331250074727[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.7169691247507[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]254[/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]1.86017625008155[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]878.904943067331[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185717&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185717&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 R0.667804237836336
R-squared0.44596250007217
Adjusted R-squared0.426331250074727
F-TEST (value)22.7169691247507
F-TEST (DF numerator)9
F-TEST (DF denominator)254
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.86017625008155
Sum Squared Residuals878.904943067331







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1319992948175-3.13199929481746
21615.7730133207950.226986679205008
31917.06432088135921.93567911864083
41512.1954414843572.80455851564302
51416.4180322249668-2.41803222496679
61314.8802268925994-1.88022689259938
71915.30738884979883.69261115020122
81517.0994109702753-2.09941097027534
91416.0716945323483-2.07169453234826
101514.47092766324020.529072336759824
111615.00483208812550.995167911874463
121616.1898230324591-0.189823032459142
131615.52988297608430.470117023915685
141615.50146168205470.498538317945334
151717.9849723007825-0.984972300782519
161515.4711693679876-0.471169367987578
171514.61151730328050.388482696719519
182016.41506761399343.58493238600663
191815.48989120805032.51010879194971
201615.55111755201860.448882447981356
211615.36086953737470.639130462625279
221615.14050556597470.859494434025267
231916.47381068475322.52618931524679
241615.08824249253160.911757507468446
251716.14898491088840.851015089111614
261716.25402615760580.745973842394168
271614.97075727681.02924272319997
281516.7314032980079-1.73140329800786
291615.71444805827950.285551941720517
301414.1776976706257-0.177697670625683
311515.7271570905492-0.727157090549234
321212.8196274232436-0.819627423243612
331414.7900282358008-0.790028235800772
341615.90923301678180.0907669832181899
351415.5241842748814-1.52418427488141
361012.9949264320293-2.99492643202926
371013.036017732808-3.03601773280801
381415.7159143656349-1.71591436563487
391614.51893224043911.48106775956088
401614.46397400331251.53602599668754
411614.69226143879911.30773856120091
421415.6917393515644-1.69173935156445
432017.42399040785722.57600959214279
441414.1268333106255-0.126833310625451
451414.4277755307961-0.427775530796144
461115.4769010866658-4.47690108666579
471416.5016700566974-2.50167005669739
481515.0872733557269-0.0872733557269008
491615.43449868155980.565501318440229
501415.5957515567831-1.59575155678309
511616.9470055618408-0.947005561840761
521414.1029056640276-0.102905664027581
531214.9814432966391-2.98144329663911
541615.78579329926690.214206700733062
55911.356371537705-2.35637153770504
561412.33493451389981.66506548610018
571615.79458143180010.205418568199872
581615.42094699845450.579053001545475
591515.0830124726959-0.0830124726958567
601614.15126792463551.84873207536446
611211.50696101907350.493038980926504
621615.61401880338330.385981196616701
631616.48100460217-0.481004602170041
641414.6386116076604-0.638611607660401
651615.21606649800860.783933501991448
661716.12835949642960.871640503570395
671816.36598102858421.6340189714158
681814.54606520960843.4539347903916
691215.9859557929282-3.98595579292816
701615.72358968010840.276410319891583
711013.4364628729273-3.4364628729273
721414.9824764129632-0.982476412963195
731817.01588301218050.984116987819457
741817.24548809358730.754511906412718
751615.32572059802390.674279401976127
761713.57575201879013.42424798120986
771616.4687902994353-0.468790299435291
781614.55221583323531.44778416676474
791315.2614002582591-2.26140025825909
801615.19645256229850.80354743770153
811615.75744966677740.242550333222626
821615.77685169303730.223148306962747
831515.7298773413516-0.72987734135157
841514.92997023040010.0700297695999076
851614.21494342275451.78505657724552
861414.2366309572854-0.236630957285394
871615.40941707492330.590582925076732
881614.90002720382131.0999727961787
891514.55752438751510.442475612484949
901213.9195769686057-1.91957696860568
911716.78002268138120.219977318618845
921615.86296481876890.13703518123109
931515.130179843395-0.130179843395034
941315.0948418111599-2.09484181115994
951614.83081725072941.16918274927058
961615.79873323490230.201266765097656
971613.75560587896972.24439412103029
981615.83442676297320.165573237026808
991414.4661328340413-0.466132834041314
1001617.0397108174705-1.0397108174705
1011614.69888001762821.30111998237178
1022017.4606549280592.53934507194102
1031514.22090689148290.779093108517128
1041615.00493246408790.995067535912078
1051314.8816285614618-1.88162856146175
1061715.70608292130381.29391707869616
1071615.7299469725030.27005302749703
1081614.37341216152491.62658783847515
1091212.3059825583899-0.305982558389896
1101615.2836170632320.716382936768048
1111616.0107716671885-0.0107716671884955
1121715.03508126417861.96491873582139
1131314.5406112403526-1.54061124035264
1141214.5772750204351-2.57727502043506
1151816.1499158080541.85008419194602
1161415.8490205615627-1.8490205615627
1171413.17143741370320.828562586296819
1181314.7890480129649-1.78904801296489
1191615.5214947103640.478505289635973
1201314.4173866932526-1.41738669325256
1211615.37326484034760.626735159652427
1221315.8239894208261-2.82398942082606
1231616.8206727627472-0.820672762747172
1241515.8151943614052-0.815194361405167
1251616.7758864663847-0.775886466384681
1261514.65631769558030.343682304419718
1271715.51354584467651.48645415532348
1281513.95619058709241.04380941290757
1291214.6649696205487-2.66496962054874
1301613.94841252720732.05158747279265
1311013.6993457863953-3.69934578639527
1321613.47873264914812.52126735085192
1331214.117119104406-2.11711910440596
1341415.4969825827167-1.49698258271668
1351515.022851451454-0.0228514514539609
1361312.07828353730440.921716462695583
1371514.41797520628360.582024793716415
1381113.4148843246344-2.41488432463444
1391212.9615388133991-0.961538813399079
1401113.3294291078386-2.32942910783856
1411612.81982867301833.18017132698171
1421513.61147651816381.38852348183618
1431716.76473616182020.235263838179766
1441614.09726648148351.90273351851652
1451013.2326063570416-3.23260635704161
1461815.42318082200582.57681917799423
1471314.8643149057298-1.86431490572983
1481614.78110400975751.21889599024253
1491312.75937389386590.240626106134091
1501012.8479456212172-2.84794562121719
1511515.8571245856987-0.857124585698667
1521613.82432036865652.17567963134348
1531611.8177905401794.18220945982104
1541412.17962526706471.82037473293531
1551012.3603674449609-2.36036744496087
1561716.35639685260250.643603147397511
1571311.63995927610471.36004072389528
1581513.760670973811.23932902619003
1591614.47778380013711.52221619986288
1601212.671268682682-0.671268682682013
1611312.55015839308350.449841606916476
1621312.69518566592350.304814334076481
1631212.5325265824158-0.532526582415843
1641716.29443654998990.705563450010052
1651513.7802694966021.21973050339805
1661011.69604843173-1.69604843172995
1671414.4098138706111-0.409813870611107
1681114.2824014929905-3.28240149299048
1691314.8696564507497-1.86965645074974
1701614.52772562337951.47227437662047
1711210.57815969722011.42184030277993
1721615.40539571243880.594604287561249
1731213.9507326026571-1.95073260265714
174911.4327559786395-2.43275597863953
1751214.9708875013237-2.97088750132374
1761514.61531181533650.384688184663489
1771212.3484847331071-0.348484733107133
1781212.7410235452396-0.741023545239648
1791413.84825693293820.151743067061837
1801213.4363825724819-1.43638257248194
1811615.05742255122530.942577448774667
1821111.5290900782482-0.529090078248177
1831916.70239346640282.29760653359724
1841515.1861080347248-0.18610803472479
185814.6098477634636-6.60984776346357
1861614.79487542842621.20512457157379
1871714.48896780864662.51103219135338
1881212.4492788886036-0.449278888603589
1891111.5258253233485-0.525825323348493
1901110.48399194092280.516008059077233
1911414.7297291026958-0.729729102695786
1921615.45734086060360.542659139396364
193129.794949979580572.20505002041943
1941614.05823760705391.94176239294606
1951313.6671267744826-0.667126774482598
1961515.0059615481752-0.00596154817518689
1971612.961275648783.03872435122004
1981615.02169595069260.97830404930736
1991412.46549516896541.53450483103465
2001614.5360348798981.463965120102
2011614.01017732156791.98982267843206
2021413.35051464494010.649485355059946
2031113.4248894641781-2.42488946417808
2041214.4629453484512-2.46294534845121
2051512.72818073292342.27181926707663
2061514.43920770290540.560792297094553
2071614.4841958335771.51580416642297
2081614.8639174831781.13608251682204
2091113.5735507320522-2.57355073205222
2101513.93299163421971.06700836578035
2111214.1909393598993-2.19093935989934
2121215.7003657756719-3.70036577567191
2131514.09847214845650.901527851543532
2141512.08980395457622.9101960454238
2151614.48611736758141.51388263241862
2161413.04479115557020.955208844429781
2171714.57038470360082.42961529639917
2181413.88987696130980.110123038690196
2191311.8763407163771.12365928362303
2201515.0824598206262-0.08245982062625
2211314.5750556822787-1.57505568227874
2221413.98603322779790.0139667722021294
2231514.18660326089680.813396739103206
2241213.0451003628764-1.04510036287638
2251312.52779271327570.472207286724255
226811.6960459918855-3.69604599188549
2271413.65619932870260.343800671297409
2281412.90007169960871.09992830039132
2291112.1551096302894-1.15510963028936
2301212.81419870208-0.814198702080007
2311311.19279847494691.80720152505309
2321013.1324013386614-3.13240133866136
2331611.32387242147464.6761275785254
2341815.70166237527842.29833762472164
2351313.6986247386433-0.698624738643324
2361113.1953213217862-2.19532132178622
237410.9144168771812-6.91441687718123
2381314.0863865432717-1.08638654327172
2391614.1114446024491.88855539755098
2401011.5810420842532-1.58104208425323
2411212.1457895056583-0.145789505658277
2421213.3223330284656-1.32233302846563
243108.801955081847941.19804491815206
2441310.96759703967272.03240296032731
2451513.4825144280371.51748557196298
2461211.77011759791040.229882402089599
2471412.67016620458431.32983379541566
2481012.2934478887145-2.29344788871453
2491210.60140736630471.3985926336953
2501211.50458606214790.495413937852081
2511111.693327081122-0.693327081122017
2521011.4970254698553-1.49702546985533
2531211.22777229258040.772227707419641
2541612.63302946348673.36697053651325
2551213.1033064447534-1.10330644475345
2561413.61151012685110.38848987314886
2571614.13802203852431.8619779614757
2581411.45129652044112.54870347955894
2591313.9883658481621-0.988365848162067
26049.2785535181503-5.2785535181503
2611513.49081781415331.50918218584672
2621114.705447878116-3.70544787811599
2631111.1003815456157-0.100381545615727
2641412.57599843794711.4240015620529

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.1319992948175 & -3.13199929481746 \tabularnewline
2 & 16 & 15.773013320795 & 0.226986679205008 \tabularnewline
3 & 19 & 17.0643208813592 & 1.93567911864083 \tabularnewline
4 & 15 & 12.195441484357 & 2.80455851564302 \tabularnewline
5 & 14 & 16.4180322249668 & -2.41803222496679 \tabularnewline
6 & 13 & 14.8802268925994 & -1.88022689259938 \tabularnewline
7 & 19 & 15.3073888497988 & 3.69261115020122 \tabularnewline
8 & 15 & 17.0994109702753 & -2.09941097027534 \tabularnewline
9 & 14 & 16.0716945323483 & -2.07169453234826 \tabularnewline
10 & 15 & 14.4709276632402 & 0.529072336759824 \tabularnewline
11 & 16 & 15.0048320881255 & 0.995167911874463 \tabularnewline
12 & 16 & 16.1898230324591 & -0.189823032459142 \tabularnewline
13 & 16 & 15.5298829760843 & 0.470117023915685 \tabularnewline
14 & 16 & 15.5014616820547 & 0.498538317945334 \tabularnewline
15 & 17 & 17.9849723007825 & -0.984972300782519 \tabularnewline
16 & 15 & 15.4711693679876 & -0.471169367987578 \tabularnewline
17 & 15 & 14.6115173032805 & 0.388482696719519 \tabularnewline
18 & 20 & 16.4150676139934 & 3.58493238600663 \tabularnewline
19 & 18 & 15.4898912080503 & 2.51010879194971 \tabularnewline
20 & 16 & 15.5511175520186 & 0.448882447981356 \tabularnewline
21 & 16 & 15.3608695373747 & 0.639130462625279 \tabularnewline
22 & 16 & 15.1405055659747 & 0.859494434025267 \tabularnewline
23 & 19 & 16.4738106847532 & 2.52618931524679 \tabularnewline
24 & 16 & 15.0882424925316 & 0.911757507468446 \tabularnewline
25 & 17 & 16.1489849108884 & 0.851015089111614 \tabularnewline
26 & 17 & 16.2540261576058 & 0.745973842394168 \tabularnewline
27 & 16 & 14.9707572768 & 1.02924272319997 \tabularnewline
28 & 15 & 16.7314032980079 & -1.73140329800786 \tabularnewline
29 & 16 & 15.7144480582795 & 0.285551941720517 \tabularnewline
30 & 14 & 14.1776976706257 & -0.177697670625683 \tabularnewline
31 & 15 & 15.7271570905492 & -0.727157090549234 \tabularnewline
32 & 12 & 12.8196274232436 & -0.819627423243612 \tabularnewline
33 & 14 & 14.7900282358008 & -0.790028235800772 \tabularnewline
34 & 16 & 15.9092330167818 & 0.0907669832181899 \tabularnewline
35 & 14 & 15.5241842748814 & -1.52418427488141 \tabularnewline
36 & 10 & 12.9949264320293 & -2.99492643202926 \tabularnewline
37 & 10 & 13.036017732808 & -3.03601773280801 \tabularnewline
38 & 14 & 15.7159143656349 & -1.71591436563487 \tabularnewline
39 & 16 & 14.5189322404391 & 1.48106775956088 \tabularnewline
40 & 16 & 14.4639740033125 & 1.53602599668754 \tabularnewline
41 & 16 & 14.6922614387991 & 1.30773856120091 \tabularnewline
42 & 14 & 15.6917393515644 & -1.69173935156445 \tabularnewline
43 & 20 & 17.4239904078572 & 2.57600959214279 \tabularnewline
44 & 14 & 14.1268333106255 & -0.126833310625451 \tabularnewline
45 & 14 & 14.4277755307961 & -0.427775530796144 \tabularnewline
46 & 11 & 15.4769010866658 & -4.47690108666579 \tabularnewline
47 & 14 & 16.5016700566974 & -2.50167005669739 \tabularnewline
48 & 15 & 15.0872733557269 & -0.0872733557269008 \tabularnewline
49 & 16 & 15.4344986815598 & 0.565501318440229 \tabularnewline
50 & 14 & 15.5957515567831 & -1.59575155678309 \tabularnewline
51 & 16 & 16.9470055618408 & -0.947005561840761 \tabularnewline
52 & 14 & 14.1029056640276 & -0.102905664027581 \tabularnewline
53 & 12 & 14.9814432966391 & -2.98144329663911 \tabularnewline
54 & 16 & 15.7857932992669 & 0.214206700733062 \tabularnewline
55 & 9 & 11.356371537705 & -2.35637153770504 \tabularnewline
56 & 14 & 12.3349345138998 & 1.66506548610018 \tabularnewline
57 & 16 & 15.7945814318001 & 0.205418568199872 \tabularnewline
58 & 16 & 15.4209469984545 & 0.579053001545475 \tabularnewline
59 & 15 & 15.0830124726959 & -0.0830124726958567 \tabularnewline
