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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 19 Nov 2010 16:51:37 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/19/t1290185446loejfnmcrmnl8i6.htm/, Retrieved Fri, 01 Nov 2024 00:38:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98058, Retrieved Fri, 01 Nov 2024 00:38:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact277

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [Multiple linear r...] [2010-11-19 16:51:37] [9ea95e194e0eb2a674315798620d5bc6] [Current]
-    D      [Multiple Regression] [Multiple linear r...] [2010-11-19 18:08:07] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
- R           [Multiple Regression] [WS 7 - Blog 3] [2011-11-21 19:24:18] [69d59b79aaf660457acc70a0ef0bfdab]
- RM          [Multiple Regression] [] [2011-11-22 11:05:22] [74be16979710d4c4e7c6647856088456]
-    D      [Multiple Regression] [] [2010-11-23 16:04:09] [ed939ef6f97e5f2afb6796311d9e7a5f]
-    D        [Multiple Regression] [] [2010-11-23 16:33:32] [ed939ef6f97e5f2afb6796311d9e7a5f]
-    D          [Multiple Regression] [] [2010-11-23 16:41:37] [ed939ef6f97e5f2afb6796311d9e7a5f]
-   PD      [Multiple Regression] [Workshop 7 Link 1] [2010-11-23 16:09:53] [945bcebba5e7ac34a41d6888338a1ba9]
-   P         [Multiple Regression] [Trend wel signifi...] [2010-11-24 17:05:14] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
- R  D      [Multiple Regression] [C7.1] [2011-11-18 11:36:25] [d1ce18d003fa52f731d1c3ce8b58d5f9]
- R P         [Multiple Regression] [Paper stat] [2011-12-22 17:46:16] [60c0c94f647e2c90e494ab0f2a2f1926]
- R         [Multiple Regression] [WS 7 - Blog 1] [2011-11-21 15:51:08] [69d59b79aaf660457acc70a0ef0bfdab]
- RM        [Multiple Regression] [] [2011-11-22 10:46:23] [74be16979710d4c4e7c6647856088456]
- RM        [Multiple Regression] [] [2011-11-22 11:00:36] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
15	10	77	5	4	46	15	11	12	13	6
12	20	63	6	4	37	9	12	7	11	4
15	16	73	4	10	45	12	12	13	14	6
12	10	76	6	6	46	15	11	11	12	5
14	8	90	3	5	55	17	11	16	12	5
8	14	67	10	8	40	14	10	10	6	4
11	19	69	8	9	43	9	11	15	10	5
15	15	70	3	6	43	12	9	5	11	3
4	23	54	4	8	33	11	10	4	10	2
13	9	54	3	11	33	13	12	7	12	5
19	12	76	5	6	47	16	12	15	15	6
10	14	75	5	8	44	16	12	5	13	6
15	13	76	6	11	47	15	13	16	18	8
6	11	80	5	5	49	10	9	15	11	6
7	11	89	3	10	55	16	12	13	12	3
14	10	73	4	7	43	12	12	13	13	6
16	12	74	8	7	46	15	12	15	14	6
16	18	78	8	13	51	13	12	15	16	7
14	12	76	8	10	47	18	13	10	16	8
15	10	69	5	8	42	13	11	17	16	6
14	15	74	8	6	42	17	12	14	15	7
12	15	82	2	8	48	14	12	9	13	4
9	12	77	0	7	45	13	15	6	8	4
12	9	84	5	5	51	13	11	11	14	2
14	11	75	2	9	46	15	12	13	15	6
12	15	54	7	9	33	13	10	12	13	6
14	16	79	5	11	47	15	11	10	16	6
10	17	79	2	11	47	13	13	4	13	6
14	12	69	12	11	42	14	6	13	12	6
16	11	88	7	9	55	13	12	15	15	7
10	13	57	0	7	36	16	12	8	11	4
8	9	69	2	6	42	14	10	10	14	3
12	11	86	3	6	51	18	12	8	13	5
11	9	65	0	6	43	15	12	7	13	6
8	20	66	9	5	40	9	11	9	12	4
13	8	54	2	4	33	16	9	14	14	6
11	12	85	3	10	52	16	10	5	13	3
12	10	79	1	8	49	17	12	7	12	3
16	11	84	10	6	50	13	12	16	14	6
16	13	70	1	5	43	17	11	14	15	6
13	13	54	4	9	33	15	12	16	16	6
14	13	70	6	10	44	14	11	15	15	8
5	15	54	6	6	33	10	14	4	5	2
14	12	69	4	9	41	13	10	12	15	6
13	13	68	4	10	40	11	10	8	8	4
16	13	68	7	6	40	11	11	17	16	7
14	9	71	7	6	41	16	11	15	16	6
15	9	71	7	6	41	16	11	16	14	6
15	14	66	0	13	42	11	10	12	16	6
11	9	67	3	8	42	15	10	12	14	5
15	9	71	8	10	45	15	12	13	13	6
16	15	54	8	5	33	12	11	14	14	6
13	10	76	10	8	46	17	8	14	14	5
11	13	77	11	6	47	15	12	15	12	6
12	8	71	6	9	44	16	10	14	13	7
12	15	69	2	9	44	14	7	11	15	5
10	13	73	6	7	46	17	11	13	15	6
8	24	46	1	20	30	10	7	4	13	6
9	11	66	5	8	42	11	11	8	10	4
12	13	77	4	8	46	15	8	13	13	5
14	12	77	6	7	46	15	11	15	14	6
12	22	70	6	7	43	7	12	15	13	6
11	11	86	4	10	52	17	8	8	13	4
14	15	38	1	5	11	14	14	17	18	6
7	7	66	6	8	41	18	14	12	12	4
16	14	75	7	9	45	14	11	13	14	7
16	19	80	7	9	49	12	12	14	16	8
11	10	64	2	20	41	14	14	7	13	6
16	9	80	7	6	47	9	9	16	16	6
13	12	86	8	10	53	14	13	11	15	6
11	16	54	5	11	35	11	8	10	14	5
13	13	74	4	7	45	16	11	14	13	6
14	11	88	2	12	54	17	9	19	12	6
15	12	85	0	12	53	16	12	14	16	4
10	11	63	7	8	36	12	7	8	9	5
15	13	81	0	6	48	15	11	15	15	8
11	13	81	5	6	48	15	12	8	16	6
11	10	74	3	9	45	15	11	8	12	6
6	11	80	3	5	47	16	12	6	11	2
11	9	80	3	11	49	16	9	7	13	2
12	13	60	3	6	38	11	11	16	13	4
13	15	65	7	6	40	15	13	15	14	6
12	14	62	6	10	46	12	12	10	15	6
8	14	63	3	8	42	14	12	8	14	5
9	11	89	0	7	54	15	11	9	12	4
10	10	76	2	8	45	17	12	8	16	4
16	11	81	0	9	53	19	12	14	14	6
15	12	72	9	8	44	15	11	14	13	5
14	14	84	10	10	51	16	11	14	12	6
12	14	76	3	13	46	14	8	15	13	7
12	21	76	7	7	46	16	9	7	12	6
10	14	78	3	7	45	15	11	7	9	4
12	13	72	6	7	44	15	12	12	13	4
8	11	81	5	8	48	17	13	7	10	3
16	12	72	0	9	44	12	12	12	15	8
11	12	78	0	9	47	18	6	6	9	4
12	11	79	4	8	47	13	12	10	13	4
9	14	52	0	7	31	14	11	12	13	5
14	13	67	0	6	44	14	13	13	13	5
15	13	74	7	8	42	14	11	14	15	7
8	12	73	3	8	41	12	12	8	13	4
12	14	69	9	4	43	14	10	14	14	5
10	12	67	4	8	41	12	10	10	11	5
16	12	76	4	10	47	15	11	14	15	8
17	12	77	15	7	45	11	11	15	14	5
8	18	63	7	8	37	11	11	10	15	2
9	11	84	8	7	54	15	9	6	12	5
8	15	90	2	10	55	14	7	9	15	4
11	13	75	8	9	45	15	11	11	14	5
16	11	76	7	8	47	16	12	16	16	7
13	11	75	3	8	46	12	12	14	14	6
5	22	53	3	5	37	14	15	8	12	3
15	10	87	6	8	53	18	11	16	11	5
15	11	78	8	9	46	14	10	16	13	6
12	15	54	5	11	33	13	13	14	12	5
12	14	58	6	7	36	14	13	12	12	6
16	11	80	10	8	49	14	11	16	16	7
12	10	74	0	4	44	17	12	15	13	6
10	14	56	5	16	37	12	12	11	12	6
12	14	82	0	9	53	16	12	6	14	5
4	11	64	0	16	40	15	8	6	4	4
11	15	67	5	12	42	10	5	16	14	6
16	11	75	10	8	45	13	11	16	15	6
7	10	69	0	4	40	15	12	8	12	3
9	10	72	5	11	44	16	12	11	11	4
14	16	71	6	11	43	15	11	12	12	4
11	12	54	1	8	33	14	12	13	11	4
10	14	68	5	8	44	11	10	11	12	5
6	15	54	3	12	33	13	7	9	11	4
14	10	71	3	8	43	17	12	15	13	6
11	12	53	6	6	32	14	12	11	12	6
11	15	54	2	8	33	16	9	12	12	4
9	12	71	5	6	43	15	11	15	15	7
16	11	69	6	14	42	12	12	8	14	4
7	10	30	2	10	0	16	12	7	12	4
8	20	53	3	5	32	8	11	10	12	4
10	19	68	7	8	41	9	11	9	12	4
14	17	69	6	12	44	13	12	13	13	5
9	8	54	3	11	33	19	12	11	11	4
13	17	66	6	8	42	11	11	12	13	7
13	11	79	9	8	46	15	12	5	12	3
12	13	67	2	9	44	11	12	12	14	5
11	9	74	5	6	45	15	8	14	15	5
10	10	86	10	5	53	16	15	15	15	6
12	13	63	9	8	38	15	11	14	13	5
14	16	69	8	7	43	12	11	13	16	6
11	12	73	8	4	43	16	6	14	17	6
13	14	69	5	9	42	15	13	14	13	3
14	11	71	9	5	42	13	12	15	14	6
13	13	77	9	9	47	14	12	13	13	5
16	15	74	14	12	44	11	12	14	16	8
13	14	82	5	6	49	15	12	11	13	6
12	14	54	12	4	33	16	12	14	14	4
9	14	54	6	6	33	14	10	11	13	3
14	10	80	6	7	47	13	12	8	14	4
15	8	76	8	9	47	15	12	12	16	7

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98058&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98058&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98058&T=0

As an alternative you can also use a QR Code:  
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 time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -1.40033179694972 -0.076094555483637Depression[t] + 0.0719081107769395Belonging[t] + 0.0941357682157757Weighted_popularity[t] + 0.083878464867039Parental_criticism[t] -0.0399931531986932Belonging_final[t] -0.0601894895057676Happiness[t] + 0.118163788663957FindingFriends[t] + 0.230029151161890KnowingPeople[t] + 0.344271299678481Liked[t] + 0.522587758027874Celebrity[t] -0.00639854118490133t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  -1.40033179694972 -0.076094555483637Depression[t] +  0.0719081107769395Belonging[t] +  0.0941357682157757Weighted_popularity[t] +  0.083878464867039Parental_criticism[t] -0.0399931531986932Belonging_final[t] -0.0601894895057676Happiness[t] +  0.118163788663957FindingFriends[t] +  0.230029151161890KnowingPeople[t] +  0.344271299678481Liked[t] +  0.522587758027874Celebrity[t] -0.00639854118490133t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98058&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  -1.40033179694972 -0.076094555483637Depression[t] +  0.0719081107769395Belonging[t] +  0.0941357682157757Weighted_popularity[t] +  0.083878464867039Parental_criticism[t] -0.0399931531986932Belonging_final[t] -0.0601894895057676Happiness[t] +  0.118163788663957FindingFriends[t] +  0.230029151161890KnowingPeople[t] +  0.344271299678481Liked[t] +  0.522587758027874Celebrity[t] -0.00639854118490133t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98058&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98058&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -1.40033179694972 -0.076094555483637Depression[t] + 0.0719081107769395Belonging[t] + 0.0941357682157757Weighted_popularity[t] + 0.083878464867039Parental_criticism[t] -0.0399931531986932Belonging_final[t] -0.0601894895057676Happiness[t] + 0.118163788663957FindingFriends[t] + 0.230029151161890KnowingPeople[t] + 0.344271299678481Liked[t] + 0.522587758027874Celebrity[t] -0.00639854118490133t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.400331796949722.56448-0.5460.5858770.292939
Depression-0.0760945554836370.063844-1.19190.235270.117635
Belonging0.07190811077693950.0514821.39680.1646320.082316
Weighted_popularity0.09413576821577570.05841.61190.1091690.054585
Parental_criticism0.0838784648670390.0659321.27220.2053530.102677
Belonging_final-0.03999315319869320.073107-0.54710.5851910.292595
Happiness-0.06018948950576760.085851-0.70110.4843740.242187
FindingFriends0.1181637886639570.0938941.25850.2102560.105128
KnowingPeople0.2300291511618900.0645893.56145e-040.00025
Liked0.3442712996784810.0941933.6550.000360.00018
Celebrity0.5225877580278740.1588553.28970.0012610.00063
t-0.006398541184901330.003744-1.70910.0895910.044795

\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) & -1.40033179694972 & 2.56448 & -0.546 & 0.585877 & 0.292939 \tabularnewline
Depression & -0.076094555483637 & 0.063844 & -1.1919 & 0.23527 & 0.117635 \tabularnewline
Belonging & 0.0719081107769395 & 0.051482 & 1.3968 & 0.164632 & 0.082316 \tabularnewline
Weighted_popularity & 0.0941357682157757 & 0.0584 & 1.6119 & 0.109169 & 0.054585 \tabularnewline
Parental_criticism & 0.083878464867039 & 0.065932 & 1.2722 & 0.205353 & 0.102677 \tabularnewline
Belonging_final & -0.0399931531986932 & 0.073107 & -0.5471 & 0.585191 & 0.292595 \tabularnewline
Happiness & -0.0601894895057676 & 0.085851 & -0.7011 & 0.484374 & 0.242187 \tabularnewline
FindingFriends & 0.118163788663957 & 0.093894 & 1.2585 & 0.210256 & 0.105128 \tabularnewline
KnowingPeople & 0.230029151161890 & 0.064589 & 3.5614 & 5e-04 & 0.00025 \tabularnewline
Liked & 0.344271299678481 & 0.094193 & 3.655 & 0.00036 & 0.00018 \tabularnewline
Celebrity & 0.522587758027874 & 0.158855 & 3.2897 & 0.001261 & 0.00063 \tabularnewline
t & -0.00639854118490133 & 0.003744 & -1.7091 & 0.089591 & 0.044795 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98058&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]-1.40033179694972[/C][C]2.56448[/C][C]-0.546[/C][C]0.585877[/C][C]0.292939[/C][/ROW]
[ROW][C]Depression[/C][C]-0.076094555483637[/C][C]0.063844[/C][C]-1.1919[/C][C]0.23527[/C][C]0.117635[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0719081107769395[/C][C]0.051482[/C][C]1.3968[/C][C]0.164632[/C][C]0.082316[/C][/ROW]
