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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 computationThu, 22 Dec 2011 12:46:16 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/22/t13245760105ty6ncjjbtcixry.htm/, Retrieved Fri, 03 May 2024 04:56:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=159778, Retrieved Fri, 03 May 2024 04:56:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112

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] [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] [7e9b6bd31a62815918579b1facd0f368] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
15	15	77	46	10	5	4	11	12	13	6
12	9	63	37	20	6	4	12	7	11	4
15	12	73	45	16	4	10	12	13	14	6
12	15	76	46	10	6	6	11	11	12	5
14	17	90	55	8	3	5	11	16	12	5
8	14	67	40	14	10	8	10	10	6	4
11	9	69	43	19	8	9	11	15	10	5
15	12	70	43	15	3	6	9	5	11	3
4	11	54	33	23	4	8	10	4	10	2
13	13	54	33	9	3	11	12	7	12	5
19	16	76	47	12	5	6	12	15	15	6
10	16	75	44	14	5	8	12	5	13	6
15	15	76	47	13	6	11	13	16	18	8
6	10	80	49	11	5	5	9	15	11	6
7	16	89	55	11	3	10	12	13	12	3
14	12	73	43	10	4	7	12	13	13	6
16	15	74	46	12	8	7	12	15	14	6
16	13	78	51	18	8	13	12	15	16	7
14	18	76	47	12	8	10	13	10	16	8
15	13	69	42	10	5	8	11	17	16	6
14	17	74	42	15	8	6	12	14	15	7
12	14	82	48	15	2	8	12	9	13	4
9	13	77	45	12	0	7	15	6	8	4
12	13	84	51	9	5	5	11	11	14	2
14	15	75	46	11	2	9	12	13	15	6
12	13	54	33	15	7	9	10	12	13	6
14	15	79	47	16	5	11	11	10	16	6
10	13	79	47	17	2	11	13	4	13	6
14	14	69	42	12	12	11	6	13	12	6
16	13	88	55	11	7	9	12	15	15	7
10	16	57	36	13	0	7	12	8	11	4
8	14	69	42	9	2	6	10	10	14	3
12	18	86	51	11	3	6	12	8	13	5
11	15	65	43	9	0	6	12	7	13	6
8	9	66	40	20	9	5	11	9	12	4
13	16	54	33	8	2	4	9	14	14	6
11	16	85	52	12	3	10	10	5	13	3
12	17	79	49	10	1	8	12	7	12	3
16	13	84	50	11	10	6	12	16	14	6
16	17	70	43	13	1	5	11	14	15	6
13	15	54	33	13	4	9	12	16	16	6
14	14	70	44	13	6	10	11	15	15	8
5	10	54	33	15	6	6	14	4	5	2
14	13	69	41	12	4	9	10	12	15	6
13	11	68	40	13	4	10	10	8	8	4
16	11	68	40	13	7	6	11	17	16	7
14	16	71	41	9	7	6	11	15	16	6
15	16	71	41	9	7	6	11	16	14	6
15	11	66	42	14	0	13	10	12	16	6
11	15	67	42	9	3	8	10	12	14	5
15	15	71	45	9	8	10	12	13	13	6
16	12	54	33	15	8	5	11	14	14	6
13	17	76	46	10	10	8	8	14	14	5
11	15	77	47	13	11	6	12	15	12	6
12	16	71	44	8	6	9	10	14	13	7
12	14	69	44	15	2	9	7	11	15	5
10	17	73	46	13	6	7	11	13	15	6
8	10	46	30	24	1	20	7	4	13	6
9	11	66	42	11	5	8	11	8	10	4
12	15	77	46	13	4	8	8	13	13	5
14	15	77	46	12	6	7	11	15	14	6
12	7	70	43	22	6	7	12	15	13	6
11	17	86	52	11	4	10	8	8	13	4
14	14	38	11	15	1	5	14	17	18	6
7	18	66	41	7	6	8	14	12	12	4
16	14	75	45	14	7	9	11	13	14	7
16	12	80	49	19	7	9	12	14	16	8
11	14	64	41	10	2	20	14	7	13	6
16	9	80	47	9	7	6	9	16	16	6
13	14	86	53	12	8	10	13	11	15	6
11	11	54	35	16	5	11	8	10	14	5
13	16	74	45	13	4	7	11	14	13	6
14	17	88	54	11	2	12	9	19	12	6
15	16	85	53	12	0	12	12	14	16	4
10	12	63	36	11	7	8	7	8	9	5
15	15	81	48	13	0	6	11	15	15	8
11	15	81	48	13	5	6	12	8	16	6
11	15	74	45	10	3	9	11	8	12	6
6	16	80	47	11	3	5	12	6	11	2
11	16	80	49	9	3	11	9	7	13	2
12	11	60	38	13	3	6	11	16	13	4
13	15	65	40	15	7	6	13	15	14	6
12	12	62	46	14	6	10	12	10	15	6
8	14	63	42	14	3	8	12	8	14	5
9	15	89	54	11	0	7	11	9	12	4
10	17	76	45	10	2	8	12	8	16	4
16	19	81	53	11	0	9	12	14	14	6
15	15	72	44	12	9	8	11	14	13	5
14	16	84	51	14	10	10	11	14	12	6
12	14	76	46	14	3	13	8	15	13	7
12	16	76	46	21	7	7	9	7	12	6
10	15	78	45	14	3	7	11	7	9	4
12	15	72	44	13	6	7	12	12	13	4
8	17	81	48	11	5	8	13	7	10	3
16	12	72	44	12	0	9	12	12	15	8
11	18	78	47	12	0	9	6	6	9	4
12	13	79	47	11	4	8	12	10	13	4
9	14	52	31	14	0	7	11	12	13	5
14	14	67	44	13	0	6	13	13	13	5
15	14	74	42	13	7	8	11	14	15	7
8	12	73	41	12	3	8	12	8	13	4
12	14	69	43	14	9	4	10	14	14	5
10	12	67	41	12	4	8	10	10	11	5
16	15	76	47	12	4	10	11	14	15	8
17	11	77	45	12	15	7	11	15	14	5
8	11	63	37	18	7	8	11	10	15	2
9	15	84	54	11	8	7	9	6	12	5
8	14	90	55	15	2	10	7	9	15	4
11	15	75	45	13	8	9	11	11	14	5
16	16	76	47	11	7	8	12	16	16	7
13	12	75	46	11	3	8	12	14	14	6
5	14	53	37	22	3	5	15	8	12	3
15	18	87	53	10	6	8	11	16	11	5
15	14	78	46	11	8	9	10	16	13	6
12	13	54	33	15	5	11	13	14	12	5
12	14	58	36	14	6	7	13	12	12	6
16	14	80	49	11	10	8	11	16	16	7
12	17	74	44	10	0	4	12	15	13	6
10	12	56	37	14	5	16	12	11	12	6
12	16	82	53	14	0	9	12	6	14	5
4	15	64	40	11	0	16	8	6	4	4
11	10	67	42	15	5	12	5	16	14	6
16	13	75	45	11	10	8	11	16	15	6
7	15	69	40	10	0	4	12	8	12	3
9	16	72	44	10	5	11	12	11	11	4
14	15	71	43	16	6	11	11	12	12	4
11	14	54	33	12	1	8	12	13	11	4
10	11	68	44	14	5	8	10	11	12	5
6	13	54	33	15	3	12	7	9	11	4
14	17	71	43	10	3	8	12	15	13	6
11	14	53	32	12	6	6	12	11	12	6
11	16	54	33	15	2	8	9	12	12	4
9	15	71	43	12	5	6	11	15	15	7
16	12	69	42	11	6	14	12	8	14	4
7	16	30	0	10	2	10	12	7	12	4
8	8	53	32	20	3	5	11	10	12	4
10	9	68	41	19	7	8	11	9	12	4
14	13	69	44	17	6	12	12	13	13	5
9	19	54	33	8	3	11	12	11	11	4
13	11	66	42	17	6	8	11	12	13	7
13	15	79	46	11	9	8	12	5	12	3
12	11	67	44	13	2	9	12	12	14	5
11	15	74	45	9	5	6	8	14	15	5
10	16	86	53	10	10	5	15	15	15	6
12	15	63	38	13	9	8	11	14	13	5
14	12	69	43	16	8	7	11	13	16	6
11	16	73	43	12	8	4	6	14	17	6
13	15	69	42	14	5	9	13	14	13	3
14	13	71	42	11	9	5	12	15	14	6
13	14	77	47	13	9	9	12	13	13	5
16	11	74	44	15	14	12	12	14	16	8
13	15	82	49	14	5	6	12	11	13	6
12	16	54	33	14	12	4	12	14	14	4
9	14	54	33	14	6	6	10	11	13	3
14	13	80	47	10	6	7	12	8	14	4
15	15	76	47	8	8	9	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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159778&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159778&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159778&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 time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Popularity[t] = -1.91206029885816 -0.0715552487226202Happiness[t] + 0.0772109348040085Belonging[t] -0.0405383947304084Belonging_alternative[t] -0.0740467965262187Depression[t] + 0.0791790323233553Weighted_popularity[t] + 0.0717849532009769Parental_criticism[t] + 0.119387401401479Finding_Friends[t] + 0.221496711464923Knowing_People[t] + 0.33030742443407Perceived_Liked[t] + 0.56755314498157Celebrity[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Popularity[t] =  -1.91206029885816 -0.0715552487226202Happiness[t] +  0.0772109348040085Belonging[t] -0.0405383947304084Belonging_alternative[t] -0.0740467965262187Depression[t] +  0.0791790323233553Weighted_popularity[t] +  0.0717849532009769Parental_criticism[t] +  0.119387401401479Finding_Friends[t] +  0.221496711464923Knowing_People[t] +  0.33030742443407Perceived_Liked[t] +  0.56755314498157Celebrity[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159778&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Popularity[t] =  -1.91206029885816 -0.0715552487226202Happiness[t] +  0.0772109348040085Belonging[t] -0.0405383947304084Belonging_alternative[t] -0.0740467965262187Depression[t] +  0.0791790323233553Weighted_popularity[t] +  0.0717849532009769Parental_criticism[t] +  0.119387401401479Finding_Friends[t] +  0.221496711464923Knowing_People[t] +  0.33030742443407Perceived_Liked[t] +  0.56755314498157Celebrity[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159778&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159778&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] = -1.91206029885816 -0.0715552487226202Happiness[t] + 0.0772109348040085Belonging[t] -0.0405383947304084Belonging_alternative[t] -0.0740467965262187Depression[t] + 0.0791790323233553Weighted_popularity[t] + 0.0717849532009769Parental_criticism[t] + 0.119387401401479Finding_Friends[t] + 0.221496711464923Knowing_People[t] + 0.33030742443407Perceived_Liked[t] + 0.56755314498157Celebrity[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.912060298858162.563756-0.74580.4569930.228496
Happiness-0.07155524872262020.086158-0.83050.4076130.203806
Belonging0.07721093480400850.0517271.49270.1377010.068851
Belonging_alternative-0.04053839473040840.073589-0.55090.5825650.291283
Depression-0.07404679652621870.064255-1.15240.2510540.125527
Weighted_popularity0.07917903232335530.0581221.36230.1752160.087608
Parental_criticism0.07178495320097690.0659841.08790.2784370.139218
Finding_Friends0.1193874014014790.0945121.26320.2085430.104271
Knowing_People0.2214967114649230.0648213.41710.0008220.000411
Perceived_Liked0.330307424434070.0944573.49690.0006250.000313
Celebrity0.567553144981570.1576963.5990.0004380.000219

\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) & -1.91206029885816 & 2.563756 & -0.7458 & 0.456993 & 0.228496 \tabularnewline
Happiness & -0.0715552487226202 & 0.086158 & -0.8305 & 0.407613 & 0.203806 \tabularnewline
Belonging & 0.0772109348040085 & 0.051727 & 1.4927 & 0.137701 & 0.068851 \tabularnewline
Belonging_alternative & -0.0405383947304084 & 0.073589 & -0.5509 & 0.582565 & 0.291283 \tabularnewline
Depression & -0.0740467965262187 & 0.064255 & -1.1524 & 0.251054 & 0.125527 \tabularnewline
Weighted_popularity & 0.0791790323233553 & 0.058122 & 1.3623 & 0.175216 & 0.087608 \tabularnewline
Parental_criticism & 0.0717849532009769 & 0.065984 & 1.0879 & 0.278437 & 0.139218 \tabularnewline
Finding_Friends & 0.119387401401479 & 0.094512 & 1.2632 & 0.208543 & 0.104271 \tabularnewline
Knowing_People & 0.221496711464923 & 0.064821 & 3.4171 & 0.000822 & 0.000411 \tabularnewline
Perceived_Liked & 0.33030742443407 & 0.094457 & 3.4969 & 0.000625 & 0.000313 \tabularnewline
Celebrity & 0.56755314498157 & 0.157696 & 3.599 & 0.000438 & 0.000219 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159778&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]-1.91206029885816[/C][C]2.563756[/C][C]-0.7458[/C][C]0.456993[/C][C]0.228496[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.0715552487226202[/C][C]0.086158[/C][C]-0.8305[/C][C]0.407613[/C][C]0.203806[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0772109348040085[/C][C]0.051727[/C][C]1.4927[/C][C]0.137701[/C][C]0.068851[/C][/ROW]
[ROW][C]Belonging_alternative[/C][C]-0.0405383947304084[/C][C]0.073589[/C][C]-0.5509[/C][C]0.582565[/C][C]0.291283[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0740467965262187[/C][C]0.064255[/C][C]-1.1524[/C][C]0.251054[/C][C]0.125527[/C][/ROW]
[ROW][C]Weighted_popularity[/C][C]0.0791790323233553[/C][C]0.058122[/C][C]1.3623[/C][C]0.175216[/C][C]0.087608[/C][/ROW]
[ROW][C]Parental_criticism[/C][C]0.0717849532009769[/C][C]0.065984[/C][C]1.0879[/C][C]0.278437[/C][C]0.139218[/C][/ROW]
[ROW][C]Finding_Friends[/C][C]0.119387401401479[/C][C]0.094512[/C][C]1.2632[/C][C]0.208543[/C][C]0.104271[/C][/ROW]
[ROW][C]Knowing_People[/C][C]0.221496711464923[/C][C]0.064821[/C][C]3.4171[/C][C]0.000822[/C][C]0.000411[/C][/ROW]
[ROW][C]Perceived_Liked[/C][C]0.33030742443407[/C][C]0.094457[/C][C]3.4969[/C][C]0.000625[/C][C]0.000313[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.56755314498157[/C][C]0.157696[/C][C]3.599[/C][C]0.000438[/C][C]0.000219[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159778&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159778&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)-1.912060298858162.563756-0.74580.4569930.228496