60 & 16 & 14.1512679246355 & 1.84873207536446 \tabularnewline
61 & 12 & 11.5069610190735 & 0.493038980926504 \tabularnewline
62 & 16 & 15.6140188033833 & 0.385981196616701 \tabularnewline
63 & 16 & 16.48100460217 & -0.481004602170041 \tabularnewline
64 & 14 & 14.6386116076604 & -0.638611607660401 \tabularnewline
65 & 16 & 15.2160664980086 & 0.783933501991448 \tabularnewline
66 & 17 & 16.1283594964296 & 0.871640503570395 \tabularnewline
67 & 18 & 16.3659810285842 & 1.6340189714158 \tabularnewline
68 & 18 & 14.5460652096084 & 3.4539347903916 \tabularnewline
69 & 12 & 15.9859557929282 & -3.98595579292816 \tabularnewline
70 & 16 & 15.7235896801084 & 0.276410319891583 \tabularnewline
71 & 10 & 13.4364628729273 & -3.4364628729273 \tabularnewline
72 & 14 & 14.9824764129632 & -0.982476412963195 \tabularnewline
73 & 18 & 17.0158830121805 & 0.984116987819457 \tabularnewline
74 & 18 & 17.2454880935873 & 0.754511906412718 \tabularnewline
75 & 16 & 15.3257205980239 & 0.674279401976127 \tabularnewline
76 & 17 & 13.5757520187901 & 3.42424798120986 \tabularnewline
77 & 16 & 16.4687902994353 & -0.468790299435291 \tabularnewline
78 & 16 & 14.5522158332353 & 1.44778416676474 \tabularnewline
79 & 13 & 15.2614002582591 & -2.26140025825909 \tabularnewline
80 & 16 & 15.1964525622985 & 0.80354743770153 \tabularnewline
81 & 16 & 15.7574496667774 & 0.242550333222626 \tabularnewline
82 & 16 & 15.7768516930373 & 0.223148306962747 \tabularnewline
83 & 15 & 15.7298773413516 & -0.72987734135157 \tabularnewline
84 & 15 & 14.9299702304001 & 0.0700297695999076 \tabularnewline
85 & 16 & 14.2149434227545 & 1.78505657724552 \tabularnewline
86 & 14 & 14.2366309572854 & -0.236630957285394 \tabularnewline
87 & 16 & 15.4094170749233 & 0.590582925076732 \tabularnewline
88 & 16 & 14.9000272038213 & 1.0999727961787 \tabularnewline
89 & 15 & 14.5575243875151 & 0.442475612484949 \tabularnewline
90 & 12 & 13.9195769686057 & -1.91957696860568 \tabularnewline
91 & 17 & 16.7800226813812 & 0.219977318618845 \tabularnewline
92 & 16 & 15.8629648187689 & 0.13703518123109 \tabularnewline
93 & 15 & 15.130179843395 & -0.130179843395034 \tabularnewline
94 & 13 & 15.0948418111599 & -2.09484181115994 \tabularnewline
95 & 16 & 14.8308172507294 & 1.16918274927058 \tabularnewline
96 & 16 & 15.7987332349023 & 0.201266765097656 \tabularnewline
97 & 16 & 13.7556058789697 & 2.24439412103029 \tabularnewline
98 & 16 & 15.8344267629732 & 0.165573237026808 \tabularnewline
99 & 14 & 14.4661328340413 & -0.466132834041314 \tabularnewline
100 & 16 & 17.0397108174705 & -1.0397108174705 \tabularnewline
101 & 16 & 14.6988800176282 & 1.30111998237178 \tabularnewline
102 & 20 & 17.460654928059 & 2.53934507194102 \tabularnewline
103 & 15 & 14.2209068914829 & 0.779093108517128 \tabularnewline
104 & 16 & 15.0049324640879 & 0.995067535912078 \tabularnewline
105 & 13 & 14.8816285614618 & -1.88162856146175 \tabularnewline
106 & 17 & 15.7060829213038 & 1.29391707869616 \tabularnewline
107 & 16 & 15.729946972503 & 0.27005302749703 \tabularnewline
108 & 16 & 14.3734121615249 & 1.62658783847515 \tabularnewline
109 & 12 & 12.3059825583899 & -0.305982558389896 \tabularnewline
110 & 16 & 15.283617063232 & 0.716382936768048 \tabularnewline
111 & 16 & 16.0107716671885 & -0.0107716671884955 \tabularnewline
112 & 17 & 15.0350812641786 & 1.96491873582139 \tabularnewline
113 & 13 & 14.5406112403526 & -1.54061124035264 \tabularnewline
114 & 12 & 14.5772750204351 & -2.57727502043506 \tabularnewline
115 & 18 & 16.149915808054 & 1.85008419194602 \tabularnewline
116 & 14 & 15.8490205615627 & -1.8490205615627 \tabularnewline
117 & 14 & 13.1714374137032 & 0.828562586296819 \tabularnewline
118 & 13 & 14.7890480129649 & -1.78904801296489 \tabularnewline
119 & 16 & 15.521494710364 & 0.478505289635973 \tabularnewline
120 & 13 & 14.4173866932526 & -1.41738669325256 \tabularnewline
121 & 16 & 15.3732648403476 & 0.626735159652427 \tabularnewline
122 & 13 & 15.8239894208261 & -2.82398942082606 \tabularnewline
123 & 16 & 16.8206727627472 & -0.820672762747172 \tabularnewline
124 & 15 & 15.8151943614052 & -0.815194361405167 \tabularnewline
125 & 16 & 16.7758864663847 & -0.775886466384681 \tabularnewline
126 & 15 & 14.6563176955803 & 0.343682304419718 \tabularnewline
127 & 17 & 15.5135458446765 & 1.48645415532348 \tabularnewline
128 & 15 & 13.9561905870924 & 1.04380941290757 \tabularnewline
129 & 12 & 14.6649696205487 & -2.66496962054874 \tabularnewline
130 & 16 & 13.9484125272073 & 2.05158747279265 \tabularnewline
131 & 10 & 13.6993457863953 & -3.69934578639527 \tabularnewline
132 & 16 & 13.4787326491481 & 2.52126735085192 \tabularnewline
133 & 12 & 14.117119104406 & -2.11711910440596 \tabularnewline
134 & 14 & 15.4969825827167 & -1.49698258271668 \tabularnewline
135 & 15 & 15.022851451454 & -0.0228514514539609 \tabularnewline
136 & 13 & 12.0782835373044 & 0.921716462695583 \tabularnewline
137 & 15 & 14.4179752062836 & 0.582024793716415 \tabularnewline
138 & 11 & 13.4148843246344 & -2.41488432463444 \tabularnewline
139 & 12 & 12.9615388133991 & -0.961538813399079 \tabularnewline
140 & 11 & 13.3294291078386 & -2.32942910783856 \tabularnewline
141 & 16 & 12.8198286730183 & 3.18017132698171 \tabularnewline
142 & 15 & 13.6114765181638 & 1.38852348183618 \tabularnewline
143 & 17 & 16.7647361618202 & 0.235263838179766 \tabularnewline
144 & 16 & 14.0972664814835 & 1.90273351851652 \tabularnewline
145 & 10 & 13.2326063570416 & -3.23260635704161 \tabularnewline
146 & 18 & 15.4231808220058 & 2.57681917799423 \tabularnewline
147 & 13 & 14.8643149057298 & -1.86431490572983 \tabularnewline
148 & 16 & 14.7811040097575 & 1.21889599024253 \tabularnewline
149 & 13 & 12.7593738938659 & 0.240626106134091 \tabularnewline
150 & 10 & 12.8479456212172 & -2.84794562121719 \tabularnewline
151 & 15 & 15.8571245856987 & -0.857124585698667 \tabularnewline
152 & 16 & 13.8243203686565 & 2.17567963134348 \tabularnewline
153 & 16 & 11.817790540179 & 4.18220945982104 \tabularnewline
154 & 14 & 12.1796252670647 & 1.82037473293531 \tabularnewline
155 & 10 & 12.3603674449609 & -2.36036744496087 \tabularnewline
156 & 17 & 16.3563968526025 & 0.643603147397511 \tabularnewline
157 & 13 & 11.6399592761047 & 1.36004072389528 \tabularnewline
158 & 15 & 13.76067097381 & 1.23932902619003 \tabularnewline
159 & 16 & 14.4777838001371 & 1.52221619986288 \tabularnewline
160 & 12 & 12.671268682682 & -0.671268682682013 \tabularnewline
161 & 13 & 12.5501583930835 & 0.449841606916476 \tabularnewline
162 & 13 & 12.6951856659235 & 0.304814334076481 \tabularnewline
163 & 12 & 12.5325265824158 & -0.532526582415843 \tabularnewline
164 & 17 & 16.2944365499899 & 0.705563450010052 \tabularnewline
165 & 15 & 13.780269496602 & 1.21973050339805 \tabularnewline
166 & 10 & 11.69604843173 & -1.69604843172995 \tabularnewline
167 & 14 & 14.4098138706111 & -0.409813870611107 \tabularnewline
168 & 11 & 14.2824014929905 & -3.28240149299048 \tabularnewline
169 & 13 & 14.8696564507497 & -1.86965645074974 \tabularnewline
170 & 16 & 14.5277256233795 & 1.47227437662047 \tabularnewline
171 & 12 & 10.5781596972201 & 1.42184030277993 \tabularnewline
172 & 16 & 15.4053957124388 & 0.594604287561249 \tabularnewline
173 & 12 & 13.9507326026571 & -1.95073260265714 \tabularnewline
174 & 9 & 11.4327559786395 & -2.43275597863953 \tabularnewline
175 & 12 & 14.9708875013237 & -2.97088750132374 \tabularnewline
176 & 15 & 14.6153118153365 & 0.384688184663489 \tabularnewline
177 & 12 & 12.3484847331071 & -0.348484733107133 \tabularnewline
178 & 12 & 12.7410235452396 & -0.741023545239648 \tabularnewline
179 & 14 & 13.8482569329382 & 0.151743067061837 \tabularnewline
180 & 12 & 13.4363825724819 & -1.43638257248194 \tabularnewline
181 & 16 & 15.0574225512253 & 0.942577448774667 \tabularnewline
182 & 11 & 11.5290900782482 & -0.529090078248177 \tabularnewline
183 & 19 & 16.7023934664028 & 2.29760653359724 \tabularnewline
184 & 15 & 15.1861080347248 & -0.18610803472479 \tabularnewline
185 & 8 & 14.6098477634636 & -6.60984776346357 \tabularnewline
186 & 16 & 14.7948754284262 & 1.20512457157379 \tabularnewline
187 & 17 & 14.4889678086466 & 2.51103219135338 \tabularnewline
188 & 12 & 12.4492788886036 & -0.449278888603589 \tabularnewline
189 & 11 & 11.5258253233485 & -0.525825323348493 \tabularnewline
190 & 11 & 10.4839919409228 & 0.516008059077233 \tabularnewline
191 & 14 & 14.7297291026958 & -0.729729102695786 \tabularnewline
192 & 16 & 15.4573408606036 & 0.542659139396364 \tabularnewline
193 & 12 & 9.79494997958057 & 2.20505002041943 \tabularnewline
194 & 16 & 14.0582376070539 & 1.94176239294606 \tabularnewline
195 & 13 & 13.6671267744826 & -0.667126774482598 \tabularnewline
196 & 15 & 15.0059615481752 & -0.00596154817518689 \tabularnewline
197 & 16 & 12.96127564878 & 3.03872435122004 \tabularnewline
198 & 16 & 15.0216959506926 & 0.97830404930736 \tabularnewline
199 & 14 & 12.4654951689654 & 1.53450483103465 \tabularnewline
200 & 16 & 14.536034879898 & 1.463965120102 \tabularnewline
201 & 16 & 14.0101773215679 & 1.98982267843206 \tabularnewline
202 & 14 & 13.3505146449401 & 0.649485355059946 \tabularnewline
203 & 11 & 13.4248894641781 & -2.42488946417808 \tabularnewline
204 & 12 & 14.4629453484512 & -2.46294534845121 \tabularnewline
205 & 15 & 12.7281807329234 & 2.27181926707663 \tabularnewline
206 & 15 & 14.4392077029054 & 0.560792297094553 \tabularnewline
207 & 16 & 14.484195833577 & 1.51580416642297 \tabularnewline
208 & 16 & 14.863917483178 & 1.13608251682204 \tabularnewline
209 & 11 & 13.5735507320522 & -2.57355073205222 \tabularnewline
210 & 15 & 13.9329916342197 & 1.06700836578035 \tabularnewline
211 & 12 & 14.1909393598993 & -2.19093935989934 \tabularnewline
212 & 12 & 15.7003657756719 & -3.70036577567191 \tabularnewline
213 & 15 & 14.0984721484565 & 0.901527851543532 \tabularnewline
214 & 15 & 12.0898039545762 & 2.9101960454238 \tabularnewline
215 & 16 & 14.4861173675814 & 1.51388263241862 \tabularnewline
216 & 14 & 13.0447911555702 & 0.955208844429781 \tabularnewline
217 & 17 & 14.5703847036008 & 2.42961529639917 \tabularnewline
218 & 14 & 13.8898769613098 & 0.110123038690196 \tabularnewline
219 & 13 & 11.876340716377 & 1.12365928362303 \tabularnewline
220 & 15 & 15.0824598206262 & -0.08245982062625 \tabularnewline
221 & 13 & 14.5750556822787 & -1.57505568227874 \tabularnewline
222 & 14 & 13.9860332277979 & 0.0139667722021294 \tabularnewline
223 & 15 & 14.1866032608968 & 0.813396739103206 \tabularnewline
224 & 12 & 13.0451003628764 & -1.04510036287638 \tabularnewline
225 & 13 & 12.5277927132757 & 0.472207286724255 \tabularnewline
226 & 8 & 11.6960459918855 & -3.69604599188549 \tabularnewline
227 & 14 & 13.6561993287026 & 0.343800671297409 \tabularnewline
228 & 14 & 12.9000716996087 & 1.09992830039132 \tabularnewline
229 & 11 & 12.1551096302894 & -1.15510963028936 \tabularnewline
230 & 12 & 12.81419870208 & -0.814198702080007 \tabularnewline
231 & 13 & 11.1927984749469 & 1.80720152505309 \tabularnewline
232 & 10 & 13.1324013386614 & -3.13240133866136 \tabularnewline
233 & 16 & 11.3238724214746 & 4.6761275785254 \tabularnewline
234 & 18 & 15.7016623752784 & 2.29833762472164 \tabularnewline
235 & 13 & 13.6986247386433 & -0.698624738643324 \tabularnewline
236 & 11 & 13.1953213217862 & -2.19532132178622 \tabularnewline
237 & 4 & 10.9144168771812 & -6.91441687718123 \tabularnewline
238 & 13 & 14.0863865432717 & -1.08638654327172 \tabularnewline
239 & 16 & 14.111444602449 & 1.88855539755098 \tabularnewline
240 & 10 & 11.5810420842532 & -1.58104208425323 \tabularnewline
241 & 12 & 12.1457895056583 & -0.145789505658277 \tabularnewline
242 & 12 & 13.3223330284656 & -1.32233302846563 \tabularnewline
243 & 10 & 8.80195508184794 & 1.19804491815206 \tabularnewline
244 & 13 & 10.9675970396727 & 2.03240296032731 \tabularnewline
245 & 15 & 13.482514428037 & 1.51748557196298 \tabularnewline
246 & 12 & 11.7701175979104 & 0.229882402089599 \tabularnewline
247 & 14 & 12.6701662045843 & 1.32983379541566 \tabularnewline
248 & 10 & 12.2934478887145 & -2.29344788871453 \tabularnewline
249 & 12 & 10.6014073663047 & 1.3985926336953 \tabularnewline
250 & 12 & 11.5045860621479 & 0.495413937852081 \tabularnewline
251 & 11 & 11.693327081122 & -0.693327081122017 \tabularnewline
252 & 10 & 11.4970254698553 & -1.49702546985533 \tabularnewline
253 & 12 & 11.2277722925804 & 0.772227707419641 \tabularnewline
254 & 16 & 12.6330294634867 & 3.36697053651325 \tabularnewline
255 & 12 & 13.1033064447534 & -1.10330644475345 \tabularnewline
256 & 14 & 13.6115101268511 & 0.38848987314886 \tabularnewline
257 & 16 & 14.1380220385243 & 1.8619779614757 \tabularnewline
258 & 14 & 11.4512965204411 & 2.54870347955894 \tabularnewline
259 & 13 & 13.9883658481621 & -0.988365848162067 \tabularnewline
260 & 4 & 9.2785535181503 & -5.2785535181503 \tabularnewline
261 & 15 & 13.4908178141533 & 1.50918218584672 \tabularnewline
262 & 11 & 14.705447878116 & -3.70544787811599 \tabularnewline
263 & 11 & 11.1003815456157 & -0.100381545615727 \tabularnewline
