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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 23 Nov 2010 02:24:42 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t1290479371ui8ntsxpg4t8ibw.htm/, Retrieved Fri, 29 Mar 2024 12:40:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=98838, Retrieved Fri, 29 Mar 2024 12:40:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact186
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Mini-Tutorial Mul...] [2010-11-23 02:24:42] [dfb0309aec67f282200eef05efe0d5bd] [Current]
F   PD      [Multiple Regression] [Mini-tutorial Int...] [2010-11-23 11:55:50] [6ca0fc48dd5333d51a15728999009c83]
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Dataseries X:
0	13	26	0	9	0	6	0	25	0	25	0
0	16	20	0	9	0	6	0	25	0	24	0
0	19	21	0	9	0	13	0	19	0	21	0
1	15	31	31	14	14	8	8	18	18	23	23
0	14	21	0	8	0	7	0	18	0	17	0
0	13	18	0	8	0	9	0	22	0	19	0
0	19	26	0	11	0	5	0	29	0	18	0
0	15	22	0	10	0	8	0	26	0	27	0
0	14	22	0	9	0	9	0	25	0	23	0
0	15	29	0	15	0	11	0	23	0	23	0
1	16	15	15	14	14	8	8	23	23	29	29
0	16	16	0	11	0	11	0	23	0	21	0
1	16	24	24	14	14	12	12	24	24	26	26
0	17	17	0	6	0	8	0	30	0	25	0
1	15	19	19	20	20	7	7	19	19	25	25
1	15	22	22	9	9	9	9	24	24	23	23
0	20	31	0	10	0	12	0	32	0	26	0
1	18	28	28	8	8	20	20	30	30	20	20
0	16	38	0	11	0	7	0	29	0	29	0
1	16	26	26	14	14	8	8	17	17	24	24
0	19	25	0	11	0	8	0	25	0	23	0
0	16	25	0	16	0	16	0	26	0	24	0
1	17	29	29	14	14	10	10	26	26	30	30
0	17	28	0	11	0	6	0	25	0	22	0
1	16	15	15	11	11	8	8	23	23	22	22
0	15	18	0	12	0	9	0	21	0	13	0
1	14	21	21	9	9	9	9	19	19	24	24
0	15	25	0	7	0	11	0	35	0	17	0
1	12	23	23	13	13	12	12	19	19	24	24
0	14	23	0	10	0	8	0	20	0	21	0
0	16	19	0	9	0	7	0	21	0	23	0
1	14	18	18	9	9	8	8	21	21	24	24
1	7	18	18	13	13	9	9	24	24	24	24
1	10	26	26	16	16	4	4	23	23	24	24
1	14	18	18	12	12	8	8	19	19	23	23
0	16	18	0	6	0	8	0	17	0	26	0
1	16	28	28	14	14	8	8	24	24	24	24
1	16	17	17	14	14	6	6	15	15	21	21
0	14	29	0	10	0	8	0	25	0	23	0
1	20	12	12	4	4	4	4	27	27	28	28
1	14	25	25	12	12	7	7	29	29	23	23
0	14	28	0	12	0	14	0	27	0	22	0
0	11	20	0	14	0	10	0	18	0	24	0
0	15	17	0	9	0	9	0	25	0	21	0
0	16	17	0	9	0	6	0	22	0	23	0
1	14	20	20	10	10	8	8	26	26	23	23
0	16	31	0	14	0	11	0	23	0	20	0
1	14	21	21	10	10	8	8	16	16	23	23
1	12	19	19	9	9	8	8	27	27	21	21
0	16	23	0	14	0	10	0	25	0	27	0
1	9	15	15	8	8	8	8	14	14	12	12
0	14	24	0	9	0	10	0	19	0	15	0
0	16	28	0	8	0	7	0	20	0	22	0
0	16	16	0	9	0	8	0	16	0	21	0
1	15	19	19	9	9	7	7	18	18	21	21
0	16	21	0	9	0	9	0	22	0	20	0
1	12	21	21	15	15	5	5	21	21	24	24
1	16	20	20	8	8	7	7	22	22	24	24
0	16	16	0	10	0	7	0	22	0	29	0
0	14	25	0	8	0	7	0	32	0	25	0
0	16	30	0	14	0	9	0	23	0	14	0
1	17	29	29	11	11	5	5	31	31	30	30
0	18	22	0	10	0	8	0	18	0	19	0
1	18	19	19	12	12	8	8	23	23	29	29
0	12	33	0	14	0	8	0	26	0	25	0
1	16	17	17	9	9	9	9	24	24	25	25
1	10	9	9	13	13	6	6	19	19	25	25
0	14	14	0	15	0	8	0	14	0	16	0
0	18	15	0	8	0	6	0	20	0	25	0
1	18	12	12	7	7	4	4	22	22	28	28
1	16	21	21	10	10	6	6	24	24	24	24
0	16	20	0	10	0	4	0	25	0	25	0
0	16	29	0	13	0	12	0	21	0	21	0
1	13	33	33	11	11	6	6	28	28	22	22
1	16	21	21	8	8	11	11	24	24	20	20
1	16	15	15	12	12	8	8	20	20	25	25
1	20	19	19	9	9	10	10	21	21	27	27
0	16	23	0	10	0	10	0	23	0	21	0
1	15	20	20	11	11	4	4	13	13	13	13
0	15	20	0	11	0	8	0	24	0	26	0
0	16	18	0	10	0	9	0	21	0	26	0
1	14	31	31	16	16	9	9	21	21	25	25
0	15	18	0	16	0	7	0	17	0	22	0
0	12	13	0	8	0	7	0	14	0	19	0
0	17	9	0	6	0	11	0	29	0	23	0
0	16	20	0	11	0	8	0	25	0	25	0
0	15	18	0	12	0	8	0	16	0	15	0
0	13	23	0	14	0	7	0	25	0	21	0
0	16	17	0	9	0	5	0	25	0	23	0
0	16	17	0	11	0	7	0	21	0	25	0
0	16	16	0	8	0	9	0	23	0	24	0
1	16	31	31	8	8	8	8	22	22	24	24
1	14	15	15	7	7	6	6	19	19	21	21
0	16	28	0	16	0	8	0	24	0	24	0
1	16	26	26	13	13	10	10	26	26	22	22
0	20	20	0	8	0	10	0	25	0	24	0
1	15	19	19	11	11	8	8	20	20	28	28
0	16	25	0	14	0	11	0	22	0	21	0
1	13	18	18	10	10	8	8	14	14	17	17
0	17	20	0	10	0	8	0	20	0	28	0
1	16	33	33	14	14	6	6	32	32	24	24
0	12	24	0	14	0	20	0	21	0	10	0
0	16	22	0	10	0	6	0	22	0	20	0
0	16	32	0	12	0	12	0	28	0	22	0
0	17	31	0	9	0	9	0	25	0	19	0
1	13	13	13	16	16	5	5	17	17	22	22
0	12	18	0	8	0	10	0	21	0	22	0
1	18	17	17	9	9	5	5	23	23	26	26
0	14	29	0	16	0	6	0	27	0	24	0
0	14	22	0	13	0	10	0	22	0	22	0
0	13	18	0	13	0	6	0	19	0	20	0
0	16	22	0	8	0	10	0	20	0	20	0
0	13	25	0	14	0	5	0	17	0	15	0
0	16	20	0	11	0	13	0	24	0	20	0
0	13	20	0	9	0	7	0	21	0	20	0
0	16	17	0	8	0	9	0	21	0	24	0
0	15	21	0	13	0	11	0	23	0	22	0
0	16	26	0	13	0	8	0	24	0	29	0
1	15	10	10	10	10	5	5	19	19	23	23
0	17	15	0	8	0	4	0	22	0	24	0
0	15	20	0	7	0	9	0	26	0	22	0
0	12	14	0	11	0	7	0	17	0	16	0
1	16	16	16	11	11	5	5	17	17	23	23
1	10	23	23	14	14	5	5	19	19	27	27
0	16	11	0	6	0	4	0	15	0	16	0
1	14	19	19	10	10	7	7	17	17	21	21
0	15	30	0	9	0	9	0	27	0	26	0
1	13	21	21	12	12	8	8	19	19	22	22
1	15	20	20	11	11	8	8	21	21	23	23
0	11	22	0	14	0	11	0	25	0	19	0
0	12	30	0	12	0	10	0	19	0	18	0
1	8	25	25	14	14	9	9	22	22	24	24
0	16	28	0	8	0	12	0	18	0	24	0
1	15	23	23	14	14	10	10	20	20	29	29
0	17	23	0	8	0	10	0	15	0	22	0
1	16	21	21	11	11	7	7	20	20	24	24
0	10	30	0	12	0	10	0	29	0	22	0
0	18	22	0	9	0	6	0	19	0	12	0
1	13	32	32	16	16	6	6	29	29	26	26
0	15	22	0	11	0	11	0	24	0	18	0
1	16	15	15	11	11	8	8	23	23	22	22
0	16	21	0	12	0	9	0	22	0	24	0
0	14	27	0	15	0	9	0	23	0	21	0
0	10	22	0	13	0	13	0	22	0	15	0
0	17	9	0	6	0	11	0	29	0	23	0
0	13	29	0	11	0	4	0	26	0	22	0
0	15	20	0	7	0	9	0	26	0	22	0
0	16	16	0	8	0	5	0	21	0	24	0
0	12	16	0	8	0	4	0	18	0	23	0
0	13	16	0	9	0	9	0	10	0	13	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 8 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98838&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98838&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98838&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Multiple Linear Regression - Estimated Regression Equation
Selfconfidence[t] = + 13.6904599062833 -3.23857734904733Gender[t] + 0.00877329489010884ConcernMistakes[t] + 0.0239140039291088ConcernMistakes_G[t] -0.212820358169707DoubtsActions[t] -0.186727182614101DoubtsActions_G[t] + 0.0272883965522743ParentalCriticism[t] + 0.0569766005229586ParentalCriticism_G[t] + 0.0323243960587162PersonalStandards[t] -0.038112033503889PersonalStandards_G[t] + 0.117975229950911Organization[t] + 0.20126225687406Organization_G[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Selfconfidence[t] =  +  13.6904599062833 -3.23857734904733Gender[t] +  0.00877329489010884ConcernMistakes[t] +  0.0239140039291088ConcernMistakes_G[t] -0.212820358169707DoubtsActions[t] -0.186727182614101DoubtsActions_G[t] +  0.0272883965522743ParentalCriticism[t] +  0.0569766005229586ParentalCriticism_G[t] +  0.0323243960587162PersonalStandards[t] -0.038112033503889PersonalStandards_G[t] +  0.117975229950911Organization[t] +  0.20126225687406Organization_G[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98838&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Selfconfidence[t] =  +  13.6904599062833 -3.23857734904733Gender[t] +  0.00877329489010884ConcernMistakes[t] +  0.0239140039291088ConcernMistakes_G[t] -0.212820358169707DoubtsActions[t] -0.186727182614101DoubtsActions_G[t] +  0.0272883965522743ParentalCriticism[t] +  0.0569766005229586ParentalCriticism_G[t] +  0.0323243960587162PersonalStandards[t] -0.038112033503889PersonalStandards_G[t] +  0.117975229950911Organization[t] +  0.20126225687406Organization_G[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98838&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98838&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Selfconfidence[t] = + 13.6904599062833 -3.23857734904733Gender[t] + 0.00877329489010884ConcernMistakes[t] + 0.0239140039291088ConcernMistakes_G[t] -0.212820358169707DoubtsActions[t] -0.186727182614101DoubtsActions_G[t] + 0.0272883965522743ParentalCriticism[t] + 0.0569766005229586ParentalCriticism_G[t] + 0.0323243960587162PersonalStandards[t] -0.038112033503889PersonalStandards_G[t] + 0.117975229950911Organization[t] + 0.20126225687406Organization_G[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.69045990628331.8180717.530200
Gender-3.238577349047332.978129-1.08750.2787320.139366
ConcernMistakes0.008773294890108840.0478080.18350.8546660.427333
ConcernMistakes_G0.02391400392910880.0775340.30840.7582180.379109
DoubtsActions-0.2128203581697070.09557-2.22690.0275780.013789
DoubtsActions_G-0.1867271826141010.146837-1.27170.2056310.102815
ParentalCriticism0.02728839655227430.0873950.31220.7553290.377665
ParentalCriticism_G0.05697660052295860.1448950.39320.6947590.34738
PersonalStandards0.03232439605871620.0612680.52760.5986310.299316
PersonalStandards_G-0.0381120335038890.104694-0.3640.7163910.358195
Organization0.1179752299509110.0630281.87180.0633540.031677
Organization_G0.201262256874060.1142741.76120.0804160.040208

\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) & 13.6904599062833 & 1.818071 & 7.5302 & 0 & 0 \tabularnewline
Gender & -3.23857734904733 & 2.978129 & -1.0875 & 0.278732 & 0.139366 \tabularnewline
ConcernMistakes & 0.00877329489010884 & 0.047808 & 0.1835 & 0.854666 & 0.427333 \tabularnewline
ConcernMistakes_G & 0.0239140039291088 & 0.077534 & 0.3084 & 0.758218 & 0.379109 \tabularnewline
DoubtsActions & -0.212820358169707 & 0.09557 & -2.2269 & 0.027578 & 0.013789 \tabularnewline
DoubtsActions_G & -0.186727182614101 & 0.146837 & -1.2717 & 0.205631 & 0.102815 \tabularnewline
ParentalCriticism & 0.0272883965522743 & 0.087395 & 0.3122 & 0.755329 & 0.377665 \tabularnewline
ParentalCriticism_G & 0.0569766005229586 & 0.144895 & 0.3932 & 0.694759 & 0.34738 \tabularnewline