Happiness-0.07155524872262020.086158-0.83050.4076130.203806
Belonging0.07721093480400850.0517271.49270.1377010.068851
Belonging_alternative-0.04053839473040840.073589-0.55090.5825650.291283
Depression-0.07404679652621870.064255-1.15240.2510540.125527
Weighted_popularity0.07917903232335530.0581221.36230.1752160.087608
Parental_criticism0.07178495320097690.0659841.08790.2784370.139218
Finding_Friends0.1193874014014790.0945121.26320.2085430.104271
Knowing_People0.2214967114649230.0648213.41710.0008220.000411
Perceived_Liked0.330307424434070.0944573.49690.0006250.000313
Celebrity0.567553144981570.1576963.5990.0004380.000219






Multiple Linear Regression - Regression Statistics
Multiple R0.741467930820018
R-squared0.549774692434519
Adjusted R-squared0.518724671223106
F-TEST (value)17.7060971614553
F-TEST (DF numerator)10
F-TEST (DF denominator)145
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.03725613186905
Sum Squared Residuals601.809819291518

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.741467930820018 \tabularnewline
R-squared & 0.549774692434519 \tabularnewline
Adjusted R-squared & 0.518724671223106 \tabularnewline
F-TEST (value) & 17.7060971614553 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 145 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.03725613186905 \tabularnewline
Sum Squared Residuals & 601.809819291518 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159778&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.741467930820018[/C][/ROW]
[ROW][C]R-squared[/C][C]0.549774692434519[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.518724671223106[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.7060971614553[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]145[/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]2.03725613186905[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]601.809819291518[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159778&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159778&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 R0.741467930820018
R-squared0.549774692434519
Adjusted R-squared0.518724671223106
F-TEST (value)17.7060971614553
F-TEST (DF numerator)10
F-TEST (DF denominator)145
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.03725613186905
Sum Squared Residuals601.809819291518






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11512.70819114229862.29180885770144
2128.97630887225863.0236911277414
31513.23299298900651.76700701099354
41211.73437186533930.265628134660696
51413.25362400278250.746375997217486
688.62807611434641-0.628076114346409
71111.6775057507434-0.677505750743415
8157.966447321283487.03355267871652
945.83841624748252-1.83841624748252
101310.13467594969452.86532405030551
111913.95885526205545.04114473794457
121011.1231538612748-1.1231538612748
131516.8613801887118-1.86138018871182
14612.9388236455309-6.93882364553089
15711.7045436701709-4.70454367017092
161413.21268827358760.787311726412405
171613.89554166163742.10445833836258
181615.35940085854490.640599141455092
191414.8177392328582-0.817739232858242
201514.78131338366410.218686616335898
211414.2970232582849-0.297023258284894
221211.08388398582670.916116014173269
2398.925132064090820.0748679359091772
241211.17351609248990.826483907510115
251413.60260910693370.39739089306631
261211.63011071736590.369889282634056
271413.42821736079810.571782639201935
281011.1786162254582-1.17861622545823
291412.52711907715211.4728809228479
301615.79105793378560.208942066214443
31108.532799869917871.46720013008213
32810.3695592626269-2.36955926262689
331211.5627442915040.437255708496018
341110.736900494230.263099505770049
35810.0495619886129-2.04956198861288
361311.83286606522241.16713393477761
37119.762827248531631.23717275146837
38129.547247998478692.45275200152131
391615.0307245476380.969275452362017
401612.78275599154343.21724400845662
411313.5132406379679-0.513240637967933
421415.0683062720306-1.06830627203055
4355.34314651009521-0.343146510095213
441413.10918572305950.890814276940468
45138.880116730244614.11988326975539
461615.28849064941380.711509350586178
471414.4074494336758-0.407449433675797
481513.96833129627261.03166870372742
491513.13276253625451.86723746374554
501111.9444307959146-0.944430795914551
511513.36864165377351.63135834622653
521612.38639343437343.6136065656266
531313.0184901826479-0.0184901826479091
541113.5177265696938-2.51772656969376
551213.7318036321033-1.73180363210333
561211.38830477526940.611695224730609
571013.210741968318-3.21074196831798
5888.86670739590379-0.86670739590379
5999.50832304041924-0.508323040419236
601211.98979289547940.0102071045206223
611413.84942900000130.150570999998748
621213.0516216420506-1.0516216420506
631110.9149772400360.0850227599640291
641413.6893814742050.31061852579499
65711.3281048114518-4.3281048114518
661614.00631584157091.99368415842914
671615.57214555819950.427854441800472
681112.1403970133465-1.14039701334651
691615.34272612824970.657273871750271
701314.3889222044741-1.38892220447407
711110.68429552429910.315704475700912
721312.80257034452510.197429655474906
731414.3341842549831-0.334184254983113
741513.41904228750411.58095771249585
75109.366419535255340.633580464744664
761514.92170372048640.0782962795136483
771113.0817104377211-2.08171043772108
781111.5013691636814-0.501369163681363
7968.52669009910313-2.52669009910313
80119.548365978029241.45163402197076
811211.52008541143730.479914588562694
821313.1901566430823-0.190156643082281
831212.3154032591704-0.315403259170415
84810.6896962797333-2.68969627973331
85910.9259242547714-1.92592425477136
861011.6672273594944-1.66722735949442
871613.22871618013852.77128381986146
881512.73441588473252.26558411526749
891413.61752415676550.382475843234498
901213.7679358594463-1.76793585944629
911210.44205733667151.55794266332847
92109.02293053566030.977069464339703
931211.45988787152530.540112128474691
9489.44365014811766-1.44365014811766
951614.34792355547561.65207644452437
96117.962880684043633.03711931595637
971211.64038678708420.359613212915758
98910.4132683087768-1.41326830877681
991411.50696655693472.49303344306525
1001514.60478607018230.395213929817689
101810.8956875435858-2.89568754358584
1021212.3905633409498-0.390563340949756
1031010.6227578833255-0.622757883325465
1041615.03259346835530.967406531644657
1051714.32124653555082.67875346444923
106810.0788369537595-2.07883695375949
107910.837590047122-1.837590047122
108811.6250504494086-3.62505044940864
1091112.5098867088048-1.50988670880483
1101615.45418731051090.545812689489123
1111313.7158582192548-0.715858219254782
11257.87499073890574-2.87499073890574
1131513.00600367819521.99399632180483
1141514.14597183673210.854028163267862
1151211.51861761683980.481382383160171
1161211.62493666123940.375063338760591
1171615.94321445327990.0567855467200786
1181212.8025070040816-0.802507004081587
1191011.7990883279653-1.79908832796526
1201210.95892563483081.04107436516919
12146.5441412556339-2.5441412556339
1221113.1600271216548-2.16002712165482
1231614.89300803748851.10699196251151
12479.13827256679518-2.13827256679518
125910.9363222325288-1.93632223252883
1261411.03851992884142.96148007115859
127119.898387086517471.10161291348253
1281011.1327984578308-1.13279845783078
12968.71037597024497-2.71037597024497
1301413.13608950417390.863910495826056
1311111.1364600931261-0.136460093126059
132119.362963740581641.63703625941836
133914.2546751592597-5.25467515925973
1341611.61890644677564.38109355322438
13578.61215086730179-1.61215086730179
13689.18810476025144-1.18810476025145
1371010.2944890549729-0.294489054972925
1381412.2231530009051.77684699909497
13999.76751753032904-0.767517530329041
1401312.72278984769190.27721015230811
141139.928365719245943.07163428075407
1421212.0847685389199-0.0847685389199198
1431112.9126063581571-1.9126063581571
1441015.3181002473857-5.31810024738574
1451212.2087010433527-0.208701043352662
1461413.64781475640840.3521852435916
1471113.7061369561275-2.70613695612749
1481311.29450361347621.70549638652377
1491413.97882885603990.0211711439601155
1501312.96603946989530.0339605301047409
1511616.4489224437214-0.448922443721431
1521312.7179040423610.282095957638981
1531211.40343152353990.596568476460074
15499.41391222683362-0.413912226833622
1551411.76553163136442.23446836863557
1561515.0128600884768-0.0128600884767955

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15 & 12.7081911422986 & 2.29180885770144 \tabularnewline
2 & 12 & 8.9763088722586 & 3.0236911277414 \tabularnewline
3 & 15 & 13.2329929890065 & 1.76700701099354 \tabularnewline
4 & 12 & 11.7343718653393 & 0.265628134660696 \tabularnewline
5 & 14 & 13.2536240027825 & 0.746375997217486 \tabularnewline
6 & 8 & 8.62807611434641 & -0.628076114346409 \tabularnewline
7 & 11 & 11.6775057507434 & -0.677505750743415 \tabularnewline
8 & 15 & 7.96644732128348 & 7.03355267871652 \tabularnewline
9 & 4 & 5.83841624748252 & -1.83841624748252 \tabularnewline
10 & 13 & 10.1346759496945 & 2.86532405030551 \tabularnewline
11 & 19 & 13.9588552620554 & 5.04114473794457 \tabularnewline
12 & 10 & 11.1231538612748 & -1.1231538612748 \tabularnewline
13 & 15 & 16.8613801887118 & -1.86138018871182 \tabularnewline
14 & 6 & 12.9388236455309 & -6.93882364553089 \tabularnewline
15 & 7 & 11.7045436701709 & -4.70454367017092 \tabularnewline
16 & 14 & 13.2126882735876 & 0.787311726412405 \tabularnewline
17 & 16 & 13.8955416616374 & 2.10445833836258 \tabularnewline
18 & 16 & 15.3594008585449 & 0.640599141455092 \tabularnewline
19 & 14 & 14.8177392328582 & -0.817739232858242 \tabularnewline
20 & 15 & 14.7813133836641 & 0.218686616335898 \tabularnewline
21 & 14 & 14.2970232582849 & -0.297023258284894 \tabularnewline
22 & 12 & 11.0838839858267 & 0.916116014173269 \tabularnewline
23 & 9 & 8.92513206409082 & 0.0748679359091772 \tabularnewline
24 & 12 & 11.1735160924899 & 0.826483907510115 \tabularnewline
25 & 14 & 13.6026091069337 & 0.39739089306631 \tabularnewline
26 & 12 & 11.6301107173659 & 0.369889282634056 \tabularnewline
27 & 14 & 13.4282173607981 & 0.571782639201935 \tabularnewline
28 & 10 & 11.1786162254582 & -1.17861622545823 \tabularnewline
29 & 14 & 12.5271190771521 & 1.4728809228479 \tabularnewline
30 & 16 & 15.7910579337856 & 0.208942066214443 \tabularnewline
31 & 10 & 8.53279986991787 & 1.46720013008213 \tabularnewline
32 & 8 & 10.3695592626269 & -2.36955926262689 \tabularnewline
33 & 12 & 11.562744291504 & 0.437255708496018 \tabularnewline
34 & 11 & 10.73690049423 & 0.263099505770049 \tabularnewline
35 & 8 & 10.0495619886129 & -2.04956198861288 \tabularnewline
36 & 13 & 11.8328660652224 & 1.16713393477761 \tabularnewline
37 & 11 & 9.76282724853163 & 1.23717275146837 \tabularnewline
38 & 12 & 9.54724799847869 & 2.45275200152131 \tabularnewline
39 & 16 & 15.030724547638 & 0.969275452362017 \tabularnewline
40 & 16 & 12.7827559915434 & 3.21724400845662 \tabularnewline
41 & 13 & 13.5132406379679 & -0.513240637967933 \tabularnewline
42 & 14 & 15.0683062720306 & -1.06830627203055 \tabularnewline