264 & 14 & 12.5759984379471 & 1.4240015620529 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185717&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]16.1319992948175[/C][C]-3.13199929481746[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.773013320795[/C][C]0.226986679205008[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]17.0643208813592[/C][C]1.93567911864083[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.195441484357[/C][C]2.80455851564302[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]16.4180322249668[/C][C]-2.41803222496679[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.8802268925994[/C][C]-1.88022689259938[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.3073888497988[/C][C]3.69261115020122[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]17.0994109702753[/C][C]-2.09941097027534[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.0716945323483[/C][C]-2.07169453234826[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.4709276632402[/C][C]0.529072336759824[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.0048320881255[/C][C]0.995167911874463[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1898230324591[/C][C]-0.189823032459142[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.5298829760843[/C][C]0.470117023915685[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.5014616820547[/C][C]0.498538317945334[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.9849723007825[/C][C]-0.984972300782519[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.4711693679876[/C][C]-0.471169367987578[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.6115173032805[/C][C]0.388482696719519[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.4150676139934[/C][C]3.58493238600663[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.4898912080503[/C][C]2.51010879194971[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.5511175520186[/C][C]0.448882447981356[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.3608695373747[/C][C]0.639130462625279[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.1405055659747[/C][C]0.859494434025267[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4738106847532[/C][C]2.52618931524679[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.0882424925316[/C][C]0.911757507468446[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]16.1489849108884[/C][C]0.851015089111614[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.2540261576058[/C][C]0.745973842394168[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9707572768[/C][C]1.02924272319997[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.7314032980079[/C][C]-1.73140329800786[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.7144480582795[/C][C]0.285551941720517[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.1776976706257[/C][C]-0.177697670625683[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7271570905492[/C][C]-0.727157090549234[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.8196274232436[/C][C]-0.819627423243612[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.7900282358008[/C][C]-0.790028235800772[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.9092330167818[/C][C]0.0907669832181899[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.5241842748814[/C][C]-1.52418427488141[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]12.9949264320293[/C][C]-2.99492643202926[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]13.036017732808[/C][C]-3.03601773280801[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7159143656349[/C][C]-1.71591436563487[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.5189322404391[/C][C]1.48106775956088[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.4639740033125[/C][C]1.53602599668754[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.6922614387991[/C][C]1.30773856120091[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.6917393515644[/C][C]-1.69173935156445[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.4239904078572[/C][C]2.57600959214279[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.1268333106255[/C][C]-0.126833310625451[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.4277755307961[/C][C]-0.427775530796144[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.4769010866658[/C][C]-4.47690108666579[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.5016700566974[/C][C]-2.50167005669739[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.0872733557269[/C][C]-0.0872733557269008[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.4344986815598[/C][C]0.565501318440229[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.5957515567831[/C][C]-1.59575155678309[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.9470055618408[/C][C]-0.947005561840761[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1029056640276[/C][C]-0.102905664027581[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.9814432966391[/C][C]-2.98144329663911[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.7857932992669[/C][C]0.214206700733062[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.356371537705[/C][C]-2.35637153770504[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.3349345138998[/C][C]1.66506548610018[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.7945814318001[/C][C]0.205418568199872[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.4209469984545[/C][C]0.579053001545475[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.0830124726959[/C][C]-0.0830124726958567[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.1512679246355[/C][C]1.84873207536446[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5069610190735[/C][C]0.493038980926504[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.6140188033833[/C][C]0.385981196616701[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.48100460217[/C][C]-0.481004602170041[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.6386116076604[/C][C]-0.638611607660401[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.2160664980086[/C][C]0.783933501991448[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.1283594964296[/C][C]0.871640503570395[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.3659810285842[/C][C]1.6340189714158[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.5460652096084[/C][C]3.4539347903916[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.9859557929282[/C][C]-3.98595579292816[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.7235896801084[/C][C]0.276410319891583[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.4364628729273[/C][C]-3.4364628729273[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.9824764129632[/C][C]-0.982476412963195[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]17.0158830121805[/C][C]0.984116987819457[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.2454880935873[/C][C]0.754511906412718[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.3257205980239[/C][C]0.674279401976127[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.5757520187901[/C][C]3.42424798120986[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.4687902994353[/C][C]-0.468790299435291[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.5522158332353[/C][C]1.44778416676474[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.2614002582591[/C][C]-2.26140025825909[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.1964525622985[/C][C]0.80354743770153[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.7574496667774[/C][C]0.242550333222626[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.7768516930373[/C][C]0.223148306962747[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.7298773413516[/C][C]-0.72987734135157[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.9299702304001[/C][C]0.0700297695999076[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]14.2149434227545[/C][C]1.78505657724552[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.2366309572854[/C][C]-0.236630957285394[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.4094170749233[/C][C]0.590582925076732[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.9000272038213[/C][C]1.0999727961787[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.5575243875151[/C][C]0.442475612484949[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.9195769686057[/C][C]-1.91957696860568[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.7800226813812[/C][C]0.219977318618845[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.8629648187689[/C][C]0.13703518123109[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.130179843395[/C][C]-0.130179843395034[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]15.0948418111599[/C][C]-2.09484181115994[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.8308172507294[/C][C]1.16918274927058[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.7987332349023[/C][C]0.201266765097656[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.7556058789697[/C][C]2.24439412103029[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.8344267629732[/C][C]0.165573237026808[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.4661328340413[/C][C]-0.466132834041314[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]17.0397108174705[/C][C]-1.0397108174705[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.6988800176282[/C][C]1.30111998237178[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.460654928059[/C][C]2.53934507194102[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.2209068914829[/C][C]0.779093108517128[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]15.0049324640879[/C][C]0.995067535912078[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]14.8816285614618[/C][C]-1.88162856146175[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.7060829213038[/C][C]1.29391707869616[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.729946972503[/C][C]0.27005302749703[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.3734121615249[/C][C]1.62658783847515[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]12.3059825583899[/C][C]-0.305982558389896[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.283617063232[/C][C]0.716382936768048[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]16.0107716671885[/C][C]-0.0107716671884955[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]15.0350812641786[/C][C]1.96491873582139[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.5406112403526[/C][C]-1.54061124035264[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.5772750204351[/C][C]-2.57727502043506[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.149915808054[/C][C]1.85008419194602[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.8490205615627[/C][C]-1.8490205615627[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.1714374137032[/C][C]0.828562586296819[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.7890480129649[/C][C]-1.78904801296489[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.521494710364[/C][C]0.478505289635973[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.4173866932526[/C][C]-1.41738669325256[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.3732648403476[/C][C]0.626735159652427[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.8239894208261[/C][C]-2.82398942082606[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]16.8206727627472[/C][C]-0.820672762747172[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.8151943614052[/C][C]-0.815194361405167[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.7758864663847[/C][C]-0.775886466384681[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]14.6563176955803[/C][C]0.343682304419718[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.5135458446765[/C][C]1.48645415532348[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]13.9561905870924[/C][C]1.04380941290757[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.6649696205487[/C][C]-2.66496962054874[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]13.9484125272073[/C][C]2.05158747279265[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.6993457863953[/C][C]-3.69934578639527[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.4787326491481[/C][C]2.52126735085192[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]14.117119104406[/C][C]-2.11711910440596[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.4969825827167[/C][C]-1.49698258271668[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.022851451454[/C][C]-0.0228514514539609[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.0782835373044[/C][C]0.921716462695583[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.4179752062836[/C][C]0.582024793716415[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.4148843246344[/C][C]-2.41488432463444[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]12.9615388133991[/C][C]-0.961538813399079[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.3294291078386[/C][C]-2.32942910783856[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.8198286730183[/C][C]3.18017132698171[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.6114765181638[/C][C]1.38852348183618[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]16.7647361618202[/C][C]0.235263838179766[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.0972664814835[/C][C]1.90273351851652[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]13.2326063570416[/C][C]-3.23260635704161[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.4231808220058[/C][C]2.57681917799423[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]14.8643149057298[/C][C]-1.86431490572983[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]14.7811040097575[/C][C]1.21889599024253[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]12.7593738938659[/C][C]0.240626106134091[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]12.8479456212172[/C][C]-2.84794562121719[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]15.8571245856987[/C][C]-0.857124585698667[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.8243203686565[/C][C]2.17567963134348[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]11.817790540179[/C][C]4.18220945982104[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.1796252670647[/C][C]1.82037473293531[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.3603674449609[/C][C]-2.36036744496087[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.3563968526025[/C][C]0.643603147397511[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.6399592761047[/C][C]1.36004072389528[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]13.76067097381[/C][C]1.23932902619003[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]14.4777838001371[/C][C]1.52221619986288[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.671268682682[/C][C]-0.671268682682013[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.5501583930835[/C][C]0.449841606916476[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6951856659235[/C][C]0.304814334076481[/C][/ROW]
[ROW][C]163[/C][C]12[/C][C]12.5325265824158[/C][C]-0.532526582415843[/C][/ROW]
[ROW][C]164[/C][C]17[/C][C]16.2944365499899[/C][C]0.705563450010052[/C][/ROW]
[ROW][C]165[/C][C]15[/C][C]13.780269496602[/C][C]1.21973050339805[/C][/ROW]
[ROW][C]166[/C][C]10[/C][C]11.69604843173[/C][C]-1.69604843172995[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.4098138706111[/C][C]-0.409813870611107[/C][/ROW]
[ROW][C]168[/C][C]11[/C][C]14.2824014929905[/C][C]-3.28240149299048[/C][/ROW]
[ROW][C]169[/C][C]13[/C][C]14.8696564507497[/C][C]-1.86965645074974[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]14.5277256233795[/C][C]1.47227437662047[/C][/ROW]
[ROW][C]171[/C][C]12[/C][C]10.5781596972201[/C][C]1.42184030277993[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]15.4053957124388[/C][C]0.594604287561249[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]13.9507326026571[/C][C]-1.95073260265714[/C][/ROW]
[ROW][C]174[/C][C]9[/C][C]11.4327559786395[/C][C]-2.43275597863953[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]14.9708875013237[/C][C]-2.97088750132374[/C][/ROW]
[ROW][C]176[/C][C]15[/C][C]14.6153118153365[/C][C]0.384688184663489[/C][/ROW]
[ROW][C]177[/C][C]12[/C][C]12.3484847331071[/C][C]-0.348484733107133[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]12.7410235452396[/C][C]-0.741023545239648[/C][/ROW]