PersonalStandards & 0.0323243960587162 & 0.061268 & 0.5276 & 0.598631 & 0.299316 \tabularnewline
PersonalStandards_G & -0.038112033503889 & 0.104694 & -0.364 & 0.716391 & 0.358195 \tabularnewline
Organization & 0.117975229950911 & 0.063028 & 1.8718 & 0.063354 & 0.031677 \tabularnewline
Organization_G & 0.20126225687406 & 0.114274 & 1.7612 & 0.080416 & 0.040208 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98838&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]13.6904599062833[/C][C]1.818071[/C][C]7.5302[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Gender[/C][C]-3.23857734904733[/C][C]2.978129[/C][C]-1.0875[/C][C]0.278732[/C][C]0.139366[/C][/ROW]
[ROW][C]ConcernMistakes[/C][C]0.00877329489010884[/C][C]0.047808[/C][C]0.1835[/C][C]0.854666[/C][C]0.427333[/C][/ROW]
[ROW][C]ConcernMistakes_G[/C][C]0.0239140039291088[/C][C]0.077534[/C][C]0.3084[/C][C]0.758218[/C][C]0.379109[/C][/ROW]
[ROW][C]DoubtsActions[/C][C]-0.212820358169707[/C][C]0.09557[/C][C]-2.2269[/C][C]0.027578[/C][C]0.013789[/C][/ROW]
[ROW][C]DoubtsActions_G[/C][C]-0.186727182614101[/C][C]0.146837[/C][C]-1.2717[/C][C]0.205631[/C][C]0.102815[/C][/ROW]
[ROW][C]ParentalCriticism[/C][C]0.0272883965522743[/C][C]0.087395[/C][C]0.3122[/C][C]0.755329[/C][C]0.377665[/C][/ROW]
[ROW][C]ParentalCriticism_G[/C][C]0.0569766005229586[/C][C]0.144895[/C][C]0.3932[/C][C]0.694759[/C][C]0.34738[/C][/ROW]
[ROW][C]PersonalStandards[/C][C]0.0323243960587162[/C][C]0.061268[/C][C]0.5276[/C][C]0.598631[/C][C]0.299316[/C][/ROW]
[ROW][C]PersonalStandards_G[/C][C]-0.038112033503889[/C][C]0.104694[/C][C]-0.364[/C][C]0.716391[/C][C]0.358195[/C][/ROW]
[ROW][C]Organization[/C][C]0.117975229950911[/C][C]0.063028[/C][C]1.8718[/C][C]0.063354[/C][C]0.031677[/C][/ROW]
[ROW][C]Organization_G[/C][C]0.20126225687406[/C][C]0.114274[/C][C]1.7612[/C][C]0.080416[/C][C]0.040208[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98838&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98838&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.69045990628331.8180717.530200
Gender-3.238577349047332.978129-1.08750.2787320.139366
ConcernMistakes0.008773294890108840.0478080.18350.8546660.427333
ConcernMistakes_G0.02391400392910880.0775340.30840.7582180.379109
DoubtsActions-0.2128203581697070.09557-2.22690.0275780.013789
DoubtsActions_G-0.1867271826141010.146837-1.27170.2056310.102815
ParentalCriticism0.02728839655227430.0873950.31220.7553290.377665
ParentalCriticism_G0.05697660052295860.1448950.39320.6947590.34738
PersonalStandards0.03232439605871620.0612680.52760.5986310.299316
PersonalStandards_G-0.0381120335038890.104694-0.3640.7163910.358195
Organization0.1179752299509110.0630281.87180.0633540.031677
Organization_G0.201262256874060.1142741.76120.0804160.040208







Multiple Linear Regression - Regression Statistics
Multiple R0.478100989985447
R-squared0.228580556625065
Adjusted R-squared0.16709060099373
F-TEST (value)3.71736414961047
F-TEST (DF numerator)11
F-TEST (DF denominator)138
p-value0.000117570465484218
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.07435659787837
Sum Squared Residuals593.80783073229

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.478100989985447 \tabularnewline
R-squared & 0.228580556625065 \tabularnewline
Adjusted R-squared & 0.16709060099373 \tabularnewline
F-TEST (value) & 3.71736414961047 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 138 \tabularnewline
p-value & 0.000117570465484218 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.07435659787837 \tabularnewline
Sum Squared Residuals & 593.80783073229 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98838&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.478100989985447[/C][/ROW]
[ROW][C]R-squared[/C][C]0.228580556625065[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.16709060099373[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.71736414961047[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]138[/C][/ROW]
[ROW][C]p-value[/C][C]0.000117570465484218[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.07435659787837[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]593.80783073229[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98838&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98838&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.478100989985447
R-squared0.228580556625065
Adjusted R-squared0.16709060099373
F-TEST (value)3.71736414961047
F-TEST (DF numerator)11
F-TEST (DF denominator)138
p-value0.000117570465484218
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.07435659787837
Sum Squared Residuals593.80783073229







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.9244033794531-2.92440337945306
21615.75378838016150.246211619838472
31915.40570838471253.59429161528748
41513.78392794922151.21607205077851
51414.9505730477062-0.950573047706224
61315.3440780002771-2.34407800027713
71914.77494524113994.22505475886011
81515.999341490788-0.99934149078803
91415.7352249296477-1.73522492964765
101514.50964384584730.490356154152711
111615.1474179018380.852582098162033
121615.01092198505290.98907801494712
131614.81516348159181.18483651840823
141716.70010357334940.299896426650644
151511.54281745781763.45718254218244
161515.5370191361718-0.537019136171766
172016.26342587740953.7365741225905
181816.06716715255251.93283284744751
191616.2325291023858-0.232529102385761
201613.94551657939552.05448342060446
211915.30861570142633.69138429857371
221614.61312070900561.38687929099442
231716.07544465394690.924555346053067
241715.16238356304121.83761643695884
251614.11139811641461.88860188358541
261513.75262079183411.24737920816588
271415.8525075114034-1.85250751140338
281515.8571549046436-0.857154904643638
291214.5724869371323-2.57248693713228
301415.1063170296204-1.10631702962038
311615.52503066763790.474969332362088
321415.6586053429802-1.65860534298015
33714.1273172645846-7.12731726458463
341012.774635684856-2.77463568485596
351414.1523005086941-0.152300508694102
361616.4066349494271-0.406634949427065
371613.97037771491782.02962228508223
381612.53666371028763.46333628971245
391415.5565292391564-1.55652923915643
402018.36538338831181.6346166116882
411414.2389702289017-0.238970228901665
421415.2325191694071-1.23251916940708
431114.5725694031111-3.57256940311107
441515.4554079952953-0.455407995295286
451615.51252007736410.487479922635864
461414.9762567257839-0.976256725783944
471614.38608510394451.61391489605552
481415.0668203990549-1.06682039905489
491214.6988543566534-2.69885435665342
501615.17908575004510.820914249954856
51912.1697546075229-3.16975460752286
521414.6423117000206-0.64231170002056
531615.6665110538090.333488946191029
541615.12842673932450.871573260675543
551514.66667809658470.333321903415258
561615.27555275672870.724447243271338
571213.1065870035093-1.10658700350926
581616.033474846882-0.0334748468819896
591616.0260662005621-0.0260662005620591
601416.382009611696-2.38200961169597
611613.61488363624442.38511636375564
621716.82382410369630.176175896303671
631814.7969444827113.20305551728899
641816.07726217868251.92273782131755
651215.0086158419986-3.00861584199858
661616.0120576157256-0.0120576157256189
671013.9285122580368-3.92851225803681
681413.1794330586540.820566941345987
691815.8790955135382.12090448646198
701817.19567895318620.80432104681376
711615.1712267921680.828773207831987
721615.60436645883820.395633541161823
731614.66197370671971.33802629328028
741314.5023013137842-1.50230131378418
751615.11469691181190.885303088188091
761614.68692594844121.31307405155878
772016.81753509642473.18246490357525
781615.25786701090110.742132989098926
791511.12251361523673.87748638476326
801515.5863505207698-0.586350520769762
811615.71193949753540.288060502464623
821413.69020992604350.309790073956472
831513.77924205137411.22075794862592
841214.9870395642523-2.98703956425231
851716.44350754792480.556492452075229
861615.50069968687760.499300313122432
871513.79966087489011.20033912510991
881314.3893691806829-1.38936918068286
891615.5822048689880.41779513101199
901615.31779382142010.682206178579898
911615.94873195631020.0512680436898163
921616.4773001309686-0.477300130968617
931415.2449713483551-1.24497134835508
941614.35648462914031.64351537085972
951613.82303040367332.17696959632668
962016.07576232454033.92423767545967
971516.1749351449768-1.17493514497681
981614.4190961684961.58090383150398
991313.0649088565378-0.0649088565377565
1001715.90582375460641.09417624539357
1011613.9189831153022.08101688469799
1021213.3258665170576-1.32586651705765
1031614.98964050379221.01035949620776
1041615.24535995192170.75464004807832
1051715.3422836638551.65771633614501
1061311.83021664830251.16978335169754
1071215.6929676906234-3.69296769062342
1081816.00002275169481.99997724830517
1091414.407654319102-0.407654319101987
1101414.696283475394-0.696283475394039
1111314.2191130615465-1.21911306154654
1121615.45978601422330.540213985776681
1131313.3858924291833-0.385892429183331
1141615.01494112382570.98505887617433
1151315.1798782726753-2.17987827267529
1161615.89285645908290.10714354091714
1171514.74712297311490.252877026885079
1181615.56727526362370.432724736376266
1191514.43710220848230.562897791517722
1201715.77119228261.22880771740001
1211516.0576682223147-1.05766822231465
1221214.1003992829567-2.10039928295671
1231614.24525373550411.75474626449588
1241014.5407968772968-4.54079687729676