43 & 5 & 5.34314651009521 & -0.343146510095213 \tabularnewline
44 & 14 & 13.1091857230595 & 0.890814276940468 \tabularnewline
45 & 13 & 8.88011673024461 & 4.11988326975539 \tabularnewline
46 & 16 & 15.2884906494138 & 0.711509350586178 \tabularnewline
47 & 14 & 14.4074494336758 & -0.407449433675797 \tabularnewline
48 & 15 & 13.9683312962726 & 1.03166870372742 \tabularnewline
49 & 15 & 13.1327625362545 & 1.86723746374554 \tabularnewline
50 & 11 & 11.9444307959146 & -0.944430795914551 \tabularnewline
51 & 15 & 13.3686416537735 & 1.63135834622653 \tabularnewline
52 & 16 & 12.3863934343734 & 3.6136065656266 \tabularnewline
53 & 13 & 13.0184901826479 & -0.0184901826479091 \tabularnewline
54 & 11 & 13.5177265696938 & -2.51772656969376 \tabularnewline
55 & 12 & 13.7318036321033 & -1.73180363210333 \tabularnewline
56 & 12 & 11.3883047752694 & 0.611695224730609 \tabularnewline
57 & 10 & 13.210741968318 & -3.21074196831798 \tabularnewline
58 & 8 & 8.86670739590379 & -0.86670739590379 \tabularnewline
59 & 9 & 9.50832304041924 & -0.508323040419236 \tabularnewline
60 & 12 & 11.9897928954794 & 0.0102071045206223 \tabularnewline
61 & 14 & 13.8494290000013 & 0.150570999998748 \tabularnewline
62 & 12 & 13.0516216420506 & -1.0516216420506 \tabularnewline
63 & 11 & 10.914977240036 & 0.0850227599640291 \tabularnewline
64 & 14 & 13.689381474205 & 0.31061852579499 \tabularnewline
65 & 7 & 11.3281048114518 & -4.3281048114518 \tabularnewline
66 & 16 & 14.0063158415709 & 1.99368415842914 \tabularnewline
67 & 16 & 15.5721455581995 & 0.427854441800472 \tabularnewline
68 & 11 & 12.1403970133465 & -1.14039701334651 \tabularnewline
69 & 16 & 15.3427261282497 & 0.657273871750271 \tabularnewline
70 & 13 & 14.3889222044741 & -1.38892220447407 \tabularnewline
71 & 11 & 10.6842955242991 & 0.315704475700912 \tabularnewline
72 & 13 & 12.8025703445251 & 0.197429655474906 \tabularnewline
73 & 14 & 14.3341842549831 & -0.334184254983113 \tabularnewline
74 & 15 & 13.4190422875041 & 1.58095771249585 \tabularnewline
75 & 10 & 9.36641953525534 & 0.633580464744664 \tabularnewline
76 & 15 & 14.9217037204864 & 0.0782962795136483 \tabularnewline
77 & 11 & 13.0817104377211 & -2.08171043772108 \tabularnewline
78 & 11 & 11.5013691636814 & -0.501369163681363 \tabularnewline
79 & 6 & 8.52669009910313 & -2.52669009910313 \tabularnewline
80 & 11 & 9.54836597802924 & 1.45163402197076 \tabularnewline
81 & 12 & 11.5200854114373 & 0.479914588562694 \tabularnewline
82 & 13 & 13.1901566430823 & -0.190156643082281 \tabularnewline
83 & 12 & 12.3154032591704 & -0.315403259170415 \tabularnewline
84 & 8 & 10.6896962797333 & -2.68969627973331 \tabularnewline
85 & 9 & 10.9259242547714 & -1.92592425477136 \tabularnewline
86 & 10 & 11.6672273594944 & -1.66722735949442 \tabularnewline
87 & 16 & 13.2287161801385 & 2.77128381986146 \tabularnewline
88 & 15 & 12.7344158847325 & 2.26558411526749 \tabularnewline
89 & 14 & 13.6175241567655 & 0.382475843234498 \tabularnewline
90 & 12 & 13.7679358594463 & -1.76793585944629 \tabularnewline
91 & 12 & 10.4420573366715 & 1.55794266332847 \tabularnewline
92 & 10 & 9.0229305356603 & 0.977069464339703 \tabularnewline
93 & 12 & 11.4598878715253 & 0.540112128474691 \tabularnewline
94 & 8 & 9.44365014811766 & -1.44365014811766 \tabularnewline
95 & 16 & 14.3479235554756 & 1.65207644452437 \tabularnewline
96 & 11 & 7.96288068404363 & 3.03711931595637 \tabularnewline
97 & 12 & 11.6403867870842 & 0.359613212915758 \tabularnewline
98 & 9 & 10.4132683087768 & -1.41326830877681 \tabularnewline
99 & 14 & 11.5069665569347 & 2.49303344306525 \tabularnewline
100 & 15 & 14.6047860701823 & 0.395213929817689 \tabularnewline
101 & 8 & 10.8956875435858 & -2.89568754358584 \tabularnewline
102 & 12 & 12.3905633409498 & -0.390563340949756 \tabularnewline
103 & 10 & 10.6227578833255 & -0.622757883325465 \tabularnewline
104 & 16 & 15.0325934683553 & 0.967406531644657 \tabularnewline
105 & 17 & 14.3212465355508 & 2.67875346444923 \tabularnewline
106 & 8 & 10.0788369537595 & -2.07883695375949 \tabularnewline
107 & 9 & 10.837590047122 & -1.837590047122 \tabularnewline
108 & 8 & 11.6250504494086 & -3.62505044940864 \tabularnewline
109 & 11 & 12.5098867088048 & -1.50988670880483 \tabularnewline
110 & 16 & 15.4541873105109 & 0.545812689489123 \tabularnewline
111 & 13 & 13.7158582192548 & -0.715858219254782 \tabularnewline
112 & 5 & 7.87499073890574 & -2.87499073890574 \tabularnewline
113 & 15 & 13.0060036781952 & 1.99399632180483 \tabularnewline
114 & 15 & 14.1459718367321 & 0.854028163267862 \tabularnewline
115 & 12 & 11.5186176168398 & 0.481382383160171 \tabularnewline
116 & 12 & 11.6249366612394 & 0.375063338760591 \tabularnewline
117 & 16 & 15.9432144532799 & 0.0567855467200786 \tabularnewline
118 & 12 & 12.8025070040816 & -0.802507004081587 \tabularnewline
119 & 10 & 11.7990883279653 & -1.79908832796526 \tabularnewline
120 & 12 & 10.9589256348308 & 1.04107436516919 \tabularnewline
121 & 4 & 6.5441412556339 & -2.5441412556339 \tabularnewline
122 & 11 & 13.1600271216548 & -2.16002712165482 \tabularnewline
123 & 16 & 14.8930080374885 & 1.10699196251151 \tabularnewline
124 & 7 & 9.13827256679518 & -2.13827256679518 \tabularnewline
125 & 9 & 10.9363222325288 & -1.93632223252883 \tabularnewline
126 & 14 & 11.0385199288414 & 2.96148007115859 \tabularnewline
127 & 11 & 9.89838708651747 & 1.10161291348253 \tabularnewline
128 & 10 & 11.1327984578308 & -1.13279845783078 \tabularnewline
129 & 6 & 8.71037597024497 & -2.71037597024497 \tabularnewline
130 & 14 & 13.1360895041739 & 0.863910495826056 \tabularnewline
131 & 11 & 11.1364600931261 & -0.136460093126059 \tabularnewline
132 & 11 & 9.36296374058164 & 1.63703625941836 \tabularnewline
133 & 9 & 14.2546751592597 & -5.25467515925973 \tabularnewline
134 & 16 & 11.6189064467756 & 4.38109355322438 \tabularnewline
135 & 7 & 8.61215086730179 & -1.61215086730179 \tabularnewline
136 & 8 & 9.18810476025144 & -1.18810476025145 \tabularnewline
137 & 10 & 10.2944890549729 & -0.294489054972925 \tabularnewline
138 & 14 & 12.223153000905 & 1.77684699909497 \tabularnewline
139 & 9 & 9.76751753032904 & -0.767517530329041 \tabularnewline
140 & 13 & 12.7227898476919 & 0.27721015230811 \tabularnewline
141 & 13 & 9.92836571924594 & 3.07163428075407 \tabularnewline
142 & 12 & 12.0847685389199 & -0.0847685389199198 \tabularnewline
143 & 11 & 12.9126063581571 & -1.9126063581571 \tabularnewline
144 & 10 & 15.3181002473857 & -5.31810024738574 \tabularnewline
145 & 12 & 12.2087010433527 & -0.208701043352662 \tabularnewline
146 & 14 & 13.6478147564084 & 0.3521852435916 \tabularnewline
147 & 11 & 13.7061369561275 & -2.70613695612749 \tabularnewline
148 & 13 & 11.2945036134762 & 1.70549638652377 \tabularnewline
149 & 14 & 13.9788288560399 & 0.0211711439601155 \tabularnewline
150 & 13 & 12.9660394698953 & 0.0339605301047409 \tabularnewline
151 & 16 & 16.4489224437214 & -0.448922443721431 \tabularnewline
152 & 13 & 12.717904042361 & 0.282095957638981 \tabularnewline
153 & 12 & 11.4034315235399 & 0.596568476460074 \tabularnewline
154 & 9 & 9.41391222683362 & -0.413912226833622 \tabularnewline
155 & 14 & 11.7655316313644 & 2.23446836863557 \tabularnewline
156 & 15 & 15.0128600884768 & -0.0128600884767955 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159778&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]12.7081911422986[/C][C]2.29180885770144[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]8.9763088722586[/C][C]3.0236911277414[/C][/ROW]
[ROW][C]3[/C][C]15[/C][C]13.2329929890065[/C][C]1.76700701099354[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]11.7343718653393[/C][C]0.265628134660696[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]13.2536240027825[/C][C]0.746375997217486[/C][/ROW]
[ROW][C]6[/C][C]8[/C][C]8.62807611434641[/C][C]-0.628076114346409[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]11.6775057507434[/C][C]-0.677505750743415[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]7.96644732128348[/C][C]7.03355267871652[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]5.83841624748252[/C][C]-1.83841624748252[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]10.1346759496945[/C][C]2.86532405030551[/C][/ROW]
[ROW][C]11[/C][C]19[/C][C]13.9588552620554[/C][C]5.04114473794457[/C][/ROW]
[ROW][C]12[/C][C]10[/C][C]11.1231538612748[/C][C]-1.1231538612748[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]16.8613801887118[/C][C]-1.86138018871182[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]12.9388236455309[/C][C]-6.93882364553089[/C][/ROW]
[ROW][C]15[/C][C]7[/C][C]11.7045436701709[/C][C]-4.70454367017092[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.2126882735876[/C][C]0.787311726412405[/C][/ROW]
[ROW][C]17[/C][C]16[/C][C]13.8955416616374[/C][C]2.10445833836258[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]15.3594008585449[/C][C]0.640599141455092[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]14.8177392328582[/C][C]-0.817739232858242[/C][/ROW]
[ROW][C]20[/C][C]15[/C][C]14.7813133836641[/C][C]0.218686616335898[/C][/ROW]
[ROW][C]21[/C][C]14[/C][C]14.2970232582849[/C][C]-0.297023258284894[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.0838839858267[/C][C]0.916116014173269[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]8.92513206409082[/C][C]0.0748679359091772[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]11.1735160924899[/C][C]0.826483907510115[/C][/ROW]
[ROW][C]25[/C][C]14[/C][C]13.6026091069337[/C][C]0.39739089306631[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]11.6301107173659[/C][C]0.369889282634056[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]13.4282173607981[/C][C]0.571782639201935[/C][/ROW]
[ROW][C]28[/C][C]10[/C][C]11.1786162254582[/C][C]-1.17861622545823[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.5271190771521[/C][C]1.4728809228479[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]15.7910579337856[/C][C]0.208942066214443[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]8.53279986991787[/C][C]1.46720013008213[/C][/ROW]
[ROW][C]32[/C][C]8[/C][C]10.3695592626269[/C][C]-2.36955926262689[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]11.562744291504[/C][C]0.437255708496018[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.73690049423[/C][C]0.263099505770049[/C][/ROW]
[ROW][C]35[/C][C]8[/C][C]10.0495619886129[/C][C]-2.04956198861288[/C][/ROW]
[ROW][C]36[/C][C]13[/C][C]11.8328660652224[/C][C]1.16713393477761[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]9.76282724853163[/C][C]1.23717275146837[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]9.54724799847869[/C][C]2.45275200152131[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]15.030724547638[/C][C]0.969275452362017[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]12.7827559915434[/C][C]3.21724400845662[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]13.5132406379679[/C][C]-0.513240637967933[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.0683062720306[/C][C]-1.06830627203055[/C][/ROW]