[ROW][C]179[/C][C]14[/C][C]13.8482569329382[/C][C]0.151743067061837[/C][/ROW]
[ROW][C]180[/C][C]12[/C][C]13.4363825724819[/C][C]-1.43638257248194[/C][/ROW]
[ROW][C]181[/C][C]16[/C][C]15.0574225512253[/C][C]0.942577448774667[/C][/ROW]
[ROW][C]182[/C][C]11[/C][C]11.5290900782482[/C][C]-0.529090078248177[/C][/ROW]
[ROW][C]183[/C][C]19[/C][C]16.7023934664028[/C][C]2.29760653359724[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]15.1861080347248[/C][C]-0.18610803472479[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]14.6098477634636[/C][C]-6.60984776346357[/C][/ROW]
[ROW][C]186[/C][C]16[/C][C]14.7948754284262[/C][C]1.20512457157379[/C][/ROW]
[ROW][C]187[/C][C]17[/C][C]14.4889678086466[/C][C]2.51103219135338[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]12.4492788886036[/C][C]-0.449278888603589[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]11.5258253233485[/C][C]-0.525825323348493[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]10.4839919409228[/C][C]0.516008059077233[/C][/ROW]
[ROW][C]191[/C][C]14[/C][C]14.7297291026958[/C][C]-0.729729102695786[/C][/ROW]
[ROW][C]192[/C][C]16[/C][C]15.4573408606036[/C][C]0.542659139396364[/C][/ROW]
[ROW][C]193[/C][C]12[/C][C]9.79494997958057[/C][C]2.20505002041943[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]14.0582376070539[/C][C]1.94176239294606[/C][/ROW]
[ROW][C]195[/C][C]13[/C][C]13.6671267744826[/C][C]-0.667126774482598[/C][/ROW]
[ROW][C]196[/C][C]15[/C][C]15.0059615481752[/C][C]-0.00596154817518689[/C][/ROW]
[ROW][C]197[/C][C]16[/C][C]12.96127564878[/C][C]3.03872435122004[/C][/ROW]
[ROW][C]198[/C][C]16[/C][C]15.0216959506926[/C][C]0.97830404930736[/C][/ROW]
[ROW][C]199[/C][C]14[/C][C]12.4654951689654[/C][C]1.53450483103465[/C][/ROW]
[ROW][C]200[/C][C]16[/C][C]14.536034879898[/C][C]1.463965120102[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]14.0101773215679[/C][C]1.98982267843206[/C][/ROW]
[ROW][C]202[/C][C]14[/C][C]13.3505146449401[/C][C]0.649485355059946[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]13.4248894641781[/C][C]-2.42488946417808[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]14.4629453484512[/C][C]-2.46294534845121[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]12.7281807329234[/C][C]2.27181926707663[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.4392077029054[/C][C]0.560792297094553[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]14.484195833577[/C][C]1.51580416642297[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]14.863917483178[/C][C]1.13608251682204[/C][/ROW]
[ROW][C]209[/C][C]11[/C][C]13.5735507320522[/C][C]-2.57355073205222[/C][/ROW]
[ROW][C]210[/C][C]15[/C][C]13.9329916342197[/C][C]1.06700836578035[/C][/ROW]
[ROW][C]211[/C][C]12[/C][C]14.1909393598993[/C][C]-2.19093935989934[/C][/ROW]
[ROW][C]212[/C][C]12[/C][C]15.7003657756719[/C][C]-3.70036577567191[/C][/ROW]
[ROW][C]213[/C][C]15[/C][C]14.0984721484565[/C][C]0.901527851543532[/C][/ROW]
[ROW][C]214[/C][C]15[/C][C]12.0898039545762[/C][C]2.9101960454238[/C][/ROW]
[ROW][C]215[/C][C]16[/C][C]14.4861173675814[/C][C]1.51388263241862[/C][/ROW]
[ROW][C]216[/C][C]14[/C][C]13.0447911555702[/C][C]0.955208844429781[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]14.5703847036008[/C][C]2.42961529639917[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]13.8898769613098[/C][C]0.110123038690196[/C][/ROW]
[ROW][C]219[/C][C]13[/C][C]11.876340716377[/C][C]1.12365928362303[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]15.0824598206262[/C][C]-0.08245982062625[/C][/ROW]
[ROW][C]221[/C][C]13[/C][C]14.5750556822787[/C][C]-1.57505568227874[/C][/ROW]
[ROW][C]222[/C][C]14[/C][C]13.9860332277979[/C][C]0.0139667722021294[/C][/ROW]
[ROW][C]223[/C][C]15[/C][C]14.1866032608968[/C][C]0.813396739103206[/C][/ROW]
[ROW][C]224[/C][C]12[/C][C]13.0451003628764[/C][C]-1.04510036287638[/C][/ROW]
[ROW][C]225[/C][C]13[/C][C]12.5277927132757[/C][C]0.472207286724255[/C][/ROW]
[ROW][C]226[/C][C]8[/C][C]11.6960459918855[/C][C]-3.69604599188549[/C][/ROW]
[ROW][C]227[/C][C]14[/C][C]13.6561993287026[/C][C]0.343800671297409[/C][/ROW]
[ROW][C]228[/C][C]14[/C][C]12.9000716996087[/C][C]1.09992830039132[/C][/ROW]
[ROW][C]229[/C][C]11[/C][C]12.1551096302894[/C][C]-1.15510963028936[/C][/ROW]
[ROW][C]230[/C][C]12[/C][C]12.81419870208[/C][C]-0.814198702080007[/C][/ROW]
[ROW][C]231[/C][C]13[/C][C]11.1927984749469[/C][C]1.80720152505309[/C][/ROW]
[ROW][C]232[/C][C]10[/C][C]13.1324013386614[/C][C]-3.13240133866136[/C][/ROW]
[ROW][C]233[/C][C]16[/C][C]11.3238724214746[/C][C]4.6761275785254[/C][/ROW]
[ROW][C]234[/C][C]18[/C][C]15.7016623752784[/C][C]2.29833762472164[/C][/ROW]
[ROW][C]235[/C][C]13[/C][C]13.6986247386433[/C][C]-0.698624738643324[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]13.1953213217862[/C][C]-2.19532132178622[/C][/ROW]
[ROW][C]237[/C][C]4[/C][C]10.9144168771812[/C][C]-6.91441687718123[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]14.0863865432717[/C][C]-1.08638654327172[/C][/ROW]
[ROW][C]239[/C][C]16[/C][C]14.111444602449[/C][C]1.88855539755098[/C][/ROW]
[ROW][C]240[/C][C]10[/C][C]11.5810420842532[/C][C]-1.58104208425323[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]12.1457895056583[/C][C]-0.145789505658277[/C][/ROW]
[ROW][C]242[/C][C]12[/C][C]13.3223330284656[/C][C]-1.32233302846563[/C][/ROW]
[ROW][C]243[/C][C]10[/C][C]8.80195508184794[/C][C]1.19804491815206[/C][/ROW]
[ROW][C]244[/C][C]13[/C][C]10.9675970396727[/C][C]2.03240296032731[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]13.482514428037[/C][C]1.51748557196298[/C][/ROW]
[ROW][C]246[/C][C]12[/C][C]11.7701175979104[/C][C]0.229882402089599[/C][/ROW]
[ROW][C]247[/C][C]14[/C][C]12.6701662045843[/C][C]1.32983379541566[/C][/ROW]
[ROW][C]248[/C][C]10[/C][C]12.2934478887145[/C][C]-2.29344788871453[/C][/ROW]
[ROW][C]249[/C][C]12[/C][C]10.6014073663047[/C][C]1.3985926336953[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.5045860621479[/C][C]0.495413937852081[/C][/ROW]
[ROW][C]251[/C][C]11[/C][C]11.693327081122[/C][C]-0.693327081122017[/C][/ROW]
[ROW][C]252[/C][C]10[/C][C]11.4970254698553[/C][C]-1.49702546985533[/C][/ROW]
[ROW][C]253[/C][C]12[/C][C]11.2277722925804[/C][C]0.772227707419641[/C][/ROW]
[ROW][C]254[/C][C]16[/C][C]12.6330294634867[/C][C]3.36697053651325[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]13.1033064447534[/C][C]-1.10330644475345[/C][/ROW]
[ROW][C]256[/C][C]14[/C][C]13.6115101268511[/C][C]0.38848987314886[/C][/ROW]
[ROW][C]257[/C][C]16[/C][C]14.1380220385243[/C][C]1.8619779614757[/C][/ROW]
[ROW][C]258[/C][C]14[/C][C]11.4512965204411[/C][C]2.54870347955894[/C][/ROW]
[ROW][C]259[/C][C]13[/C][C]13.9883658481621[/C][C]-0.988365848162067[/C][/ROW]
[ROW][C]260[/C][C]4[/C][C]9.2785535181503[/C][C]-5.2785535181503[/C][/ROW]
[ROW][C]261[/C][C]15[/C][C]13.4908178141533[/C][C]1.50918218584672[/C][/ROW]
[ROW][C]262[/C][C]11[/C][C]14.705447878116[/C][C]-3.70544787811599[/C][/ROW]
[ROW][C]263[/C][C]11[/C][C]11.1003815456157[/C][C]-0.100381545615727[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]12.5759984379471[/C][C]1.4240015620529[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185717&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185717&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 IndexActualsInterpolationForecastResidualsPrediction Error
11316.1319992948175-3.13199929481746
21615.7730133207950.226986679205008
31917.06432088135921.93567911864083
41512.1954414843572.80455851564302
51416.4180322249668-2.41803222496679
61314.8802268925994-1.88022689259938
71915.30738884979883.69261115020122
81517.0994109702753-2.09941097027534
91416.0716945323483-2.07169453234826
101514.47092766324020.529072336759824
111615.00483208812550.995167911874463
121616.1898230324591-0.189823032459142
131615.52988297608430.470117023915685
141615.50146168205470.498538317945334
151717.9849723007825-0.984972300782519
161515.4711693679876-0.471169367987578
171514.61151730328050.388482696719519
182016.41506761399343.58493238600663
191815.48989120805032.51010879194971
201615.55111755201860.448882447981356
211615.36086953737470.639130462625279
221615.14050556597470.859494434025267
231916.47381068475322.52618931524679
241615.08824249253160.911757507468446
251716.14898491088840.851015089111614
261716.25402615760580.745973842394168
271614.97075727681.02924272319997
281516.7314032980079-1.73140329800786
291615.71444805827950.285551941720517
301414.1776976706257-0.177697670625683
311515.7271570905492-0.727157090549234
321212.8196274232436-0.819627423243612
331414.7900282358008-0.790028235800772
341615.90923301678180.0907669832181899
351415.5241842748814-1.52418427488141
361012.9949264320293-2.99492643202926
371013.036017732808-3.03601773280801
381415.7159143656349-1.71591436563487
391614.51893224043911.48106775956088
401614.46397400331251.53602599668754
411614.69226143879911.30773856120091
421415.6917393515644-1.69173935156445
432017.42399040785722.57600959214279
441414.1268333106255-0.126833310625451
451414.4277755307961-0.427775530796144
461115.4769010866658-4.47690108666579
471416.5016700566974-2.50167005669739
481515.0872733557269-0.0872733557269008
491615.43449868155980.565501318440229
501415.5957515567831-1.59575155678309
511616.9470055618408-0.947005561840761
521414.1029056640276-0.102905664027581
531214.9814432966391-2.98144329663911
541615.78579329926690.214206700733062
55911.356371537705-2.35637153770504
561412.33493451389981.66506548610018
571615.79458143180010.205418568199872
581615.42094699845450.579053001545475
591515.0830124726959-0.0830124726958567
601614.15126792463551.84873207536446
611211.50696101907350.493038980926504
621615.61401880338330.385981196616701
631616.48100460217-0.481004602170041
641414.6386116076604-0.638611607660401
651615.21606649800860.783933501991448
661716.12835949642960.871640503570395
671816.36598102858421.6340189714158
681814.54606520960843.4539347903916
691215.9859557929282-3.98595579292816
701615.72358968010840.276410319891583
711013.4364628729273-3.4364628729273
721414.9824764129632-0.982476412963195
731817.01588301218050.984116987819457
741817.24548809358730.754511906412718
751615.32572059802390.674279401976127
761713.57575201879013.42424798120986
771616.4687902994353-0.468790299435291
781614.55221583323531.44778416676474
791315.2614002582591-2.26140025825909
801615.19645256229850.80354743770153
811615.75744966677740.242550333222626
821615.77685169303730.223148306962747
831515.7298773413516-0.72987734135157
841514.92997023040010.0700297695999076
851614.21494342275451.78505657724552
861414.2366309572854-0.236630957285394
871615.40941707492330.590582925076732
881614.90002720382131.0999727961787
891514.55752438751510.442475612484949
901213.9195769686057-1.91957696860568
911716.78002268138120.219977318618845
921615.86296481876890.13703518123109
931515.130179843395-0.130179843395034
941315.0948418111599-2.09484181115994
951614.83081725072941.16918274927058
961615.79873323490230.201266765097656
971613.75560587896972.24439412103029
981615.83442676297320.165573237026808
991414.4661328340413-0.466132834041314
1001617.0397108174705-1.0397108174705
1011614.69888001762821.30111998237178
1022017.4606549280592.53934507194102
1031514.22090689148290.779093108517128
1041615.00493246408790.995067535912078
1051314.8816285614618-1.88162856146175
1061715.70608292130381.29391707869616
1071615.7299469725030.27005302749703
1081614.37341216152491.62658783847515
1091212.3059825583899-0.305982558389896
1101615.2836170632320.716382936768048
1111616.0107716671885-0.0107716671884955
1121715.03508126417861.96491873582139
1131314.5406112403526-1.54061124035264
1141214.5772750204351-2.57727502043506
1151816.1499158080541.85008419194602
1161415.8490205615627-1.8490205615627
1171413.17143741370320.828562586296819
1181314.7890480129649-1.78904801296489
1191615.5214947103640.478505289635973
1201314.4173866932526-1.41738669325256
1211615.37326484034760.626735159652427
1221315.8239894208261-2.82398942082606
1231616.8206727627472-0.820672762747172
1241515.8151943614052-0.815194361405167
1251616.7758864663847-0.775886466384681
1261514.65631769558030.343682304419718
1271715.51354584467651.48645415532348
1281513.95619058709241.04380941290757
1291214.6649696205487-2.66496962054874
1301613.94841252720732.05158747279265
1311013.6993457863953-3.69934578639527
1321613.47873264914812.52126735085192
1331214.117119104406-2.11711910440596
1341415.4969825827167-1.49698258271668
1351515.022851451454-0.0228514514539609
1361312.07828353730440.921716462695583
1371514.41797520628360.582024793716415
1381113.4148843246344-2.41488432463444
1391212.9615388133991-0.961538813399079
1401113.3294291078386-2.32942910783856
1411612.81982867301833.18017132698171
1421513.61147651816381.38852348183618
1431716.76473616182020.235263838179766
1441614.09726648148351.90273351851652
1451013.2326063570416-3.23260635704161
1461815.42318082200582.57681917799423
1471314.8643149057298-1.86431490572983
1481614.78110400975751.21889599024253
1491312.75937389386590.240626106134091
1501012.8479456212172-2.84794562121719
1511515.8571245856987-0.857124585698667
1521613.82432036865652.17567963134348
1531611.8177905401794.18220945982104
1541412.17962526706471.82037473293531
1551012.3603674449609-2.36036744496087
1561716.35639685260250.643603147397511
1571311.63995927610471.36004072389528
1581513.760670973811.23932902619003
1591614.47778380013711.52221619986288
1601212.671268682682-0.671268682682013
1611312.55015839308350.449841606916476
1621312.69518566592350.304814334076481
1631212.5325265824158-0.532526582415843
1641716.29443654998990.705563450010052
1651513.7802694966021.21973050339805
1661011.69604843173-1.69604843172995
1671414.4098138706111-0.409813870611107
1681114.2824014929905-3.28240149299048
1691314.8696564507497-1.86965645074974
1701614.52772562337951.47227437662047
1711210.57815969722011.42184030277993
1721615.40539571243880.594604287561249
1731213.9507326026571-1.95073260265714
174911.4327559786395-2.43275597863953
1751214.9708875013237-2.97088750132374
1761514.61531181533650.384688184663489
1771212.3484847331071-0.348484733107133
1781212.7410235452396-0.741023545239648
1791413.84825693293820.151743067061837
1801213.4363825724819-1.43638257248194
1811615.05742255122530.942577448774667
1821111.5290900782482-0.529090078248177
1831916.70239346640282.29760653359724
1841515.1861080347248-0.18610803472479
185814.6098477634636-6.60984776346357
1861614.79487542842621.20512457157379
1871714.48896780864662.51103219135338
1881212.4492788886036-0.449278888603589
1891111.5258253233485-0.525825323348493
1901110.48399194092280.516008059077233
1911414.7297291026958-0.729729102695786
1921615.45734086060360.542659139396364