1251614.99166720736071.00833279263933
1261414.2729181932461-0.272918193246106
1271516.2239857707387-1.22398577073869
1281313.9311249183268-0.931124918326784
1291514.6056473722260.394352627774
1301114.2537990121-3.25379901210002
1311214.4104160847048-2.41041608470483
132813.9681560904257-5.96815609042569
1331615.97425470435470.025745295645268
1341515.5948091988777-0.594809198877692
1351715.54288778872171.45711221127833
1361614.87909479824011.12090520175987
1371015.2055609650956-5.20556096509563
1381814.16168583417853.83831416582149
1391313.7430386209006-0.743038620900636
1401514.74196046059950.258039539400483
1411614.11139811641461.88860188358541
1421615.10899260202320.891007397976815
1431414.2015700030607-0.201570003060701
1441013.9523220553945-3.95232205539449
1451716.44350754792480.556492452075229
1461315.1489044608854-2.14890446088543
1471516.0576682223147-1.05766822231465
1481615.77492957798370.225070422016346
1491215.5326927633043-3.53269276330432
1501314.0179669199171-1.01796691991715

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 15.9244033794531 & -2.92440337945306 \tabularnewline
2 & 16 & 15.7537883801615 & 0.246211619838472 \tabularnewline
3 & 19 & 15.4057083847125 & 3.59429161528748 \tabularnewline
4 & 15 & 13.7839279492215 & 1.21607205077851 \tabularnewline
5 & 14 & 14.9505730477062 & -0.950573047706224 \tabularnewline
6 & 13 & 15.3440780002771 & -2.34407800027713 \tabularnewline
7 & 19 & 14.7749452411399 & 4.22505475886011 \tabularnewline
8 & 15 & 15.999341490788 & -0.99934149078803 \tabularnewline
9 & 14 & 15.7352249296477 & -1.73522492964765 \tabularnewline
10 & 15 & 14.5096438458473 & 0.490356154152711 \tabularnewline
11 & 16 & 15.147417901838 & 0.852582098162033 \tabularnewline
12 & 16 & 15.0109219850529 & 0.98907801494712 \tabularnewline
13 & 16 & 14.8151634815918 & 1.18483651840823 \tabularnewline
14 & 17 & 16.7001035733494 & 0.299896426650644 \tabularnewline
15 & 15 & 11.5428174578176 & 3.45718254218244 \tabularnewline
16 & 15 & 15.5370191361718 & -0.537019136171766 \tabularnewline
17 & 20 & 16.2634258774095 & 3.7365741225905 \tabularnewline
18 & 18 & 16.0671671525525 & 1.93283284744751 \tabularnewline
19 & 16 & 16.2325291023858 & -0.232529102385761 \tabularnewline
20 & 16 & 13.9455165793955 & 2.05448342060446 \tabularnewline
21 & 19 & 15.3086157014263 & 3.69138429857371 \tabularnewline
22 & 16 & 14.6131207090056 & 1.38687929099442 \tabularnewline
23 & 17 & 16.0754446539469 & 0.924555346053067 \tabularnewline
24 & 17 & 15.1623835630412 & 1.83761643695884 \tabularnewline
25 & 16 & 14.1113981164146 & 1.88860188358541 \tabularnewline
26 & 15 & 13.7526207918341 & 1.24737920816588 \tabularnewline
27 & 14 & 15.8525075114034 & -1.85250751140338 \tabularnewline
28 & 15 & 15.8571549046436 & -0.857154904643638 \tabularnewline
29 & 12 & 14.5724869371323 & -2.57248693713228 \tabularnewline
30 & 14 & 15.1063170296204 & -1.10631702962038 \tabularnewline
31 & 16 & 15.5250306676379 & 0.474969332362088 \tabularnewline
32 & 14 & 15.6586053429802 & -1.65860534298015 \tabularnewline
33 & 7 & 14.1273172645846 & -7.12731726458463 \tabularnewline
34 & 10 & 12.774635684856 & -2.77463568485596 \tabularnewline
35 & 14 & 14.1523005086941 & -0.152300508694102 \tabularnewline
36 & 16 & 16.4066349494271 & -0.406634949427065 \tabularnewline
37 & 16 & 13.9703777149178 & 2.02962228508223 \tabularnewline
38 & 16 & 12.5366637102876 & 3.46333628971245 \tabularnewline
39 & 14 & 15.5565292391564 & -1.55652923915643 \tabularnewline
40 & 20 & 18.3653833883118 & 1.6346166116882 \tabularnewline
41 & 14 & 14.2389702289017 & -0.238970228901665 \tabularnewline
42 & 14 & 15.2325191694071 & -1.23251916940708 \tabularnewline
43 & 11 & 14.5725694031111 & -3.57256940311107 \tabularnewline
44 & 15 & 15.4554079952953 & -0.455407995295286 \tabularnewline
45 & 16 & 15.5125200773641 & 0.487479922635864 \tabularnewline
46 & 14 & 14.9762567257839 & -0.976256725783944 \tabularnewline
47 & 16 & 14.3860851039445 & 1.61391489605552 \tabularnewline
48 & 14 & 15.0668203990549 & -1.06682039905489 \tabularnewline
49 & 12 & 14.6988543566534 & -2.69885435665342 \tabularnewline
50 & 16 & 15.1790857500451 & 0.820914249954856 \tabularnewline
51 & 9 & 12.1697546075229 & -3.16975460752286 \tabularnewline
52 & 14 & 14.6423117000206 & -0.64231170002056 \tabularnewline
53 & 16 & 15.666511053809 & 0.333488946191029 \tabularnewline
54 & 16 & 15.1284267393245 & 0.871573260675543 \tabularnewline
55 & 15 & 14.6666780965847 & 0.333321903415258 \tabularnewline
56 & 16 & 15.2755527567287 & 0.724447243271338 \tabularnewline
57 & 12 & 13.1065870035093 & -1.10658700350926 \tabularnewline
58 & 16 & 16.033474846882 & -0.0334748468819896 \tabularnewline
59 & 16 & 16.0260662005621 & -0.0260662005620591 \tabularnewline
60 & 14 & 16.382009611696 & -2.38200961169597 \tabularnewline
61 & 16 & 13.6148836362444 & 2.38511636375564 \tabularnewline
62 & 17 & 16.8238241036963 & 0.176175896303671 \tabularnewline
63 & 18 & 14.796944482711 & 3.20305551728899 \tabularnewline
64 & 18 & 16.0772621786825 & 1.92273782131755 \tabularnewline
65 & 12 & 15.0086158419986 & -3.00861584199858 \tabularnewline
66 & 16 & 16.0120576157256 & -0.0120576157256189 \tabularnewline
67 & 10 & 13.9285122580368 & -3.92851225803681 \tabularnewline
68 & 14 & 13.179433058654 & 0.820566941345987 \tabularnewline
69 & 18 & 15.879095513538 & 2.12090448646198 \tabularnewline
70 & 18 & 17.1956789531862 & 0.80432104681376 \tabularnewline
71 & 16 & 15.171226792168 & 0.828773207831987 \tabularnewline
72 & 16 & 15.6043664588382 & 0.395633541161823 \tabularnewline
73 & 16 & 14.6619737067197 & 1.33802629328028 \tabularnewline
74 & 13 & 14.5023013137842 & -1.50230131378418 \tabularnewline
75 & 16 & 15.1146969118119 & 0.885303088188091 \tabularnewline
76 & 16 & 14.6869259484412 & 1.31307405155878 \tabularnewline
77 & 20 & 16.8175350964247 & 3.18246490357525 \tabularnewline
78 & 16 & 15.2578670109011 & 0.742132989098926 \tabularnewline
79 & 15 & 11.1225136152367 & 3.87748638476326 \tabularnewline
80 & 15 & 15.5863505207698 & -0.586350520769762 \tabularnewline
81 & 16 & 15.7119394975354 & 0.288060502464623 \tabularnewline
82 & 14 & 13.6902099260435 & 0.309790073956472 \tabularnewline
83 & 15 & 13.7792420513741 & 1.22075794862592 \tabularnewline
84 & 12 & 14.9870395642523 & -2.98703956425231 \tabularnewline
85 & 17 & 16.4435075479248 & 0.556492452075229 \tabularnewline
86 & 16 & 15.5006996868776 & 0.499300313122432 \tabularnewline
87 & 15 & 13.7996608748901 & 1.20033912510991 \tabularnewline
88 & 13 & 14.3893691806829 & -1.38936918068286 \tabularnewline
89 & 16 & 15.582204868988 & 0.41779513101199 \tabularnewline
90 & 16 & 15.3177938214201 & 0.682206178579898 \tabularnewline
91 & 16 & 15.9487319563102 & 0.0512680436898163 \tabularnewline
92 & 16 & 16.4773001309686 & -0.477300130968617 \tabularnewline
93 & 14 & 15.2449713483551 & -1.24497134835508 \tabularnewline
94 & 16 & 14.3564846291403 & 1.64351537085972 \tabularnewline
95 & 16 & 13.8230304036733 & 2.17696959632668 \tabularnewline
96 & 20 & 16.0757623245403 & 3.92423767545967 \tabularnewline
97 & 15 & 16.1749351449768 & -1.17493514497681 \tabularnewline
98 & 16 & 14.419096168496 & 1.58090383150398 \tabularnewline
99 & 13 & 13.0649088565378 & -0.0649088565377565 \tabularnewline
100 & 17 & 15.9058237546064 & 1.09417624539357 \tabularnewline
101 & 16 & 13.918983115302 & 2.08101688469799 \tabularnewline
102 & 12 & 13.3258665170576 & -1.32586651705765 \tabularnewline
103 & 16 & 14.9896405037922 & 1.01035949620776 \tabularnewline
104 & 16 & 15.2453599519217 & 0.75464004807832 \tabularnewline
105 & 17 & 15.342283663855 & 1.65771633614501 \tabularnewline
106 & 13 & 11.8302166483025 & 1.16978335169754 \tabularnewline
107 & 12 & 15.6929676906234 & -3.69296769062342 \tabularnewline
108 & 18 & 16.0000227516948 & 1.99997724830517 \tabularnewline
109 & 14 & 14.407654319102 & -0.407654319101987 \tabularnewline
110 & 14 & 14.696283475394 & -0.696283475394039 \tabularnewline
111 & 13 & 14.2191130615465 & -1.21911306154654 \tabularnewline
112 & 16 & 15.4597860142233 & 0.540213985776681 \tabularnewline
113 & 13 & 13.3858924291833 & -0.385892429183331 \tabularnewline
114 & 16 & 15.0149411238257 & 0.98505887617433 \tabularnewline
115 & 13 & 15.1798782726753 & -2.17987827267529 \tabularnewline
116 & 16 & 15.8928564590829 & 0.10714354091714 \tabularnewline
117 & 15 & 14.7471229731149 & 0.252877026885079 \tabularnewline
118 & 16 & 15.5672752636237 & 0.432724736376266 \tabularnewline
119 & 15 & 14.4371022084823 & 0.562897791517722 \tabularnewline
120 & 17 & 15.7711922826 & 1.22880771740001 \tabularnewline
121 & 15 & 16.0576682223147 & -1.05766822231465 \tabularnewline
122 & 12 & 14.1003992829567 & -2.10039928295671 \tabularnewline
123 & 16 & 14.2452537355041 & 1.75474626449588 \tabularnewline
124 & 10 & 14.5407968772968 & -4.54079687729676 \tabularnewline
125 & 16 & 14.9916672073607 & 1.00833279263933 \tabularnewline
126 & 14 & 14.2729181932461 & -0.272918193246106 \tabularnewline
127 & 15 & 16.2239857707387 & -1.22398577073869 \tabularnewline
128 & 13 & 13.9311249183268 & -0.931124918326784 \tabularnewline
129 & 15 & 14.605647372226 & 0.394352627774 \tabularnewline
130 & 11 & 14.2537990121 & -3.25379901210002 \tabularnewline
131 & 12 & 14.4104160847048 & -2.41041608470483 \tabularnewline
132 & 8 & 13.9681560904257 & -5.96815609042569 \tabularnewline