[ROW][C]Weighted_popularity[/C][C]0.0941357682157757[/C][C]0.0584[/C][C]1.6119[/C][C]0.109169[/C][C]0.054585[/C][/ROW]
[ROW][C]Parental_criticism[/C][C]0.083878464867039[/C][C]0.065932[/C][C]1.2722[/C][C]0.205353[/C][C]0.102677[/C][/ROW]
[ROW][C]Belonging_final[/C][C]-0.0399931531986932[/C][C]0.073107[/C][C]-0.5471[/C][C]0.585191[/C][C]0.292595[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.0601894895057676[/C][C]0.085851[/C][C]-0.7011[/C][C]0.484374[/C][C]0.242187[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.118163788663957[/C][C]0.093894[/C][C]1.2585[/C][C]0.210256[/C][C]0.105128[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.230029151161890[/C][C]0.064589[/C][C]3.5614[/C][C]5e-04[/C][C]0.00025[/C][/ROW]
[ROW][C]Liked[/C][C]0.344271299678481[/C][C]0.094193[/C][C]3.655[/C][C]0.00036[/C][C]0.00018[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.522587758027874[/C][C]0.158855[/C][C]3.2897[/C][C]0.001261[/C][C]0.00063[/C][/ROW]
[ROW][C]t[/C][C]-0.00639854118490133[/C][C]0.003744[/C][C]-1.7091[/C][C]0.089591[/C][C]0.044795[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98058&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98058&T=2

As an alternative you can also use a QR Code:  
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)-1.400331796949722.56448-0.5460.5858770.292939
Depression-0.0760945554836370.063844-1.19190.235270.117635
Belonging0.07190811077693950.0514821.39680.1646320.082316
Weighted_popularity0.09413576821577570.05841.61190.1091690.054585
Parental_criticism0.0838784648670390.0659321.27220.2053530.102677
Belonging_final-0.03999315319869320.073107-0.54710.5851910.292595
Happiness-0.06018948950576760.085851-0.70110.4843740.242187
FindingFriends0.1181637886639570.0938941.25850.2102560.105128
KnowingPeople0.2300291511618900.0645893.56145e-040.00025
Liked0.3442712996784810.0941933.6550.000360.00018
Celebrity0.5225877580278740.1588553.28970.0012610.00063
t-0.006398541184901330.003744-1.70910.0895910.044795






Multiple Linear Regression - Regression Statistics
Multiple R0.747479529845818
R-squared0.558725647538525
Adjusted R-squared0.525017190058829
F-TEST (value)16.5752362852874
F-TEST (DF numerator)11
F-TEST (DF denominator)144
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.02389408044419
Sum Squared Residuals589.845203835411

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.747479529845818 \tabularnewline
R-squared & 0.558725647538525 \tabularnewline
Adjusted R-squared & 0.525017190058829 \tabularnewline
F-TEST (value) & 16.5752362852874 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 144 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.02389408044419 \tabularnewline
Sum Squared Residuals & 589.845203835411 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98058&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.747479529845818[/C][/ROW]
[ROW][C]R-squared[/C][C]0.558725647538525[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.525017190058829[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.5752362852874[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]144[/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]2.02389408044419[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]589.845203835411[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98058&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98058&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.747479529845818
R-squared0.558725647538525
Adjusted R-squared0.525017190058829
F-TEST (value)16.5752362852874
F-TEST (DF numerator)11
F-TEST (DF denominator)144
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.02389408044419
Sum Squared Residuals589.845203835411






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11513.10411888090751.89588111909250
2129.379572235489662.62042776451034
31513.66928290473511.33071709526493
41212.1780196356576-0.17801963565763
51413.63406638481250.365933615187533
689.12171124973133-1.12171124973133
71112.1232135702656-1.12321357026557
8158.352695352270796.64730464772921
946.33029989402401-2.33029989402401
101310.6090666812382.39093331876199
111914.38040437585064.61959562414936
121011.4488108912758-1.44881089127583
131517.291412674971-2.29141267497098
14613.1906313519517-7.19063135195172
15712.1323718286991-5.1323718286991
161413.52674881435100.47325118564904
171614.32039401972771.67960598027230
181615.77987494835390.220125051646122
191415.1842230800641-1.18422308006412
201515.2061068182984-0.206106818298382
211414.6590612639979-0.659061263997852
221211.3650278495430.634972150456999
2399.07843878284137-0.0784387828413666
241211.56458655886740.435413441132648
251413.90436367430320.0956363256968038
261212.0395860686947-0.0395860686946777
271413.74491739633450.255082603665467
281011.3237347453513-1.32373474535133
291412.95870637259151.04129362740849
301616.0209419101327-0.0209419101327301
31108.830744427978021.16925557202198
32810.7103914354383-2.71039143543826
331211.74485458927990.255145410720076
341110.89123982907210.108760170927926
35810.3166168319753-2.31661683197533
361312.12378872463810.876211275361948
371110.01556689669140.98443310330859
38129.785784885710042.21421511428996
391615.26963036585610.730369634143928
401612.97847210707633.02152789292375
411313.8822688592405-0.882268859240485
421415.2715261726568-1.27152617265677
4355.55350869007313-0.553508690073128
441413.31750772459150.682492275408454
45139.032165895770663.96783410422934
461615.48302062039740.516979379602634
471414.6731379723986-0.673137972398646
481514.20822598301870.791774016981327
491513.30122948746531.69877051253468
501112.1583384993798-1.15833849937977
511513.60270189935871.3972981006413
521612.61452878680683.38547121319315
531313.3125508280339-0.312550828033874
541113.6932708599582-2.69327085995817
551213.8771452843589-1.87714528435892
561211.53689280320680.463107196793213
571013.3738483993187-3.37384839931873
5889.03838872227876-1.03838872227876
5999.60405812870067-0.604058128700668
601212.0926494029421-0.0926494029421035
611413.94814721482740.0518527851726437
621213.0528342079952-1.05283420799521
631111.0075014050402-0.00750140504018929
641413.90940055219770.0905994478022505
65711.5459981683963-4.54599816839630
661614.04475397274011.95524602725989
671615.53715287144920.462847128550839
681112.2647601803260-1.26476018032602
691615.35461214450550.645387855494538
701314.4183599616621-1.41835996166212
711110.82073288780610.17926711219393
721312.80317605040.19682394960001
731414.3362199694722-0.336219969472208
741513.48616893958641.51383106041364
75109.359659407222940.640340592777062
761514.72447441985310.275525580146891
771112.9958102339005-1.99581023390055
781111.4024329141157-0.402432914115743
7968.49918198128773-2.49918198128773
80119.632337418401931.36766258159807
811211.55664370196360.443356298036368
821313.2091406540391-0.209140654039053
831212.3210617317896-0.321061731789568
84810.6490833407533-2.64908334075331
85910.7349212539284-1.73492125392839
861011.6467410654182-1.64674106541819
871613.11587907013892.88412092986111
881512.76522991599382.23477008400617
891413.56960718756760.430392812432445
901213.6433703651392-1.64337036513920
911210.26827476188071.73172523811934
92108.820332062713181.17966793728682
931211.42637461338360.573625386616377
9489.3413456607946-1.34134566079460
951614.05207650692261.94792349307736
96117.750873764631233.24912623536877
971211.49227448057700.507725519422968
98910.2998349775947-1.29983497759473
991411.31071992558732.68928007441269
1001514.44179146272960.558208537270431
101810.7050914338505-2.70509143385053
1021212.2985131903579-0.298513190357948
1031010.4127573387016-0.412757338701639
1041614.78389020264291.21610979735713
1051714.27199666982762.72800333017243
106810.0794170593795-2.07941705937948
107910.5838716669164-1.58387166691636
108811.3755467072374-3.37554670723741
1091112.3744237176035-1.37442371760347
1101615.27596002906290.724039970937076
1111313.4306727557511-0.430672755751130
11257.71119025790909-2.71119025790909
1131512.78467871000662.21532128999339
1141513.9408392167791.05916078322099
1151211.39729190715030.60270809284972
1161211.39560277990660.60439722009336
1171615.72343887247390.276561127526108
1181212.4369449584-0.436944958399993
1191011.6255542363726-1.62555423637263
1201210.56609915655751.43390084344252
12146.22293211352812-2.22293211352812
1221112.9176944639492-1.91769446394925
1231614.67881011607401.32118988392603
12478.79712611731686-1.79712611731686
125910.7125218131455-1.71252181314545
1261410.82810290137843.17189709862156
127119.645373124880191.35462687511981
1281010.7811590578546-0.781159057854605
12968.4773317134976-2.47733171349761
1301412.80235244916451.19764755083547
1311110.82017442731760.179825572682416
132119.118604324233731.88139567576627
133913.8648278690724-4.86482786907245
1341611.3723579274544.62764207254599
13578.44596341492404-1.44596341492404
13688.78090798916263-0.780907989162627
137109.907247113123780.0927528868762226
1381411.91072591833422.08927408166581
13999.55187003377597-0.551870033775968
1401312.24403850580420.755961494195762
141139.58402687019163.41597312980839
1421211.65213641403920.347863585960838
1431112.5351681163983-1.53516811639827
1441014.9020014400070-4.90200144000698
1451211.91720443517050.0827955648294793
1461413.24193186753880.758068132461205
1471113.3186321462926-2.3186321462926
1481310.99187776127512.00812223872485
1491413.54288723676160.457112763238448
1501312.56418949320980.435810506790199
1511616.0433459968011-0.0433459968011405
1521312.12901360157780.87098639842222
1531211.16926560519660.83073439480335
15499.09291427494277-0.0929142749427714
1551411.25776782703252.74223217296746
1561514.52799791894940.472002081050603

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15 & 13.1041188809075 & 1.89588111909250 \tabularnewline
2 & 12 & 9.37957223548966 & 2.62042776451034 \tabularnewline
3 & 15 & 13.6692829047351 & 1.33071709526493 \tabularnewline
4 & 12 & 12.1780196356576 & -0.17801963565763 \tabularnewline
5 & 14 & 13.6340663848125 & 0.365933615187533 \tabularnewline
6 & 8 & 9.12171124973133 & -1.12171124973133 \tabularnewline
7 & 11 & 12.1232135702656 & -1.12321357026557 \tabularnewline
8 & 15 & 8.35269535227079 & 6.64730464772921 \tabularnewline
9 & 4 & 6.33029989402401 & -2.33029989402401 \tabularnewline
10 & 13 & 10.609066681238 & 2.39093331876199 \tabularnewline
11 & 19 & 14.3804043758506 & 4.61959562414936 \tabularnewline
12 & 10 & 11.4488108912758 & -1.44881089127583 \tabularnewline
13 & 15 & 17.291412674971 & -2.29141267497098 \tabularnewline
14 & 6 & 13.1906313519517 & -7.19063135195172 \tabularnewline
15 & 7 & 12.1323718286991 & -5.1323718286991 \tabularnewline
16 & 14 & 13.5267488143510 & 0.47325118564904 \tabularnewline
17 & 16 & 14.3203940197277 & 1.67960598027230 \tabularnewline
18 & 16 & 15.7798749483539 & 0.220125051646122 \tabularnewline
19 & 14 & 15.1842230800641 & -1.18422308006412 \tabularnewline
20 & 15 & 15.2061068182984 & -0.206106818298382 \tabularnewline
21 & 14 & 14.6590612639979 & -0.659061263997852 \tabularnewline