193129.794949979580572.20505002041943
1941614.05823760705391.94176239294606
1951313.6671267744826-0.667126774482598
1961515.0059615481752-0.00596154817518689
1971612.961275648783.03872435122004
1981615.02169595069260.97830404930736
1991412.46549516896541.53450483103465
2001614.5360348798981.463965120102
2011614.01017732156791.98982267843206
2021413.35051464494010.649485355059946
2031113.4248894641781-2.42488946417808
2041214.4629453484512-2.46294534845121
2051512.72818073292342.27181926707663
2061514.43920770290540.560792297094553
2071614.4841958335771.51580416642297
2081614.8639174831781.13608251682204
2091113.5735507320522-2.57355073205222
2101513.93299163421971.06700836578035
2111214.1909393598993-2.19093935989934
2121215.7003657756719-3.70036577567191
2131514.09847214845650.901527851543532
2141512.08980395457622.9101960454238
2151614.48611736758141.51388263241862
2161413.04479115557020.955208844429781
2171714.57038470360082.42961529639917
2181413.88987696130980.110123038690196
2191311.8763407163771.12365928362303
2201515.0824598206262-0.08245982062625
2211314.5750556822787-1.57505568227874
2221413.98603322779790.0139667722021294
2231514.18660326089680.813396739103206
2241213.0451003628764-1.04510036287638
2251312.52779271327570.472207286724255
226811.6960459918855-3.69604599188549
2271413.65619932870260.343800671297409
2281412.90007169960871.09992830039132
2291112.1551096302894-1.15510963028936
2301212.81419870208-0.814198702080007
2311311.19279847494691.80720152505309
2321013.1324013386614-3.13240133866136
2331611.32387242147464.6761275785254
2341815.70166237527842.29833762472164
2351313.6986247386433-0.698624738643324
2361113.1953213217862-2.19532132178622
237410.9144168771812-6.91441687718123
2381314.0863865432717-1.08638654327172
2391614.1114446024491.88855539755098
2401011.5810420842532-1.58104208425323
2411212.1457895056583-0.145789505658277
2421213.3223330284656-1.32233302846563
243108.801955081847941.19804491815206
2441310.96759703967272.03240296032731
2451513.4825144280371.51748557196298
2461211.77011759791040.229882402089599
2471412.67016620458431.32983379541566
2481012.2934478887145-2.29344788871453
2491210.60140736630471.3985926336953
2501211.50458606214790.495413937852081
2511111.693327081122-0.693327081122017
2521011.4970254698553-1.49702546985533
2531211.22777229258040.772227707419641
2541612.63302946348673.36697053651325
2551213.1033064447534-1.10330644475345
2561413.61151012685110.38848987314886
2571614.13802203852431.8619779614757
2581411.45129652044112.54870347955894
2591313.9883658481621-0.988365848162067
26049.2785535181503-5.2785535181503
2611513.49081781415331.50918218584672
2621114.705447878116-3.70544787811599
2631111.1003815456157-0.100381545615727
2641412.57599843794711.4240015620529







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.3098076562868190.6196153125736370.690192343713181
140.2982458895083990.5964917790167970.701754110491601
150.180880645654020.3617612913080410.81911935434598
160.1379595279697360.2759190559394720.862040472030264
170.143729595500070.287459191000140.85627040449993
180.2956726119172730.5913452238345470.704327388082727
190.2459530517545690.4919061035091380.754046948245431
200.1975315826423510.3950631652847020.802468417357649
210.1546385984340610.3092771968681210.845361401565939
220.1811797940705840.3623595881411680.818820205929416
230.2923707220197970.5847414440395940.707629277980203
240.4135630532472670.8271261064945330.586436946752733
250.3438715144549040.6877430289098080.656128485545096
260.2927453957228830.5854907914457670.707254604277117
270.2802750084189120.5605500168378240.719724991581088
280.3963794298911410.7927588597822830.603620570108859
290.3427054950612210.6854109901224420.657294504938779
300.4535908923182440.9071817846364880.546409107681756
310.4098203042313980.8196406084627950.590179695768602
320.3797004077828050.7594008155656110.620299592217195
330.3621991750907350.724398350181470.637800824909265
340.3230614192767030.6461228385534050.676938580723297
350.2774193711413440.5548387422826880.722580628858656
360.4125993740547510.8251987481095030.587400625945249
370.4404867410818060.8809734821636120.559513258918194
380.4147512409377360.8295024818754730.585248759062264
390.4589965817755790.9179931635511570.541003418224422
400.4380772810874010.8761545621748020.561922718912599
410.3976066324475270.7952132648950540.602393367552473
420.3570110205573520.7140220411147040.642988979442648
430.3758033239779860.7516066479559720.624196676022014
440.3262191959369550.6524383918739110.673780804063045
450.2972285976381190.5944571952762370.702771402361881
460.47776757671470.9555351534294010.5222324232853
470.5669830094703450.866033981059310.433016990529655
480.5236943455500880.9526113088998250.476305654449913
490.5470906284513140.9058187430973710.452909371548686
500.519795954142250.9604080917154990.48020404585775
510.4743618932566040.9487237865132070.525638106743396
520.4323254143523730.8646508287047470.567674585647627
530.4325852567479120.8651705134958240.567414743252088
540.3893686754559180.7787373509118350.610631324544082
550.3778085137905960.7556170275811930.622191486209404
560.373594207972990.7471884159459790.626405792027011
570.3328522754056870.6657045508113730.667147724594313
580.312980767817860.625961535635720.68701923218214
590.281726108362710.563452216725420.71827389163729
600.3015549681005980.6031099362011960.698445031899402
610.2767868351991080.5535736703982170.723213164800892
620.250508189541220.501016379082440.74949181045878
630.2206830002329820.4413660004659650.779316999767018
640.1917082812773360.3834165625546710.808291718722664
650.1693681956767310.3387363913534620.830631804323269
660.1437839319250250.2875678638500490.856216068074975
670.124528037277640.249056074555280.87547196272236
680.1507141268579960.3014282537159920.849285873142004
690.3438886689519220.6877773379038450.656111331048078
700.3056432567799190.6112865135598390.694356743220081
710.4568775855796530.9137551711593070.543122414420347
720.4199286978973710.8398573957947410.580071302102629
730.4102502676853240.8205005353706480.589749732314676
740.3894757878510020.7789515757020040.610524212148998
750.3530671532661420.7061343065322840.646932846733858
760.4024786615460310.8049573230920630.597521338453968
770.3675803013216610.7351606026433220.632419698678339
780.3389469117914920.6778938235829850.661053088208508
790.3663871668170840.7327743336341690.633612833182916
800.334440607313750.6688812146275010.66555939268625
810.3026602800865650.605320560173130.697339719913435
820.268712181387130.5374243627742610.73128781861287
830.2434651651269040.4869303302538090.756534834873096
840.2133951700540430.4267903401080850.786604829945957
850.2044737233266640.4089474466533280.795526276673336
860.1775890557445860.3551781114891710.822410944255414
870.1551740734164460.3103481468328910.844825926583554
880.1411343802845170.2822687605690330.858865619715483
890.1209886343409550.241977268681910.879011365659045
900.1202551592357210.2405103184714430.879744840764279
910.1024791541696440.2049583083392880.897520845830356
920.08914610202308230.1782922040461650.910853897976918
930.07551078691453740.1510215738290750.924489213085463
940.07953270421450530.1590654084290110.920467295785495
950.07183277807612140.1436655561522430.928167221923879
960.05977188118396190.1195437623679240.940228118816038
970.06365130835156120.1273026167031220.936348691648439
980.052572922589570.105145845179140.94742707741043
990.04334852788316240.08669705576632470.956651472116838
1000.03753924089161130.07507848178322260.962460759108389
1010.03299041089105950.06598082178211890.967009589108941
1020.03983937967767910.07967875935535830.960160620322321
1030.0335490789628820.06709815792576390.966450921037118
1040.03052017460761660.06104034921523320.969479825392383
1050.03570514471445460.07141028942890920.964294855285545
1060.03126503917001510.06253007834003020.968734960829985
1070.02530967683720280.05061935367440560.974690323162797
1080.02460496769296060.04920993538592120.975395032307039
1090.01984064776979220.03968129553958440.980159352230208
1100.01624007140688650.03248014281377290.983759928593114
1110.01281132361245620.02562264722491240.987188676387544
1120.0132259030977180.02645180619543610.986774096902282
1130.01212227737624730.02424455475249460.987877722623753
1140.01698041891064570.03396083782129130.983019581089354
1150.0161927520666970.0323855041333940.983807247933303
1160.01548673497034280.03097346994068550.984513265029657
1170.01279342485440280.02558684970880570.987206575145597
1180.0126934873005720.0253869746011440.987306512699428
1190.01012742435129640.02025484870259280.989872575648704
1200.008949415656714290.01789883131342860.991050584343286
1210.007135534773742170.01427106954748430.992864465226258
1220.01030091704980430.02060183409960850.989699082950196
1230.008418225579053950.01683645115810790.991581774420946
1240.007014958593532520.0140299171870650.992985041406467
1250.005612308142537930.01122461628507590.994387691857462
1260.004373091948813260.008746183897626520.995626908051187
1270.004114882308206620.008229764616413240.995885117691793
1280.003371336242236070.006742672484472140.996628663757764
1290.004595192412916990.009190384825833980.995404807587083
1300.005208333436493750.01041666687298750.994791666563506
1310.01065716548059790.02131433096119570.989342834519402
1320.01316378374194220.02632756748388440.986836216258058
1330.01405986139114380.02811972278228770.985940138608856
1340.01302663520592910.02605327041185810.986973364794071
1350.01026830039202160.02053660078404330.989731699607978
1360.008693013370512240.01738602674102450.991306986629488
1370.00691930633764560.01383861267529120.993080693662354
1380.008731244146020170.01746248829204030.99126875585398
1390.007445631284598030.01489126256919610.992554368715402
1400.009115021525194620.01823004305038920.990884978474805
1410.01492449788796980.02984899577593950.98507550211203
1420.01428802690950940.02857605381901880.985711973090491
1430.01178751919102710.02357503838205430.988212480808973
1440.01174247362107220.02348494724214440.988257526378928
1450.02085450788042560.04170901576085120.979145492119574
1460.02489666808312320.04979333616624630.975103331916877
1470.02642358234085880.05284716468171750.973576417659141
1480.0230885578000690.04617711560013810.976911442199931
1490.01857537741227480.03715075482454950.981424622587725
1500.02658264134176150.05316528268352290.973417358658239
1510.02268517783066220.04537035566132450.977314822169338
1520.02527519662139690.05055039324279380.974724803378603
1530.05101438518797870.1020287703759570.948985614812021
1540.04999733427735050.09999466855470090.95000266572265
1550.05905625823206360.1181125164641270.940943741767936
1560.0500529447297150.100105889459430.949947055270285
1570.04423785787402510.08847571574805030.955762142125975
1580.03797772267168220.07595544534336450.962022277328318
1590.03675453814585660.07350907629171310.963245461854143
1600.03105938517441330.06211877034882660.968940614825587
1610.02517503779155380.05035007558310750.974824962208446
1620.02033153230116950.04066306460233910.97966846769883
1630.01688684725346990.03377369450693980.98311315274653
1640.01408506905374590.02817013810749180.985914930946254
1650.01203776963979180.02407553927958360.987962230360208
1660.01271903993476150.02543807986952310.987280960065238
1670.01015662610664350.0203132522132870.989843373893357
1680.01572539382854310.03145078765708620.984274606171457
1690.01608870079771710.03217740159543430.983911299202283
1700.01500082693420220.03000165386840430.984999173065798
1710.01358950266183270.02717900532366550.986410497338167
1720.01083942079672250.02167884159344490.989160579203277
1730.01084416257913780.02168832515827560.989155837420862
1740.01259645952338080.02519291904676160.987403540476619
1750.01663112030229520.03326224060459030.983368879697705
1760.01324807451995940.02649614903991880.986751925480041
1770.01045292098781330.02090584197562670.989547079012187
1780.008768598309440910.01753719661888180.991231401690559
1790.006752180994416220.01350436198883240.993247819005584
1800.005952275777861570.01190455155572310.994047724222138
1810.004820212552379010.009640425104758020.995179787447621
1820.003708346311680420.007416692623360840.99629165368832
1830.003986923990583410.007973847981166820.996013076009417
1840.003009610086656640.006019220173313280.996990389913343
1850.08534147718383930.1706829543676790.914658522816161
1860.07602153353057240.1520430670611450.923978466469428
1870.08374139238210130.1674827847642030.916258607617899
1880.07004031937687150.1400806387537430.929959680623129
1890.06228759675132870.1245751935026570.937712403248671
1900.05174481009591280.1034896201918260.948255189904087
1910.04773149933135830.09546299866271650.952268500668642
1920.03922575193981920.07845150387963830.960774248060181
1930.04002435218004950.08004870436009910.95997564781995
1940.04030470811104250.08060941622208490.959695291888958
1950.03470060814855670.06940121629711340.965299391851443
1960.02776615183788410.05553230367576820.972233848162116
1970.03510520148723090.07021040297446180.964894798512769
1980.02850141846129390.05700283692258780.971498581538706
1990.02553144438275370.05106288876550740.974468555617246
2000.02181961012182370.04363922024364740.978180389878176
2010.02032694564840540.04065389129681090.979673054351595
2020.01708995824873960.03417991649747930.98291004175126
2030.0224643847935020.0449287695870040.977535615206498
2040.02615488779815660.05230977559631310.973845112201843
2050.02732199565107330.05464399130214660.972678004348927
2060.02125701190681490.04251402381362980.978742988093185
2070.02026531063659360.04053062127318710.979734689363406
2080.01646882596881970.03293765193763940.98353117403118
2090.0183272916694640.03665458333892790.981672708330536
2100.01450100379924260.02900200759848530.985498996200757
2110.01661828671498880.03323657342997760.983381713285011
2120.02709009545871450.0541801909174290.972909904541285
2130.02091311624195930.04182623248391850.979086883758041
2140.02711393349488850.0542278669897770.972886066505111
2150.02287254941602480.04574509883204960.977127450583975
2160.01753197827981620.03506395655963240.982468021720184
2170.01925595938692850.0385119187738570.980744040613072
2180.01609807138193110.03219614276386210.983901928618069
2190.01487379328870670.02974758657741340.985126206711293
2200.01124522826294170.02249045652588340.988754771737058
2210.009261827372234950.01852365474446990.990738172627765
2220.006565627505843110.01313125501168620.993434372494157
2230.004982036278469950.00996407255693990.99501796372153
2240.003705866237020060.007411732474040110.99629413376298
2250.003129105724860460.006258211449720920.99687089427514