[ROW][C]43[/C][C]5[/C][C]5.34314651009521[/C][C]-0.343146510095213[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]13.1091857230595[/C][C]0.890814276940468[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]8.88011673024461[/C][C]4.11988326975539[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]15.2884906494138[/C][C]0.711509350586178[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]14.4074494336758[/C][C]-0.407449433675797[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]13.9683312962726[/C][C]1.03166870372742[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]13.1327625362545[/C][C]1.86723746374554[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]11.9444307959146[/C][C]-0.944430795914551[/C][/ROW]
[ROW][C]51[/C][C]15[/C][C]13.3686416537735[/C][C]1.63135834622653[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]12.3863934343734[/C][C]3.6136065656266[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]13.0184901826479[/C][C]-0.0184901826479091[/C][/ROW]
[ROW][C]54[/C][C]11[/C][C]13.5177265696938[/C][C]-2.51772656969376[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.7318036321033[/C][C]-1.73180363210333[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]11.3883047752694[/C][C]0.611695224730609[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]13.210741968318[/C][C]-3.21074196831798[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]8.86670739590379[/C][C]-0.86670739590379[/C][/ROW]
[ROW][C]59[/C][C]9[/C][C]9.50832304041924[/C][C]-0.508323040419236[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]11.9897928954794[/C][C]0.0102071045206223[/C][/ROW]
[ROW][C]61[/C][C]14[/C][C]13.8494290000013[/C][C]0.150570999998748[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.0516216420506[/C][C]-1.0516216420506[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]10.914977240036[/C][C]0.0850227599640291[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.689381474205[/C][C]0.31061852579499[/C][/ROW]
[ROW][C]65[/C][C]7[/C][C]11.3281048114518[/C][C]-4.3281048114518[/C][/ROW]
[ROW][C]66[/C][C]16[/C][C]14.0063158415709[/C][C]1.99368415842914[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]15.5721455581995[/C][C]0.427854441800472[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]12.1403970133465[/C][C]-1.14039701334651[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]15.3427261282497[/C][C]0.657273871750271[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]14.3889222044741[/C][C]-1.38892220447407[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]10.6842955242991[/C][C]0.315704475700912[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]12.8025703445251[/C][C]0.197429655474906[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]14.3341842549831[/C][C]-0.334184254983113[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]13.4190422875041[/C][C]1.58095771249585[/C][/ROW]
[ROW][C]75[/C][C]10[/C][C]9.36641953525534[/C][C]0.633580464744664[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]14.9217037204864[/C][C]0.0782962795136483[/C][/ROW]
[ROW][C]77[/C][C]11[/C][C]13.0817104377211[/C][C]-2.08171043772108[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]11.5013691636814[/C][C]-0.501369163681363[/C][/ROW]
[ROW][C]79[/C][C]6[/C][C]8.52669009910313[/C][C]-2.52669009910313[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]9.54836597802924[/C][C]1.45163402197076[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.5200854114373[/C][C]0.479914588562694[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]13.1901566430823[/C][C]-0.190156643082281[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]12.3154032591704[/C][C]-0.315403259170415[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]10.6896962797333[/C][C]-2.68969627973331[/C][/ROW]
[ROW][C]85[/C][C]9[/C][C]10.9259242547714[/C][C]-1.92592425477136[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]11.6672273594944[/C][C]-1.66722735949442[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]13.2287161801385[/C][C]2.77128381986146[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]12.7344158847325[/C][C]2.26558411526749[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]13.6175241567655[/C][C]0.382475843234498[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.7679358594463[/C][C]-1.76793585944629[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]10.4420573366715[/C][C]1.55794266332847[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]9.0229305356603[/C][C]0.977069464339703[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.4598878715253[/C][C]0.540112128474691[/C][/ROW]
[ROW][C]94[/C][C]8[/C][C]9.44365014811766[/C][C]-1.44365014811766[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.3479235554756[/C][C]1.65207644452437[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]7.96288068404363[/C][C]3.03711931595637[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]11.6403867870842[/C][C]0.359613212915758[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]10.4132683087768[/C][C]-1.41326830877681[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]11.5069665569347[/C][C]2.49303344306525[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.6047860701823[/C][C]0.395213929817689[/C][/ROW]
[ROW][C]101[/C][C]8[/C][C]10.8956875435858[/C][C]-2.89568754358584[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.3905633409498[/C][C]-0.390563340949756[/C][/ROW]
[ROW][C]103[/C][C]10[/C][C]10.6227578833255[/C][C]-0.622757883325465[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]15.0325934683553[/C][C]0.967406531644657[/C][/ROW]
[ROW][C]105[/C][C]17[/C][C]14.3212465355508[/C][C]2.67875346444923[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]10.0788369537595[/C][C]-2.07883695375949[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.837590047122[/C][C]-1.837590047122[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]11.6250504494086[/C][C]-3.62505044940864[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]12.5098867088048[/C][C]-1.50988670880483[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.4541873105109[/C][C]0.545812689489123[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]13.7158582192548[/C][C]-0.715858219254782[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]7.87499073890574[/C][C]-2.87499073890574[/C][/ROW]
[ROW][C]113[/C][C]15[/C][C]13.0060036781952[/C][C]1.99399632180483[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]14.1459718367321[/C][C]0.854028163267862[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]11.5186176168398[/C][C]0.481382383160171[/C][/ROW]
[ROW][C]116[/C][C]12[/C][C]11.6249366612394[/C][C]0.375063338760591[/C][/ROW]
[ROW][C]117[/C][C]16[/C][C]15.9432144532799[/C][C]0.0567855467200786[/C][/ROW]
[ROW][C]118[/C][C]12[/C][C]12.8025070040816[/C][C]-0.802507004081587[/C][/ROW]
[ROW][C]119[/C][C]10[/C][C]11.7990883279653[/C][C]-1.79908832796526[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]10.9589256348308[/C][C]1.04107436516919[/C][/ROW]
[ROW][C]121[/C][C]4[/C][C]6.5441412556339[/C][C]-2.5441412556339[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]13.1600271216548[/C][C]-2.16002712165482[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]14.8930080374885[/C][C]1.10699196251151[/C][/ROW]
[ROW][C]124[/C][C]7[/C][C]9.13827256679518[/C][C]-2.13827256679518[/C][/ROW]
[ROW][C]125[/C][C]9[/C][C]10.9363222325288[/C][C]-1.93632223252883[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]11.0385199288414[/C][C]2.96148007115859[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]9.89838708651747[/C][C]1.10161291348253[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]11.1327984578308[/C][C]-1.13279845783078[/C][/ROW]
[ROW][C]129[/C][C]6[/C][C]8.71037597024497[/C][C]-2.71037597024497[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]13.1360895041739[/C][C]0.863910495826056[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]11.1364600931261[/C][C]-0.136460093126059[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]9.36296374058164[/C][C]1.63703625941836[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]14.2546751592597[/C][C]-5.25467515925973[/C][/ROW]
[ROW][C]134[/C][C]16[/C][C]11.6189064467756[/C][C]4.38109355322438[/C][/ROW]
[ROW][C]135[/C][C]7[/C][C]8.61215086730179[/C][C]-1.61215086730179[/C][/ROW]
[ROW][C]136[/C][C]8[/C][C]9.18810476025144[/C][C]-1.18810476025145[/C][/ROW]
[ROW][C]137[/C][C]10[/C][C]10.2944890549729[/C][C]-0.294489054972925[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]12.223153000905[/C][C]1.77684699909497[/C][/ROW]
[ROW][C]139[/C][C]9[/C][C]9.76751753032904[/C][C]-0.767517530329041[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]12.7227898476919[/C][C]0.27721015230811[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]9.92836571924594[/C][C]3.07163428075407[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]12.0847685389199[/C][C]-0.0847685389199198[/C][/ROW]
[ROW][C]143[/C][C]11[/C][C]12.9126063581571[/C][C]-1.9126063581571[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]15.3181002473857[/C][C]-5.31810024738574[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]12.2087010433527[/C][C]-0.208701043352662[/C][/ROW]
[ROW][C]146[/C][C]14[/C][C]13.6478147564084[/C][C]0.3521852435916[/C][/ROW]
[ROW][C]147[/C][C]11[/C][C]13.7061369561275[/C][C]-2.70613695612749[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]11.2945036134762[/C][C]1.70549638652377[/C][/ROW]
[ROW][C]149[/C][C]14[/C][C]13.9788288560399[/C][C]0.0211711439601155[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.9660394698953[/C][C]0.0339605301047409[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]16.4489224437214[/C][C]-0.448922443721431[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]12.717904042361[/C][C]0.282095957638981[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]11.4034315235399[/C][C]0.596568476460074[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]9.41391222683362[/C][C]-0.413912226833622[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]11.7655316313644[/C][C]2.23446836863557[/C][/ROW]
[ROW][C]156[/C][C]15[/C][C]15.0128600884768[/C][C]-0.0128600884767955[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159778&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159778&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
11512.70819114229862.29180885770144
2128.97630887225863.0236911277414
31513.23299298900651.76700701099354
41211.73437186533930.265628134660696
51413.25362400278250.746375997217486
688.62807611434641-0.628076114346409
71111.6775057507434-0.677505750743415
8157.966447321283487.03355267871652
945.83841624748252-1.83841624748252
101310.13467594969452.86532405030551
111913.95885526205545.04114473794457
121011.1231538612748-1.1231538612748
131516.8613801887118-1.86138018871182
14612.9388236455309-6.93882364553089
15711.7045436701709-4.70454367017092