22 & 12 & 11.365027849543 & 0.634972150456999 \tabularnewline
23 & 9 & 9.07843878284137 & -0.0784387828413666 \tabularnewline
24 & 12 & 11.5645865588674 & 0.435413441132648 \tabularnewline
25 & 14 & 13.9043636743032 & 0.0956363256968038 \tabularnewline
26 & 12 & 12.0395860686947 & -0.0395860686946777 \tabularnewline
27 & 14 & 13.7449173963345 & 0.255082603665467 \tabularnewline
28 & 10 & 11.3237347453513 & -1.32373474535133 \tabularnewline
29 & 14 & 12.9587063725915 & 1.04129362740849 \tabularnewline
30 & 16 & 16.0209419101327 & -0.0209419101327301 \tabularnewline
31 & 10 & 8.83074442797802 & 1.16925557202198 \tabularnewline
32 & 8 & 10.7103914354383 & -2.71039143543826 \tabularnewline
33 & 12 & 11.7448545892799 & 0.255145410720076 \tabularnewline
34 & 11 & 10.8912398290721 & 0.108760170927926 \tabularnewline
35 & 8 & 10.3166168319753 & -2.31661683197533 \tabularnewline
36 & 13 & 12.1237887246381 & 0.876211275361948 \tabularnewline
37 & 11 & 10.0155668966914 & 0.98443310330859 \tabularnewline
38 & 12 & 9.78578488571004 & 2.21421511428996 \tabularnewline
39 & 16 & 15.2696303658561 & 0.730369634143928 \tabularnewline
40 & 16 & 12.9784721070763 & 3.02152789292375 \tabularnewline
41 & 13 & 13.8822688592405 & -0.882268859240485 \tabularnewline
42 & 14 & 15.2715261726568 & -1.27152617265677 \tabularnewline
43 & 5 & 5.55350869007313 & -0.553508690073128 \tabularnewline
44 & 14 & 13.3175077245915 & 0.682492275408454 \tabularnewline
45 & 13 & 9.03216589577066 & 3.96783410422934 \tabularnewline
46 & 16 & 15.4830206203974 & 0.516979379602634 \tabularnewline
47 & 14 & 14.6731379723986 & -0.673137972398646 \tabularnewline
48 & 15 & 14.2082259830187 & 0.791774016981327 \tabularnewline
49 & 15 & 13.3012294874653 & 1.69877051253468 \tabularnewline
50 & 11 & 12.1583384993798 & -1.15833849937977 \tabularnewline
51 & 15 & 13.6027018993587 & 1.3972981006413 \tabularnewline
52 & 16 & 12.6145287868068 & 3.38547121319315 \tabularnewline
53 & 13 & 13.3125508280339 & -0.312550828033874 \tabularnewline
54 & 11 & 13.6932708599582 & -2.69327085995817 \tabularnewline
55 & 12 & 13.8771452843589 & -1.87714528435892 \tabularnewline
56 & 12 & 11.5368928032068 & 0.463107196793213 \tabularnewline
57 & 10 & 13.3738483993187 & -3.37384839931873 \tabularnewline
58 & 8 & 9.03838872227876 & -1.03838872227876 \tabularnewline
59 & 9 & 9.60405812870067 & -0.604058128700668 \tabularnewline
60 & 12 & 12.0926494029421 & -0.0926494029421035 \tabularnewline
61 & 14 & 13.9481472148274 & 0.0518527851726437 \tabularnewline
62 & 12 & 13.0528342079952 & -1.05283420799521 \tabularnewline
63 & 11 & 11.0075014050402 & -0.00750140504018929 \tabularnewline
64 & 14 & 13.9094005521977 & 0.0905994478022505 \tabularnewline
65 & 7 & 11.5459981683963 & -4.54599816839630 \tabularnewline
66 & 16 & 14.0447539727401 & 1.95524602725989 \tabularnewline
67 & 16 & 15.5371528714492 & 0.462847128550839 \tabularnewline
68 & 11 & 12.2647601803260 & -1.26476018032602 \tabularnewline
69 & 16 & 15.3546121445055 & 0.645387855494538 \tabularnewline
70 & 13 & 14.4183599616621 & -1.41835996166212 \tabularnewline
71 & 11 & 10.8207328878061 & 0.17926711219393 \tabularnewline
72 & 13 & 12.8031760504 & 0.19682394960001 \tabularnewline
73 & 14 & 14.3362199694722 & -0.336219969472208 \tabularnewline
74 & 15 & 13.4861689395864 & 1.51383106041364 \tabularnewline
75 & 10 & 9.35965940722294 & 0.640340592777062 \tabularnewline
76 & 15 & 14.7244744198531 & 0.275525580146891 \tabularnewline
77 & 11 & 12.9958102339005 & -1.99581023390055 \tabularnewline
78 & 11 & 11.4024329141157 & -0.402432914115743 \tabularnewline
79 & 6 & 8.49918198128773 & -2.49918198128773 \tabularnewline
80 & 11 & 9.63233741840193 & 1.36766258159807 \tabularnewline
81 & 12 & 11.5566437019636 & 0.443356298036368 \tabularnewline
82 & 13 & 13.2091406540391 & -0.209140654039053 \tabularnewline
83 & 12 & 12.3210617317896 & -0.321061731789568 \tabularnewline
84 & 8 & 10.6490833407533 & -2.64908334075331 \tabularnewline
85 & 9 & 10.7349212539284 & -1.73492125392839 \tabularnewline
86 & 10 & 11.6467410654182 & -1.64674106541819 \tabularnewline
87 & 16 & 13.1158790701389 & 2.88412092986111 \tabularnewline
88 & 15 & 12.7652299159938 & 2.23477008400617 \tabularnewline
89 & 14 & 13.5696071875676 & 0.430392812432445 \tabularnewline
90 & 12 & 13.6433703651392 & -1.64337036513920 \tabularnewline
91 & 12 & 10.2682747618807 & 1.73172523811934 \tabularnewline
92 & 10 & 8.82033206271318 & 1.17966793728682 \tabularnewline
93 & 12 & 11.4263746133836 & 0.573625386616377 \tabularnewline
94 & 8 & 9.3413456607946 & -1.34134566079460 \tabularnewline
95 & 16 & 14.0520765069226 & 1.94792349307736 \tabularnewline
96 & 11 & 7.75087376463123 & 3.24912623536877 \tabularnewline
97 & 12 & 11.4922744805770 & 0.507725519422968 \tabularnewline
98 & 9 & 10.2998349775947 & -1.29983497759473 \tabularnewline
99 & 14 & 11.3107199255873 & 2.68928007441269 \tabularnewline
100 & 15 & 14.4417914627296 & 0.558208537270431 \tabularnewline
101 & 8 & 10.7050914338505 & -2.70509143385053 \tabularnewline
102 & 12 & 12.2985131903579 & -0.298513190357948 \tabularnewline
103 & 10 & 10.4127573387016 & -0.412757338701639 \tabularnewline
104 & 16 & 14.7838902026429 & 1.21610979735713 \tabularnewline
105 & 17 & 14.2719966698276 & 2.72800333017243 \tabularnewline
106 & 8 & 10.0794170593795 & -2.07941705937948 \tabularnewline
107 & 9 & 10.5838716669164 & -1.58387166691636 \tabularnewline
108 & 8 & 11.3755467072374 & -3.37554670723741 \tabularnewline
109 & 11 & 12.3744237176035 & -1.37442371760347 \tabularnewline
110 & 16 & 15.2759600290629 & 0.724039970937076 \tabularnewline
111 & 13 & 13.4306727557511 & -0.430672755751130 \tabularnewline
112 & 5 & 7.71119025790909 & -2.71119025790909 \tabularnewline
113 & 15 & 12.7846787100066 & 2.21532128999339 \tabularnewline
114 & 15 & 13.940839216779 & 1.05916078322099 \tabularnewline
115 & 12 & 11.3972919071503 & 0.60270809284972 \tabularnewline
116 & 12 & 11.3956027799066 & 0.60439722009336 \tabularnewline
117 & 16 & 15.7234388724739 & 0.276561127526108 \tabularnewline
118 & 12 & 12.4369449584 & -0.436944958399993 \tabularnewline
119 & 10 & 11.6255542363726 & -1.62555423637263 \tabularnewline
120 & 12 & 10.5660991565575 & 1.43390084344252 \tabularnewline
121 & 4 & 6.22293211352812 & -2.22293211352812 \tabularnewline
122 & 11 & 12.9176944639492 & -1.91769446394925 \tabularnewline
123 & 16 & 14.6788101160740 & 1.32118988392603 \tabularnewline
124 & 7 & 8.79712611731686 & -1.79712611731686 \tabularnewline
125 & 9 & 10.7125218131455 & -1.71252181314545 \tabularnewline
126 & 14 & 10.8281029013784 & 3.17189709862156 \tabularnewline
127 & 11 & 9.64537312488019 & 1.35462687511981 \tabularnewline
128 & 10 & 10.7811590578546 & -0.781159057854605 \tabularnewline
129 & 6 & 8.4773317134976 & -2.47733171349761 \tabularnewline
130 & 14 & 12.8023524491645 & 1.19764755083547 \tabularnewline
131 & 11 & 10.8201744273176 & 0.179825572682416 \tabularnewline
132 & 11 & 9.11860432423373 & 1.88139567576627 \tabularnewline
133 & 9 & 13.8648278690724 & -4.86482786907245 \tabularnewline
134 & 16 & 11.372357927454 & 4.62764207254599 \tabularnewline
135 & 7 & 8.44596341492404 & -1.44596341492404 \tabularnewline
136 & 8 & 8.78090798916263 & -0.780907989162627 \tabularnewline
137 & 10 & 9.90724711312378 & 0.0927528868762226 \tabularnewline
138 & 14 & 11.9107259183342 & 2.08927408166581 \tabularnewline
139 & 9 & 9.55187003377597 & -0.551870033775968 \tabularnewline
140 & 13 & 12.2440385058042 & 0.755961494195762 \tabularnewline
141 & 13 & 9.5840268701916 & 3.41597312980839 \tabularnewline
142 & 12 & 11.6521364140392 & 0.347863585960838 \tabularnewline
143 & 11 & 12.5351681163983 & -1.53516811639827 \tabularnewline
144 & 10 & 14.9020014400070 & -4.90200144000698 \tabularnewline
145 & 12 & 11.9172044351705 & 0.0827955648294793 \tabularnewline
146 & 14 & 13.2419318675388 & 0.758068132461205 \tabularnewline
147 & 11 & 13.3186321462926 & -2.3186321462926 \tabularnewline
148 & 13 & 10.9918777612751 & 2.00812223872485 \tabularnewline
149 & 14 & 13.5428872367616 & 0.457112763238448 \tabularnewline
150 & 13 & 12.5641894932098 & 0.435810506790199 \tabularnewline
151 & 16 & 16.0433459968011 & -0.0433459968011405 \tabularnewline
152 & 13 & 12.1290136015778 & 0.87098639842222 \tabularnewline
153 & 12 & 11.1692656051966 & 0.83073439480335 \tabularnewline
154 & 9 & 9.09291427494277 & -0.0929142749427714 \tabularnewline
155 & 14 & 11.2577678270325 & 2.74223217296746 \tabularnewline
156 & 15 & 14.5279979189494 & 0.472002081050603 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98058&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]15[/C][C]13.1041188809075[/C][C]1.89588111909250[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]9.37957223548966[/C][C]2.62042776451034[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.6692829047351[/C][C]1.33071709526493[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.1780196356576[/C][C]-0.17801963565763[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]13.6340663848125[/C][C]0.365933615187533[/C][/ROW]
[ROW][C]6[/C][C]8[/C][C]9.12171124973133[/C][C]-1.12171124973133[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]12.1232135702656[/C][C]-1.12321357026557[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]8.35269535227079[/C][C]6.64730464772921[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]6.33029989402401[/C][C]-2.33029989402401[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]10.609066681238[/C][C]2.39093331876199[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]14.3804043758506[/C][C]4.61959562414936[/C][/ROW]
[ROW][C]12[/C][C]10[/C][C]11.4488108912758[/C][C]-1.44881089127583[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]17.291412674971[/C][C]-2.29141267497098[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]13.1906313519517[/C][C]-7.19063135195172[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]12.1323718286991[/C][C]-5.1323718286991[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.5267488143510[/C][C]0.47325118564904[/C][/ROW]
[ROW][C]17[/C][C]16[/C][C]14.3203940197277[/C][C]1.67960598027230[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]15.7798749483539[/C][C]0.220125051646122[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.1842230800641[/C][C]-1.18422308006412[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]15.2061068182984[/C][C]-0.206106818298382[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]14.6590612639979[/C][C]-0.659061263997852[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.365027849543[/C][C]0.634972150456999[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]9.07843878284137[/C][C]-0.0784387828413666[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]11.5645865588674[/C][C]0.435413441132648[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.9043636743032[/C][C]0.0956363256968038[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]12.0395860686947[/C][C]-0.0395860686946777[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]13.7449173963345[/C][C]0.255082603665467[/C][/ROW]