133 & 16 & 15.9742547043547 & 0.025745295645268 \tabularnewline
134 & 15 & 15.5948091988777 & -0.594809198877692 \tabularnewline
135 & 17 & 15.5428877887217 & 1.45711221127833 \tabularnewline
136 & 16 & 14.8790947982401 & 1.12090520175987 \tabularnewline
137 & 10 & 15.2055609650956 & -5.20556096509563 \tabularnewline
138 & 18 & 14.1616858341785 & 3.83831416582149 \tabularnewline
139 & 13 & 13.7430386209006 & -0.743038620900636 \tabularnewline
140 & 15 & 14.7419604605995 & 0.258039539400483 \tabularnewline
141 & 16 & 14.1113981164146 & 1.88860188358541 \tabularnewline
142 & 16 & 15.1089926020232 & 0.891007397976815 \tabularnewline
143 & 14 & 14.2015700030607 & -0.201570003060701 \tabularnewline
144 & 10 & 13.9523220553945 & -3.95232205539449 \tabularnewline
145 & 17 & 16.4435075479248 & 0.556492452075229 \tabularnewline
146 & 13 & 15.1489044608854 & -2.14890446088543 \tabularnewline
147 & 15 & 16.0576682223147 & -1.05766822231465 \tabularnewline
148 & 16 & 15.7749295779837 & 0.225070422016346 \tabularnewline
149 & 12 & 15.5326927633043 & -3.53269276330432 \tabularnewline
150 & 13 & 14.0179669199171 & -1.01796691991715 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98838&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]15.9244033794531[/C][C]-2.92440337945306[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.7537883801615[/C][C]0.246211619838472[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]15.4057083847125[/C][C]3.59429161528748[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]13.7839279492215[/C][C]1.21607205077851[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]14.9505730477062[/C][C]-0.950573047706224[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.3440780002771[/C][C]-2.34407800027713[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]14.7749452411399[/C][C]4.22505475886011[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]15.999341490788[/C][C]-0.99934149078803[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.7352249296477[/C][C]-1.73522492964765[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.5096438458473[/C][C]0.490356154152711[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.147417901838[/C][C]0.852582098162033[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]15.0109219850529[/C][C]0.98907801494712[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]14.8151634815918[/C][C]1.18483651840823[/C][/ROW]
[ROW][C]14[/C][C]17[/C][C]16.7001035733494[/C][C]0.299896426650644[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]11.5428174578176[/C][C]3.45718254218244[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.5370191361718[/C][C]-0.537019136171766[/C][/ROW]
[ROW][C]17[/C][C]20[/C][C]16.2634258774095[/C][C]3.7365741225905[/C][/ROW]
[ROW][C]18[/C][C]18[/C][C]16.0671671525525[/C][C]1.93283284744751[/C][/ROW]
[ROW][C]19[/C][C]16[/C][C]16.2325291023858[/C][C]-0.232529102385761[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.9455165793955[/C][C]2.05448342060446[/C][/ROW]
[ROW][C]21[/C][C]19[/C][C]15.3086157014263[/C][C]3.69138429857371[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.6131207090056[/C][C]1.38687929099442[/C][/ROW]
[ROW][C]23[/C][C]17[/C][C]16.0754446539469[/C][C]0.924555346053067[/C][/ROW]
[ROW][C]24[/C][C]17[/C][C]15.1623835630412[/C][C]1.83761643695884[/C][/ROW]
[ROW][C]25[/C][C]16[/C][C]14.1113981164146[/C][C]1.88860188358541[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]13.7526207918341[/C][C]1.24737920816588[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]15.8525075114034[/C][C]-1.85250751140338[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]15.8571549046436[/C][C]-0.857154904643638[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]14.5724869371323[/C][C]-2.57248693713228[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]15.1063170296204[/C][C]-1.10631702962038[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.5250306676379[/C][C]0.474969332362088[/C][/ROW]
[ROW][C]32[/C][C]14[/C][C]15.6586053429802[/C][C]-1.65860534298015[/C][/ROW]
[ROW][C]33[/C][C]7[/C][C]14.1273172645846[/C][C]-7.12731726458463[/C][/ROW]
[ROW][C]34[/C][C]10[/C][C]12.774635684856[/C][C]-2.77463568485596[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.1523005086941[/C][C]-0.152300508694102[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]16.4066349494271[/C][C]-0.406634949427065[/C][/ROW]
[ROW][C]37[/C][C]16[/C][C]13.9703777149178[/C][C]2.02962228508223[/C][/ROW]
[ROW][C]38[/C][C]16[/C][C]12.5366637102876[/C][C]3.46333628971245[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]15.5565292391564[/C][C]-1.55652923915643[/C][/ROW]
[ROW][C]40[/C][C]20[/C][C]18.3653833883118[/C][C]1.6346166116882[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]14.2389702289017[/C][C]-0.238970228901665[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.2325191694071[/C][C]-1.23251916940708[/C][/ROW]
[ROW][C]43[/C][C]11[/C][C]14.5725694031111[/C][C]-3.57256940311107[/C][/ROW]
[ROW][C]44[/C][C]15[/C][C]15.4554079952953[/C][C]-0.455407995295286[/C][/ROW]
[ROW][C]45[/C][C]16[/C][C]15.5125200773641[/C][C]0.487479922635864[/C][/ROW]
[ROW][C]46[/C][C]14[/C][C]14.9762567257839[/C][C]-0.976256725783944[/C][/ROW]
[ROW][C]47[/C][C]16[/C][C]14.3860851039445[/C][C]1.61391489605552[/C][/ROW]
[ROW][C]48[/C][C]14[/C][C]15.0668203990549[/C][C]-1.06682039905489[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]14.6988543566534[/C][C]-2.69885435665342[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]15.1790857500451[/C][C]0.820914249954856[/C][/ROW]
[ROW][C]51[/C][C]9[/C][C]12.1697546075229[/C][C]-3.16975460752286[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.6423117000206[/C][C]-0.64231170002056[/C][/ROW]
[ROW][C]53[/C][C]16[/C][C]15.666511053809[/C][C]0.333488946191029[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.1284267393245[/C][C]0.871573260675543[/C][/ROW]
[ROW][C]55[/C][C]15[/C][C]14.6666780965847[/C][C]0.333321903415258[/C][/ROW]
[ROW][C]56[/C][C]16[/C][C]15.2755527567287[/C][C]0.724447243271338[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]13.1065870035093[/C][C]-1.10658700350926[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]16.033474846882[/C][C]-0.0334748468819896[/C][/ROW]
[ROW][C]59[/C][C]16[/C][C]16.0260662005621[/C][C]-0.0260662005620591[/C][/ROW]
[ROW][C]60[/C][C]14[/C][C]16.382009611696[/C][C]-2.38200961169597[/C][/ROW]
[ROW][C]61[/C][C]16[/C][C]13.6148836362444[/C][C]2.38511636375564[/C][/ROW]
[ROW][C]62[/C][C]17[/C][C]16.8238241036963[/C][C]0.176175896303671[/C][/ROW]
[ROW][C]63[/C][C]18[/C][C]14.796944482711[/C][C]3.20305551728899[/C][/ROW]
[ROW][C]64[/C][C]18[/C][C]16.0772621786825[/C][C]1.92273782131755[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]15.0086158419986[/C][C]-3.00861584199858[/C][/ROW]
[ROW][C]66[/C][C]16[/C][C]16.0120576157256[/C][C]-0.0120576157256189[/C][/ROW]
[ROW][C]67[/C][C]10[/C][C]13.9285122580368[/C][C]-3.92851225803681[/C][/ROW]
[ROW][C]68[/C][C]14[/C][C]13.179433058654[/C][C]0.820566941345987[/C][/ROW]
[ROW][C]69[/C][C]18[/C][C]15.879095513538[/C][C]2.12090448646198[/C][/ROW]
[ROW][C]70[/C][C]18[/C][C]17.1956789531862[/C][C]0.80432104681376[/C][/ROW]
[ROW][C]71[/C][C]16[/C][C]15.171226792168[/C][C]0.828773207831987[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]15.6043664588382[/C][C]0.395633541161823[/C][/ROW]
[ROW][C]73[/C][C]16[/C][C]14.6619737067197[/C][C]1.33802629328028[/C][/ROW]
[ROW][C]74[/C][C]13[/C][C]14.5023013137842[/C][C]-1.50230131378418[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.1146969118119[/C][C]0.885303088188091[/C][/ROW]
[ROW][C]76[/C][C]16[/C][C]14.6869259484412[/C][C]1.31307405155878[/C][/ROW]
[ROW][C]77[/C][C]20[/C][C]16.8175350964247[/C][C]3.18246490357525[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]15.2578670109011[/C][C]0.742132989098926[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]11.1225136152367[/C][C]3.87748638476326[/C][/ROW]
[ROW][C]80[/C][C]15[/C][C]15.5863505207698[/C][C]-0.586350520769762[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.7119394975354[/C][C]0.288060502464623[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]13.6902099260435[/C][C]0.309790073956472[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]13.7792420513741[/C][C]1.22075794862592[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]14.9870395642523[/C][C]-2.98703956425231[/C][/ROW]
[ROW][C]85[/C][C]17[/C][C]16.4435075479248[/C][C]0.556492452075229[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]15.5006996868776[/C][C]0.499300313122432[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]13.7996608748901[/C][C]1.20033912510991[/C][/ROW]
[ROW][C]88[/C][C]13[/C][C]14.3893691806829[/C][C]-1.38936918068286[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]15.582204868988[/C][C]0.41779513101199[/C][/ROW]
[ROW][C]90[/C][C]16[/C][C]15.3177938214201[/C][C]0.682206178579898[/C][/ROW]
[ROW][C]91[/C][C]16[/C][C]15.9487319563102[/C][C]0.0512680436898163[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]16.4773001309686[/C][C]-0.477300130968617[/C][/ROW]
[ROW][C]93[/C][C]14[/C][C]15.2449713483551[/C][C]-1.24497134835508[/C][/ROW]
[ROW][C]94[/C][C]16[/C][C]14.3564846291403[/C][C]1.64351537085972[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]13.8230304036733[/C][C]2.17696959632668[/C][/ROW]