161413.21268827358760.787311726412405
171613.89554166163742.10445833836258
181615.35940085854490.640599141455092
191414.8177392328582-0.817739232858242
201514.78131338366410.218686616335898
211414.2970232582849-0.297023258284894
221211.08388398582670.916116014173269
2398.925132064090820.0748679359091772
241211.17351609248990.826483907510115
251413.60260910693370.39739089306631
261211.63011071736590.369889282634056
271413.42821736079810.571782639201935
281011.1786162254582-1.17861622545823
291412.52711907715211.4728809228479
301615.79105793378560.208942066214443
31108.532799869917871.46720013008213
32810.3695592626269-2.36955926262689
331211.5627442915040.437255708496018
341110.736900494230.263099505770049
35810.0495619886129-2.04956198861288
361311.83286606522241.16713393477761
37119.762827248531631.23717275146837
38129.547247998478692.45275200152131
391615.0307245476380.969275452362017
401612.78275599154343.21724400845662
411313.5132406379679-0.513240637967933
421415.0683062720306-1.06830627203055
4355.34314651009521-0.343146510095213
441413.10918572305950.890814276940468
45138.880116730244614.11988326975539
461615.28849064941380.711509350586178
471414.4074494336758-0.407449433675797
481513.96833129627261.03166870372742
491513.13276253625451.86723746374554
501111.9444307959146-0.944430795914551
511513.36864165377351.63135834622653
521612.38639343437343.6136065656266
531313.0184901826479-0.0184901826479091
541113.5177265696938-2.51772656969376
551213.7318036321033-1.73180363210333
561211.38830477526940.611695224730609
571013.210741968318-3.21074196831798
5888.86670739590379-0.86670739590379
5999.50832304041924-0.508323040419236
601211.98979289547940.0102071045206223
611413.84942900000130.150570999998748
621213.0516216420506-1.0516216420506
631110.9149772400360.0850227599640291
641413.6893814742050.31061852579499
65711.3281048114518-4.3281048114518
661614.00631584157091.99368415842914
671615.57214555819950.427854441800472
681112.1403970133465-1.14039701334651
691615.34272612824970.657273871750271
701314.3889222044741-1.38892220447407
711110.68429552429910.315704475700912
721312.80257034452510.197429655474906
731414.3341842549831-0.334184254983113
741513.41904228750411.58095771249585
75109.366419535255340.633580464744664
761514.92170372048640.0782962795136483
771113.0817104377211-2.08171043772108
781111.5013691636814-0.501369163681363
7968.52669009910313-2.52669009910313
80119.548365978029241.45163402197076
811211.52008541143730.479914588562694
821313.1901566430823-0.190156643082281
831212.3154032591704-0.315403259170415
84810.6896962797333-2.68969627973331
85910.9259242547714-1.92592425477136
861011.6672273594944-1.66722735949442
871613.22871618013852.77128381986146
881512.73441588473252.26558411526749
891413.61752415676550.382475843234498
901213.7679358594463-1.76793585944629
911210.44205733667151.55794266332847
92109.02293053566030.977069464339703
931211.45988787152530.540112128474691
9489.44365014811766-1.44365014811766
951614.34792355547561.65207644452437
96117.962880684043633.03711931595637
971211.64038678708420.359613212915758
98910.4132683087768-1.41326830877681
991411.50696655693472.49303344306525
1001514.60478607018230.395213929817689
101810.8956875435858-2.89568754358584
1021212.3905633409498-0.390563340949756
1031010.6227578833255-0.622757883325465
1041615.03259346835530.967406531644657
1051714.32124653555082.67875346444923
106810.0788369537595-2.07883695375949
107910.837590047122-1.837590047122
108811.6250504494086-3.62505044940864
1091112.5098867088048-1.50988670880483
1101615.45418731051090.545812689489123
1111313.7158582192548-0.715858219254782
11257.87499073890574-2.87499073890574
1131513.00600367819521.99399632180483
1141514.14597183673210.854028163267862
1151211.51861761683980.481382383160171
1161211.62493666123940.375063338760591
1171615.94321445327990.0567855467200786
1181212.8025070040816-0.802507004081587
1191011.7990883279653-1.79908832796526
1201210.95892563483081.04107436516919
12146.5441412556339-2.5441412556339
1221113.1600271216548-2.16002712165482
1231614.89300803748851.10699196251151
12479.13827256679518-2.13827256679518
125910.9363222325288-1.93632223252883
1261411.03851992884142.96148007115859
127119.898387086517471.10161291348253
1281011.1327984578308-1.13279845783078
12968.71037597024497-2.71037597024497
1301413.13608950417390.863910495826056
1311111.1364600931261-0.136460093126059
132119.362963740581641.63703625941836
133914.2546751592597-5.25467515925973
1341611.61890644677564.38109355322438
13578.61215086730179-1.61215086730179
13689.18810476025144-1.18810476025145
1371010.2944890549729-0.294489054972925
1381412.2231530009051.77684699909497
13999.76751753032904-0.767517530329041
1401312.72278984769190.27721015230811
141139.928365719245943.07163428075407
1421212.0847685389199-0.0847685389199198
1431112.9126063581571-1.9126063581571
1441015.3181002473857-5.31810024738574
1451212.2087010433527-0.208701043352662
1461413.64781475640840.3521852435916
1471113.7061369561275-2.70613695612749
1481311.29450361347621.70549638652377
1491413.97882885603990.0211711439601155
1501312.96603946989530.0339605301047409
1511616.4489224437214-0.448922443721431
1521312.7179040423610.282095957638981
1531211.40343152353990.596568476460074
15499.41391222683362-0.413912226833622
1551411.76553163136442.23446836863557
1561515.0128600884768-0.0128600884767955






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
140.9972297838341190.005540432331762140.00277021616588107
150.997837772997930.004324454004139370.00216222700206968
160.9999369537308640.0001260925382716546.30462691358268e-05
170.9998705727287940.0002588545424123540.000129427271206177
180.9996884111470810.0006231777058387290.000311588852919365
190.9997931731265490.0004136537469017030.000206826873450851
200.9995672442353850.0008655115292291940.000432755764614597
210.9993502115199590.001299576960081420.00064978848004071
220.999416305036790.001167389926419380.00058369496320969
230.9989795411576480.00204091768470330.00102045884235165
240.9982762726631780.003447454673644850.00172372733682242
250.9970330183390330.005933963321933780.00296698166096689
260.9953160817495220.009367836500956610.00468391825047831
270.9954897854517910.009020429096418530.00451021454820926
280.9928079548297920.01438409034041610.00719204517020807
290.9954514384646160.009097123070768180.00454856153538409
300.9932783115898220.01344337682035490.00672168841017745
310.9913364206834440.01732715863311140.00866357931655569
320.9964141050684420.007171789863116450.00358589493155823
330.9944317725853180.01113645482936430.00556822741468215
340.9936301792273840.01273964154523280.00636982077261641
350.9955861494756210.008827701048757680.00441385052437884
360.9940764589381310.0118470821237370.00592354106186849
370.9919637530099280.01607249398014470.00803624699007233
380.9915023805232690.01699523895346130.00849761947673066
390.9883895952421460.02322080951570740.0116104047578537
400.9907519955303160.01849600893936810.00924800446968405
410.9882204905729970.02355901885400560.0117795094270028
420.9842770191357410.03144596172851840.0157229808642592
430.9785703298400550.04285934031989090.0214296701599454
440.9720437653614060.05591246927718780.0279562346385939
450.9905938428775440.01881231424491280.00940615712245642
460.9869526157523620.02609476849527640.0130473842476382
470.9830457385198410.03390852296031760.0169542614801588
480.9777356994041140.04452860119177160.0222643005958858
490.9747294464998480.05054110700030410.0252705535001521
500.9709069475835080.05818610483298420.0290930524164921
510.9673331315109510.06533373697809730.0326668684890487
520.9795301637115420.04093967257691650.0204698362884582
530.9727543443849930.05449131123001440.0272456556150072
540.9754515924403230.04909681511935420.0245484075596771
550.9732135266865350.05357294662693030.0267864733134652
560.9663947288464920.06721054230701660.0336052711535083
570.9780591205021440.04388175899571120.0219408794978556
580.973294948113450.05341010377310070.0267050518865503
590.9657806897148270.06843862057034660.0342193102851733
600.9551084815629680.0897830368740630.0448915184370315
610.9419446854282790.1161106291434420.058055314571721
620.9311078233029670.1377843533940660.0688921766970332
630.9133420869033930.1733158261932140.0866579130966071
640.9019430535769880.1961138928460240.0980569464230119
650.9536712539585420.09265749208291560.0463287460414578
660.9531304422541090.09373911549178190.0468695577458909
670.9403650127594040.1192699744811930.0596349872405964
680.9286664294934150.142667141013170.0713335705065849
690.9148318158312830.1703363683374350.0851681841687173
700.9046390975376250.1907218049247490.0953609024623747
710.8866568683704820.2266862632590360.113343131629518
720.8616414615659970.2767170768680070.138358538434003
730.842959951937160.314080096125680.15704004806284
740.8324871911820870.3350256176358260.167512808817913
750.8103066093232450.379386781353510.189693390676755
760.7809260010982870.4381479978034270.219073998901713
770.7862117417511920.4275765164976170.213788258248808
780.7519567020888430.4960865958223130.248043297911157
790.7731241350541310.4537517298917380.226875864945869
800.755696147411750.48860770517650.24430385258825
810.7224413605948980.5551172788102040.277558639405102
820.6804035981685210.6391928036629580.319596401831479
830.6369143215582150.726171356883570.363085678441785
840.6601631817378660.6796736365242680.339836818262134
850.65564719406740.6887056118652010.3443528059326
860.638718704691760.7225625906164790.36128129530824
870.6695503956522670.6608992086954670.330449604347733
880.6803061624494850.639387675101030.319693837550515
890.6393119882856290.7213760234287410.360688011714371
900.6308853447563980.7382293104872050.369114655243602
910.6130655199597570.7738689600804870.386934480040243
920.5764687850996310.8470624298007380.423531214900369
930.5299025063630660.9401949872738690.470097493636934
940.5151221080774450.969755783845110.484877891922555
950.5231374544733660.9537250910532680.476862545526634
960.6552038754440340.6895922491119330.344796124555966
970.6082631475164280.7834737049671440.391736852483572
980.5802985585228440.8394028829543130.419701441477156
990.6294406712587230.7411186574825550.370559328741277
1000.5883709324130920.8232581351738170.411629067586908
1010.6290566358572060.7418867282855890.370943364142794
1020.5835114983629320.8329770032741360.416488501637068
1030.5356482203634350.9287035592731290.464351779636565
1040.5261983795889920.9476032408220160.473801620411008
1050.5367704568812870.9264590862374270.463229543118713
1060.5781503467093160.8436993065813670.421849653290684
1070.5444342936312020.9111314127375960.455565706368798
1080.6603864431199870.6792271137600260.339613556880013
1090.6510928068055380.6978143863889230.348907193194462
1100.6082701452417780.7834597095164440.391729854758222
1110.5561376489758340.8877247020483330.443862351024167
1120.6646271470818530.6707457058362950.335372852918147