[ROW][C]28[/C][C]10[/C][C]11.3237347453513[/C][C]-1.32373474535133[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.9587063725915[/C][C]1.04129362740849[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]16.0209419101327[/C][C]-0.0209419101327301[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]8.83074442797802[/C][C]1.16925557202198[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]10.7103914354383[/C][C]-2.71039143543826[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]11.7448545892799[/C][C]0.255145410720076[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.8912398290721[/C][C]0.108760170927926[/C][/ROW]
[ROW][C]35[/C][C]8[/C][C]10.3166168319753[/C][C]-2.31661683197533[/C][/ROW]
[ROW][C]36[/C][C]13[/C][C]12.1237887246381[/C][C]0.876211275361948[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]10.0155668966914[/C][C]0.98443310330859[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]9.78578488571004[/C][C]2.21421511428996[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]15.2696303658561[/C][C]0.730369634143928[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]12.9784721070763[/C][C]3.02152789292375[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]13.8822688592405[/C][C]-0.882268859240485[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.2715261726568[/C][C]-1.27152617265677[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]5.55350869007313[/C][C]-0.553508690073128[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]13.3175077245915[/C][C]0.682492275408454[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]9.03216589577066[/C][C]3.96783410422934[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]15.4830206203974[/C][C]0.516979379602634[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]14.6731379723986[/C][C]-0.673137972398646[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.2082259830187[/C][C]0.791774016981327[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]13.3012294874653[/C][C]1.69877051253468[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.1583384993798[/C][C]-1.15833849937977[/C][/ROW]
[ROW][C]51[/C][C]15[/C][C]13.6027018993587[/C][C]1.3972981006413[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]12.6145287868068[/C][C]3.38547121319315[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]13.3125508280339[/C][C]-0.312550828033874[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]13.6932708599582[/C][C]-2.69327085995817[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.8771452843589[/C][C]-1.87714528435892[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]11.5368928032068[/C][C]0.463107196793213[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]13.3738483993187[/C][C]-3.37384839931873[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]9.03838872227876[/C][C]-1.03838872227876[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]9.60405812870067[/C][C]-0.604058128700668[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]12.0926494029421[/C][C]-0.0926494029421035[/C][/ROW]
[ROW][C]61[/C][C]14[/C][C]13.9481472148274[/C][C]0.0518527851726437[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.0528342079952[/C][C]-1.05283420799521[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]11.0075014050402[/C][C]-0.00750140504018929[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.9094005521977[/C][C]0.0905994478022505[/C][/ROW]
[ROW][C]65[/C][C]7[/C][C]11.5459981683963[/C][C]-4.54599816839630[/C][/ROW]
[ROW][C]66[/C][C]16[/C][C]14.0447539727401[/C][C]1.95524602725989[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]15.5371528714492[/C][C]0.462847128550839[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.2647601803260[/C][C]-1.26476018032602[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]15.3546121445055[/C][C]0.645387855494538[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]14.4183599616621[/C][C]-1.41835996166212[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]10.8207328878061[/C][C]0.17926711219393[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.8031760504[/C][C]0.19682394960001[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]14.3362199694722[/C][C]-0.336219969472208[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]13.4861689395864[/C][C]1.51383106041364[/C][/ROW]
[ROW][C]75[/C][C]10[/C][C]9.35965940722294[/C][C]0.640340592777062[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]14.7244744198531[/C][C]0.275525580146891[/C][/ROW]
[ROW][C]77[/C][C]11[/C][C]12.9958102339005[/C][C]-1.99581023390055[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.4024329141157[/C][C]-0.402432914115743[/C][/ROW]
[ROW][C]79[/C][C]6[/C][C]8.49918198128773[/C][C]-2.49918198128773[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]9.63233741840193[/C][C]1.36766258159807[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.5566437019636[/C][C]0.443356298036368[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]13.2091406540391[/C][C]-0.209140654039053[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]12.3210617317896[/C][C]-0.321061731789568[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]10.6490833407533[/C][C]-2.64908334075331[/C][/ROW]
[ROW][C]85[/C][C]9[/C][C]10.7349212539284[/C][C]-1.73492125392839[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.6467410654182[/C][C]-1.64674106541819[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]13.1158790701389[/C][C]2.88412092986111[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]12.7652299159938[/C][C]2.23477008400617[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]13.5696071875676[/C][C]0.430392812432445[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.6433703651392[/C][C]-1.64337036513920[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]10.2682747618807[/C][C]1.73172523811934[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]8.82033206271318[/C][C]1.17966793728682[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.4263746133836[/C][C]0.573625386616377[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]9.3413456607946[/C][C]-1.34134566079460[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.0520765069226[/C][C]1.94792349307736[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]7.75087376463123[/C][C]3.24912623536877[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]11.4922744805770[/C][C]0.507725519422968[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]10.2998349775947[/C][C]-1.29983497759473[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]11.3107199255873[/C][C]2.68928007441269[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.4417914627296[/C][C]0.558208537270431[/C][/ROW]
[ROW][C]101[/C][C]8[/C][C]10.7050914338505[/C][C]-2.70509143385053[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.2985131903579[/C][C]-0.298513190357948[/C][/ROW]
[ROW][C]103[/C][C]10[/C][C]10.4127573387016[/C][C]-0.412757338701639[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.7838902026429[/C][C]1.21610979735713[/C][/ROW]
[ROW][C]105[/C][C]17[/C][C]14.2719966698276[/C][C]2.72800333017243[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]10.0794170593795[/C][C]-2.07941705937948[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.5838716669164[/C][C]-1.58387166691636[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]11.3755467072374[/C][C]-3.37554670723741[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]12.3744237176035[/C][C]-1.37442371760347[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.2759600290629[/C][C]0.724039970937076[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]13.4306727557511[/C][C]-0.430672755751130[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]7.71119025790909[/C][C]-2.71119025790909[/C][/ROW]
[ROW][C]113[/C][C]15[/C][C]12.7846787100066[/C][C]2.21532128999339[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.940839216779[/C][C]1.05916078322099[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]11.3972919071503[/C][C]0.60270809284972[/C][/ROW]
[ROW][C]116[/C][C]12[/C][C]11.3956027799066[/C][C]0.60439722009336[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]15.7234388724739[/C][C]0.276561127526108[/C][/ROW]
[ROW][C]118[/C][C]12[/C][C]12.4369449584[/C][C]-0.436944958399993[/C][/ROW]
[ROW][C]119[/C][C]10[/C][C]11.6255542363726[/C][C]-1.62555423637263[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]10.5660991565575[/C][C]1.43390084344252[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]6.22293211352812[/C][C]-2.22293211352812[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]12.9176944639492[/C][C]-1.91769446394925[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]14.6788101160740[/C][C]1.32118988392603[/C][/ROW]
[ROW][C]124[/C][C]7[/C][C]8.79712611731686[/C][C]-1.79712611731686[/C][/ROW]
[ROW][C]125[/C][C]9[/C][C]10.7125218131455[/C][C]-1.71252181314545[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]10.8281029013784[/C][C]3.17189709862156[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]9.64537312488019[/C][C]1.35462687511981[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]10.7811590578546[/C][C]-0.781159057854605[/C][/ROW]
[ROW][C]129[/C][C]6[/C][C]8.4773317134976[/C][C]-2.47733171349761[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]12.8023524491645[/C][C]1.19764755083547[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]10.8201744273176[/C][C]0.179825572682416[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]9.11860432423373[/C][C]1.88139567576627[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]13.8648278690724[/C][C]-4.86482786907245[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]11.372357927454[/C][C]4.62764207254599[/C][/ROW]
[ROW][C]135[/C][C]7[/C][C]8.44596341492404[/C][C]-1.44596341492404[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]8.78090798916263[/C][C]-0.780907989162627[/C][/ROW]
[ROW][C]137[/C][C]10[/C][C]9.90724711312378[/C][C]0.0927528868762226[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]11.9107259183342[/C][C]2.08927408166581[/C][/ROW]
[ROW][C]139[/C][C]9[/C][C]9.55187003377597[/C][C]-0.551870033775968[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]12.2440385058042[/C][C]0.755961494195762[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]9.5840268701916[/C][C]3.41597312980839[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]11.6521364140392[/C][C]0.347863585960838[/C][/ROW]
[ROW][C]143[/C][C]11[/C][C]12.5351681163983[/C][C]-1.53516811639827[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]14.9020014400070[/C][C]-4.90200144000698[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.9172044351705[/C][C]0.0827955648294793[/C][/ROW]
[ROW][C]146[/C][C]14[/C][C]13.2419318675388[/C][C]0.758068132461205[/C][/ROW]
[ROW][C]147[/C][C]11[/C][C]13.3186321462926[/C][C]-2.3186321462926[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]10.9918777612751[/C][C]2.00812223872485[/C][/ROW]