[ROW][C]96[/C][C]20[/C][C]16.0757623245403[/C][C]3.92423767545967[/C][/ROW]
[ROW][C]97[/C][C]15[/C][C]16.1749351449768[/C][C]-1.17493514497681[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.419096168496[/C][C]1.58090383150398[/C][/ROW]
[ROW][C]99[/C][C]13[/C][C]13.0649088565378[/C][C]-0.0649088565377565[/C][/ROW]
[ROW][C]100[/C][C]17[/C][C]15.9058237546064[/C][C]1.09417624539357[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]13.918983115302[/C][C]2.08101688469799[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]13.3258665170576[/C][C]-1.32586651705765[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]14.9896405037922[/C][C]1.01035949620776[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]15.2453599519217[/C][C]0.75464004807832[/C][/ROW]
[ROW][C]105[/C][C]17[/C][C]15.342283663855[/C][C]1.65771633614501[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]11.8302166483025[/C][C]1.16978335169754[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]15.6929676906234[/C][C]-3.69296769062342[/C][/ROW]
[ROW][C]108[/C][C]18[/C][C]16.0000227516948[/C][C]1.99997724830517[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]14.407654319102[/C][C]-0.407654319101987[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]14.696283475394[/C][C]-0.696283475394039[/C][/ROW]
[ROW][C]111[/C][C]13[/C][C]14.2191130615465[/C][C]-1.21911306154654[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.4597860142233[/C][C]0.540213985776681[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]13.3858924291833[/C][C]-0.385892429183331[/C][/ROW]
[ROW][C]114[/C][C]16[/C][C]15.0149411238257[/C][C]0.98505887617433[/C][/ROW]
[ROW][C]115[/C][C]13[/C][C]15.1798782726753[/C][C]-2.17987827267529[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]15.8928564590829[/C][C]0.10714354091714[/C][/ROW]
[ROW][C]117[/C][C]15[/C][C]14.7471229731149[/C][C]0.252877026885079[/C][/ROW]
[ROW][C]118[/C][C]16[/C][C]15.5672752636237[/C][C]0.432724736376266[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]14.4371022084823[/C][C]0.562897791517722[/C][/ROW]
[ROW][C]120[/C][C]17[/C][C]15.7711922826[/C][C]1.22880771740001[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]16.0576682223147[/C][C]-1.05766822231465[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]14.1003992829567[/C][C]-2.10039928295671[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]14.2452537355041[/C][C]1.75474626449588[/C][/ROW]
[ROW][C]124[/C][C]10[/C][C]14.5407968772968[/C][C]-4.54079687729676[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]14.9916672073607[/C][C]1.00833279263933[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]14.2729181932461[/C][C]-0.272918193246106[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]16.2239857707387[/C][C]-1.22398577073869[/C][/ROW]
[ROW][C]128[/C][C]13[/C][C]13.9311249183268[/C][C]-0.931124918326784[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]14.605647372226[/C][C]0.394352627774[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]14.2537990121[/C][C]-3.25379901210002[/C][/ROW]
[ROW][C]131[/C][C]12[/C][C]14.4104160847048[/C][C]-2.41041608470483[/C][/ROW]
[ROW][C]132[/C][C]8[/C][C]13.9681560904257[/C][C]-5.96815609042569[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]15.9742547043547[/C][C]0.025745295645268[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]15.5948091988777[/C][C]-0.594809198877692[/C][/ROW]
[ROW][C]135[/C][C]17[/C][C]15.5428877887217[/C][C]1.45711221127833[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]14.8790947982401[/C][C]1.12090520175987[/C][/ROW]
[ROW][C]137[/C][C]10[/C][C]15.2055609650956[/C][C]-5.20556096509563[/C][/ROW]
[ROW][C]138[/C][C]18[/C][C]14.1616858341785[/C][C]3.83831416582149[/C][/ROW]
[ROW][C]139[/C][C]13[/C][C]13.7430386209006[/C][C]-0.743038620900636[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]14.7419604605995[/C][C]0.258039539400483[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]14.1113981164146[/C][C]1.88860188358541[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]15.1089926020232[/C][C]0.891007397976815[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]14.2015700030607[/C][C]-0.201570003060701[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]13.9523220553945[/C][C]-3.95232205539449[/C][/ROW]
[ROW][C]145[/C][C]17[/C][C]16.4435075479248[/C][C]0.556492452075229[/C][/ROW]
[ROW][C]146[/C][C]13[/C][C]15.1489044608854[/C][C]-2.14890446088543[/C][/ROW]
[ROW][C]147[/C][C]15[/C][C]16.0576682223147[/C][C]-1.05766822231465[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]15.7749295779837[/C][C]0.225070422016346[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]15.5326927633043[/C][C]-3.53269276330432[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]14.0179669199171[/C][C]-1.01796691991715[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98838&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98838&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.9244033794531-2.92440337945306
21615.75378838016150.246211619838472
31915.40570838471253.59429161528748
41513.78392794922151.21607205077851
51414.9505730477062-0.950573047706224
61315.3440780002771-2.34407800027713
71914.77494524113994.22505475886011
81515.999341490788-0.99934149078803
91415.7352249296477-1.73522492964765
101514.50964384584730.490356154152711
111615.1474179018380.852582098162033
121615.01092198505290.98907801494712
131614.81516348159181.18483651840823
141716.70010357334940.299896426650644
151511.54281745781763.45718254218244
161515.5370191361718-0.537019136171766
172016.26342587740953.7365741225905
181816.06716715255251.93283284744751
191616.2325291023858-0.232529102385761
201613.94551657939552.05448342060446
211915.30861570142633.69138429857371
221614.61312070900561.38687929099442
231716.07544465394690.924555346053067
241715.16238356304121.83761643695884
251614.11139811641461.88860188358541
261513.75262079183411.24737920816588
271415.8525075114034-1.85250751140338
281515.8571549046436-0.857154904643638
291214.5724869371323-2.57248693713228
301415.1063170296204-1.10631702962038
311615.52503066763790.474969332362088
321415.6586053429802-1.65860534298015
33714.1273172645846-7.12731726458463
341012.774635684856-2.77463568485596
351414.1523005086941-0.152300508694102
361616.4066349494271-0.406634949427065
371613.97037771491782.02962228508223
381612.53666371028763.46333628971245
391415.5565292391564-1.55652923915643
402018.36538338831181.6346166116882
411414.2389702289017-0.238970228901665
421415.2325191694071-1.23251916940708
431114.5725694031111-3.57256940311107
441515.4554079952953-0.455407995295286
451615.51252007736410.487479922635864
461414.9762567257839-0.976256725783944
471614.38608510394451.61391489605552
481415.0668203990549-1.06682039905489
491214.6988543566534-2.69885435665342
501615.17908575004510.820914249954856
51912.1697546075229-3.16975460752286
521414.6423117000206-0.64231170002056
531615.6665110538090.333488946191029
541615.12842673932450.871573260675543
551514.66667809658470.333321903415258
561615.27555275672870.724447243271338
571213.1065870035093-1.10658700350926
581616.033474846882-0.0334748468819896
591616.0260662005621-0.0260662005620591
601416.382009611696-2.38200961169597
611613.61488363624442.38511636375564
621716.82382410369630.176175896303671
631814.7969444827113.20305551728899
641816.07726217868251.92273782131755
651215.0086158419986-3.00861584199858
661616.0120576157256-0.0120576157256189
671013.9285122580368-3.92851225803681
681413.1794330586540.820566941345987
691815.8790955135382.12090448646198
701817.19567895318620.80432104681376
711615.1712267921680.828773207831987
721615.60436645883820.395633541161823
731614.66197370671971.33802629328028
741314.5023013137842-1.50230131378418
751615.11469691181190.885303088188091
761614.68692594844121.31307405155878
772016.81753509642473.18246490357525
781615.25786701090110.742132989098926
791511.12251361523673.87748638476326
801515.5863505207698-0.586350520769762
811615.71193949753540.288060502464623
821413.69020992604350.309790073956472
831513.77924205137411.22075794862592
841214.9870395642523-2.98703956425231
851716.44350754792480.556492452075229
861615.50069968687760.499300313122432
871513.79966087489011.20033912510991
881314.3893691806829-1.38936918068286
891615.5822048689880.41779513101199
901615.31779382142010.682206178579898
911615.94873195631020.0512680436898163
921616.4773001309686-0.477300130968617
931415.2449713483551-1.24497134835508
941614.35648462914031.64351537085972
951613.82303040367332.17696959632668
962016.07576232454033.92423767545967
971516.1749351449768-1.17493514497681
981614.4190961684961.58090383150398
991313.0649088565378-0.0649088565377565
1001715.90582375460641.09417624539357
1011613.9189831153022.08101688469799
1021213.3258665170576-1.32586651705765
1031614.98964050379221.01035949620776
1041615.24535995192170.75464004807832
1051715.3422836638551.65771633614501
1061311.83021664830251.16978335169754
1071215.6929676906234-3.69296769062342
1081816.00002275169481.99997724830517
1091414.407654319102-0.407654319101987
1101414.696283475394-0.696283475394039
1111314.2191130615465-1.21911306154654
1121615.45978601422330.540213985776681
1131313.3858924291833-0.385892429183331
1141615.01494112382570.98505887617433
1151315.1798782726753-2.17987827267529
1161615.89285645908290.10714354091714