1130.6756874703466550.6486250593066910.324312529653345
1140.6718945043825960.6562109912348080.328105495617404
1150.6175423520821570.7649152958356850.382457647917843
1160.5810611295460920.8378777409078170.418938870453908
1170.5332128069942210.9335743860115580.466787193005779
1180.5171355874967770.9657288250064460.482864412503223
1190.5627531679043580.8744936641912830.437246832095642
1200.5067467426498160.9865065147003680.493253257350184
1210.4801325773184460.9602651546368930.519867422681554
1220.4400932318084290.8801864636168570.559906768191571
1230.4465684435146320.8931368870292650.553431556485368
1240.4401982108627960.8803964217255930.559801789137204
1250.4927864400029790.9855728800059580.507213559997021
1260.4915640774549770.9831281549099540.508435922545023
1270.4648987360166770.9297974720333540.535101263983323
1280.4032382237686130.8064764475372250.596761776231387
1290.6494644445509730.7010711108980540.350535555449027
1300.7218037935653430.5563924128693130.278196206434657
1310.6738713737106440.6522572525787110.326128626289356
1320.6672988521762530.6654022956474930.332701147823747
1330.733552291916240.5328954161675190.26644770808376
1340.7059040883217030.5881918233565930.294095911678297
1350.6433136688081110.7133726623837790.356686331191889
1360.6528746632794550.694250673441090.347125336720545
1370.8190586125945290.3618827748109430.180941387405471
1380.7670063021087440.4659873957825130.232993697891256
1390.7681699138528630.4636601722942740.231830086147137
1400.6508928848323070.6982142303353860.349107115167693
1410.5194024403040570.9611951193918870.480597559695943
1420.3607120764992590.7214241529985170.639287923500741

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 0.997229783834119 & 0.00554043233176214 & 0.00277021616588107 \tabularnewline
15 & 0.99783777299793 & 0.00432445400413937 & 0.00216222700206968 \tabularnewline
16 & 0.999936953730864 & 0.000126092538271654 & 6.30462691358268e-05 \tabularnewline
17 & 0.999870572728794 & 0.000258854542412354 & 0.000129427271206177 \tabularnewline
18 & 0.999688411147081 & 0.000623177705838729 & 0.000311588852919365 \tabularnewline
19 & 0.999793173126549 & 0.000413653746901703 & 0.000206826873450851 \tabularnewline
20 & 0.999567244235385 & 0.000865511529229194 & 0.000432755764614597 \tabularnewline
21 & 0.999350211519959 & 0.00129957696008142 & 0.00064978848004071 \tabularnewline
22 & 0.99941630503679 & 0.00116738992641938 & 0.00058369496320969 \tabularnewline
23 & 0.998979541157648 & 0.0020409176847033 & 0.00102045884235165 \tabularnewline
24 & 0.998276272663178 & 0.00344745467364485 & 0.00172372733682242 \tabularnewline
25 & 0.997033018339033 & 0.00593396332193378 & 0.00296698166096689 \tabularnewline
26 & 0.995316081749522 & 0.00936783650095661 & 0.00468391825047831 \tabularnewline
27 & 0.995489785451791 & 0.00902042909641853 & 0.00451021454820926 \tabularnewline
28 & 0.992807954829792 & 0.0143840903404161 & 0.00719204517020807 \tabularnewline
29 & 0.995451438464616 & 0.00909712307076818 & 0.00454856153538409 \tabularnewline
30 & 0.993278311589822 & 0.0134433768203549 & 0.00672168841017745 \tabularnewline
31 & 0.991336420683444 & 0.0173271586331114 & 0.00866357931655569 \tabularnewline
32 & 0.996414105068442 & 0.00717178986311645 & 0.00358589493155823 \tabularnewline
33 & 0.994431772585318 & 0.0111364548293643 & 0.00556822741468215 \tabularnewline
34 & 0.993630179227384 & 0.0127396415452328 & 0.00636982077261641 \tabularnewline
35 & 0.995586149475621 & 0.00882770104875768 & 0.00441385052437884 \tabularnewline
36 & 0.994076458938131 & 0.011847082123737 & 0.00592354106186849 \tabularnewline
37 & 0.991963753009928 & 0.0160724939801447 & 0.00803624699007233 \tabularnewline
38 & 0.991502380523269 & 0.0169952389534613 & 0.00849761947673066 \tabularnewline
39 & 0.988389595242146 & 0.0232208095157074 & 0.0116104047578537 \tabularnewline
40 & 0.990751995530316 & 0.0184960089393681 & 0.00924800446968405 \tabularnewline
41 & 0.988220490572997 & 0.0235590188540056 & 0.0117795094270028 \tabularnewline
42 & 0.984277019135741 & 0.0314459617285184 & 0.0157229808642592 \tabularnewline
43 & 0.978570329840055 & 0.0428593403198909 & 0.0214296701599454 \tabularnewline
44 & 0.972043765361406 & 0.0559124692771878 & 0.0279562346385939 \tabularnewline
45 & 0.990593842877544 & 0.0188123142449128 & 0.00940615712245642 \tabularnewline
46 & 0.986952615752362 & 0.0260947684952764 & 0.0130473842476382 \tabularnewline
47 & 0.983045738519841 & 0.0339085229603176 & 0.0169542614801588 \tabularnewline
48 & 0.977735699404114 & 0.0445286011917716 & 0.0222643005958858 \tabularnewline
49 & 0.974729446499848 & 0.0505411070003041 & 0.0252705535001521 \tabularnewline
50 & 0.970906947583508 & 0.0581861048329842 & 0.0290930524164921 \tabularnewline
51 & 0.967333131510951 & 0.0653337369780973 & 0.0326668684890487 \tabularnewline
52 & 0.979530163711542 & 0.0409396725769165 & 0.0204698362884582 \tabularnewline
53 & 0.972754344384993 & 0.0544913112300144 & 0.0272456556150072 \tabularnewline
54 & 0.975451592440323 & 0.0490968151193542 & 0.0245484075596771 \tabularnewline
55 & 0.973213526686535 & 0.0535729466269303 & 0.0267864733134652 \tabularnewline
56 & 0.966394728846492 & 0.0672105423070166 & 0.0336052711535083 \tabularnewline
57 & 0.978059120502144 & 0.0438817589957112 & 0.0219408794978556 \tabularnewline
58 & 0.97329494811345 & 0.0534101037731007 & 0.0267050518865503 \tabularnewline
59 & 0.965780689714827 & 0.0684386205703466 & 0.0342193102851733 \tabularnewline
60 & 0.955108481562968 & 0.089783036874063 & 0.0448915184370315 \tabularnewline
61 & 0.941944685428279 & 0.116110629143442 & 0.058055314571721 \tabularnewline
62 & 0.931107823302967 & 0.137784353394066 & 0.0688921766970332 \tabularnewline
63 & 0.913342086903393 & 0.173315826193214 & 0.0866579130966071 \tabularnewline
64 & 0.901943053576988 & 0.196113892846024 & 0.0980569464230119 \tabularnewline
65 & 0.953671253958542 & 0.0926574920829156 & 0.0463287460414578 \tabularnewline
66 & 0.953130442254109 & 0.0937391154917819 & 0.0468695577458909 \tabularnewline
67 & 0.940365012759404 & 0.119269974481193 & 0.0596349872405964 \tabularnewline
68 & 0.928666429493415 & 0.14266714101317 & 0.0713335705065849 \tabularnewline
69 & 0.914831815831283 & 0.170336368337435 & 0.0851681841687173 \tabularnewline
70 & 0.904639097537625 & 0.190721804924749 & 0.0953609024623747 \tabularnewline
71 & 0.886656868370482 & 0.226686263259036 & 0.113343131629518 \tabularnewline
72 & 0.861641461565997 & 0.276717076868007 & 0.138358538434003 \tabularnewline
73 & 0.84295995193716 & 0.31408009612568 & 0.15704004806284 \tabularnewline
74 & 0.832487191182087 & 0.335025617635826 & 0.167512808817913 \tabularnewline
75 & 0.810306609323245 & 0.37938678135351 & 0.189693390676755 \tabularnewline
76 & 0.780926001098287 & 0.438147997803427 & 0.219073998901713 \tabularnewline
77 & 0.786211741751192 & 0.427576516497617 & 0.213788258248808 \tabularnewline
78 & 0.751956702088843 & 0.496086595822313 & 0.248043297911157 \tabularnewline
79 & 0.773124135054131 & 0.453751729891738 & 0.226875864945869 \tabularnewline
80 & 0.75569614741175 & 0.4886077051765 & 0.24430385258825 \tabularnewline
81 & 0.722441360594898 & 0.555117278810204 & 0.277558639405102 \tabularnewline
82 & 0.680403598168521 & 0.639192803662958 & 0.319596401831479 \tabularnewline
83 & 0.636914321558215 & 0.72617135688357 & 0.363085678441785 \tabularnewline
84 & 0.660163181737866 & 0.679673636524268 & 0.339836818262134 \tabularnewline
85 & 0.6556471940674 & 0.688705611865201 & 0.3443528059326 \tabularnewline
86 & 0.63871870469176 & 0.722562590616479 & 0.36128129530824 \tabularnewline
87 & 0.669550395652267 & 0.660899208695467 & 0.330449604347733 \tabularnewline
88 & 0.680306162449485 & 0.63938767510103 & 0.319693837550515 \tabularnewline
89 & 0.639311988285629 & 0.721376023428741 & 0.360688011714371 \tabularnewline
90 & 0.630885344756398 & 0.738229310487205 & 0.369114655243602 \tabularnewline
91 & 0.613065519959757 & 0.773868960080487 & 0.386934480040243 \tabularnewline
92 & 0.576468785099631 & 0.847062429800738 & 0.423531214900369 \tabularnewline
93 & 0.529902506363066 & 0.940194987273869 & 0.470097493636934 \tabularnewline
94 & 0.515122108077445 & 0.96975578384511 & 0.484877891922555 \tabularnewline
95 & 0.523137454473366 & 0.953725091053268 & 0.476862545526634 \tabularnewline
96 & 0.655203875444034 & 0.689592249111933 & 0.344796124555966 \tabularnewline
97 & 0.608263147516428 & 0.783473704967144 & 0.391736852483572 \tabularnewline
98 & 0.580298558522844 & 0.839402882954313 & 0.419701441477156 \tabularnewline
99 & 0.629440671258723 & 0.741118657482555 & 0.370559328741277 \tabularnewline
100 & 0.588370932413092 & 0.823258135173817 & 0.411629067586908 \tabularnewline
101 & 0.629056635857206 & 0.741886728285589 & 0.370943364142794 \tabularnewline
102 & 0.583511498362932 & 0.832977003274136 & 0.416488501637068 \tabularnewline
103 & 0.535648220363435 & 0.928703559273129 & 0.464351779636565 \tabularnewline
104 & 0.526198379588992 & 0.947603240822016 & 0.473801620411008 \tabularnewline
105 & 0.536770456881287 & 0.926459086237427 & 0.463229543118713 \tabularnewline
106 & 0.578150346709316 & 0.843699306581367 & 0.421849653290684 \tabularnewline
107 & 0.544434293631202 & 0.911131412737596 & 0.455565706368798 \tabularnewline
108 & 0.660386443119987 & 0.679227113760026 & 0.339613556880013 \tabularnewline
109 & 0.651092806805538 & 0.697814386388923 & 0.348907193194462 \tabularnewline
110 & 0.608270145241778 & 0.783459709516444 & 0.391729854758222 \tabularnewline
111 & 0.556137648975834 & 0.887724702048333 & 0.443862351024167 \tabularnewline
112 & 0.664627147081853 & 0.670745705836295 & 0.335372852918147 \tabularnewline
113 & 0.675687470346655 & 0.648625059306691 & 0.324312529653345 \tabularnewline
114 & 0.671894504382596 & 0.656210991234808 & 0.328105495617404 \tabularnewline
115 & 0.617542352082157 & 0.764915295835685 & 0.382457647917843 \tabularnewline
116 & 0.581061129546092 & 0.837877740907817 & 0.418938870453908 \tabularnewline
117 & 0.533212806994221 & 0.933574386011558 & 0.466787193005779 \tabularnewline
118 & 0.517135587496777 & 0.965728825006446 & 0.482864412503223 \tabularnewline
119 & 0.562753167904358 & 0.874493664191283 & 0.437246832095642 \tabularnewline
120 & 0.506746742649816 & 0.986506514700368 & 0.493253257350184 \tabularnewline
121 & 0.480132577318446 & 0.960265154636893 & 0.519867422681554 \tabularnewline
122 & 0.440093231808429 & 0.880186463616857 & 0.559906768191571 \tabularnewline
123 & 0.446568443514632 & 0.893136887029265 & 0.553431556485368 \tabularnewline
124 & 0.440198210862796 & 0.880396421725593 & 0.559801789137204 \tabularnewline
125 & 0.492786440002979 & 0.985572880005958 & 0.507213559997021 \tabularnewline
126 & 0.491564077454977 & 0.983128154909954 & 0.508435922545023 \tabularnewline
127 & 0.464898736016677 & 0.929797472033354 & 0.535101263983323 \tabularnewline
128 & 0.403238223768613 & 0.806476447537225 & 0.596761776231387 \tabularnewline