2260.007308023833457170.01461604766691430.992691976166543
2270.005656586972485760.01131317394497150.994343413027514
2280.004130035904279870.008260071808559730.99586996409572
2290.002900956262569140.005801912525138290.997099043737431
2300.002100065268682330.004200130537364650.997899934731318
2310.001874350061501410.003748700123002810.998125649938499
2320.004452684319662040.008905368639324080.995547315680338
2330.01865136071435560.03730272142871120.981348639285644
2340.02870562366639240.05741124733278470.971294376333608
2350.02024081568753410.04048163137506820.979759184312466
2360.01663851784656380.03327703569312770.983361482153436
2370.1588115271456040.3176230542912080.841188472854396
2380.1220536595929050.2441073191858090.877946340407095
2390.09960486963204310.1992097392640860.900395130367957
2400.07508577164683040.1501715432936610.92491422835317
2410.06464264177535980.129285283550720.93535735822464
2420.1024906555532860.2049813111065720.897509344446714
2430.08149172083289850.1629834416657970.918508279167101
2440.0842682626125320.1685365252250640.915731737387468
2450.06658711930415780.1331742386083160.933412880695842
2460.05443568400435390.1088713680087080.945564315995646
2470.03299075440982110.06598150881964220.967009245590179
2480.02663916407596290.05327832815192580.973360835924037
2490.02249135767596260.04498271535192520.977508642324037
2500.06644733884633630.1328946776926730.933552661153664
2510.04869724832382630.09739449664765270.951302751676174

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.309807656286819 & 0.619615312573637 & 0.690192343713181 \tabularnewline
14 & 0.298245889508399 & 0.596491779016797 & 0.701754110491601 \tabularnewline
15 & 0.18088064565402 & 0.361761291308041 & 0.81911935434598 \tabularnewline
16 & 0.137959527969736 & 0.275919055939472 & 0.862040472030264 \tabularnewline
17 & 0.14372959550007 & 0.28745919100014 & 0.85627040449993 \tabularnewline
18 & 0.295672611917273 & 0.591345223834547 & 0.704327388082727 \tabularnewline
19 & 0.245953051754569 & 0.491906103509138 & 0.754046948245431 \tabularnewline
20 & 0.197531582642351 & 0.395063165284702 & 0.802468417357649 \tabularnewline
21 & 0.154638598434061 & 0.309277196868121 & 0.845361401565939 \tabularnewline
22 & 0.181179794070584 & 0.362359588141168 & 0.818820205929416 \tabularnewline
23 & 0.292370722019797 & 0.584741444039594 & 0.707629277980203 \tabularnewline
24 & 0.413563053247267 & 0.827126106494533 & 0.586436946752733 \tabularnewline
25 & 0.343871514454904 & 0.687743028909808 & 0.656128485545096 \tabularnewline
26 & 0.292745395722883 & 0.585490791445767 & 0.707254604277117 \tabularnewline
27 & 0.280275008418912 & 0.560550016837824 & 0.719724991581088 \tabularnewline
28 & 0.396379429891141 & 0.792758859782283 & 0.603620570108859 \tabularnewline
29 & 0.342705495061221 & 0.685410990122442 & 0.657294504938779 \tabularnewline
30 & 0.453590892318244 & 0.907181784636488 & 0.546409107681756 \tabularnewline
31 & 0.409820304231398 & 0.819640608462795 & 0.590179695768602 \tabularnewline
32 & 0.379700407782805 & 0.759400815565611 & 0.620299592217195 \tabularnewline
33 & 0.362199175090735 & 0.72439835018147 & 0.637800824909265 \tabularnewline
34 & 0.323061419276703 & 0.646122838553405 & 0.676938580723297 \tabularnewline
35 & 0.277419371141344 & 0.554838742282688 & 0.722580628858656 \tabularnewline
36 & 0.412599374054751 & 0.825198748109503 & 0.587400625945249 \tabularnewline
37 & 0.440486741081806 & 0.880973482163612 & 0.559513258918194 \tabularnewline
38 & 0.414751240937736 & 0.829502481875473 & 0.585248759062264 \tabularnewline
39 & 0.458996581775579 & 0.917993163551157 & 0.541003418224422 \tabularnewline
40 & 0.438077281087401 & 0.876154562174802 & 0.561922718912599 \tabularnewline
41 & 0.397606632447527 & 0.795213264895054 & 0.602393367552473 \tabularnewline
42 & 0.357011020557352 & 0.714022041114704 & 0.642988979442648 \tabularnewline
43 & 0.375803323977986 & 0.751606647955972 & 0.624196676022014 \tabularnewline
44 & 0.326219195936955 & 0.652438391873911 & 0.673780804063045 \tabularnewline
45 & 0.297228597638119 & 0.594457195276237 & 0.702771402361881 \tabularnewline
46 & 0.4777675767147 & 0.955535153429401 & 0.5222324232853 \tabularnewline
47 & 0.566983009470345 & 0.86603398105931 & 0.433016990529655 \tabularnewline
48 & 0.523694345550088 & 0.952611308899825 & 0.476305654449913 \tabularnewline
49 & 0.547090628451314 & 0.905818743097371 & 0.452909371548686 \tabularnewline
50 & 0.51979595414225 & 0.960408091715499 & 0.48020404585775 \tabularnewline
51 & 0.474361893256604 & 0.948723786513207 & 0.525638106743396 \tabularnewline
52 & 0.432325414352373 & 0.864650828704747 & 0.567674585647627 \tabularnewline
53 & 0.432585256747912 & 0.865170513495824 & 0.567414743252088 \tabularnewline
54 & 0.389368675455918 & 0.778737350911835 & 0.610631324544082 \tabularnewline
55 & 0.377808513790596 & 0.755617027581193 & 0.622191486209404 \tabularnewline
56 & 0.37359420797299 & 0.747188415945979 & 0.626405792027011 \tabularnewline
57 & 0.332852275405687 & 0.665704550811373 & 0.667147724594313 \tabularnewline
58 & 0.31298076781786 & 0.62596153563572 & 0.68701923218214 \tabularnewline
59 & 0.28172610836271 & 0.56345221672542 & 0.71827389163729 \tabularnewline
60 & 0.301554968100598 & 0.603109936201196 & 0.698445031899402 \tabularnewline
61 & 0.276786835199108 & 0.553573670398217 & 0.723213164800892 \tabularnewline
62 & 0.25050818954122 & 0.50101637908244 & 0.74949181045878 \tabularnewline
63 & 0.220683000232982 & 0.441366000465965 & 0.779316999767018 \tabularnewline
64 & 0.191708281277336 & 0.383416562554671 & 0.808291718722664 \tabularnewline
65 & 0.169368195676731 & 0.338736391353462 & 0.830631804323269 \tabularnewline
66 & 0.143783931925025 & 0.287567863850049 & 0.856216068074975 \tabularnewline
67 & 0.12452803727764 & 0.24905607455528 & 0.87547196272236 \tabularnewline
68 & 0.150714126857996 & 0.301428253715992 & 0.849285873142004 \tabularnewline
69 & 0.343888668951922 & 0.687777337903845 & 0.656111331048078 \tabularnewline
70 & 0.305643256779919 & 0.611286513559839 & 0.694356743220081 \tabularnewline
71 & 0.456877585579653 & 0.913755171159307 & 0.543122414420347 \tabularnewline
72 & 0.419928697897371 & 0.839857395794741 & 0.580071302102629 \tabularnewline
73 & 0.410250267685324 & 0.820500535370648 & 0.589749732314676 \tabularnewline
74 & 0.389475787851002 & 0.778951575702004 & 0.610524212148998 \tabularnewline
75 & 0.353067153266142 & 0.706134306532284 & 0.646932846733858 \tabularnewline
76 & 0.402478661546031 & 0.804957323092063 & 0.597521338453968 \tabularnewline
77 & 0.367580301321661 & 0.735160602643322 & 0.632419698678339 \tabularnewline
78 & 0.338946911791492 & 0.677893823582985 & 0.661053088208508 \tabularnewline
79 & 0.366387166817084 & 0.732774333634169 & 0.633612833182916 \tabularnewline
80 & 0.33444060731375 & 0.668881214627501 & 0.66555939268625 \tabularnewline
81 & 0.302660280086565 & 0.60532056017313 & 0.697339719913435 \tabularnewline
82 & 0.26871218138713 & 0.537424362774261 & 0.73128781861287 \tabularnewline
83 & 0.243465165126904 & 0.486930330253809 & 0.756534834873096 \tabularnewline
84 & 0.213395170054043 & 0.426790340108085 & 0.786604829945957 \tabularnewline
85 & 0.204473723326664 & 0.408947446653328 & 0.795526276673336 \tabularnewline
86 & 0.177589055744586 & 0.355178111489171 & 0.822410944255414 \tabularnewline
87 & 0.155174073416446 & 0.310348146832891 & 0.844825926583554 \tabularnewline
88 & 0.141134380284517 & 0.282268760569033 & 0.858865619715483 \tabularnewline
89 & 0.120988634340955 & 0.24197726868191 & 0.879011365659045 \tabularnewline
90 & 0.120255159235721 & 0.240510318471443 & 0.879744840764279 \tabularnewline
91 & 0.102479154169644 & 0.204958308339288 & 0.897520845830356 \tabularnewline
92 & 0.0891461020230823 & 0.178292204046165 & 0.910853897976918 \tabularnewline
93 & 0.0755107869145374 & 0.151021573829075 & 0.924489213085463 \tabularnewline
94 & 0.0795327042145053 & 0.159065408429011 & 0.920467295785495 \tabularnewline
95 & 0.0718327780761214 & 0.143665556152243 & 0.928167221923879 \tabularnewline
96 & 0.0597718811839619 & 0.119543762367924 & 0.940228118816038 \tabularnewline
97 & 0.0636513083515612 & 0.127302616703122 & 0.936348691648439 \tabularnewline
98 & 0.05257292258957 & 0.10514584517914 & 0.94742707741043 \tabularnewline
99 & 0.0433485278831624 & 0.0866970557663247 & 0.956651472116838 \tabularnewline
100 & 0.0375392408916113 & 0.0750784817832226 & 0.962460759108389 \tabularnewline
101 & 0.0329904108910595 & 0.0659808217821189 & 0.967009589108941 \tabularnewline
102 & 0.0398393796776791 & 0.0796787593553583 & 0.960160620322321 \tabularnewline
103 & 0.033549078962882 & 0.0670981579257639 & 0.966450921037118 \tabularnewline
104 & 0.0305201746076166 & 0.0610403492152332 & 0.969479825392383 \tabularnewline
105 & 0.0357051447144546 & 0.0714102894289092 & 0.964294855285545 \tabularnewline
106 & 0.0312650391700151 & 0.0625300783400302 & 0.968734960829985 \tabularnewline
107 & 0.0253096768372028 & 0.0506193536744056 & 0.974690323162797 \tabularnewline
108 & 0.0246049676929606 & 0.0492099353859212 & 0.975395032307039 \tabularnewline
109 & 0.0198406477697922 & 0.0396812955395844 & 0.980159352230208 \tabularnewline
110 & 0.0162400714068865 & 0.0324801428137729 & 0.983759928593114 \tabularnewline
111 & 0.0128113236124562 & 0.0256226472249124 & 0.987188676387544 \tabularnewline
112 & 0.013225903097718 & 0.0264518061954361 & 0.986774096902282 \tabularnewline
113 & 0.0121222773762473 & 0.0242445547524946 & 0.987877722623753 \tabularnewline
114 & 0.0169804189106457 & 0.0339608378212913 & 0.983019581089354 \tabularnewline
115 & 0.016192752066697 & 0.032385504133394 & 0.983807247933303 \tabularnewline
116 & 0.0154867349703428 & 0.0309734699406855 & 0.984513265029657 \tabularnewline
117 & 0.0127934248544028 & 0.0255868497088057 & 0.987206575145597 \tabularnewline
118 & 0.012693487300572 & 0.025386974601144 & 0.987306512699428 \tabularnewline
119 & 0.0101274243512964 & 0.0202548487025928 & 0.989872575648704 \tabularnewline
120 & 0.00894941565671429 & 0.0178988313134286 & 0.991050584343286 \tabularnewline
121 & 0.00713553477374217 & 0.0142710695474843 & 0.992864465226258 \tabularnewline
122 & 0.0103009170498043 & 0.0206018340996085 & 0.989699082950196 \tabularnewline
123 & 0.00841822557905395 & 0.0168364511581079 & 0.991581774420946 \tabularnewline
124 & 0.00701495859353252 & 0.014029917187065 & 0.992985041406467 \tabularnewline
125 & 0.00561230814253793 & 0.0112246162850759 & 0.994387691857462 \tabularnewline
126 & 0.00437309194881326 & 0.00874618389762652 & 0.995626908051187 \tabularnewline
127 & 0.00411488230820662 & 0.00822976461641324 & 0.995885117691793 \tabularnewline
128 & 0.00337133624223607 & 0.00674267248447214 & 0.996628663757764 \tabularnewline
129 & 0.00459519241291699 & 0.00919038482583398 & 0.995404807587083 \tabularnewline
130 & 0.00520833343649375 & 0.0104166668729875 & 0.994791666563506 \tabularnewline
131 & 0.0106571654805979 & 0.0213143309611957 & 0.989342834519402 \tabularnewline
132 & 0.0131637837419422 & 0.0263275674838844 & 0.986836216258058 \tabularnewline
133 & 0.0140598613911438 & 0.0281197227822877 & 0.985940138608856 \tabularnewline
134 & 0.0130266352059291 & 0.0260532704118581 & 0.986973364794071 \tabularnewline
135 & 0.0102683003920216 & 0.0205366007840433 & 0.989731699607978 \tabularnewline
136 & 0.00869301337051224 & 0.0173860267410245 & 0.991306986629488 \tabularnewline
137 & 0.0069193063376456 & 0.0138386126752912 & 0.993080693662354 \tabularnewline
138 & 0.00873124414602017 & 0.0174624882920403 & 0.99126875585398 \tabularnewline
139 & 0.00744563128459803 & 0.0148912625691961 & 0.992554368715402 \tabularnewline
140 & 0.00911502152519462 & 0.0182300430503892 & 0.990884978474805 \tabularnewline
141 & 0.0149244978879698 & 0.0298489957759395 & 0.98507550211203 \tabularnewline
142 & 0.0142880269095094 & 0.0285760538190188 & 0.985711973090491 \tabularnewline
143 & 0.0117875191910271 & 0.0235750383820543 & 0.988212480808973 \tabularnewline
144 & 0.0117424736210722 & 0.0234849472421444 & 0.988257526378928 \tabularnewline
145 & 0.0208545078804256 & 0.0417090157608512 & 0.979145492119574 \tabularnewline
146 & 0.0248966680831232 & 0.0497933361662463 & 0.975103331916877 \tabularnewline
147 & 0.0264235823408588 & 0.0528471646817175 & 0.973576417659141 \tabularnewline
148 & 0.023088557800069 & 0.0461771156001381 & 0.976911442199931 \tabularnewline
149 & 0.0185753774122748 & 0.0371507548245495 & 0.981424622587725 \tabularnewline
150 & 0.0265826413417615 & 0.0531652826835229 & 0.973417358658239 \tabularnewline
151 & 0.0226851778306622 & 0.0453703556613245 & 0.977314822169338 \tabularnewline
152 & 0.0252751966213969 & 0.0505503932427938 & 0.974724803378603 \tabularnewline
153 & 0.0510143851879787 & 0.102028770375957 & 0.948985614812021 \tabularnewline
154 & 0.0499973342773505 & 0.0999946685547009 & 0.95000266572265 \tabularnewline
155 & 0.0590562582320636 & 0.118112516464127 & 0.940943741767936 \tabularnewline
156 & 0.050052944729715 & 0.10010588945943 & 0.949947055270285 \tabularnewline
157 & 0.0442378578740251 & 0.0884757157480503 & 0.955762142125975 \tabularnewline
158 & 0.0379777226716822 & 0.0759554453433645 & 0.962022277328318 \tabularnewline
159 & 0.0367545381458566 & 0.0735090762917131 & 0.963245461854143 \tabularnewline
160 & 0.0310593851744133 & 0.0621187703488266 & 0.968940614825587 \tabularnewline
161 & 0.0251750377915538 & 0.0503500755831075 & 0.974824962208446 \tabularnewline
162 & 0.0203315323011695 & 0.0406630646023391 & 0.97966846769883 \tabularnewline
163 & 0.0168868472534699 & 0.0337736945069398 & 0.98311315274653 \tabularnewline
164 & 0.0140850690537459 & 0.0281701381074918 & 0.985914930946254 \tabularnewline
165 & 0.0120377696397918 & 0.0240755392795836 & 0.987962230360208 \tabularnewline
166 & 0.0127190399347615 & 0.0254380798695231 & 0.987280960065238 \tabularnewline
167 & 0.0101566261066435 & 0.020313252213287 & 0.989843373893357 \tabularnewline
168 & 0.0157253938285431 & 0.0314507876570862 & 0.984274606171457 \tabularnewline
169 & 0.0160887007977171 & 0.0321774015954343 & 0.983911299202283 \tabularnewline
170 & 0.0150008269342022 & 0.0300016538684043 & 0.984999173065798 \tabularnewline
171 & 0.0135895026618327 & 0.0271790053236655 & 0.986410497338167 \tabularnewline
172 & 0.0108394207967225 & 0.0216788415934449 & 0.989160579203277 \tabularnewline
173 & 0.0108441625791378 & 0.0216883251582756 & 0.989155837420862 \tabularnewline
174 & 0.0125964595233808 & 0.0251929190467616 & 0.987403540476619 \tabularnewline
175 & 0.0166311203022952 & 0.0332622406045903 & 0.983368879697705 \tabularnewline
176 & 0.0132480745199594 & 0.0264961490399188 & 0.986751925480041 \tabularnewline