[ROW][C]149[/C][C]14[/C][C]13.5428872367616[/C][C]0.457112763238448[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.5641894932098[/C][C]0.435810506790199[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]16.0433459968011[/C][C]-0.0433459968011405[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]12.1290136015778[/C][C]0.87098639842222[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]11.1692656051966[/C][C]0.83073439480335[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]9.09291427494277[/C][C]-0.0929142749427714[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]11.2577678270325[/C][C]2.74223217296746[/C][/ROW]
[ROW][C]156[/C][C]15[/C][C]14.5279979189494[/C][C]0.472002081050603[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98058&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98058&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11513.10411888090751.89588111909250
2129.379572235489662.62042776451034
31513.66928290473511.33071709526493
41212.1780196356576-0.17801963565763
51413.63406638481250.365933615187533
689.12171124973133-1.12171124973133
71112.1232135702656-1.12321357026557
8158.352695352270796.64730464772921
946.33029989402401-2.33029989402401
101310.6090666812382.39093331876199
111914.38040437585064.61959562414936
121011.4488108912758-1.44881089127583
131517.291412674971-2.29141267497098
14613.1906313519517-7.19063135195172
15712.1323718286991-5.1323718286991
161413.52674881435100.47325118564904
171614.32039401972771.67960598027230
181615.77987494835390.220125051646122
191415.1842230800641-1.18422308006412
201515.2061068182984-0.206106818298382
211414.6590612639979-0.659061263997852
221211.3650278495430.634972150456999
2399.07843878284137-0.0784387828413666
241211.56458655886740.435413441132648
251413.90436367430320.0956363256968038
261212.0395860686947-0.0395860686946777
271413.74491739633450.255082603665467
281011.3237347453513-1.32373474535133
291412.95870637259151.04129362740849
301616.0209419101327-0.0209419101327301
31108.830744427978021.16925557202198
32810.7103914354383-2.71039143543826
331211.74485458927990.255145410720076
341110.89123982907210.108760170927926
35810.3166168319753-2.31661683197533
361312.12378872463810.876211275361948
371110.01556689669140.98443310330859
38129.785784885710042.21421511428996
391615.26963036585610.730369634143928
401612.97847210707633.02152789292375
411313.8822688592405-0.882268859240485
421415.2715261726568-1.27152617265677
4355.55350869007313-0.553508690073128
441413.31750772459150.682492275408454
45139.032165895770663.96783410422934
461615.48302062039740.516979379602634
471414.6731379723986-0.673137972398646
481514.20822598301870.791774016981327
491513.30122948746531.69877051253468
501112.1583384993798-1.15833849937977
511513.60270189935871.3972981006413
521612.61452878680683.38547121319315
531313.3125508280339-0.312550828033874
541113.6932708599582-2.69327085995817
551213.8771452843589-1.87714528435892
561211.53689280320680.463107196793213
571013.3738483993187-3.37384839931873
5889.03838872227876-1.03838872227876
5999.60405812870067-0.604058128700668
601212.0926494029421-0.0926494029421035
611413.94814721482740.0518527851726437
621213.0528342079952-1.05283420799521
631111.0075014050402-0.00750140504018929
641413.90940055219770.0905994478022505
65711.5459981683963-4.54599816839630
661614.04475397274011.95524602725989
671615.53715287144920.462847128550839
681112.2647601803260-1.26476018032602
691615.35461214450550.645387855494538
701314.4183599616621-1.41835996166212
711110.82073288780610.17926711219393
721312.80317605040.19682394960001
731414.3362199694722-0.336219969472208
741513.48616893958641.51383106041364
75109.359659407222940.640340592777062
761514.72447441985310.275525580146891
771112.9958102339005-1.99581023390055
781111.4024329141157-0.402432914115743
7968.49918198128773-2.49918198128773
80119.632337418401931.36766258159807
811211.55664370196360.443356298036368
821313.2091406540391-0.209140654039053
831212.3210617317896-0.321061731789568
84810.6490833407533-2.64908334075331
85910.7349212539284-1.73492125392839
861011.6467410654182-1.64674106541819
871613.11587907013892.88412092986111
881512.76522991599382.23477008400617
891413.56960718756760.430392812432445
901213.6433703651392-1.64337036513920
911210.26827476188071.73172523811934
92108.820332062713181.17966793728682
931211.42637461338360.573625386616377
9489.3413456607946-1.34134566079460
951614.05207650692261.94792349307736
96117.750873764631233.24912623536877
971211.49227448057700.507725519422968
98910.2998349775947-1.29983497759473
991411.31071992558732.68928007441269
1001514.44179146272960.558208537270431
101810.7050914338505-2.70509143385053
1021212.2985131903579-0.298513190357948
1031010.4127573387016-0.412757338701639
1041614.78389020264291.21610979735713
1051714.27199666982762.72800333017243
106810.0794170593795-2.07941705937948
107910.5838716669164-1.58387166691636
108811.3755467072374-3.37554670723741
1091112.3744237176035-1.37442371760347
1101615.27596002906290.724039970937076
1111313.4306727557511-0.430672755751130
11257.71119025790909-2.71119025790909
1131512.78467871000662.21532128999339
1141513.9408392167791.05916078322099
1151211.39729190715030.60270809284972
1161211.39560277990660.60439722009336
1171615.72343887247390.276561127526108
1181212.4369449584-0.436944958399993
1191011.6255542363726-1.62555423637263
1201210.56609915655751.43390084344252
12146.22293211352812-2.22293211352812
1221112.9176944639492-1.91769446394925
1231614.67881011607401.32118988392603
12478.79712611731686-1.79712611731686
125910.7125218131455-1.71252181314545
1261410.82810290137843.17189709862156
127119.645373124880191.35462687511981
1281010.7811590578546-0.781159057854605
12968.4773317134976-2.47733171349761
1301412.80235244916451.19764755083547
1311110.82017442731760.179825572682416
132119.118604324233731.88139567576627
133913.8648278690724-4.86482786907245
1341611.3723579274544.62764207254599
13578.44596341492404-1.44596341492404
13688.78090798916263-0.780907989162627
137109.907247113123780.0927528868762226
1381411.91072591833422.08927408166581
13999.55187003377597-0.551870033775968
1401312.24403850580420.755961494195762
141139.58402687019163.41597312980839
1421211.65213641403920.347863585960838
1431112.5351681163983-1.53516811639827
1441014.9020014400070-4.90200144000698
1451211.91720443517050.0827955648294793
1461413.24193186753880.758068132461205
1471113.3186321462926-2.3186321462926
1481310.99187776127512.00812223872485
1491413.54288723676160.457112763238448
1501312.56418949320980.435810506790199
1511616.0433459968011-0.0433459968011405
1521312.12901360157780.87098639842222
1531211.16926560519660.83073439480335
15499.09291427494277-0.0929142749427714
1551411.25776782703252.74223217296746
1561514.52799791894940.472002081050603






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.9336492573955630.1327014852088740.066350742604437
160.9999306218741460.0001387562517079236.93781258539613e-05
170.999825330422720.0003493391545604130.000174669577280207
180.9995788242493360.0008423515013271790.000421175750663589
190.999442462963340.001115074073318620.000557537036659311
200.9989417082623730.002116583475253860.00105829173762693
210.9990347553211670.001930489357665010.000965244678832506
220.9995672504713650.0008654990572709780.000432749528635489
230.9991578278316070.001684344336786150.000842172168393073
240.9984453260016660.003109347996668610.00155467399833431
250.9972355174606210.005528965078757280.00276448253937864
260.9952678823144680.009464235371065050.00473211768553253
270.9959426887683370.008114622463326840.00405731123166342
280.9935169324214010.01296613515719700.00648306757859852
290.9965824577184790.006835084563042070.00341754228152104
300.9952171762680080.00956564746398340.0047828237319917
310.9929213369923820.01415732601523650.00707866300761823
320.9962843345489040.007431330902191590.00371566545109579
330.9943308617574320.01133827648513560.00566913824256779
340.9922774370725640.01544512585487140.00772256292743568
350.9912793561000530.01744128779989320.00872064389994661
360.9877578153960070.02448436920798570.0122421846039929
370.9848936426377310.03021271472453720.0151063573622686
380.9860230592496970.02795388150060670.0139769407503034
390.9852617601434230.02947647971315370.0147382398565768
400.9898363558670450.02032728826591010.0101636441329550
410.986022983535750.02795403292850060.0139770164642503
420.980778542402160.03844291519568090.0192214575978405
430.9734890365830660.05302192683386820.0265109634169341
440.967690414269650.0646191714607010.0323095857303505
450.9912925549644440.01741489007111160.00870744503555578
460.9880136781771970.02397264364560560.0119863218228028
470.9839868942327420.03202621153451670.0160131057672583
480.9790217195390530.04195656092189360.0209782804609468
490.9765208322078370.04695833558432520.0234791677921626
500.9720102590318660.05597948193626890.0279897409681344
510.9694319506334640.06113609873307160.0305680493665358
520.9821389886582160.03572202268356780.0178610113417839
530.9760673762037820.04786524759243530.0239326237962177
540.9767379353712320.0465241292575350.0232620646287675
550.9733760106219360.0532479787561280.026623989378064
560.9663856462991970.06722870740160550.0336143537008028
570.9750728129680490.04985437406390270.0249271870319513
580.9686046853847950.06279062923040990.0313953146152050
590.9587840317144120.08243193657117520.0412159682855876
600.9467447109821430.1065105780357140.0532552890178569
610.932843091196050.1343138176078990.0671569088039494
620.9198991157672450.1602017684655100.0801008842327551
630.9002519497902520.1994961004194970.0997480502097483
640.8846227746413660.2307544507172680.115377225358634
650.9361582272630730.1276835454738540.0638417727369268
660.9413024341610880.1173951316778230.0586975658389117
670.9281614025785540.1436771948428920.0718385974214462
680.9155660044640060.1688679910719890.0844339955359943
690.8984462267925830.2031075464148350.101553773207417
700.8854602364057880.2290795271884240.114539763594212
710.8627780799441780.2744438401116440.137221920055822
720.836868337466270.3262633250674590.163131662533729
730.8207122115727170.3585755768545670.179287788427283
740.8151383342403230.3697233315193530.184861665759677
750.790247551034460.419504897931080.20975244896554
760.7561521080818760.4876957838362480.243847891918124
770.746658574939360.5066828501212820.253341425060641
780.7057336850527970.5885326298944050.294266314947203
790.7113377505204570.5773244989590870.288662249479543
800.6972040707100980.6055918585798050.302795929289902
810.6618986882728770.6762026234542460.338101311727123
820.6199648520953040.7600702958093920.380035147904696
830.572654456594140.854691086811720.42734554340586
840.5814236588777490.8371526822445020.418576341122251
850.5630902037335140.8738195925329720.436909796266486
860.5395088497290240.9209823005419520.460491150270976
870.591790927840140.816418144319720.40820907215986
880.620743725162610.7585125496747810.379256274837390
890.585861475621070.828277048757860.41413852437893
900.5699168260686210.8601663478627580.430083173931379
910.5582096358566160.8835807282867680.441790364143384
920.5264658416113350.947068316777330.473534158388665