129 & 0.649464444550973 & 0.701071110898054 & 0.350535555449027 \tabularnewline
130 & 0.721803793565343 & 0.556392412869313 & 0.278196206434657 \tabularnewline
131 & 0.673871373710644 & 0.652257252578711 & 0.326128626289356 \tabularnewline
132 & 0.667298852176253 & 0.665402295647493 & 0.332701147823747 \tabularnewline
133 & 0.73355229191624 & 0.532895416167519 & 0.26644770808376 \tabularnewline
134 & 0.705904088321703 & 0.588191823356593 & 0.294095911678297 \tabularnewline
135 & 0.643313668808111 & 0.713372662383779 & 0.356686331191889 \tabularnewline
136 & 0.652874663279455 & 0.69425067344109 & 0.347125336720545 \tabularnewline
137 & 0.819058612594529 & 0.361882774810943 & 0.180941387405471 \tabularnewline
138 & 0.767006302108744 & 0.465987395782513 & 0.232993697891256 \tabularnewline
139 & 0.768169913852863 & 0.463660172294274 & 0.231830086147137 \tabularnewline
140 & 0.650892884832307 & 0.698214230335386 & 0.349107115167693 \tabularnewline
141 & 0.519402440304057 & 0.961195119391887 & 0.480597559695943 \tabularnewline
142 & 0.360712076499259 & 0.721424152998517 & 0.639287923500741 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159778&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]14[/C][C]0.997229783834119[/C][C]0.00554043233176214[/C][C]0.00277021616588107[/C][/ROW]
[ROW][C]15[/C][C]0.99783777299793[/C][C]0.00432445400413937[/C][C]0.00216222700206968[/C][/ROW]
[ROW][C]16[/C][C]0.999936953730864[/C][C]0.000126092538271654[/C][C]6.30462691358268e-05[/C][/ROW]
[ROW][C]17[/C][C]0.999870572728794[/C][C]0.000258854542412354[/C][C]0.000129427271206177[/C][/ROW]
[ROW][C]18[/C][C]0.999688411147081[/C][C]0.000623177705838729[/C][C]0.000311588852919365[/C][/ROW]
[ROW][C]19[/C][C]0.999793173126549[/C][C]0.000413653746901703[/C][C]0.000206826873450851[/C][/ROW]
[ROW][C]20[/C][C]0.999567244235385[/C][C]0.000865511529229194[/C][C]0.000432755764614597[/C][/ROW]
[ROW][C]21[/C][C]0.999350211519959[/C][C]0.00129957696008142[/C][C]0.00064978848004071[/C][/ROW]
[ROW][C]22[/C][C]0.99941630503679[/C][C]0.00116738992641938[/C][C]0.00058369496320969[/C][/ROW]
[ROW][C]23[/C][C]0.998979541157648[/C][C]0.0020409176847033[/C][C]0.00102045884235165[/C][/ROW]
[ROW][C]24[/C][C]0.998276272663178[/C][C]0.00344745467364485[/C][C]0.00172372733682242[/C][/ROW]
[ROW][C]25[/C][C]0.997033018339033[/C][C]0.00593396332193378[/C][C]0.00296698166096689[/C][/ROW]
[ROW][C]26[/C][C]0.995316081749522[/C][C]0.00936783650095661[/C][C]0.00468391825047831[/C][/ROW]
[ROW][C]27[/C][C]0.995489785451791[/C][C]0.00902042909641853[/C][C]0.00451021454820926[/C][/ROW]
[ROW][C]28[/C][C]0.992807954829792[/C][C]0.0143840903404161[/C][C]0.00719204517020807[/C][/ROW]
[ROW][C]29[/C][C]0.995451438464616[/C][C]0.00909712307076818[/C][C]0.00454856153538409[/C][/ROW]
[ROW][C]30[/C][C]0.993278311589822[/C][C]0.0134433768203549[/C][C]0.00672168841017745[/C][/ROW]
[ROW][C]31[/C][C]0.991336420683444[/C][C]0.0173271586331114[/C][C]0.00866357931655569[/C][/ROW]
[ROW][C]32[/C][C]0.996414105068442[/C][C]0.00717178986311645[/C][C]0.00358589493155823[/C][/ROW]
[ROW][C]33[/C][C]0.994431772585318[/C][C]0.0111364548293643[/C][C]0.00556822741468215[/C][/ROW]
[ROW][C]34[/C][C]0.993630179227384[/C][C]0.0127396415452328[/C][C]0.00636982077261641[/C][/ROW]
[ROW][C]35[/C][C]0.995586149475621[/C][C]0.00882770104875768[/C][C]0.00441385052437884[/C][/ROW]
[ROW][C]36[/C][C]0.994076458938131[/C][C]0.011847082123737[/C][C]0.00592354106186849[/C][/ROW]
[ROW][C]37[/C][C]0.991963753009928[/C][C]0.0160724939801447[/C][C]0.00803624699007233[/C][/ROW]
[ROW][C]38[/C][C]0.991502380523269[/C][C]0.0169952389534613[/C][C]0.00849761947673066[/C][/ROW]
[ROW][C]39[/C][C]0.988389595242146[/C][C]0.0232208095157074[/C][C]0.0116104047578537[/C][/ROW]
[ROW][C]40[/C][C]0.990751995530316[/C][C]0.0184960089393681[/C][C]0.00924800446968405[/C][/ROW]
[ROW][C]41[/C][C]0.988220490572997[/C][C]0.0235590188540056[/C][C]0.0117795094270028[/C][/ROW]
[ROW][C]42[/C][C]0.984277019135741[/C][C]0.0314459617285184[/C][C]0.0157229808642592[/C][/ROW]
[ROW][C]43[/C][C]0.978570329840055[/C][C]0.0428593403198909[/C][C]0.0214296701599454[/C][/ROW]
[ROW][C]44[/C][C]0.972043765361406[/C][C]0.0559124692771878[/C][C]0.0279562346385939[/C][/ROW]
[ROW][C]45[/C][C]0.990593842877544[/C][C]0.0188123142449128[/C][C]0.00940615712245642[/C][/ROW]
[ROW][C]46[/C][C]0.986952615752362[/C][C]0.0260947684952764[/C][C]0.0130473842476382[/C][/ROW]
[ROW][C]47[/C][C]0.983045738519841[/C][C]0.0339085229603176[/C][C]0.0169542614801588[/C][/ROW]
[ROW][C]48[/C][C]0.977735699404114[/C][C]0.0445286011917716[/C][C]0.0222643005958858[/C][/ROW]
[ROW][C]49[/C][C]0.974729446499848[/C][C]0.0505411070003041[/C][C]0.0252705535001521[/C][/ROW]
[ROW][C]50[/C][C]0.970906947583508[/C][C]0.0581861048329842[/C][C]0.0290930524164921[/C][/ROW]
[ROW][C]51[/C][C]0.967333131510951[/C][C]0.0653337369780973[/C][C]0.0326668684890487[/C][/ROW]
[ROW][C]52[/C][C]0.979530163711542[/C][C]0.0409396725769165[/C][C]0.0204698362884582[/C][/ROW]
[ROW][C]53[/C][C]0.972754344384993[/C][C]0.0544913112300144[/C][C]0.0272456556150072[/C][/ROW]
[ROW][C]54[/C][C]0.975451592440323[/C][C]0.0490968151193542[/C][C]0.0245484075596771[/C][/ROW]
[ROW][C]55[/C][C]0.973213526686535[/C][C]0.0535729466269303[/C][C]0.0267864733134652[/C][/ROW]
[ROW][C]56[/C][C]0.966394728846492[/C][C]0.0672105423070166[/C][C]0.0336052711535083[/C][/ROW]
[ROW][C]57[/C][C]0.978059120502144[/C][C]0.0438817589957112[/C][C]0.0219408794978556[/C][/ROW]
[ROW][C]58[/C][C]0.97329494811345[/C][C]0.0534101037731007[/C][C]0.0267050518865503[/C][/ROW]
[ROW][C]59[/C][C]0.965780689714827[/C][C]0.0684386205703466[/C][C]0.0342193102851733[/C][/ROW]
[ROW][C]60[/C][C]0.955108481562968[/C][C]0.089783036874063[/C][C]0.0448915184370315[/C][/ROW]
[ROW][C]61[/C][C]0.941944685428279[/C][C]0.116110629143442[/C][C]0.058055314571721[/C][/ROW]
[ROW][C]62[/C][C]0.931107823302967[/C][C]0.137784353394066[/C][C]0.0688921766970332[/C][/ROW]
[ROW][C]63[/C][C]0.913342086903393[/C][C]0.173315826193214[/C][C]0.0866579130966071[/C][/ROW]
[ROW][C]64[/C][C]0.901943053576988[/C][C]0.196113892846024[/C][C]0.0980569464230119[/C][/ROW]
[ROW][C]65[/C][C]0.953671253958542[/C][C]0.0926574920829156[/C][C]0.0463287460414578[/C][/ROW]
[ROW][C]66[/C][C]0.953130442254109[/C][C]0.0937391154917819[/C][C]0.0468695577458909[/C][/ROW]
[ROW][C]67[/C][C]0.940365012759404[/C][C]0.119269974481193[/C][C]0.0596349872405964[/C][/ROW]
[ROW][C]68[/C][C]0.928666429493415[/C][C]0.14266714101317[/C][C]0.0713335705065849[/C][/ROW]
[ROW][C]69[/C][C]0.914831815831283[/C][C]0.170336368337435[/C][C]0.0851681841687173[/C][/ROW]
[ROW][C]70[/C][C]0.904639097537625[/C][C]0.190721804924749[/C][C]0.0953609024623747[/C][/ROW]
[ROW][C]71[/C][C]0.886656868370482[/C][C]0.226686263259036[/C][C]0.113343131629518[/C][/ROW]
[ROW][C]72[/C][C]0.861641461565997[/C][C]0.276717076868007[/C][C]0.138358538434003[/C][/ROW]
[ROW][C]73[/C][C]0.84295995193716[/C][C]0.31408009612568[/C][C]0.15704004806284[/C][/ROW]
[ROW][C]74[/C][C]0.832487191182087[/C][C]0.335025617635826[/C][C]0.167512808817913[/C][/ROW]
[ROW][C]75[/C][C]0.810306609323245[/C][C]0.37938678135351[/C][C]0.189693390676755[/C][/ROW]
[ROW][C]76[/C][C]0.780926001098287[/C][C]0.438147997803427[/C][C]0.219073998901713[/C][/ROW]
[ROW][C]77[/C][C]0.786211741751192[/C][C]0.427576516497617[/C][C]0.213788258248808[/C][/ROW]
[ROW][C]78[/C][C]0.751956702088843[/C][C]0.496086595822313[/C][C]0.248043297911157[/C][/ROW]
[ROW][C]79[/C][C]0.773124135054131[/C][C]0.453751729891738[/C][C]0.226875864945869[/C][/ROW]
[ROW][C]80[/C][C]0.75569614741175[/C][C]0.4886077051765[/C][C]0.24430385258825[/C][/ROW]
[ROW][C]81[/C][C]0.722441360594898[/C][C]0.555117278810204[/C][C]0.277558639405102[/C][/ROW]
[ROW][C]82[/C][C]0.680403598168521[/C][C]0.639192803662958[/C][C]0.319596401831479[/C][/ROW]
[ROW][C]83[/C][C]0.636914321558215[/C][C]0.72617135688357[/C][C]0.363085678441785[/C][/ROW]
[ROW][C]84[/C][C]0.660163181737866[/C][C]0.679673636524268[/C][C]0.339836818262134[/C][/ROW]
[ROW][C]85[/C][C]0.6556471940674[/C][C]0.688705611865201[/C][C]0.3443528059326[/C][/ROW]
[ROW][C]86[/C][C]0.63871870469176[/C][C]0.722562590616479[/C][C]0.36128129530824[/C][/ROW]
[ROW][C]87[/C][C]0.669550395652267[/C][C]0.660899208695467[/C][C]0.330449604347733[/C][/ROW]
[ROW][C]88[/C][C]0.680306162449485[/C][C]0.63938767510103[/C][C]0.319693837550515[/C][/ROW]
[ROW][C]89[/C][C]0.639311988285629[/C][C]0.721376023428741[/C][C]0.360688011714371[/C][/ROW]
[ROW][C]90[/C][C]0.630885344756398[/C][C]0.738229310487205[/C][C]0.369114655243602[/C][/ROW]
[ROW][C]91[/C][C]0.613065519959757[/C][C]0.773868960080487[/C][C]0.386934480040243[/C][/ROW]
[ROW][C]92[/C][C]0.576468785099631[/C][C]0.847062429800738[/C][C]0.423531214900369[/C][/ROW]
[ROW][C]93[/C][C]0.529902506363066[/C][C]0.940194987273869[/C][C]0.470097493636934[/C][/ROW]
[ROW][C]94[/C][C]0.515122108077445[/C][C]0.96975578384511[/C][C]0.484877891922555[/C][/ROW]
[ROW][C]95[/C][C]0.523137454473366[/C][C]0.953725091053268[/C][C]0.476862545526634[/C][/ROW]
[ROW][C]96[/C][C]0.655203875444034[/C][C]0.689592249111933[/C][C]0.344796124555966[/C][/ROW]
[ROW][C]97[/C][C]0.608263147516428[/C][C]0.783473704967144[/C][C]0.391736852483572[/C][/ROW]
[ROW][C]98[/C][C]0.580298558522844[/C][C]0.839402882954313[/C][C]0.419701441477156[/C][/ROW]
[ROW][C]99[/C][C]0.629440671258723[/C][C]0.741118657482555[/C][C]0.370559328741277[/C][/ROW]
[ROW][C]100[/C][C]0.588370932413092[/C][C]0.823258135173817[/C][C]0.411629067586908[/C][/ROW]
[ROW][C]101[/C][C]0.629056635857206[/C][C]0.741886728285589[/C][C]0.370943364142794[/C][/ROW]
[ROW][C]102[/C][C]0.583511498362932[/C][C]0.832977003274136[/C][C]0.416488501637068[/C][/ROW]
[ROW][C]103[/C][C]0.535648220363435[/C][C]0.928703559273129[/C][C]0.464351779636565[/C][/ROW]
[ROW][C]104[/C][C]0.526198379588992[/C][C]0.947603240822016[/C][C]0.473801620411008[/C][/ROW]
[ROW][C]105[/C][C]0.536770456881287[/C][C]0.926459086237427[/C][C]0.463229543118713[/C][/ROW]
[ROW][C]106[/C][C]0.578150346709316[/C][C]0.843699306581367[/C][C]0.421849653290684[/C][/ROW]
[ROW][C]107[/C][C]0.544434293631202[/C][C]0.911131412737596[/C][C]0.455565706368798[/C][/ROW]
[ROW][C]108[/C][C]0.660386443119987[/C][C]0.679227113760026[/C][C]0.339613556880013[/C][/ROW]
[ROW][C]109[/C][C]0.651092806805538[/C][C]0.697814386388923[/C][C]0.348907193194462[/C][/ROW]
[ROW][C]110[/C][C]0.608270145241778[/C][C]0.783459709516444[/C][C]0.391729854758222[/C][/ROW]
[ROW][C]111[/C][C]0.556137648975834[/C][C]0.887724702048333[/C][C]0.443862351024167[/C][/ROW]
[ROW][C]112[/C][C]0.664627147081853[/C][C]0.670745705836295[/C][C]0.335372852918147[/C][/ROW]
[ROW][C]113[/C][C]0.675687470346655[/C][C]0.648625059306691[/C][C]0.324312529653345[/C][/ROW]
[ROW][C]114[/C][C]0.671894504382596[/C][C]0.656210991234808[/C][C]0.328105495617404[/C][/ROW]
[ROW][C]115[/C][C]0.617542352082157[/C][C]0.764915295835685[/C][C]0.382457647917843[/C][/ROW]
[ROW][C]116[/C][C]0.581061129546092[/C][C]0.837877740907817[/C][C]0.418938870453908[/C][/ROW]