177 & 0.0104529209878133 & 0.0209058419756267 & 0.989547079012187 \tabularnewline
178 & 0.00876859830944091 & 0.0175371966188818 & 0.991231401690559 \tabularnewline
179 & 0.00675218099441622 & 0.0135043619888324 & 0.993247819005584 \tabularnewline
180 & 0.00595227577786157 & 0.0119045515557231 & 0.994047724222138 \tabularnewline
181 & 0.00482021255237901 & 0.00964042510475802 & 0.995179787447621 \tabularnewline
182 & 0.00370834631168042 & 0.00741669262336084 & 0.99629165368832 \tabularnewline
183 & 0.00398692399058341 & 0.00797384798116682 & 0.996013076009417 \tabularnewline
184 & 0.00300961008665664 & 0.00601922017331328 & 0.996990389913343 \tabularnewline
185 & 0.0853414771838393 & 0.170682954367679 & 0.914658522816161 \tabularnewline
186 & 0.0760215335305724 & 0.152043067061145 & 0.923978466469428 \tabularnewline
187 & 0.0837413923821013 & 0.167482784764203 & 0.916258607617899 \tabularnewline
188 & 0.0700403193768715 & 0.140080638753743 & 0.929959680623129 \tabularnewline
189 & 0.0622875967513287 & 0.124575193502657 & 0.937712403248671 \tabularnewline
190 & 0.0517448100959128 & 0.103489620191826 & 0.948255189904087 \tabularnewline
191 & 0.0477314993313583 & 0.0954629986627165 & 0.952268500668642 \tabularnewline
192 & 0.0392257519398192 & 0.0784515038796383 & 0.960774248060181 \tabularnewline
193 & 0.0400243521800495 & 0.0800487043600991 & 0.95997564781995 \tabularnewline
194 & 0.0403047081110425 & 0.0806094162220849 & 0.959695291888958 \tabularnewline
195 & 0.0347006081485567 & 0.0694012162971134 & 0.965299391851443 \tabularnewline
196 & 0.0277661518378841 & 0.0555323036757682 & 0.972233848162116 \tabularnewline
197 & 0.0351052014872309 & 0.0702104029744618 & 0.964894798512769 \tabularnewline
198 & 0.0285014184612939 & 0.0570028369225878 & 0.971498581538706 \tabularnewline
199 & 0.0255314443827537 & 0.0510628887655074 & 0.974468555617246 \tabularnewline
200 & 0.0218196101218237 & 0.0436392202436474 & 0.978180389878176 \tabularnewline
201 & 0.0203269456484054 & 0.0406538912968109 & 0.979673054351595 \tabularnewline
202 & 0.0170899582487396 & 0.0341799164974793 & 0.98291004175126 \tabularnewline
203 & 0.022464384793502 & 0.044928769587004 & 0.977535615206498 \tabularnewline
204 & 0.0261548877981566 & 0.0523097755963131 & 0.973845112201843 \tabularnewline
205 & 0.0273219956510733 & 0.0546439913021466 & 0.972678004348927 \tabularnewline
206 & 0.0212570119068149 & 0.0425140238136298 & 0.978742988093185 \tabularnewline
207 & 0.0202653106365936 & 0.0405306212731871 & 0.979734689363406 \tabularnewline
208 & 0.0164688259688197 & 0.0329376519376394 & 0.98353117403118 \tabularnewline
209 & 0.018327291669464 & 0.0366545833389279 & 0.981672708330536 \tabularnewline
210 & 0.0145010037992426 & 0.0290020075984853 & 0.985498996200757 \tabularnewline
211 & 0.0166182867149888 & 0.0332365734299776 & 0.983381713285011 \tabularnewline
212 & 0.0270900954587145 & 0.054180190917429 & 0.972909904541285 \tabularnewline
213 & 0.0209131162419593 & 0.0418262324839185 & 0.979086883758041 \tabularnewline
214 & 0.0271139334948885 & 0.054227866989777 & 0.972886066505111 \tabularnewline
215 & 0.0228725494160248 & 0.0457450988320496 & 0.977127450583975 \tabularnewline
216 & 0.0175319782798162 & 0.0350639565596324 & 0.982468021720184 \tabularnewline
217 & 0.0192559593869285 & 0.038511918773857 & 0.980744040613072 \tabularnewline
218 & 0.0160980713819311 & 0.0321961427638621 & 0.983901928618069 \tabularnewline
219 & 0.0148737932887067 & 0.0297475865774134 & 0.985126206711293 \tabularnewline
220 & 0.0112452282629417 & 0.0224904565258834 & 0.988754771737058 \tabularnewline
221 & 0.00926182737223495 & 0.0185236547444699 & 0.990738172627765 \tabularnewline
222 & 0.00656562750584311 & 0.0131312550116862 & 0.993434372494157 \tabularnewline
223 & 0.00498203627846995 & 0.0099640725569399 & 0.99501796372153 \tabularnewline
224 & 0.00370586623702006 & 0.00741173247404011 & 0.99629413376298 \tabularnewline
225 & 0.00312910572486046 & 0.00625821144972092 & 0.99687089427514 \tabularnewline
226 & 0.00730802383345717 & 0.0146160476669143 & 0.992691976166543 \tabularnewline
227 & 0.00565658697248576 & 0.0113131739449715 & 0.994343413027514 \tabularnewline
228 & 0.00413003590427987 & 0.00826007180855973 & 0.99586996409572 \tabularnewline
229 & 0.00290095626256914 & 0.00580191252513829 & 0.997099043737431 \tabularnewline
230 & 0.00210006526868233 & 0.00420013053736465 & 0.997899934731318 \tabularnewline
231 & 0.00187435006150141 & 0.00374870012300281 & 0.998125649938499 \tabularnewline
232 & 0.00445268431966204 & 0.00890536863932408 & 0.995547315680338 \tabularnewline
233 & 0.0186513607143556 & 0.0373027214287112 & 0.981348639285644 \tabularnewline
234 & 0.0287056236663924 & 0.0574112473327847 & 0.971294376333608 \tabularnewline
235 & 0.0202408156875341 & 0.0404816313750682 & 0.979759184312466 \tabularnewline
236 & 0.0166385178465638 & 0.0332770356931277 & 0.983361482153436 \tabularnewline
237 & 0.158811527145604 & 0.317623054291208 & 0.841188472854396 \tabularnewline
238 & 0.122053659592905 & 0.244107319185809 & 0.877946340407095 \tabularnewline
239 & 0.0996048696320431 & 0.199209739264086 & 0.900395130367957 \tabularnewline
240 & 0.0750857716468304 & 0.150171543293661 & 0.92491422835317 \tabularnewline
241 & 0.0646426417753598 & 0.12928528355072 & 0.93535735822464 \tabularnewline
242 & 0.102490655553286 & 0.204981311106572 & 0.897509344446714 \tabularnewline
243 & 0.0814917208328985 & 0.162983441665797 & 0.918508279167101 \tabularnewline
244 & 0.084268262612532 & 0.168536525225064 & 0.915731737387468 \tabularnewline
245 & 0.0665871193041578 & 0.133174238608316 & 0.933412880695842 \tabularnewline
246 & 0.0544356840043539 & 0.108871368008708 & 0.945564315995646 \tabularnewline
247 & 0.0329907544098211 & 0.0659815088196422 & 0.967009245590179 \tabularnewline
248 & 0.0266391640759629 & 0.0532783281519258 & 0.973360835924037 \tabularnewline
249 & 0.0224913576759626 & 0.0449827153519252 & 0.977508642324037 \tabularnewline
250 & 0.0664473388463363 & 0.132894677692673 & 0.933552661153664 \tabularnewline
251 & 0.0486972483238263 & 0.0973944966476527 & 0.951302751676174 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185717&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]13[/C][C]0.309807656286819[/C][C]0.619615312573637[/C][C]0.690192343713181[/C][/ROW]
[ROW][C]14[/C][C]0.298245889508399[/C][C]0.596491779016797[/C][C]0.701754110491601[/C][/ROW]
[ROW][C]15[/C][C]0.18088064565402[/C][C]0.361761291308041[/C][C]0.81911935434598[/C][/ROW]
[ROW][C]16[/C][C]0.137959527969736[/C][C]0.275919055939472[/C][C]0.862040472030264[/C][/ROW]
[ROW][C]17[/C][C]0.14372959550007[/C][C]0.28745919100014[/C][C]0.85627040449993[/C][/ROW]
[ROW][C]18[/C][C]0.295672611917273[/C][C]0.591345223834547[/C][C]0.704327388082727[/C][/ROW]
[ROW][C]19[/C][C]0.245953051754569[/C][C]0.491906103509138[/C][C]0.754046948245431[/C][/ROW]
[ROW][C]20[/C][C]0.197531582642351[/C][C]0.395063165284702[/C][C]0.802468417357649[/C][/ROW]
[ROW][C]21[/C][C]0.154638598434061[/C][C]0.309277196868121[/C][C]0.845361401565939[/C][/ROW]
[ROW][C]22[/C][C]0.181179794070584[/C][C]0.362359588141168[/C][C]0.818820205929416[/C][/ROW]
[ROW][C]23[/C][C]0.292370722019797[/C][C]0.584741444039594[/C][C]0.707629277980203[/C][/ROW]
[ROW][C]24[/C][C]0.413563053247267[/C][C]0.827126106494533[/C][C]0.586436946752733[/C][/ROW]
[ROW][C]25[/C][C]0.343871514454904[/C][C]0.687743028909808[/C][C]0.656128485545096[/C][/ROW]
[ROW][C]26[/C][C]0.292745395722883[/C][C]0.585490791445767[/C][C]0.707254604277117[/C][/ROW]
[ROW][C]27[/C][C]0.280275008418912[/C][C]0.560550016837824[/C][C]0.719724991581088[/C][/ROW]
[ROW][C]28[/C][C]0.396379429891141[/C][C]0.792758859782283[/C][C]0.603620570108859[/C][/ROW]
[ROW][C]29[/C][C]0.342705495061221[/C][C]0.685410990122442[/C][C]0.657294504938779[/C][/ROW]
[ROW][C]30[/C][C]0.453590892318244[/C][C]0.907181784636488[/C][C]0.546409107681756[/C][/ROW]
[ROW][C]31[/C][C]0.409820304231398[/C][C]0.819640608462795[/C][C]0.590179695768602[/C][/ROW]
[ROW][C]32[/C][C]0.379700407782805[/C][C]0.759400815565611[/C][C]0.620299592217195[/C][/ROW]
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[ROW][C]106[/C][C]0.0312650391700151[/C][C]0.0625300783400302[/C][C]0.968734960829985[/C][/ROW]
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[ROW][C]110[/C][C]0.0162400714068865[/C][C]0.0324801428137729[/C][C]0.983759928593114[/C][/ROW]
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[ROW][C]112[/C][C]0.013225903097718[/C][C]0.0264518061954361[/C][C]0.986774096902282[/C][/ROW]
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[ROW][C]118[/C][C]0.012693487300572[/C][C]0.025386974601144[/C][C]0.987306512699428[/C][/ROW]
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[ROW][C]125[/C][C]0.00561230814253793[/C][C]0.0112246162850759[/C][C]0.994387691857462[/C][/ROW]
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[ROW][C]127[/C][C]0.00411488230820662[/C][C]0.00822976461641324[/C][C]0.995885117691793[/C][/ROW]
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[ROW][C]130[/C][C]0.00520833343649375[/C][C]0.0104166668729875[/C][C]0.994791666563506[/C][/ROW]
[ROW][C]131[/C][C]0.0106571654805979[/C][C]0.0213143309611957[/C][C]0.989342834519402[/C][/ROW]
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[ROW][C]162[/C][C]0.0203315323011695[/C][C]0.0406630646023391[/C][C]0.97966846769883[/C][/ROW]
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[ROW][C]165[/C][C]0.0120377696397918[/C][C]0.0240755392795836[/C][C]0.987962230360208[/C][/ROW]
[ROW][C]166[/C][C]0.0127190399347615[/C][C]0.0254380798695231[/C][C]0.987280960065238[/C][/ROW]
[ROW][C]167[/C][C]0.0101566261066435[/C][C]0.020313252213287[/C][C]0.989843373893357[/C][/ROW]
[ROW][C]168[/C][C]0.0157253938285431[/C][C]0.0314507876570862[/C][C]0.984274606171457[/C][/ROW]
[ROW][C]169[/C][C]0.0160887007977171[/C][C]0.0321774015954343[/C][C]0.983911299202283[/C][/ROW]
[ROW][C]170[/C][C]0.0150008269342022[/C][C]0.0300016538684043[/C][C]0.984999173065798[/C][/ROW]
[ROW][C]171[/C][C]0.0135895026618327[/C][C]0.0271790053236655[/C][C]0.986410497338167[/C][/ROW]
[ROW][C]172[/C][C]0.0108394207967225[/C][C]0.0216788415934449[/C][C]0.989160579203277[/C][/ROW]
[ROW][C]173[/C][C]0.0108441625791378[/C][C]0.0216883251582756[/C][C]0.989155837420862[/C][/ROW]
[ROW][C]174[/C][C]0.0125964595233808[/C][C]0.0251929190467616[/C][C]0.987403540476619[/C][/ROW]
[ROW][C]175[/C][C]0.0166311203022952[/C][C]0.0332622406045903[/C][C]0.983368879697705[/C][/ROW]
[ROW][C]176[/C][C]0.0132480745199594[/C][C]0.0264961490399188[/C][C]0.986751925480041[/C][/ROW]
[ROW][C]177[/C][C]0.0104529209878133[/C][C]0.0209058419756267[/C][C]0.989547079012187[/C][/ROW]
[ROW][C]178[/C][C]0.00876859830944091[/C][C]0.0175371966188818[/C][C]0.991231401690559[/C][/ROW]
[ROW][C]179[/C][C]0.00675218099441622[/C][C]0.0135043619888324[/C][C]0.993247819005584[/C][/ROW]
[ROW][C]180[/C][C]0.00595227577786157[/C][C]0.0119045515557231[/C][C]0.994047724222138[/C][/ROW]
[ROW][C]181[/C][C]0.00482021255237901[/C][C]0.00964042510475802[/C][C]0.995179787447621[/C][/ROW]
[ROW][C]182[/C][C]0.00370834631168042[/C][C]0.00741669262336084[/C][C]0.99629165368832[/C][/ROW]
[ROW][C]183[/C][C]0.00398692399058341[/C][C]0.00797384798116682[/C][C]0.996013076009417[/C][/ROW]
[ROW][C]184[/C][C]0.00300961008665664[/C][C]0.00601922017331328[/C][C]0.996990389913343[/C][/ROW]
[ROW][C]185[/C][C]0.0853414771838393[/C][C]0.170682954367679[/C][C]0.914658522816161[/C][/ROW]
[ROW][C]186[/C][C]0.0760215335305724[/C][C]0.152043067061145[/C][C]0.923978466469428[/C][/ROW]
[ROW][C]187[/C][C]0.0837413923821013[/C][C]0.167482784764203[/C][C]0.916258607617899[/C][/ROW]
[ROW][C]188[/C][C]0.0700403193768715[/C][C]0.140080638753743[/C][C]0.929959680623129[/C][/ROW]
[ROW][C]189[/C][C]0.0622875967513287[/C][C]0.124575193502657[/C][C]0.937712403248671[/C][/ROW]
[ROW][C]190[/C][C]0.0517448100959128[/C][C]0.103489620191826[/C][C]0.948255189904087[/C][/ROW]
[ROW][C]191[/C][C]0.0477314993313583[/C][C]0.0954629986627165[/C][C]0.952268500668642[/C][/ROW]
[ROW][C]192[/C][C]0.0392257519398192[/C][C]0.0784515038796383[/C][C]0.960774248060181[/C][/ROW]
[ROW][C]193[/C][C]0.0400243521800495[/C][C]0.0800487043600991[/C][C]0.95997564781995[/C][/ROW]
[ROW][C]194[/C][C]0.0403047081110425[/C][C]0.0806094162220849[/C][C]0.959695291888958[/C][/ROW]
[ROW][C]195[/C][C]0.0347006081485567[/C][C]0.0694012162971134[/C][C]0.965299391851443[/C][/ROW]
[ROW][C]196[/C][C]0.0277661518378841[/C][C]0.0555323036757682[/C][C]0.972233848162116[/C][/ROW]
[ROW][C]197[/C][C]0.0351052014872309[/C][C]0.0702104029744618[/C][C]0.964894798512769[/C][/ROW]
[ROW][C]198[/C][C]0.0285014184612939[/C][C]0.0570028369225878[/C][C]0.971498581538706[/C][/ROW]
[ROW][C]199[/C][C]0.0255314443827537[/C][C]0.0510628887655074[/C][C]0.974468555617246[/C][/ROW]
[ROW][C]200[/C][C]0.0218196101218237[/C][C]0.0436392202436474[/C][C]0.978180389878176[/C][/ROW]
[ROW][C]201[/C][C]0.0203269456484054[/C][C]0.0406538912968109[/C][C]0.979673054351595[/C][/ROW]
[ROW][C]202[/C][C]0.0170899582487396[/C][C]0.0341799164974793[/C][C]0.98291004175126[/C][/ROW]
[ROW][C]203[/C][C]0.022464384793502[/C][C]0.044928769587004[/C][C]0.977535615206498[/C][/ROW]
[ROW][C]204[/C][C]0.0261548877981566[/C][C]0.0523097755963131[/C][C]0.973845112201843[/C][/ROW]
[ROW][C]205[/C][C]0.0273219956510733[/C][C]0.0546439913021466[/C][C]0.972678004348927[/C][/ROW]
[ROW][C]206[/C][C]0.0212570119068149[/C][C]0.0425140238136298[/C][C]0.978742988093185[/C][/ROW]
[ROW][C]207[/C][C]0.0202653106365936[/C][C]0.0405306212731871[/C][C]0.979734689363406[/C][/ROW]
[ROW][C]208[/C][C]0.0164688259688197[/C][C]0.0329376519376394[/C][C]0.98353117403118[/C][/ROW]
[ROW][C]209[/C][C]0.018327291669464[/C][C]0.0366545833389279[/C][C]0.981672708330536[/C][/ROW]
[ROW][C]210[/C][C]0.0145010037992426[/C][C]0.0290020075984853[/C][C]0.985498996200757[/C][/ROW]
[ROW][C]211[/C][C]0.0166182867149888[/C][C]0.0332365734299776[/C][C]0.983381713285011[/C][/ROW]
[ROW][C]212[/C][C]0.0270900954587145[/C][C]0.054180190917429[/C][C]0.972909904541285[/C][/ROW]
[ROW][C]213[/C][C]0.0209131162419593[/C][C]0.0418262324839185[/C][C]0.979086883758041[/C][/ROW]
[ROW][C]214[/C][C]0.0271139334948885[/C][C]0.054227866989777[/C][C]0.972886066505111[/C][/ROW]
[ROW][C]215[/C][C]0.0228725494160248[/C][C]0.0457450988320496[/C][C]0.977127450583975[/C][/ROW]
[ROW][C]216[/C][C]0.0175319782798162[/C][C]0.0350639565596324[/C][C]0.982468021720184[/C][/ROW]
[ROW][C]217[/C][C]0.0192559593869285[/C][C]0.038511918773857[/C][C]0.980744040613072[/C][/ROW]
[ROW][C]218[/C][C]0.0160980713819311[/C][C]0.0321961427638621[/C][C]0.983901928618069[/C][/ROW]