930.4829474754425660.9658949508851320.517052524557434
940.4654655263681090.9309310527362170.534534473631892
950.4793073149375690.9586146298751370.520692685062431
960.6190566541635230.7618866916729540.380943345836477
970.5739056003687620.8521887992624750.426094399631238
980.5379113310184870.9241773379630260.462088668981513
990.6049847962496090.7900304075007820.395015203750391
1000.570591213403750.85881757319250.42940878659625
1010.5908218220078040.8183563559843920.409178177992196
1020.5444237587305190.9111524825389610.455576241269481
1030.4942123088512290.9884246177024570.505787691148771
1040.5002203976503560.9995592046992870.499779602349644
1050.5519343664408120.8961312671183770.448065633559188
1060.5548921509762650.8902156980474710.445107849023735
1070.5119420381920780.9761159236158440.488057961807922
1080.609724562712760.780550874574480.39027543728724
1090.5930667409027040.8138665181945920.406933259097296
1100.5505772612457470.8988454775085050.449422738754253
1110.4948265996685130.9896531993370270.505173400331487
1120.6168093432778450.766381313444310.383190656722155
1130.6261543908035780.7476912183928430.373845609196422
1140.6152354880973120.7695290238053770.384764511902688
1150.5599876296091190.8800247407817620.440012370390881
1160.5211388776550420.9577222446899160.478861122344958
1170.4770161818485170.9540323636970340.522983818151483
1180.4638834165828050.927766833165610.536116583417195
1190.494739372148820.989478744297640.50526062785118
1200.441530858356720.883061716713440.558469141643279
1210.4134829522043010.8269659044086030.586517047795699
1220.3685878329170530.7371756658341070.631412167082947
1230.4260640319806920.8521280639613840.573935968019308
1240.3873901037021720.7747802074043440.612609896297828
1250.4151331684270880.8302663368541770.584866831572912
1260.4448625504681380.8897251009362750.555137449531862
1270.4320321230372650.864064246074530.567967876962735
1280.3616135946336770.7232271892673530.638386405366323
1290.5980661188481580.8038677623036840.401933881151842
1300.7384688316503330.5230623366993340.261531168349667
1310.7418200110498450.5163599779003090.258179988950155
1320.7892672181567970.4214655636864070.210732781843203
1330.7626227260879270.4747545478241460.237377273912073
1340.8089580224469450.3820839551061100.191041977553055
1350.7326375291746140.5347249416507720.267362470825386
1360.6681748812628130.6636502374743740.331825118737187
1370.7582451793360020.4835096413279950.241754820663998
1380.7042372239546890.5915255520906230.295762776045311
1390.6469509147001210.7060981705997570.353049085299879
1400.5030939508278420.9938120983443160.496906049172158
1410.402127666541330.804255333082660.59787233345867

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.933649257395563 & 0.132701485208874 & 0.066350742604437 \tabularnewline
16 & 0.999930621874146 & 0.000138756251707923 & 6.93781258539613e-05 \tabularnewline
17 & 0.99982533042272 & 0.000349339154560413 & 0.000174669577280207 \tabularnewline
18 & 0.999578824249336 & 0.000842351501327179 & 0.000421175750663589 \tabularnewline
19 & 0.99944246296334 & 0.00111507407331862 & 0.000557537036659311 \tabularnewline
20 & 0.998941708262373 & 0.00211658347525386 & 0.00105829173762693 \tabularnewline
21 & 0.999034755321167 & 0.00193048935766501 & 0.000965244678832506 \tabularnewline
22 & 0.999567250471365 & 0.000865499057270978 & 0.000432749528635489 \tabularnewline
23 & 0.999157827831607 & 0.00168434433678615 & 0.000842172168393073 \tabularnewline
24 & 0.998445326001666 & 0.00310934799666861 & 0.00155467399833431 \tabularnewline
25 & 0.997235517460621 & 0.00552896507875728 & 0.00276448253937864 \tabularnewline
26 & 0.995267882314468 & 0.00946423537106505 & 0.00473211768553253 \tabularnewline
27 & 0.995942688768337 & 0.00811462246332684 & 0.00405731123166342 \tabularnewline
28 & 0.993516932421401 & 0.0129661351571970 & 0.00648306757859852 \tabularnewline
29 & 0.996582457718479 & 0.00683508456304207 & 0.00341754228152104 \tabularnewline
30 & 0.995217176268008 & 0.0095656474639834 & 0.0047828237319917 \tabularnewline
31 & 0.992921336992382 & 0.0141573260152365 & 0.00707866300761823 \tabularnewline
32 & 0.996284334548904 & 0.00743133090219159 & 0.00371566545109579 \tabularnewline
33 & 0.994330861757432 & 0.0113382764851356 & 0.00566913824256779 \tabularnewline
34 & 0.992277437072564 & 0.0154451258548714 & 0.00772256292743568 \tabularnewline
35 & 0.991279356100053 & 0.0174412877998932 & 0.00872064389994661 \tabularnewline
36 & 0.987757815396007 & 0.0244843692079857 & 0.0122421846039929 \tabularnewline
37 & 0.984893642637731 & 0.0302127147245372 & 0.0151063573622686 \tabularnewline
38 & 0.986023059249697 & 0.0279538815006067 & 0.0139769407503034 \tabularnewline
39 & 0.985261760143423 & 0.0294764797131537 & 0.0147382398565768 \tabularnewline
40 & 0.989836355867045 & 0.0203272882659101 & 0.0101636441329550 \tabularnewline
41 & 0.98602298353575 & 0.0279540329285006 & 0.0139770164642503 \tabularnewline
42 & 0.98077854240216 & 0.0384429151956809 & 0.0192214575978405 \tabularnewline
43 & 0.973489036583066 & 0.0530219268338682 & 0.0265109634169341 \tabularnewline
44 & 0.96769041426965 & 0.064619171460701 & 0.0323095857303505 \tabularnewline
45 & 0.991292554964444 & 0.0174148900711116 & 0.00870744503555578 \tabularnewline
46 & 0.988013678177197 & 0.0239726436456056 & 0.0119863218228028 \tabularnewline
47 & 0.983986894232742 & 0.0320262115345167 & 0.0160131057672583 \tabularnewline
48 & 0.979021719539053 & 0.0419565609218936 & 0.0209782804609468 \tabularnewline
49 & 0.976520832207837 & 0.0469583355843252 & 0.0234791677921626 \tabularnewline
50 & 0.972010259031866 & 0.0559794819362689 & 0.0279897409681344 \tabularnewline
51 & 0.969431950633464 & 0.0611360987330716 & 0.0305680493665358 \tabularnewline
52 & 0.982138988658216 & 0.0357220226835678 & 0.0178610113417839 \tabularnewline
53 & 0.976067376203782 & 0.0478652475924353 & 0.0239326237962177 \tabularnewline
54 & 0.976737935371232 & 0.046524129257535 & 0.0232620646287675 \tabularnewline
55 & 0.973376010621936 & 0.053247978756128 & 0.026623989378064 \tabularnewline
56 & 0.966385646299197 & 0.0672287074016055 & 0.0336143537008028 \tabularnewline
57 & 0.975072812968049 & 0.0498543740639027 & 0.0249271870319513 \tabularnewline
58 & 0.968604685384795 & 0.0627906292304099 & 0.0313953146152050 \tabularnewline
59 & 0.958784031714412 & 0.0824319365711752 & 0.0412159682855876 \tabularnewline
60 & 0.946744710982143 & 0.106510578035714 & 0.0532552890178569 \tabularnewline
61 & 0.93284309119605 & 0.134313817607899 & 0.0671569088039494 \tabularnewline
62 & 0.919899115767245 & 0.160201768465510 & 0.0801008842327551 \tabularnewline
63 & 0.900251949790252 & 0.199496100419497 & 0.0997480502097483 \tabularnewline
64 & 0.884622774641366 & 0.230754450717268 & 0.115377225358634 \tabularnewline
65 & 0.936158227263073 & 0.127683545473854 & 0.0638417727369268 \tabularnewline
66 & 0.941302434161088 & 0.117395131677823 & 0.0586975658389117 \tabularnewline
67 & 0.928161402578554 & 0.143677194842892 & 0.0718385974214462 \tabularnewline
68 & 0.915566004464006 & 0.168867991071989 & 0.0844339955359943 \tabularnewline
69 & 0.898446226792583 & 0.203107546414835 & 0.101553773207417 \tabularnewline
70 & 0.885460236405788 & 0.229079527188424 & 0.114539763594212 \tabularnewline
71 & 0.862778079944178 & 0.274443840111644 & 0.137221920055822 \tabularnewline
72 & 0.83686833746627 & 0.326263325067459 & 0.163131662533729 \tabularnewline
73 & 0.820712211572717 & 0.358575576854567 & 0.179287788427283 \tabularnewline
74 & 0.815138334240323 & 0.369723331519353 & 0.184861665759677 \tabularnewline
75 & 0.79024755103446 & 0.41950489793108 & 0.20975244896554 \tabularnewline
76 & 0.756152108081876 & 0.487695783836248 & 0.243847891918124 \tabularnewline
77 & 0.74665857493936 & 0.506682850121282 & 0.253341425060641 \tabularnewline
78 & 0.705733685052797 & 0.588532629894405 & 0.294266314947203 \tabularnewline
79 & 0.711337750520457 & 0.577324498959087 & 0.288662249479543 \tabularnewline
80 & 0.697204070710098 & 0.605591858579805 & 0.302795929289902 \tabularnewline
81 & 0.661898688272877 & 0.676202623454246 & 0.338101311727123 \tabularnewline
82 & 0.619964852095304 & 0.760070295809392 & 0.380035147904696 \tabularnewline
83 & 0.57265445659414 & 0.85469108681172 & 0.42734554340586 \tabularnewline
84 & 0.581423658877749 & 0.837152682244502 & 0.418576341122251 \tabularnewline
85 & 0.563090203733514 & 0.873819592532972 & 0.436909796266486 \tabularnewline
86 & 0.539508849729024 & 0.920982300541952 & 0.460491150270976 \tabularnewline
87 & 0.59179092784014 & 0.81641814431972 & 0.40820907215986 \tabularnewline
88 & 0.62074372516261 & 0.758512549674781 & 0.379256274837390 \tabularnewline
89 & 0.58586147562107 & 0.82827704875786 & 0.41413852437893 \tabularnewline
90 & 0.569916826068621 & 0.860166347862758 & 0.430083173931379 \tabularnewline
91 & 0.558209635856616 & 0.883580728286768 & 0.441790364143384 \tabularnewline
92 & 0.526465841611335 & 0.94706831677733 & 0.473534158388665 \tabularnewline
93 & 0.482947475442566 & 0.965894950885132 & 0.517052524557434 \tabularnewline
94 & 0.465465526368109 & 0.930931052736217 & 0.534534473631892 \tabularnewline
95 & 0.479307314937569 & 0.958614629875137 & 0.520692685062431 \tabularnewline
96 & 0.619056654163523 & 0.761886691672954 & 0.380943345836477 \tabularnewline
97 & 0.573905600368762 & 0.852188799262475 & 0.426094399631238 \tabularnewline
98 & 0.537911331018487 & 0.924177337963026 & 0.462088668981513 \tabularnewline
99 & 0.604984796249609 & 0.790030407500782 & 0.395015203750391 \tabularnewline
100 & 0.57059121340375 & 0.8588175731925 & 0.42940878659625 \tabularnewline
101 & 0.590821822007804 & 0.818356355984392 & 0.409178177992196 \tabularnewline
102 & 0.544423758730519 & 0.911152482538961 & 0.455576241269481 \tabularnewline
103 & 0.494212308851229 & 0.988424617702457 & 0.505787691148771 \tabularnewline
104 & 0.500220397650356 & 0.999559204699287 & 0.499779602349644 \tabularnewline
105 & 0.551934366440812 & 0.896131267118377 & 0.448065633559188 \tabularnewline
106 & 0.554892150976265 & 0.890215698047471 & 0.445107849023735 \tabularnewline
107 & 0.511942038192078 & 0.976115923615844 & 0.488057961807922 \tabularnewline
108 & 0.60972456271276 & 0.78055087457448 & 0.39027543728724 \tabularnewline
109 & 0.593066740902704 & 0.813866518194592 & 0.406933259097296 \tabularnewline
110 & 0.550577261245747 & 0.898845477508505 & 0.449422738754253 \tabularnewline
111 & 0.494826599668513 & 0.989653199337027 & 0.505173400331487 \tabularnewline
112 & 0.616809343277845 & 0.76638131344431 & 0.383190656722155 \tabularnewline
113 & 0.626154390803578 & 0.747691218392843 & 0.373845609196422 \tabularnewline
114 & 0.615235488097312 & 0.769529023805377 & 0.384764511902688 \tabularnewline
115 & 0.559987629609119 & 0.880024740781762 & 0.440012370390881 \tabularnewline
116 & 0.521138877655042 & 0.957722244689916 & 0.478861122344958 \tabularnewline
117 & 0.477016181848517 & 0.954032363697034 & 0.522983818151483 \tabularnewline
118 & 0.463883416582805 & 0.92776683316561 & 0.536116583417195 \tabularnewline