1171514.74712297311490.252877026885079
1181615.56727526362370.432724736376266
1191514.43710220848230.562897791517722
1201715.77119228261.22880771740001
1211516.0576682223147-1.05766822231465
1221214.1003992829567-2.10039928295671
1231614.24525373550411.75474626449588
1241014.5407968772968-4.54079687729676
1251614.99166720736071.00833279263933
1261414.2729181932461-0.272918193246106
1271516.2239857707387-1.22398577073869
1281313.9311249183268-0.931124918326784
1291514.6056473722260.394352627774
1301114.2537990121-3.25379901210002
1311214.4104160847048-2.41041608470483
132813.9681560904257-5.96815609042569
1331615.97425470435470.025745295645268
1341515.5948091988777-0.594809198877692
1351715.54288778872171.45711221127833
1361614.87909479824011.12090520175987
1371015.2055609650956-5.20556096509563
1381814.16168583417853.83831416582149
1391313.7430386209006-0.743038620900636
1401514.74196046059950.258039539400483
1411614.11139811641461.88860188358541
1421615.10899260202320.891007397976815
1431414.2015700030607-0.201570003060701
1441013.9523220553945-3.95232205539449
1451716.44350754792480.556492452075229
1461315.1489044608854-2.14890446088543
1471516.0576682223147-1.05766822231465
1481615.77492957798370.225070422016346
1491215.5326927633043-3.53269276330432
1501314.0179669199171-1.01796691991715







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.9252540898488040.1494918203023910.0747459101511956
160.858189477374650.2836210452506990.14181052262535
170.7843451536995940.4313096926008130.215654846300406
180.692137037253870.6157259254922610.307862962746131
190.5842199484757810.8315601030484370.415780051524219
200.4846348826363140.9692697652726290.515365117363686
210.6424200809153220.7151598381693560.357579919084678
220.6513534130455790.6972931739088420.348646586954421
230.5844319474111970.8311361051776050.415568052588803
240.5306806361614210.9386387276771580.469319363838579
250.475954927998610.951909855997220.52404507200139
260.4344944510614210.8689889021228410.56550554893858
270.3862629215546370.7725258431092750.613737078445363
280.5798258912323210.8403482175353590.420174108767679
290.6005436438770450.798912712245910.399456356122955
300.5508943156949960.8982113686100090.449105684305004
310.4932815943794010.9865631887588020.506718405620599
320.4331796289109590.8663592578219190.56682037108904
330.9675135736943580.06497285261128410.0324864263056421
340.9737402995410290.05251940091794270.0262597004589713
350.9630327360425120.07393452791497650.0369672639574882
360.9523227727542450.09535445449150920.0476772272457546
370.9513177261880060.09736454762398890.0486822738119944
380.9669707875586770.0660584248826460.033029212441323
390.9632267050996360.07354658980072760.0367732949003638
400.977288405857240.04542318828551850.0227115941427592
410.9682987219825850.06340255603482930.0317012780174147
420.9660046011118640.06799079777627150.0339953988881357
430.981518486049720.03696302790055960.0184815139502798
440.9744908202570120.05101835948597590.025509179742988
450.9662603585080430.06747928298391350.0337396414919567
460.9566165133128280.08676697337434360.0433834866871718
470.9476586333175030.1046827333649940.052341366682497
480.9356501014153850.1286997971692290.0643498985846147
490.9431536750398840.1136926499202330.0568463249601164
500.9279942902526750.1440114194946510.0720057097473255
510.9416906607719550.116618678456090.0583093392280449
520.926063447443730.1478731051125410.0739365525562703
530.9076434874818960.1847130250362080.0923565125181039
540.8925637841264740.2148724317470520.107436215873526
550.8707554193313860.2584891613372280.129244580668614
560.844736885690580.3105262286188410.155263114309421
570.817656868278910.3646862634421820.182343131721091
580.7837893885688910.4324212228622180.216210611431109
590.7449977317934080.5100045364131840.255002268206592
600.7552172653794250.489565469241150.244782734620575
610.7578651441201090.4842697117597820.242134855879891
620.7161777019927420.5676445960145160.283822298007258
630.764909132590240.4701817348195190.23509086740976
640.7607634459269610.4784731081460780.239236554073039
650.8101385429985310.3797229140029370.189861457001469
660.775950610782630.4480987784347410.224049389217371
670.8609571747742460.2780856504515080.139042825225754
680.8363407351803440.3273185296393110.163659264819656
690.840216976686320.3195660466273610.15978302331368
700.8131995475942630.3736009048114750.186800452405737
710.7838900916770190.4322198166459620.216109908322981
720.7466107474601510.5067785050796980.253389252539849
730.7215568101169080.5568863797661850.278443189883092
740.7059966949705940.5880066100588130.294003305029406
750.6862681601305710.6274636797388570.313731839869429
760.6558022452548930.6883955094902140.344197754745107
770.7207792049237390.5584415901525230.279220795076261
780.683709962268520.632580075462960.31629003773148
790.784504448469260.430991103061480.21549555153074
800.7487720519062740.5024558961874530.251227948093726
810.7071985673735380.5856028652529240.292801432626462
820.708155357168050.5836892856638990.29184464283195
830.6802954053479570.6394091893040860.319704594652043
840.7353281211845290.5293437576309420.264671878815471
850.6956199063443790.6087601873112420.304380093655621
860.6528734897835730.6942530204328540.347126510216427
870.626429806222220.747140387555560.37357019377778
880.6006769636214780.7986460727570440.399323036378522
890.5533318223396620.8933363553206760.446668177660338
900.5080263859283060.9839472281433880.491973614071694
910.4559820189620080.9119640379240160.544017981037992
920.4250340651914440.8500681303828870.574965934808556
930.4401375233585350.8802750467170690.559862476641465
940.4411058003969440.8822116007938880.558894199603056
950.4374157166474150.874831433294830.562584283352585
960.5907513660414950.818497267917010.409248633958505
970.5497690780591260.9004618438817480.450230921940874
980.5566153547609420.8867692904781160.443384645239058
990.5066399965993450.986720006801310.493360003400655
1000.4777576050296460.955515210059290.522242394970354
1010.4734849405900070.9469698811800140.526515059409993
1020.447597245682680.8951944913653610.55240275431732
1030.4136361801623160.8272723603246330.586363819837683
1040.3922962952702740.7845925905405490.607703704729726
1050.4066805079447090.8133610158894170.593319492055291
1060.4308567632743460.8617135265486920.569143236725654
1070.547581681343850.90483663731230.45241831865615
1080.5981874196438460.8036251607123080.401812580356154
1090.5754793752850980.8490412494298050.424520624714902
1100.5206273267819220.9587453464361560.479372673218078
1110.469863875666690.939727751333380.53013612433331
1120.4167620118793550.833524023758710.583237988120645
1130.3719357139185180.7438714278370360.628064286081482
1140.361924754328270.7238495086565390.63807524567173
1150.3494050622105590.6988101244211170.650594937789441
1160.2908370498446120.5816740996892240.709162950155388
1170.2678673266195250.535734653239050.732132673380475
1180.2635285689087910.5270571378175810.73647143109121
1190.2549358840650910.5098717681301820.745064115934909
1200.2312800604364790.4625601208729590.76871993956352
1210.1875062337622980.3750124675245950.812493766237703
1220.1647702653084060.3295405306168110.835229734691594
1230.1739643371093540.3479286742187080.826035662890646
1240.2520150730300270.5040301460600540.747984926969973
1250.1962594283955440.3925188567910890.803740571604456
1260.1484159863408890.2968319726817780.851584013659111
1270.1074862372234070.2149724744468140.892513762776593
1280.0961715354672520.1923430709345040.903828464532748
1290.06306165512634850.1261233102526970.936938344873651
1300.04901950714512780.09803901429025560.950980492854872
1310.03517916732024440.07035833464048880.964820832679756
1320.02891853569434670.05783707138869340.971081464305653
1330.0153597818539130.0307195637078260.984640218146087
1340.007022379659338090.01404475931867620.992977620340662
1350.01248050118681110.02496100237362210.987519498813189

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.925254089848804 & 0.149491820302391 & 0.0747459101511956 \tabularnewline
16 & 0.85818947737465 & 0.283621045250699 & 0.14181052262535 \tabularnewline
17 & 0.784345153699594 & 0.431309692600813 & 0.215654846300406 \tabularnewline
18 & 0.69213703725387 & 0.615725925492261 & 0.307862962746131 \tabularnewline
19 & 0.584219948475781 & 0.831560103048437 & 0.415780051524219 \tabularnewline
20 & 0.484634882636314 & 0.969269765272629 & 0.515365117363686 \tabularnewline
21 & 0.642420080915322 & 0.715159838169356 & 0.357579919084678 \tabularnewline
22 & 0.651353413045579 & 0.697293173908842 & 0.348646586954421 \tabularnewline
23 & 0.584431947411197 & 0.831136105177605 & 0.415568052588803 \tabularnewline
24 & 0.530680636161421 & 0.938638727677158 & 0.469319363838579 \tabularnewline
25 & 0.47595492799861 & 0.95190985599722 & 0.52404507200139 \tabularnewline
26 & 0.434494451061421 & 0.868988902122841 & 0.56550554893858 \tabularnewline
27 & 0.386262921554637 & 0.772525843109275 & 0.613737078445363 \tabularnewline
28 & 0.579825891232321 & 0.840348217535359 & 0.420174108767679 \tabularnewline
29 & 0.600543643877045 & 0.79891271224591 & 0.399456356122955 \tabularnewline
30 & 0.550894315694996 & 0.898211368610009 & 0.449105684305004 \tabularnewline
31 & 0.493281594379401 & 0.986563188758802 & 0.506718405620599 \tabularnewline