[ROW][C]219[/C][C]0.0148737932887067[/C][C]0.0297475865774134[/C][C]0.985126206711293[/C][/ROW]
[ROW][C]220[/C][C]0.0112452282629417[/C][C]0.0224904565258834[/C][C]0.988754771737058[/C][/ROW]
[ROW][C]221[/C][C]0.00926182737223495[/C][C]0.0185236547444699[/C][C]0.990738172627765[/C][/ROW]
[ROW][C]222[/C][C]0.00656562750584311[/C][C]0.0131312550116862[/C][C]0.993434372494157[/C][/ROW]
[ROW][C]223[/C][C]0.00498203627846995[/C][C]0.0099640725569399[/C][C]0.99501796372153[/C][/ROW]
[ROW][C]224[/C][C]0.00370586623702006[/C][C]0.00741173247404011[/C][C]0.99629413376298[/C][/ROW]
[ROW][C]225[/C][C]0.00312910572486046[/C][C]0.00625821144972092[/C][C]0.99687089427514[/C][/ROW]
[ROW][C]226[/C][C]0.00730802383345717[/C][C]0.0146160476669143[/C][C]0.992691976166543[/C][/ROW]
[ROW][C]227[/C][C]0.00565658697248576[/C][C]0.0113131739449715[/C][C]0.994343413027514[/C][/ROW]
[ROW][C]228[/C][C]0.00413003590427987[/C][C]0.00826007180855973[/C][C]0.99586996409572[/C][/ROW]
[ROW][C]229[/C][C]0.00290095626256914[/C][C]0.00580191252513829[/C][C]0.997099043737431[/C][/ROW]
[ROW][C]230[/C][C]0.00210006526868233[/C][C]0.00420013053736465[/C][C]0.997899934731318[/C][/ROW]
[ROW][C]231[/C][C]0.00187435006150141[/C][C]0.00374870012300281[/C][C]0.998125649938499[/C][/ROW]
[ROW][C]232[/C][C]0.00445268431966204[/C][C]0.00890536863932408[/C][C]0.995547315680338[/C][/ROW]
[ROW][C]233[/C][C]0.0186513607143556[/C][C]0.0373027214287112[/C][C]0.981348639285644[/C][/ROW]
[ROW][C]234[/C][C]0.0287056236663924[/C][C]0.0574112473327847[/C][C]0.971294376333608[/C][/ROW]
[ROW][C]235[/C][C]0.0202408156875341[/C][C]0.0404816313750682[/C][C]0.979759184312466[/C][/ROW]
[ROW][C]236[/C][C]0.0166385178465638[/C][C]0.0332770356931277[/C][C]0.983361482153436[/C][/ROW]
[ROW][C]237[/C][C]0.158811527145604[/C][C]0.317623054291208[/C][C]0.841188472854396[/C][/ROW]
[ROW][C]238[/C][C]0.122053659592905[/C][C]0.244107319185809[/C][C]0.877946340407095[/C][/ROW]
[ROW][C]239[/C][C]0.0996048696320431[/C][C]0.199209739264086[/C][C]0.900395130367957[/C][/ROW]
[ROW][C]240[/C][C]0.0750857716468304[/C][C]0.150171543293661[/C][C]0.92491422835317[/C][/ROW]
[ROW][C]241[/C][C]0.0646426417753598[/C][C]0.12928528355072[/C][C]0.93535735822464[/C][/ROW]
[ROW][C]242[/C][C]0.102490655553286[/C][C]0.204981311106572[/C][C]0.897509344446714[/C][/ROW]
[ROW][C]243[/C][C]0.0814917208328985[/C][C]0.162983441665797[/C][C]0.918508279167101[/C][/ROW]
[ROW][C]244[/C][C]0.084268262612532[/C][C]0.168536525225064[/C][C]0.915731737387468[/C][/ROW]
[ROW][C]245[/C][C]0.0665871193041578[/C][C]0.133174238608316[/C][C]0.933412880695842[/C][/ROW]
[ROW][C]246[/C][C]0.0544356840043539[/C][C]0.108871368008708[/C][C]0.945564315995646[/C][/ROW]
[ROW][C]247[/C][C]0.0329907544098211[/C][C]0.0659815088196422[/C][C]0.967009245590179[/C][/ROW]
[ROW][C]248[/C][C]0.0266391640759629[/C][C]0.0532783281519258[/C][C]0.973360835924037[/C][/ROW]
[ROW][C]249[/C][C]0.0224913576759626[/C][C]0.0449827153519252[/C][C]0.977508642324037[/C][/ROW]
[ROW][C]250[/C][C]0.0664473388463363[/C][C]0.132894677692673[/C][C]0.933552661153664[/C][/ROW]
[ROW][C]251[/C][C]0.0486972483238263[/C][C]0.0973944966476527[/C][C]0.951302751676174[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185717&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185717&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.3098076562868190.6196153125736370.690192343713181
140.2982458895083990.5964917790167970.701754110491601
150.180880645654020.3617612913080410.81911935434598
160.1379595279697360.2759190559394720.862040472030264
170.143729595500070.287459191000140.85627040449993
180.2956726119172730.5913452238345470.704327388082727
190.2459530517545690.4919061035091380.754046948245431
200.1975315826423510.3950631652847020.802468417357649
210.1546385984340610.3092771968681210.845361401565939
220.1811797940705840.3623595881411680.818820205929416
230.2923707220197970.5847414440395940.707629277980203
240.4135630532472670.8271261064945330.586436946752733
250.3438715144549040.6877430289098080.656128485545096
260.2927453957228830.5854907914457670.707254604277117
270.2802750084189120.5605500168378240.719724991581088
280.3963794298911410.7927588597822830.603620570108859
290.3427054950612210.6854109901224420.657294504938779
300.4535908923182440.9071817846364880.546409107681756
310.4098203042313980.8196406084627950.590179695768602
320.3797004077828050.7594008155656110.620299592217195
330.3621991750907350.724398350181470.637800824909265
340.3230614192767030.6461228385534050.676938580723297
350.2774193711413440.5548387422826880.722580628858656
360.4125993740547510.8251987481095030.587400625945249
370.4404867410818060.8809734821636120.559513258918194
380.4147512409377360.8295024818754730.585248759062264
390.4589965817755790.9179931635511570.541003418224422
400.4380772810874010.8761545621748020.561922718912599
410.3976066324475270.7952132648950540.602393367552473
420.3570110205573520.7140220411147040.642988979442648
430.3758033239779860.7516066479559720.624196676022014
440.3262191959369550.6524383918739110.673780804063045
450.2972285976381190.5944571952762370.702771402361881
460.47776757671470.9555351534294010.5222324232853
470.5669830094703450.866033981059310.433016990529655
480.5236943455500880.9526113088998250.476305654449913
490.5470906284513140.9058187430973710.452909371548686
500.519795954142250.9604080917154990.48020404585775
510.4743618932566040.9487237865132070.525638106743396
520.4323254143523730.8646508287047470.567674585647627
530.4325852567479120.8651705134958240.567414743252088
540.3893686754559180.7787373509118350.610631324544082
550.3778085137905960.7556170275811930.622191486209404
560.373594207972990.7471884159459790.626405792027011
570.3328522754056870.6657045508113730.667147724594313
580.312980767817860.625961535635720.68701923218214
590.281726108362710.563452216725420.71827389163729
600.3015549681005980.6031099362011960.698445031899402
610.2767868351991080.5535736703982170.723213164800892
620.250508189541220.501016379082440.74949181045878
630.2206830002329820.4413660004659650.779316999767018
640.1917082812773360.3834165625546710.808291718722664
650.1693681956767310.3387363913534620.830631804323269
660.1437839319250250.2875678638500490.856216068074975
670.124528037277640.249056074555280.87547196272236
680.1507141268579960.3014282537159920.849285873142004
690.3438886689519220.6877773379038450.656111331048078
700.3056432567799190.6112865135598390.694356743220081
710.4568775855796530.9137551711593070.543122414420347
720.4199286978973710.8398573957947410.580071302102629
730.4102502676853240.8205005353706480.589749732314676
740.3894757878510020.7789515757020040.610524212148998
750.3530671532661420.7061343065322840.646932846733858
760.4024786615460310.8049573230920630.597521338453968
770.3675803013216610.7351606026433220.632419698678339
780.3389469117914920.6778938235829850.661053088208508
790.3663871668170840.7327743336341690.633612833182916
800.334440607313750.6688812146275010.66555939268625
810.3026602800865650.605320560173130.697339719913435
820.268712181387130.5374243627742610.73128781861287
830.2434651651269040.4869303302538090.756534834873096
840.2133951700540430.4267903401080850.786604829945957
850.2044737233266640.4089474466533280.795526276673336
860.1775890557445860.3551781114891710.822410944255414
870.1551740734164460.3103481468328910.844825926583554
880.1411343802845170.2822687605690330.858865619715483
890.1209886343409550.241977268681910.879011365659045
900.1202551592357210.2405103184714430.879744840764279
910.1024791541696440.2049583083392880.897520845830356
920.08914610202308230.1782922040461650.910853897976918
930.07551078691453740.1510215738290750.924489213085463
940.07953270421450530.1590654084290110.920467295785495
950.07183277807612140.1436655561522430.928167221923879
960.05977188118396190.1195437623679240.940228118816038
970.06365130835156120.1273026167031220.936348691648439
980.052572922589570.105145845179140.94742707741043
990.04334852788316240.08669705576632470.956651472116838
1000.03753924089161130.07507848178322260.962460759108389
1010.03299041089105950.06598082178211890.967009589108941
1020.03983937967767910.07967875935535830.960160620322321
1030.0335490789628820.06709815792576390.966450921037118
1040.03052017460761660.06104034921523320.969479825392383
1050.03570514471445460.07141028942890920.964294855285545
1060.03126503917001510.06253007834003020.968734960829985
1070.02530967683720280.05061935367440560.974690323162797
1080.02460496769296060.04920993538592120.975395032307039
1090.01984064776979220.03968129553958440.980159352230208
1100.01624007140688650.03248014281377290.983759928593114
1110.01281132361245620.02562264722491240.987188676387544
1120.0132259030977180.02645180619543610.986774096902282
1130.01212227737624730.02424455475249460.987877722623753
1140.01698041891064570.03396083782129130.983019581089354
1150.0161927520666970.0323855041333940.983807247933303
1160.01548673497034280.03097346994068550.984513265029657
1170.01279342485440280.02558684970880570.987206575145597
1180.0126934873005720.0253869746011440.987306512699428
1190.01012742435129640.02025484870259280.989872575648704
1200.008949415656714290.01789883131342860.991050584343286
1210.007135534773742170.01427106954748430.992864465226258
1220.01030091704980430.02060183409960850.989699082950196
1230.008418225579053950.01683645115810790.991581774420946
1240.007014958593532520.0140299171870650.992985041406467
1250.005612308142537930.01122461628507590.994387691857462
1260.004373091948813260.008746183897626520.995626908051187
1270.004114882308206620.008229764616413240.995885117691793
1280.003371336242236070.006742672484472140.996628663757764
1290.004595192412916990.009190384825833980.995404807587083
1300.005208333436493750.01041666687298750.994791666563506
1310.01065716548059790.02131433096119570.989342834519402
1320.01316378374194220.02632756748388440.986836216258058
1330.01405986139114380.02811972278228770.985940138608856
1340.01302663520592910.02605327041185810.986973364794071
1350.01026830039202160.02053660078404330.989731699607978
1360.008693013370512240.01738602674102450.991306986629488
1370.00691930633764560.01383861267529120.993080693662354
1380.008731244146020170.01746248829204030.99126875585398
1390.007445631284598030.01489126256919610.992554368715402
1400.009115021525194620.01823004305038920.990884978474805
1410.01492449788796980.02984899577593950.98507550211203
1420.01428802690950940.02857605381901880.985711973090491
1430.01178751919102710.02357503838205430.988212480808973
1440.01174247362107220.02348494724214440.988257526378928
1450.02085450788042560.04170901576085120.979145492119574
1460.02489666808312320.04979333616624630.975103331916877
1470.02642358234085880.05284716468171750.973576417659141
1480.0230885578000690.04617711560013810.976911442199931
1490.01857537741227480.03715075482454950.981424622587725
1500.02658264134176150.05316528268352290.973417358658239
1510.02268517783066220.04537035566132450.977314822169338
1520.02527519662139690.05055039324279380.974724803378603
1530.05101438518797870.1020287703759570.948985614812021
1540.04999733427735050.09999466855470090.95000266572265
1550.05905625823206360.1181125164641270.940943741767936
1560.0500529447297150.100105889459430.949947055270285
1570.04423785787402510.08847571574805030.955762142125975
1580.03797772267168220.07595544534336450.962022277328318
1590.03675453814585660.07350907629171310.963245461854143
1600.03105938517441330.06211877034882660.968940614825587
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1890.06228759675132870.1245751935026570.937712403248671
1900.05174481009591280.1034896201918260.948255189904087
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1920.03922575193981920.07845150387963830.960774248060181
1930.04002435218004950.08004870436009910.95997564781995
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1950.03470060814855670.06940121629711340.965299391851443
1960.02776615183788410.05553230367576820.972233848162116
1970.03510520148723090.07021040297446180.964894798512769
1980.02850141846129390.05700283692258780.971498581538706
1990.02553144438275370.05106288876550740.974468555617246
2000.02181961012182370.04363922024364740.978180389878176
2010.02032694564840540.04065389129681090.979673054351595
2020.01708995824873960.03417991649747930.98291004175126
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2140.02711393349488850.0542278669897770.972886066505111
2150.02287254941602480.04574509883204960.977127450583975
2160.01753197827981620.03506395655963240.982468021720184
2170.01925595938692850.0385119187738570.980744040613072
2180.01609807138193110.03219614276386210.983901928618069
2190.01487379328870670.02974758657741340.985126206711293
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2220.006565627505843110.01313125501168620.993434372494157
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2370.1588115271456040.3176230542912080.841188472854396
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2410.06464264177535980.129285283550720.93535735822464
2420.1024906555532860.2049813111065720.897509344446714
2430.08149172083289850.1629834416657970.918508279167101
2440.0842682626125320.1685365252250640.915731737387468
2450.06658711930415780.1331742386083160.933412880695842
2460.05443568400435390.1088713680087080.945564315995646
2470.03299075440982110.06598150881964220.967009245590179
2480.02663916407596290.05327832815192580.973360835924037
2490.02249135767596260.04498271535192520.977508642324037
2500.06644733884633630.1328946776926730.933552661153664
2510.04869724832382630.09739449664765270.951302751676174







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.0669456066945607NOK
5% type I error level980.410041841004184NOK
10% type I error level1330.556485355648536NOK

\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 & 16 & 0.0669456066945607 & NOK \tabularnewline
5% type I error level & 98 & 0.410041841004184 & NOK \tabularnewline
10% type I error level & 133 & 0.556485355648536 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185717&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]16[/C][C]0.0669456066945607[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]98[/C][C]0.410041841004184[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]133[/C][C]0.556485355648536[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185717&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185717&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 testsOK/NOK
1% type I error level160.0669456066945607NOK
5% type I error level980.410041841004184NOK
10% type I error level1330.556485355648536NOK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}