[ROW][C]117[/C][C]0.533212806994221[/C][C]0.933574386011558[/C][C]0.466787193005779[/C][/ROW]
[ROW][C]118[/C][C]0.517135587496777[/C][C]0.965728825006446[/C][C]0.482864412503223[/C][/ROW]
[ROW][C]119[/C][C]0.562753167904358[/C][C]0.874493664191283[/C][C]0.437246832095642[/C][/ROW]
[ROW][C]120[/C][C]0.506746742649816[/C][C]0.986506514700368[/C][C]0.493253257350184[/C][/ROW]
[ROW][C]121[/C][C]0.480132577318446[/C][C]0.960265154636893[/C][C]0.519867422681554[/C][/ROW]
[ROW][C]122[/C][C]0.440093231808429[/C][C]0.880186463616857[/C][C]0.559906768191571[/C][/ROW]
[ROW][C]123[/C][C]0.446568443514632[/C][C]0.893136887029265[/C][C]0.553431556485368[/C][/ROW]
[ROW][C]124[/C][C]0.440198210862796[/C][C]0.880396421725593[/C][C]0.559801789137204[/C][/ROW]
[ROW][C]125[/C][C]0.492786440002979[/C][C]0.985572880005958[/C][C]0.507213559997021[/C][/ROW]
[ROW][C]126[/C][C]0.491564077454977[/C][C]0.983128154909954[/C][C]0.508435922545023[/C][/ROW]
[ROW][C]127[/C][C]0.464898736016677[/C][C]0.929797472033354[/C][C]0.535101263983323[/C][/ROW]
[ROW][C]128[/C][C]0.403238223768613[/C][C]0.806476447537225[/C][C]0.596761776231387[/C][/ROW]
[ROW][C]129[/C][C]0.649464444550973[/C][C]0.701071110898054[/C][C]0.350535555449027[/C][/ROW]
[ROW][C]130[/C][C]0.721803793565343[/C][C]0.556392412869313[/C][C]0.278196206434657[/C][/ROW]
[ROW][C]131[/C][C]0.673871373710644[/C][C]0.652257252578711[/C][C]0.326128626289356[/C][/ROW]
[ROW][C]132[/C][C]0.667298852176253[/C][C]0.665402295647493[/C][C]0.332701147823747[/C][/ROW]
[ROW][C]133[/C][C]0.73355229191624[/C][C]0.532895416167519[/C][C]0.26644770808376[/C][/ROW]
[ROW][C]134[/C][C]0.705904088321703[/C][C]0.588191823356593[/C][C]0.294095911678297[/C][/ROW]
[ROW][C]135[/C][C]0.643313668808111[/C][C]0.713372662383779[/C][C]0.356686331191889[/C][/ROW]
[ROW][C]136[/C][C]0.652874663279455[/C][C]0.69425067344109[/C][C]0.347125336720545[/C][/ROW]
[ROW][C]137[/C][C]0.819058612594529[/C][C]0.361882774810943[/C][C]0.180941387405471[/C][/ROW]
[ROW][C]138[/C][C]0.767006302108744[/C][C]0.465987395782513[/C][C]0.232993697891256[/C][/ROW]
[ROW][C]139[/C][C]0.768169913852863[/C][C]0.463660172294274[/C][C]0.231830086147137[/C][/ROW]
[ROW][C]140[/C][C]0.650892884832307[/C][C]0.698214230335386[/C][C]0.349107115167693[/C][/ROW]
[ROW][C]141[/C][C]0.519402440304057[/C][C]0.961195119391887[/C][C]0.480597559695943[/C][/ROW]
[ROW][C]142[/C][C]0.360712076499259[/C][C]0.721424152998517[/C][C]0.639287923500741[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159778&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159778&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
140.9972297838341190.005540432331762140.00277021616588107
150.997837772997930.004324454004139370.00216222700206968
160.9999369537308640.0001260925382716546.30462691358268e-05
170.9998705727287940.0002588545424123540.000129427271206177
180.9996884111470810.0006231777058387290.000311588852919365
190.9997931731265490.0004136537469017030.000206826873450851
200.9995672442353850.0008655115292291940.000432755764614597
210.9993502115199590.001299576960081420.00064978848004071
220.999416305036790.001167389926419380.00058369496320969
230.9989795411576480.00204091768470330.00102045884235165
240.9982762726631780.003447454673644850.00172372733682242
250.9970330183390330.005933963321933780.00296698166096689
260.9953160817495220.009367836500956610.00468391825047831
270.9954897854517910.009020429096418530.00451021454820926
280.9928079548297920.01438409034041610.00719204517020807
290.9954514384646160.009097123070768180.00454856153538409
300.9932783115898220.01344337682035490.00672168841017745
310.9913364206834440.01732715863311140.00866357931655569
320.9964141050684420.007171789863116450.00358589493155823
330.9944317725853180.01113645482936430.00556822741468215
340.9936301792273840.01273964154523280.00636982077261641
350.9955861494756210.008827701048757680.00441385052437884
360.9940764589381310.0118470821237370.00592354106186849
370.9919637530099280.01607249398014470.00803624699007233
380.9915023805232690.01699523895346130.00849761947673066
390.9883895952421460.02322080951570740.0116104047578537
400.9907519955303160.01849600893936810.00924800446968405
410.9882204905729970.02355901885400560.0117795094270028
420.9842770191357410.03144596172851840.0157229808642592
430.9785703298400550.04285934031989090.0214296701599454
440.9720437653614060.05591246927718780.0279562346385939
450.9905938428775440.01881231424491280.00940615712245642
460.9869526157523620.02609476849527640.0130473842476382
470.9830457385198410.03390852296031760.0169542614801588
480.9777356994041140.04452860119177160.0222643005958858
490.9747294464998480.05054110700030410.0252705535001521
500.9709069475835080.05818610483298420.0290930524164921
510.9673331315109510.06533373697809730.0326668684890487
520.9795301637115420.04093967257691650.0204698362884582
530.9727543443849930.05449131123001440.0272456556150072
540.9754515924403230.04909681511935420.0245484075596771
550.9732135266865350.05357294662693030.0267864733134652
560.9663947288464920.06721054230701660.0336052711535083
570.9780591205021440.04388175899571120.0219408794978556
580.973294948113450.05341010377310070.0267050518865503
590.9657806897148270.06843862057034660.0342193102851733
600.9551084815629680.0897830368740630.0448915184370315
610.9419446854282790.1161106291434420.058055314571721
620.9311078233029670.1377843533940660.0688921766970332
630.9133420869033930.1733158261932140.0866579130966071
640.9019430535769880.1961138928460240.0980569464230119
650.9536712539585420.09265749208291560.0463287460414578
660.9531304422541090.09373911549178190.0468695577458909
670.9403650127594040.1192699744811930.0596349872405964
680.9286664294934150.142667141013170.0713335705065849
690.9148318158312830.1703363683374350.0851681841687173
700.9046390975376250.1907218049247490.0953609024623747
710.8866568683704820.2266862632590360.113343131629518
720.8616414615659970.2767170768680070.138358538434003
730.842959951937160.314080096125680.15704004806284
740.8324871911820870.3350256176358260.167512808817913
750.8103066093232450.379386781353510.189693390676755
760.7809260010982870.4381479978034270.219073998901713
770.7862117417511920.4275765164976170.213788258248808
780.7519567020888430.4960865958223130.248043297911157
790.7731241350541310.4537517298917380.226875864945869
800.755696147411750.48860770517650.24430385258825
810.7224413605948980.5551172788102040.277558639405102
820.6804035981685210.6391928036629580.319596401831479
830.6369143215582150.726171356883570.363085678441785
840.6601631817378660.6796736365242680.339836818262134
850.65564719406740.6887056118652010.3443528059326
860.638718704691760.7225625906164790.36128129530824
870.6695503956522670.6608992086954670.330449604347733
880.6803061624494850.639387675101030.319693837550515
890.6393119882856290.7213760234287410.360688011714371
900.6308853447563980.7382293104872050.369114655243602
910.6130655199597570.7738689600804870.386934480040243
920.5764687850996310.8470624298007380.423531214900369
930.5299025063630660.9401949872738690.470097493636934
940.5151221080774450.969755783845110.484877891922555
950.5231374544733660.9537250910532680.476862545526634
960.6552038754440340.6895922491119330.344796124555966
970.6082631475164280.7834737049671440.391736852483572
980.5802985585228440.8394028829543130.419701441477156
990.6294406712587230.7411186574825550.370559328741277
1000.5883709324130920.8232581351738170.411629067586908
1010.6290566358572060.7418867282855890.370943364142794
1020.5835114983629320.8329770032741360.416488501637068
1030.5356482203634350.9287035592731290.464351779636565
1040.5261983795889920.9476032408220160.473801620411008
1050.5367704568812870.9264590862374270.463229543118713
1060.5781503467093160.8436993065813670.421849653290684
1070.5444342936312020.9111314127375960.455565706368798
1080.6603864431199870.6792271137600260.339613556880013
1090.6510928068055380.6978143863889230.348907193194462
1100.6082701452417780.7834597095164440.391729854758222
1110.5561376489758340.8877247020483330.443862351024167
1120.6646271470818530.6707457058362950.335372852918147
1130.6756874703466550.6486250593066910.324312529653345
1140.6718945043825960.6562109912348080.328105495617404
1150.6175423520821570.7649152958356850.382457647917843
1160.5810611295460920.8378777409078170.418938870453908
1170.5332128069942210.9335743860115580.466787193005779
1180.5171355874967770.9657288250064460.482864412503223
1190.5627531679043580.8744936641912830.437246832095642
1200.5067467426498160.9865065147003680.493253257350184
1210.4801325773184460.9602651546368930.519867422681554
1220.4400932318084290.8801864636168570.559906768191571
1230.4465684435146320.8931368870292650.553431556485368
1240.4401982108627960.8803964217255930.559801789137204
1250.4927864400029790.9855728800059580.507213559997021
1260.4915640774549770.9831281549099540.508435922545023
1270.4648987360166770.9297974720333540.535101263983323
1280.4032382237686130.8064764475372250.596761776231387
1290.6494644445509730.7010711108980540.350535555449027
1300.7218037935653430.5563924128693130.278196206434657
1310.6738713737106440.6522572525787110.326128626289356
1320.6672988521762530.6654022956474930.332701147823747
1330.733552291916240.5328954161675190.26644770808376
1340.7059040883217030.5881918233565930.294095911678297
1350.6433136688081110.7133726623837790.356686331191889
1360.6528746632794550.694250673441090.347125336720545
1370.8190586125945290.3618827748109430.180941387405471
1380.7670063021087440.4659873957825130.232993697891256
1390.7681699138528630.4636601722942740.231830086147137
1400.6508928848323070.6982142303353860.349107115167693
1410.5194024403040570.9611951193918870.480597559695943
1420.3607120764992590.7214241529985170.639287923500741






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level170.131782945736434NOK
5% type I error level370.286821705426357NOK
10% type I error level490.37984496124031NOK

\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 & 17 & 0.131782945736434 & NOK \tabularnewline
5% type I error level & 37 & 0.286821705426357 & NOK \tabularnewline
10% type I error level & 49 & 0.37984496124031 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=159778&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]17[/C][C]0.131782945736434[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]37[/C][C]0.286821705426357[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]49[/C][C]0.37984496124031[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=159778&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=159778&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 level170.131782945736434NOK
5% type I error level370.286821705426357NOK
10% type I error level490.37984496124031NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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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 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}