119 & 0.49473937214882 & 0.98947874429764 & 0.50526062785118 \tabularnewline
120 & 0.44153085835672 & 0.88306171671344 & 0.558469141643279 \tabularnewline
121 & 0.413482952204301 & 0.826965904408603 & 0.586517047795699 \tabularnewline
122 & 0.368587832917053 & 0.737175665834107 & 0.631412167082947 \tabularnewline
123 & 0.426064031980692 & 0.852128063961384 & 0.573935968019308 \tabularnewline
124 & 0.387390103702172 & 0.774780207404344 & 0.612609896297828 \tabularnewline
125 & 0.415133168427088 & 0.830266336854177 & 0.584866831572912 \tabularnewline
126 & 0.444862550468138 & 0.889725100936275 & 0.555137449531862 \tabularnewline
127 & 0.432032123037265 & 0.86406424607453 & 0.567967876962735 \tabularnewline
128 & 0.361613594633677 & 0.723227189267353 & 0.638386405366323 \tabularnewline
129 & 0.598066118848158 & 0.803867762303684 & 0.401933881151842 \tabularnewline
130 & 0.738468831650333 & 0.523062336699334 & 0.261531168349667 \tabularnewline
131 & 0.741820011049845 & 0.516359977900309 & 0.258179988950155 \tabularnewline
132 & 0.789267218156797 & 0.421465563686407 & 0.210732781843203 \tabularnewline
133 & 0.762622726087927 & 0.474754547824146 & 0.237377273912073 \tabularnewline
134 & 0.808958022446945 & 0.382083955106110 & 0.191041977553055 \tabularnewline
135 & 0.732637529174614 & 0.534724941650772 & 0.267362470825386 \tabularnewline
136 & 0.668174881262813 & 0.663650237474374 & 0.331825118737187 \tabularnewline
137 & 0.758245179336002 & 0.483509641327995 & 0.241754820663998 \tabularnewline
138 & 0.704237223954689 & 0.591525552090623 & 0.295762776045311 \tabularnewline
139 & 0.646950914700121 & 0.706098170599757 & 0.353049085299879 \tabularnewline
140 & 0.503093950827842 & 0.993812098344316 & 0.496906049172158 \tabularnewline
141 & 0.40212766654133 & 0.80425533308266 & 0.59787233345867 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98058&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]15[/C][C]0.933649257395563[/C][C]0.132701485208874[/C][C]0.066350742604437[/C][/ROW]
[ROW][C]16[/C][C]0.999930621874146[/C][C]0.000138756251707923[/C][C]6.93781258539613e-05[/C][/ROW]
[ROW][C]17[/C][C]0.99982533042272[/C][C]0.000349339154560413[/C][C]0.000174669577280207[/C][/ROW]
[ROW][C]18[/C][C]0.999578824249336[/C][C]0.000842351501327179[/C][C]0.000421175750663589[/C][/ROW]
[ROW][C]19[/C][C]0.99944246296334[/C][C]0.00111507407331862[/C][C]0.000557537036659311[/C][/ROW]
[ROW][C]20[/C][C]0.998941708262373[/C][C]0.00211658347525386[/C][C]0.00105829173762693[/C][/ROW]
[ROW][C]21[/C][C]0.999034755321167[/C][C]0.00193048935766501[/C][C]0.000965244678832506[/C][/ROW]
[ROW][C]22[/C][C]0.999567250471365[/C][C]0.000865499057270978[/C][C]0.000432749528635489[/C][/ROW]
[ROW][C]23[/C][C]0.999157827831607[/C][C]0.00168434433678615[/C][C]0.000842172168393073[/C][/ROW]
[ROW][C]24[/C][C]0.998445326001666[/C][C]0.00310934799666861[/C][C]0.00155467399833431[/C][/ROW]
[ROW][C]25[/C][C]0.997235517460621[/C][C]0.00552896507875728[/C][C]0.00276448253937864[/C][/ROW]
[ROW][C]26[/C][C]0.995267882314468[/C][C]0.00946423537106505[/C][C]0.00473211768553253[/C][/ROW]
[ROW][C]27[/C][C]0.995942688768337[/C][C]0.00811462246332684[/C][C]0.00405731123166342[/C][/ROW]
[ROW][C]28[/C][C]0.993516932421401[/C][C]0.0129661351571970[/C][C]0.00648306757859852[/C][/ROW]
[ROW][C]29[/C][C]0.996582457718479[/C][C]0.00683508456304207[/C][C]0.00341754228152104[/C][/ROW]
[ROW][C]30[/C][C]0.995217176268008[/C][C]0.0095656474639834[/C][C]0.0047828237319917[/C][/ROW]
[ROW][C]31[/C][C]0.992921336992382[/C][C]0.0141573260152365[/C][C]0.00707866300761823[/C][/ROW]
[ROW][C]32[/C][C]0.996284334548904[/C][C]0.00743133090219159[/C][C]0.00371566545109579[/C][/ROW]
[ROW][C]33[/C][C]0.994330861757432[/C][C]0.0113382764851356[/C][C]0.00566913824256779[/C][/ROW]
[ROW][C]34[/C][C]0.992277437072564[/C][C]0.0154451258548714[/C][C]0.00772256292743568[/C][/ROW]
[ROW][C]35[/C][C]0.991279356100053[/C][C]0.0174412877998932[/C][C]0.00872064389994661[/C][/ROW]
[ROW][C]36[/C][C]0.987757815396007[/C][C]0.0244843692079857[/C][C]0.0122421846039929[/C][/ROW]
[ROW][C]37[/C][C]0.984893642637731[/C][C]0.0302127147245372[/C][C]0.0151063573622686[/C][/ROW]
[ROW][C]38[/C][C]0.986023059249697[/C][C]0.0279538815006067[/C][C]0.0139769407503034[/C][/ROW]
[ROW][C]39[/C][C]0.985261760143423[/C][C]0.0294764797131537[/C][C]0.0147382398565768[/C][/ROW]
[ROW][C]40[/C][C]0.989836355867045[/C][C]0.0203272882659101[/C][C]0.0101636441329550[/C][/ROW]
[ROW][C]41[/C][C]0.98602298353575[/C][C]0.0279540329285006[/C][C]0.0139770164642503[/C][/ROW]
[ROW][C]42[/C][C]0.98077854240216[/C][C]0.0384429151956809[/C][C]0.0192214575978405[/C][/ROW]
[ROW][C]43[/C][C]0.973489036583066[/C][C]0.0530219268338682[/C][C]0.0265109634169341[/C][/ROW]
[ROW][C]44[/C][C]0.96769041426965[/C][C]0.064619171460701[/C][C]0.0323095857303505[/C][/ROW]
[ROW][C]45[/C][C]0.991292554964444[/C][C]0.0174148900711116[/C][C]0.00870744503555578[/C][/ROW]
[ROW][C]46[/C][C]0.988013678177197[/C][C]0.0239726436456056[/C][C]0.0119863218228028[/C][/ROW]
[ROW][C]47[/C][C]0.983986894232742[/C][C]0.0320262115345167[/C][C]0.0160131057672583[/C][/ROW]
[ROW][C]48[/C][C]0.979021719539053[/C][C]0.0419565609218936[/C][C]0.0209782804609468[/C][/ROW]
[ROW][C]49[/C][C]0.976520832207837[/C][C]0.0469583355843252[/C][C]0.0234791677921626[/C][/ROW]
[ROW][C]50[/C][C]0.972010259031866[/C][C]0.0559794819362689[/C][C]0.0279897409681344[/C][/ROW]
[ROW][C]51[/C][C]0.969431950633464[/C][C]0.0611360987330716[/C][C]0.0305680493665358[/C][/ROW]
[ROW][C]52[/C][C]0.982138988658216[/C][C]0.0357220226835678[/C][C]0.0178610113417839[/C][/ROW]
[ROW][C]53[/C][C]0.976067376203782[/C][C]0.0478652475924353[/C][C]0.0239326237962177[/C][/ROW]
[ROW][C]54[/C][C]0.976737935371232[/C][C]0.046524129257535[/C][C]0.0232620646287675[/C][/ROW]
[ROW][C]55[/C][C]0.973376010621936[/C][C]0.053247978756128[/C][C]0.026623989378064[/C][/ROW]
[ROW][C]56[/C][C]0.966385646299197[/C][C]0.0672287074016055[/C][C]0.0336143537008028[/C][/ROW]
[ROW][C]57[/C][C]0.975072812968049[/C][C]0.0498543740639027[/C][C]0.0249271870319513[/C][/ROW]
[ROW][C]58[/C][C]0.968604685384795[/C][C]0.0627906292304099[/C][C]0.0313953146152050[/C][/ROW]
[ROW][C]59[/C][C]0.958784031714412[/C][C]0.0824319365711752[/C][C]0.0412159682855876[/C][/ROW]
[ROW][C]60[/C][C]0.946744710982143[/C][C]0.106510578035714[/C][C]0.0532552890178569[/C][/ROW]
[ROW][C]61[/C][C]0.93284309119605[/C][C]0.134313817607899[/C][C]0.0671569088039494[/C][/ROW]
[ROW][C]62[/C][C]0.919899115767245[/C][C]0.160201768465510[/C][C]0.0801008842327551[/C][/ROW]
[ROW][C]63[/C][C]0.900251949790252[/C][C]0.199496100419497[/C][C]0.0997480502097483[/C][/ROW]
[ROW][C]64[/C][C]0.884622774641366[/C][C]0.230754450717268[/C][C]0.115377225358634[/C][/ROW]
[ROW][C]65[/C][C]0.936158227263073[/C][C]0.127683545473854[/C][C]0.0638417727369268[/C][/ROW]
[ROW][C]66[/C][C]0.941302434161088[/C][C]0.117395131677823[/C][C]0.0586975658389117[/C][/ROW]
[ROW][C]67[/C][C]0.928161402578554[/C][C]0.143677194842892[/C][C]0.0718385974214462[/C][/ROW]
[ROW][C]68[/C][C]0.915566004464006[/C][C]0.168867991071989[/C][C]0.0844339955359943[/C][/ROW]
[ROW][C]69[/C][C]0.898446226792583[/C][C]0.203107546414835[/C][C]0.101553773207417[/C][/ROW]
[ROW][C]70[/C][C]0.885460236405788[/C][C]0.229079527188424[/C][C]0.114539763594212[/C][/ROW]
[ROW][C]71[/C][C]0.862778079944178[/C][C]0.274443840111644[/C][C]0.137221920055822[/C][/ROW]
[ROW][C]72[/C][C]0.83686833746627[/C][C]0.326263325067459[/C][C]0.163131662533729[/C][/ROW]
[ROW][C]73[/C][C]0.820712211572717[/C][C]0.358575576854567[/C][C]0.179287788427283[/C][/ROW]
[ROW][C]74[/C][C]0.815138334240323[/C][C]0.369723331519353[/C][C]0.184861665759677[/C][/ROW]
[ROW][C]75[/C][C]0.79024755103446[/C][C]0.41950489793108[/C][C]0.20975244896554[/C][/ROW]
[ROW][C]76[/C][C]0.756152108081876[/C][C]0.487695783836248[/C][C]0.243847891918124[/C][/ROW]
[ROW][C]77[/C][C]0.74665857493936[/C][C]0.506682850121282[/C][C]0.253341425060641[/C][/ROW]
[ROW][C]78[/C][C]0.705733685052797[/C][C]0.588532629894405[/C][C]0.294266314947203[/C][/ROW]
[ROW][C]79[/C][C]0.711337750520457[/C][C]0.577324498959087[/C][C]0.288662249479543[/C][/ROW]
[ROW][C]80[/C][C]0.697204070710098[/C][C]0.605591858579805[/C][C]0.302795929289902[/C][/ROW]
[ROW][C]81[/C][C]0.661898688272877[/C][C]0.676202623454246[/C][C]0.338101311727123[/C][/ROW]
[ROW][C]82[/C][C]0.619964852095304[/C][C]0.760070295809392[/C][C]0.380035147904696[/C][/ROW]
[ROW][C]83[/C][C]0.57265445659414[/C][C]0.85469108681172[/C][C]0.42734554340586[/C][/ROW]
[ROW][C]84[/C][C]0.581423658877749[/C][C]0.837152682244502[/C][C]0.418576341122251[/C][/ROW]
[ROW][C]85[/C][C]0.563090203733514[/C][C]0.873819592532972[/C][C]0.436909796266486[/C][/ROW]
[ROW][C]86[/C][C]0.539508849729024[/C][C]0.920982300541952[/C][C]0.460491150270976[/C][/ROW]
[ROW][C]87[/C][C]0.59179092784014[/C][C]0.81641814431972[/C][C]0.40820907215986[/C][/ROW]
[ROW][C]88[/C][C]0.62074372516261[/C][C]0.758512549674781[/C][C]0.379256274837390[/C][/ROW]
[ROW][C]89[/C][C]0.58586147562107[/C][C]0.82827704875786[/C][C]0.41413852437893[/C][/ROW]
[ROW][C]90[/C][C]0.569916826068621[/C][C]0.860166347862758[/C][C]0.430083173931379[/C][/ROW]
[ROW][C]91[/C][C]0.558209635856616[/C][C]0.883580728286768[/C][C]0.441790364143384[/C][/ROW]
[ROW][C]92[/C][C]0.526465841611335[/C][C]0.94706831677733[/C][C]0.473534158388665[/C][/ROW]
[ROW][C]93[/C][C]0.482947475442566[/C][C]0.965894950885132[/C][C]0.517052524557434[/C][/ROW]
[ROW][C]94[/C][C]0.465465526368109[/C][C]0.930931052736217[/C][C]0.534534473631892[/C][/ROW]
[ROW][C]95[/C][C]0.479307314937569[/C][C]0.958614629875137[/C][C]0.520692685062431[/C][/ROW]
[ROW][C]96[/C][C]0.619056654163523[/C][C]0.761886691672954[/C][C]0.380943345836477[/C][/ROW]
[ROW][C]97[/C][C]0.573905600368762[/C][C]0.852188799262475[/C][C]0.426094399631238[/C][/ROW]
[ROW][C]98[/C][C]0.537911331018487[/C][C]0.924177337963026[/C][C]0.462088668981513[/C][/ROW]
[ROW][C]99[/C][C]0.604984796249609[/C][C]0.790030407500782[/C][C]0.395015203750391[/C][/ROW]
[ROW][C]100[/C][C]0.57059121340375[/C][C]0.8588175731925[/C][C]0.42940878659625[/C][/ROW]
[ROW][C]101[/C][C]0.590821822007804[/C][C]0.818356355984392[/C][C]0.409178177992196[/C][/ROW]
[ROW][C]102[/C][C]0.544423758730519[/C][C]0.911152482538961[/C][C]0.455576241269481[/C][/ROW]
[ROW][C]103[/C][C]0.494212308851229[/C][C]0.988424617702457[/C][C]0.505787691148771[/C][/ROW]
[ROW][C]104[/C][C]0.500220397650356[/C][C]0.999559204699287[/C][C]0.499779602349644[/C][/ROW]
[ROW][C]105[/C][C]0.551934366440812[/C][C]0.896131267118377[/C][C]0.448065633559188[/C][/ROW]
[ROW][C]106[/C][C]0.554892150976265[/C][C]0.890215698047471[/C][C]0.445107849023735[/C][/ROW]
[ROW][C]107[/C][C]0.511942038192078[/C][C]0.976115923615844[/C][C]0.488057961807922[/C][/ROW]
[ROW][C]108[/C][C]0.60972456271276[/C][C]0.78055087457448[/C][C]0.39027543728724[/C][/ROW]
[ROW][C]109[/C][C]0.593066740902704[/C][C]0.813866518194592[/C][C]0.406933259097296[/C][/ROW]
[ROW][C]110[/C][C]0.550577261245747[/C][C]0.898845477508505[/C][C]0.449422738754253[/C][/ROW]
[ROW][C]111[/C][C]0.494826599668513[/C][C]0.989653199337027[/C][C]0.505173400331487[/C][/ROW]