32 & 0.433179628910959 & 0.866359257821919 & 0.56682037108904 \tabularnewline
33 & 0.967513573694358 & 0.0649728526112841 & 0.0324864263056421 \tabularnewline
34 & 0.973740299541029 & 0.0525194009179427 & 0.0262597004589713 \tabularnewline
35 & 0.963032736042512 & 0.0739345279149765 & 0.0369672639574882 \tabularnewline
36 & 0.952322772754245 & 0.0953544544915092 & 0.0476772272457546 \tabularnewline
37 & 0.951317726188006 & 0.0973645476239889 & 0.0486822738119944 \tabularnewline
38 & 0.966970787558677 & 0.066058424882646 & 0.033029212441323 \tabularnewline
39 & 0.963226705099636 & 0.0735465898007276 & 0.0367732949003638 \tabularnewline
40 & 0.97728840585724 & 0.0454231882855185 & 0.0227115941427592 \tabularnewline
41 & 0.968298721982585 & 0.0634025560348293 & 0.0317012780174147 \tabularnewline
42 & 0.966004601111864 & 0.0679907977762715 & 0.0339953988881357 \tabularnewline
43 & 0.98151848604972 & 0.0369630279005596 & 0.0184815139502798 \tabularnewline
44 & 0.974490820257012 & 0.0510183594859759 & 0.025509179742988 \tabularnewline
45 & 0.966260358508043 & 0.0674792829839135 & 0.0337396414919567 \tabularnewline
46 & 0.956616513312828 & 0.0867669733743436 & 0.0433834866871718 \tabularnewline
47 & 0.947658633317503 & 0.104682733364994 & 0.052341366682497 \tabularnewline
48 & 0.935650101415385 & 0.128699797169229 & 0.0643498985846147 \tabularnewline
49 & 0.943153675039884 & 0.113692649920233 & 0.0568463249601164 \tabularnewline
50 & 0.927994290252675 & 0.144011419494651 & 0.0720057097473255 \tabularnewline
51 & 0.941690660771955 & 0.11661867845609 & 0.0583093392280449 \tabularnewline
52 & 0.92606344744373 & 0.147873105112541 & 0.0739365525562703 \tabularnewline
53 & 0.907643487481896 & 0.184713025036208 & 0.0923565125181039 \tabularnewline
54 & 0.892563784126474 & 0.214872431747052 & 0.107436215873526 \tabularnewline
55 & 0.870755419331386 & 0.258489161337228 & 0.129244580668614 \tabularnewline
56 & 0.84473688569058 & 0.310526228618841 & 0.155263114309421 \tabularnewline
57 & 0.81765686827891 & 0.364686263442182 & 0.182343131721091 \tabularnewline
58 & 0.783789388568891 & 0.432421222862218 & 0.216210611431109 \tabularnewline
59 & 0.744997731793408 & 0.510004536413184 & 0.255002268206592 \tabularnewline
60 & 0.755217265379425 & 0.48956546924115 & 0.244782734620575 \tabularnewline
61 & 0.757865144120109 & 0.484269711759782 & 0.242134855879891 \tabularnewline
62 & 0.716177701992742 & 0.567644596014516 & 0.283822298007258 \tabularnewline
63 & 0.76490913259024 & 0.470181734819519 & 0.23509086740976 \tabularnewline
64 & 0.760763445926961 & 0.478473108146078 & 0.239236554073039 \tabularnewline
65 & 0.810138542998531 & 0.379722914002937 & 0.189861457001469 \tabularnewline
66 & 0.77595061078263 & 0.448098778434741 & 0.224049389217371 \tabularnewline
67 & 0.860957174774246 & 0.278085650451508 & 0.139042825225754 \tabularnewline
68 & 0.836340735180344 & 0.327318529639311 & 0.163659264819656 \tabularnewline
69 & 0.84021697668632 & 0.319566046627361 & 0.15978302331368 \tabularnewline
70 & 0.813199547594263 & 0.373600904811475 & 0.186800452405737 \tabularnewline
71 & 0.783890091677019 & 0.432219816645962 & 0.216109908322981 \tabularnewline
72 & 0.746610747460151 & 0.506778505079698 & 0.253389252539849 \tabularnewline
73 & 0.721556810116908 & 0.556886379766185 & 0.278443189883092 \tabularnewline
74 & 0.705996694970594 & 0.588006610058813 & 0.294003305029406 \tabularnewline
75 & 0.686268160130571 & 0.627463679738857 & 0.313731839869429 \tabularnewline
76 & 0.655802245254893 & 0.688395509490214 & 0.344197754745107 \tabularnewline
77 & 0.720779204923739 & 0.558441590152523 & 0.279220795076261 \tabularnewline
78 & 0.68370996226852 & 0.63258007546296 & 0.31629003773148 \tabularnewline
79 & 0.78450444846926 & 0.43099110306148 & 0.21549555153074 \tabularnewline
80 & 0.748772051906274 & 0.502455896187453 & 0.251227948093726 \tabularnewline
81 & 0.707198567373538 & 0.585602865252924 & 0.292801432626462 \tabularnewline
82 & 0.70815535716805 & 0.583689285663899 & 0.29184464283195 \tabularnewline
83 & 0.680295405347957 & 0.639409189304086 & 0.319704594652043 \tabularnewline
84 & 0.735328121184529 & 0.529343757630942 & 0.264671878815471 \tabularnewline
85 & 0.695619906344379 & 0.608760187311242 & 0.304380093655621 \tabularnewline
86 & 0.652873489783573 & 0.694253020432854 & 0.347126510216427 \tabularnewline
87 & 0.62642980622222 & 0.74714038755556 & 0.37357019377778 \tabularnewline
88 & 0.600676963621478 & 0.798646072757044 & 0.399323036378522 \tabularnewline
89 & 0.553331822339662 & 0.893336355320676 & 0.446668177660338 \tabularnewline
90 & 0.508026385928306 & 0.983947228143388 & 0.491973614071694 \tabularnewline
91 & 0.455982018962008 & 0.911964037924016 & 0.544017981037992 \tabularnewline
92 & 0.425034065191444 & 0.850068130382887 & 0.574965934808556 \tabularnewline
93 & 0.440137523358535 & 0.880275046717069 & 0.559862476641465 \tabularnewline
94 & 0.441105800396944 & 0.882211600793888 & 0.558894199603056 \tabularnewline
95 & 0.437415716647415 & 0.87483143329483 & 0.562584283352585 \tabularnewline
96 & 0.590751366041495 & 0.81849726791701 & 0.409248633958505 \tabularnewline
97 & 0.549769078059126 & 0.900461843881748 & 0.450230921940874 \tabularnewline
98 & 0.556615354760942 & 0.886769290478116 & 0.443384645239058 \tabularnewline
99 & 0.506639996599345 & 0.98672000680131 & 0.493360003400655 \tabularnewline
100 & 0.477757605029646 & 0.95551521005929 & 0.522242394970354 \tabularnewline
101 & 0.473484940590007 & 0.946969881180014 & 0.526515059409993 \tabularnewline
102 & 0.44759724568268 & 0.895194491365361 & 0.55240275431732 \tabularnewline
103 & 0.413636180162316 & 0.827272360324633 & 0.586363819837683 \tabularnewline
104 & 0.392296295270274 & 0.784592590540549 & 0.607703704729726 \tabularnewline
105 & 0.406680507944709 & 0.813361015889417 & 0.593319492055291 \tabularnewline
106 & 0.430856763274346 & 0.861713526548692 & 0.569143236725654 \tabularnewline
107 & 0.54758168134385 & 0.9048366373123 & 0.45241831865615 \tabularnewline
108 & 0.598187419643846 & 0.803625160712308 & 0.401812580356154 \tabularnewline
109 & 0.575479375285098 & 0.849041249429805 & 0.424520624714902 \tabularnewline
110 & 0.520627326781922 & 0.958745346436156 & 0.479372673218078 \tabularnewline
111 & 0.46986387566669 & 0.93972775133338 & 0.53013612433331 \tabularnewline
112 & 0.416762011879355 & 0.83352402375871 & 0.583237988120645 \tabularnewline
113 & 0.371935713918518 & 0.743871427837036 & 0.628064286081482 \tabularnewline
114 & 0.36192475432827 & 0.723849508656539 & 0.63807524567173 \tabularnewline
115 & 0.349405062210559 & 0.698810124421117 & 0.650594937789441 \tabularnewline
116 & 0.290837049844612 & 0.581674099689224 & 0.709162950155388 \tabularnewline
117 & 0.267867326619525 & 0.53573465323905 & 0.732132673380475 \tabularnewline
118 & 0.263528568908791 & 0.527057137817581 & 0.73647143109121 \tabularnewline
119 & 0.254935884065091 & 0.509871768130182 & 0.745064115934909 \tabularnewline
120 & 0.231280060436479 & 0.462560120872959 & 0.76871993956352 \tabularnewline
121 & 0.187506233762298 & 0.375012467524595 & 0.812493766237703 \tabularnewline
122 & 0.164770265308406 & 0.329540530616811 & 0.835229734691594 \tabularnewline
123 & 0.173964337109354 & 0.347928674218708 & 0.826035662890646 \tabularnewline
124 & 0.252015073030027 & 0.504030146060054 & 0.747984926969973 \tabularnewline
125 & 0.196259428395544 & 0.392518856791089 & 0.803740571604456 \tabularnewline
126 & 0.148415986340889 & 0.296831972681778 & 0.851584013659111 \tabularnewline
127 & 0.107486237223407 & 0.214972474446814 & 0.892513762776593 \tabularnewline
128 & 0.096171535467252 & 0.192343070934504 & 0.903828464532748 \tabularnewline
129 & 0.0630616551263485 & 0.126123310252697 & 0.936938344873651 \tabularnewline
130 & 0.0490195071451278 & 0.0980390142902556 & 0.950980492854872 \tabularnewline
131 & 0.0351791673202444 & 0.0703583346404888 & 0.964820832679756 \tabularnewline
132 & 0.0289185356943467 & 0.0578370713886934 & 0.971081464305653 \tabularnewline
133 & 0.015359781853913 & 0.030719563707826 & 0.984640218146087 \tabularnewline
134 & 0.00702237965933809 & 0.0140447593186762 & 0.992977620340662 \tabularnewline
135 & 0.0124805011868111 & 0.0249610023736221 & 0.987519498813189 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98838&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]15[/C][C]0.925254089848804[/C][C]0.149491820302391[/C][C]0.0747459101511956[/C][/ROW]
[ROW][C]16[/C][C]0.85818947737465[/C][C]0.283621045250699[/C][C]0.14181052262535[/C][/ROW]
[ROW][C]17[/C][C]0.784345153699594[/C][C]0.431309692600813[/C][C]0.215654846300406[/C][/ROW]
[ROW][C]18[/C][C]0.69213703725387[/C][C]0.615725925492261[/C][C]0.307862962746131[/C][/ROW]
[ROW][C]19[/C][C]0.584219948475781[/C][C]0.831560103048437[/C][C]0.415780051524219[/C][/ROW]
[ROW][C]20[/C][C]0.484634882636314[/C][C]0.969269765272629[/C][C]0.515365117363686[/C][/ROW]
[ROW][C]21[/C][C]0.642420080915322[/C][C]0.715159838169356[/C][C]0.357579919084678[/C][/ROW]
[ROW][C]22[/C][C]0.651353413045579[/C][C]0.697293173908842[/C][C]0.348646586954421[/C][/ROW]
[ROW][C]23[/C][C]0.584431947411197[/C][C]0.831136105177605[/C][C]0.415568052588803[/C][/ROW]
[ROW][C]24[/C][C]0.530680636161421[/C][C]0.938638727677158[/C][C]0.469319363838579[/C][/ROW]
[ROW][C]25[/C][C]0.47595492799861[/C][C]0.95190985599722[/C][C]0.52404507200139[/C][/ROW]