[ROW][C]112[/C][C]0.616809343277845[/C][C]0.76638131344431[/C][C]0.383190656722155[/C][/ROW]
[ROW][C]113[/C][C]0.626154390803578[/C][C]0.747691218392843[/C][C]0.373845609196422[/C][/ROW]
[ROW][C]114[/C][C]0.615235488097312[/C][C]0.769529023805377[/C][C]0.384764511902688[/C][/ROW]
[ROW][C]115[/C][C]0.559987629609119[/C][C]0.880024740781762[/C][C]0.440012370390881[/C][/ROW]
[ROW][C]116[/C][C]0.521138877655042[/C][C]0.957722244689916[/C][C]0.478861122344958[/C][/ROW]
[ROW][C]117[/C][C]0.477016181848517[/C][C]0.954032363697034[/C][C]0.522983818151483[/C][/ROW]
[ROW][C]118[/C][C]0.463883416582805[/C][C]0.92776683316561[/C][C]0.536116583417195[/C][/ROW]
[ROW][C]119[/C][C]0.49473937214882[/C][C]0.98947874429764[/C][C]0.50526062785118[/C][/ROW]
[ROW][C]120[/C][C]0.44153085835672[/C][C]0.88306171671344[/C][C]0.558469141643279[/C][/ROW]
[ROW][C]121[/C][C]0.413482952204301[/C][C]0.826965904408603[/C][C]0.586517047795699[/C][/ROW]
[ROW][C]122[/C][C]0.368587832917053[/C][C]0.737175665834107[/C][C]0.631412167082947[/C][/ROW]
[ROW][C]123[/C][C]0.426064031980692[/C][C]0.852128063961384[/C][C]0.573935968019308[/C][/ROW]
[ROW][C]124[/C][C]0.387390103702172[/C][C]0.774780207404344[/C][C]0.612609896297828[/C][/ROW]
[ROW][C]125[/C][C]0.415133168427088[/C][C]0.830266336854177[/C][C]0.584866831572912[/C][/ROW]
[ROW][C]126[/C][C]0.444862550468138[/C][C]0.889725100936275[/C][C]0.555137449531862[/C][/ROW]
[ROW][C]127[/C][C]0.432032123037265[/C][C]0.86406424607453[/C][C]0.567967876962735[/C][/ROW]
[ROW][C]128[/C][C]0.361613594633677[/C][C]0.723227189267353[/C][C]0.638386405366323[/C][/ROW]
[ROW][C]129[/C][C]0.598066118848158[/C][C]0.803867762303684[/C][C]0.401933881151842[/C][/ROW]
[ROW][C]130[/C][C]0.738468831650333[/C][C]0.523062336699334[/C][C]0.261531168349667[/C][/ROW]
[ROW][C]131[/C][C]0.741820011049845[/C][C]0.516359977900309[/C][C]0.258179988950155[/C][/ROW]
[ROW][C]132[/C][C]0.789267218156797[/C][C]0.421465563686407[/C][C]0.210732781843203[/C][/ROW]
[ROW][C]133[/C][C]0.762622726087927[/C][C]0.474754547824146[/C][C]0.237377273912073[/C][/ROW]
[ROW][C]134[/C][C]0.808958022446945[/C][C]0.382083955106110[/C][C]0.191041977553055[/C][/ROW]
[ROW][C]135[/C][C]0.732637529174614[/C][C]0.534724941650772[/C][C]0.267362470825386[/C][/ROW]
[ROW][C]136[/C][C]0.668174881262813[/C][C]0.663650237474374[/C][C]0.331825118737187[/C][/ROW]
[ROW][C]137[/C][C]0.758245179336002[/C][C]0.483509641327995[/C][C]0.241754820663998[/C][/ROW]
[ROW][C]138[/C][C]0.704237223954689[/C][C]0.591525552090623[/C][C]0.295762776045311[/C][/ROW]
[ROW][C]139[/C][C]0.646950914700121[/C][C]0.706098170599757[/C][C]0.353049085299879[/C][/ROW]
[ROW][C]140[/C][C]0.503093950827842[/C][C]0.993812098344316[/C][C]0.496906049172158[/C][/ROW]
[ROW][C]141[/C][C]0.40212766654133[/C][C]0.80425533308266[/C][C]0.59787233345867[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98058&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98058&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.9336492573955630.1327014852088740.066350742604437
160.9999306218741460.0001387562517079236.93781258539613e-05
170.999825330422720.0003493391545604130.000174669577280207
180.9995788242493360.0008423515013271790.000421175750663589
190.999442462963340.001115074073318620.000557537036659311
200.9989417082623730.002116583475253860.00105829173762693
210.9990347553211670.001930489357665010.000965244678832506
220.9995672504713650.0008654990572709780.000432749528635489
230.9991578278316070.001684344336786150.000842172168393073
240.9984453260016660.003109347996668610.00155467399833431
250.9972355174606210.005528965078757280.00276448253937864
260.9952678823144680.009464235371065050.00473211768553253
270.9959426887683370.008114622463326840.00405731123166342
280.9935169324214010.01296613515719700.00648306757859852
290.9965824577184790.006835084563042070.00341754228152104
300.9952171762680080.00956564746398340.0047828237319917
310.9929213369923820.01415732601523650.00707866300761823
320.9962843345489040.007431330902191590.00371566545109579
330.9943308617574320.01133827648513560.00566913824256779
340.9922774370725640.01544512585487140.00772256292743568
350.9912793561000530.01744128779989320.00872064389994661
360.9877578153960070.02448436920798570.0122421846039929
370.9848936426377310.03021271472453720.0151063573622686
380.9860230592496970.02795388150060670.0139769407503034
390.9852617601434230.02947647971315370.0147382398565768
400.9898363558670450.02032728826591010.0101636441329550
410.986022983535750.02795403292850060.0139770164642503
420.980778542402160.03844291519568090.0192214575978405
430.9734890365830660.05302192683386820.0265109634169341
440.967690414269650.0646191714607010.0323095857303505
450.9912925549644440.01741489007111160.00870744503555578
460.9880136781771970.02397264364560560.0119863218228028
470.9839868942327420.03202621153451670.0160131057672583
480.9790217195390530.04195656092189360.0209782804609468
490.9765208322078370.04695833558432520.0234791677921626
500.9720102590318660.05597948193626890.0279897409681344
510.9694319506334640.06113609873307160.0305680493665358
520.9821389886582160.03572202268356780.0178610113417839
530.9760673762037820.04786524759243530.0239326237962177
540.9767379353712320.0465241292575350.0232620646287675
550.9733760106219360.0532479787561280.026623989378064
560.9663856462991970.06722870740160550.0336143537008028
570.9750728129680490.04985437406390270.0249271870319513
580.9686046853847950.06279062923040990.0313953146152050
590.9587840317144120.08243193657117520.0412159682855876
600.9467447109821430.1065105780357140.0532552890178569
610.932843091196050.1343138176078990.0671569088039494
620.9198991157672450.1602017684655100.0801008842327551
630.9002519497902520.1994961004194970.0997480502097483
640.8846227746413660.2307544507172680.115377225358634
650.9361582272630730.1276835454738540.0638417727369268
660.9413024341610880.1173951316778230.0586975658389117
670.9281614025785540.1436771948428920.0718385974214462
680.9155660044640060.1688679910719890.0844339955359943
690.8984462267925830.2031075464148350.101553773207417
700.8854602364057880.2290795271884240.114539763594212
710.8627780799441780.2744438401116440.137221920055822
720.836868337466270.3262633250674590.163131662533729
730.8207122115727170.3585755768545670.179287788427283
740.8151383342403230.3697233315193530.184861665759677
750.790247551034460.419504897931080.20975244896554
760.7561521080818760.4876957838362480.243847891918124
770.746658574939360.5066828501212820.253341425060641
780.7057336850527970.5885326298944050.294266314947203
790.7113377505204570.5773244989590870.288662249479543
800.6972040707100980.6055918585798050.302795929289902
810.6618986882728770.6762026234542460.338101311727123
820.6199648520953040.7600702958093920.380035147904696
830.572654456594140.854691086811720.42734554340586
840.5814236588777490.8371526822445020.418576341122251
850.5630902037335140.8738195925329720.436909796266486
860.5395088497290240.9209823005419520.460491150270976
870.591790927840140.816418144319720.40820907215986
880.620743725162610.7585125496747810.379256274837390
890.585861475621070.828277048757860.41413852437893
900.5699168260686210.8601663478627580.430083173931379
910.5582096358566160.8835807282867680.441790364143384
920.5264658416113350.947068316777330.473534158388665
930.4829474754425660.9658949508851320.517052524557434
940.4654655263681090.9309310527362170.534534473631892
950.4793073149375690.9586146298751370.520692685062431
960.6190566541635230.7618866916729540.380943345836477
970.5739056003687620.8521887992624750.426094399631238
980.5379113310184870.9241773379630260.462088668981513
990.6049847962496090.7900304075007820.395015203750391
1000.570591213403750.85881757319250.42940878659625
1010.5908218220078040.8183563559843920.409178177992196
1020.5444237587305190.9111524825389610.455576241269481
1030.4942123088512290.9884246177024570.505787691148771
1040.5002203976503560.9995592046992870.499779602349644
1050.5519343664408120.8961312671183770.448065633559188
1060.5548921509762650.8902156980474710.445107849023735
1070.5119420381920780.9761159236158440.488057961807922
1080.609724562712760.780550874574480.39027543728724
1090.5930667409027040.8138665181945920.406933259097296
1100.5505772612457470.8988454775085050.449422738754253
1110.4948265996685130.9896531993370270.505173400331487
1120.6168093432778450.766381313444310.383190656722155
1130.6261543908035780.7476912183928430.373845609196422
1140.6152354880973120.7695290238053770.384764511902688
1150.5599876296091190.8800247407817620.440012370390881
1160.5211388776550420.9577222446899160.478861122344958
1170.4770161818485170.9540323636970340.522983818151483
1180.4638834165828050.927766833165610.536116583417195
1190.494739372148820.989478744297640.50526062785118
1200.441530858356720.883061716713440.558469141643279
1210.4134829522043010.8269659044086030.586517047795699
1220.3685878329170530.7371756658341070.631412167082947
1230.4260640319806920.8521280639613840.573935968019308
1240.3873901037021720.7747802074043440.612609896297828
1250.4151331684270880.8302663368541770.584866831572912
1260.4448625504681380.8897251009362750.555137449531862
1270.4320321230372650.864064246074530.567967876962735
1280.3616135946336770.7232271892673530.638386405366323
1290.5980661188481580.8038677623036840.401933881151842
1300.7384688316503330.5230623366993340.261531168349667
1310.7418200110498450.5163599779003090.258179988950155
1320.7892672181567970.4214655636864070.210732781843203
1330.7626227260879270.4747545478241460.237377273912073
1340.8089580224469450.3820839551061100.191041977553055
1350.7326375291746140.5347249416507720.267362470825386
1360.6681748812628130.6636502374743740.331825118737187
1370.7582451793360020.4835096413279950.241754820663998
1380.7042372239546890.5915255520906230.295762776045311
1390.6469509147001210.7060981705997570.353049085299879
1400.5030939508278420.9938120983443160.496906049172158
1410.402127666541330.804255333082660.59787233345867






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level150.118110236220472NOK
5% type I error level360.283464566929134NOK
10% type I error level440.346456692913386NOK

\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 & 15 & 0.118110236220472 & NOK \tabularnewline
5% type I error level & 36 & 0.283464566929134 & NOK \tabularnewline
10% type I error level & 44 & 0.346456692913386 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98058&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]15[/C][C]0.118110236220472[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]36[/C][C]0.283464566929134[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]0.346456692913386[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98058&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98058&T=6

As an alternative you can also use a QR Code:  
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 level150.118110236220472NOK
5% type I error level360.283464566929134NOK
10% type I error level440.346456692913386NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}