[ROW][C]26[/C][C]0.434494451061421[/C][C]0.868988902122841[/C][C]0.56550554893858[/C][/ROW]
[ROW][C]27[/C][C]0.386262921554637[/C][C]0.772525843109275[/C][C]0.613737078445363[/C][/ROW]
[ROW][C]28[/C][C]0.579825891232321[/C][C]0.840348217535359[/C][C]0.420174108767679[/C][/ROW]
[ROW][C]29[/C][C]0.600543643877045[/C][C]0.79891271224591[/C][C]0.399456356122955[/C][/ROW]
[ROW][C]30[/C][C]0.550894315694996[/C][C]0.898211368610009[/C][C]0.449105684305004[/C][/ROW]
[ROW][C]31[/C][C]0.493281594379401[/C][C]0.986563188758802[/C][C]0.506718405620599[/C][/ROW]
[ROW][C]32[/C][C]0.433179628910959[/C][C]0.866359257821919[/C][C]0.56682037108904[/C][/ROW]
[ROW][C]33[/C][C]0.967513573694358[/C][C]0.0649728526112841[/C][C]0.0324864263056421[/C][/ROW]
[ROW][C]34[/C][C]0.973740299541029[/C][C]0.0525194009179427[/C][C]0.0262597004589713[/C][/ROW]
[ROW][C]35[/C][C]0.963032736042512[/C][C]0.0739345279149765[/C][C]0.0369672639574882[/C][/ROW]
[ROW][C]36[/C][C]0.952322772754245[/C][C]0.0953544544915092[/C][C]0.0476772272457546[/C][/ROW]
[ROW][C]37[/C][C]0.951317726188006[/C][C]0.0973645476239889[/C][C]0.0486822738119944[/C][/ROW]
[ROW][C]38[/C][C]0.966970787558677[/C][C]0.066058424882646[/C][C]0.033029212441323[/C][/ROW]
[ROW][C]39[/C][C]0.963226705099636[/C][C]0.0735465898007276[/C][C]0.0367732949003638[/C][/ROW]
[ROW][C]40[/C][C]0.97728840585724[/C][C]0.0454231882855185[/C][C]0.0227115941427592[/C][/ROW]
[ROW][C]41[/C][C]0.968298721982585[/C][C]0.0634025560348293[/C][C]0.0317012780174147[/C][/ROW]
[ROW][C]42[/C][C]0.966004601111864[/C][C]0.0679907977762715[/C][C]0.0339953988881357[/C][/ROW]
[ROW][C]43[/C][C]0.98151848604972[/C][C]0.0369630279005596[/C][C]0.0184815139502798[/C][/ROW]
[ROW][C]44[/C][C]0.974490820257012[/C][C]0.0510183594859759[/C][C]0.025509179742988[/C][/ROW]
[ROW][C]45[/C][C]0.966260358508043[/C][C]0.0674792829839135[/C][C]0.0337396414919567[/C][/ROW]
[ROW][C]46[/C][C]0.956616513312828[/C][C]0.0867669733743436[/C][C]0.0433834866871718[/C][/ROW]
[ROW][C]47[/C][C]0.947658633317503[/C][C]0.104682733364994[/C][C]0.052341366682497[/C][/ROW]
[ROW][C]48[/C][C]0.935650101415385[/C][C]0.128699797169229[/C][C]0.0643498985846147[/C][/ROW]
[ROW][C]49[/C][C]0.943153675039884[/C][C]0.113692649920233[/C][C]0.0568463249601164[/C][/ROW]
[ROW][C]50[/C][C]0.927994290252675[/C][C]0.144011419494651[/C][C]0.0720057097473255[/C][/ROW]
[ROW][C]51[/C][C]0.941690660771955[/C][C]0.11661867845609[/C][C]0.0583093392280449[/C][/ROW]
[ROW][C]52[/C][C]0.92606344744373[/C][C]0.147873105112541[/C][C]0.0739365525562703[/C][/ROW]
[ROW][C]53[/C][C]0.907643487481896[/C][C]0.184713025036208[/C][C]0.0923565125181039[/C][/ROW]
[ROW][C]54[/C][C]0.892563784126474[/C][C]0.214872431747052[/C][C]0.107436215873526[/C][/ROW]
[ROW][C]55[/C][C]0.870755419331386[/C][C]0.258489161337228[/C][C]0.129244580668614[/C][/ROW]
[ROW][C]56[/C][C]0.84473688569058[/C][C]0.310526228618841[/C][C]0.155263114309421[/C][/ROW]
[ROW][C]57[/C][C]0.81765686827891[/C][C]0.364686263442182[/C][C]0.182343131721091[/C][/ROW]
[ROW][C]58[/C][C]0.783789388568891[/C][C]0.432421222862218[/C][C]0.216210611431109[/C][/ROW]
[ROW][C]59[/C][C]0.744997731793408[/C][C]0.510004536413184[/C][C]0.255002268206592[/C][/ROW]
[ROW][C]60[/C][C]0.755217265379425[/C][C]0.48956546924115[/C][C]0.244782734620575[/C][/ROW]
[ROW][C]61[/C][C]0.757865144120109[/C][C]0.484269711759782[/C][C]0.242134855879891[/C][/ROW]
[ROW][C]62[/C][C]0.716177701992742[/C][C]0.567644596014516[/C][C]0.283822298007258[/C][/ROW]
[ROW][C]63[/C][C]0.76490913259024[/C][C]0.470181734819519[/C][C]0.23509086740976[/C][/ROW]
[ROW][C]64[/C][C]0.760763445926961[/C][C]0.478473108146078[/C][C]0.239236554073039[/C][/ROW]
[ROW][C]65[/C][C]0.810138542998531[/C][C]0.379722914002937[/C][C]0.189861457001469[/C][/ROW]
[ROW][C]66[/C][C]0.77595061078263[/C][C]0.448098778434741[/C][C]0.224049389217371[/C][/ROW]
[ROW][C]67[/C][C]0.860957174774246[/C][C]0.278085650451508[/C][C]0.139042825225754[/C][/ROW]
[ROW][C]68[/C][C]0.836340735180344[/C][C]0.327318529639311[/C][C]0.163659264819656[/C][/ROW]
[ROW][C]69[/C][C]0.84021697668632[/C][C]0.319566046627361[/C][C]0.15978302331368[/C][/ROW]
[ROW][C]70[/C][C]0.813199547594263[/C][C]0.373600904811475[/C][C]0.186800452405737[/C][/ROW]
[ROW][C]71[/C][C]0.783890091677019[/C][C]0.432219816645962[/C][C]0.216109908322981[/C][/ROW]
[ROW][C]72[/C][C]0.746610747460151[/C][C]0.506778505079698[/C][C]0.253389252539849[/C][/ROW]
[ROW][C]73[/C][C]0.721556810116908[/C][C]0.556886379766185[/C][C]0.278443189883092[/C][/ROW]
[ROW][C]74[/C][C]0.705996694970594[/C][C]0.588006610058813[/C][C]0.294003305029406[/C][/ROW]
[ROW][C]75[/C][C]0.686268160130571[/C][C]0.627463679738857[/C][C]0.313731839869429[/C][/ROW]
[ROW][C]76[/C][C]0.655802245254893[/C][C]0.688395509490214[/C][C]0.344197754745107[/C][/ROW]
[ROW][C]77[/C][C]0.720779204923739[/C][C]0.558441590152523[/C][C]0.279220795076261[/C][/ROW]
[ROW][C]78[/C][C]0.68370996226852[/C][C]0.63258007546296[/C][C]0.31629003773148[/C][/ROW]
[ROW][C]79[/C][C]0.78450444846926[/C][C]0.43099110306148[/C][C]0.21549555153074[/C][/ROW]
[ROW][C]80[/C][C]0.748772051906274[/C][C]0.502455896187453[/C][C]0.251227948093726[/C][/ROW]
[ROW][C]81[/C][C]0.707198567373538[/C][C]0.585602865252924[/C][C]0.292801432626462[/C][/ROW]
[ROW][C]82[/C][C]0.70815535716805[/C][C]0.583689285663899[/C][C]0.29184464283195[/C][/ROW]
[ROW][C]83[/C][C]0.680295405347957[/C][C]0.639409189304086[/C][C]0.319704594652043[/C][/ROW]
[ROW][C]84[/C][C]0.735328121184529[/C][C]0.529343757630942[/C][C]0.264671878815471[/C][/ROW]
[ROW][C]85[/C][C]0.695619906344379[/C][C]0.608760187311242[/C][C]0.304380093655621[/C][/ROW]
[ROW][C]86[/C][C]0.652873489783573[/C][C]0.694253020432854[/C][C]0.347126510216427[/C][/ROW]
[ROW][C]87[/C][C]0.62642980622222[/C][C]0.74714038755556[/C][C]0.37357019377778[/C][/ROW]
[ROW][C]88[/C][C]0.600676963621478[/C][C]0.798646072757044[/C][C]0.399323036378522[/C][/ROW]
[ROW][C]89[/C][C]0.553331822339662[/C][C]0.893336355320676[/C][C]0.446668177660338[/C][/ROW]
[ROW][C]90[/C][C]0.508026385928306[/C][C]0.983947228143388[/C][C]0.491973614071694[/C][/ROW]
[ROW][C]91[/C][C]0.455982018962008[/C][C]0.911964037924016[/C][C]0.544017981037992[/C][/ROW]
[ROW][C]92[/C][C]0.425034065191444[/C][C]0.850068130382887[/C][C]0.574965934808556[/C][/ROW]
[ROW][C]93[/C][C]0.440137523358535[/C][C]0.880275046717069[/C][C]0.559862476641465[/C][/ROW]
[ROW][C]94[/C][C]0.441105800396944[/C][C]0.882211600793888[/C][C]0.558894199603056[/C][/ROW]
[ROW][C]95[/C][C]0.437415716647415[/C][C]0.87483143329483[/C][C]0.562584283352585[/C][/ROW]
[ROW][C]96[/C][C]0.590751366041495[/C][C]0.81849726791701[/C][C]0.409248633958505[/C][/ROW]
[ROW][C]97[/C][C]0.549769078059126[/C][C]0.900461843881748[/C][C]0.450230921940874[/C][/ROW]
[ROW][C]98[/C][C]0.556615354760942[/C][C]0.886769290478116[/C][C]0.443384645239058[/C][/ROW]
[ROW][C]99[/C][C]0.506639996599345[/C][C]0.98672000680131[/C][C]0.493360003400655[/C][/ROW]
[ROW][C]100[/C][C]0.477757605029646[/C][C]0.95551521005929[/C][C]0.522242394970354[/C][/ROW]
[ROW][C]101[/C][C]0.473484940590007[/C][C]0.946969881180014[/C][C]0.526515059409993[/C][/ROW]
[ROW][C]102[/C][C]0.44759724568268[/C][C]0.895194491365361[/C][C]0.55240275431732[/C][/ROW]
[ROW][C]103[/C][C]0.413636180162316[/C][C]0.827272360324633[/C][C]0.586363819837683[/C][/ROW]
[ROW][C]104[/C][C]0.392296295270274[/C][C]0.784592590540549[/C][C]0.607703704729726[/C][/ROW]
[ROW][C]105[/C][C]0.406680507944709[/C][C]0.813361015889417[/C][C]0.593319492055291[/C][/ROW]
[ROW][C]106[/C][C]0.430856763274346[/C][C]0.861713526548692[/C][C]0.569143236725654[/C][/ROW]
[ROW][C]107[/C][C]0.54758168134385[/C][C]0.9048366373123[/C][C]0.45241831865615[/C][/ROW]
[ROW][C]108[/C][C]0.598187419643846[/C][C]0.803625160712308[/C][C]0.401812580356154[/C][/ROW]
[ROW][C]109[/C][C]0.575479375285098[/C][C]0.849041249429805[/C][C]0.424520624714902[/C][/ROW]
[ROW][C]110[/C][C]0.520627326781922[/C][C]0.958745346436156[/C][C]0.479372673218078[/C][/ROW]
[ROW][C]111[/C][C]0.46986387566669[/C][C]0.93972775133338[/C][C]0.53013612433331[/C][/ROW]
[ROW][C]112[/C][C]0.416762011879355[/C][C]0.83352402375871[/C][C]0.583237988120645[/C][/ROW]
[ROW][C]113[/C][C]0.371935713918518[/C][C]0.743871427837036[/C][C]0.628064286081482[/C][/ROW]
[ROW][C]114[/C][C]0.36192475432827[/C][C]0.723849508656539[/C][C]0.63807524567173[/C][/ROW]
[ROW][C]115[/C][C]0.349405062210559[/C][C]0.698810124421117[/C][C]0.650594937789441[/C][/ROW]
[ROW][C]116[/C][C]0.290837049844612[/C][C]0.581674099689224[/C][C]0.709162950155388[/C][/ROW]
[ROW][C]117[/C][C]0.267867326619525[/C][C]0.53573465323905[/C][C]0.732132673380475[/C][/ROW]
[ROW][C]118[/C][C]0.263528568908791[/C][C]0.527057137817581[/C][C]0.73647143109121[/C][/ROW]
[ROW][C]119[/C][C]0.254935884065091[/C][C]0.509871768130182[/C][C]0.745064115934909[/C][/ROW]
[ROW][C]120[/C][C]0.231280060436479[/C][C]0.462560120872959[/C][C]0.76871993956352[/C][/ROW]
[ROW][C]121[/C][C]0.187506233762298[/C][C]0.375012467524595[/C][C]0.812493766237703[/C][/ROW]
[ROW][C]122[/C][C]0.164770265308406[/C][C]0.329540530616811[/C][C]0.835229734691594[/C][/ROW]
[ROW][C]123[/C][C]0.173964337109354[/C][C]0.347928674218708[/C][C]0.826035662890646[/C][/ROW]
[ROW][C]124[/C][C]0.252015073030027[/C][C]0.504030146060054[/C][C]0.747984926969973[/C][/ROW]
[ROW][C]125[/C][C]0.196259428395544[/C][C]0.392518856791089[/C][C]0.803740571604456[/C][/ROW]
[ROW][C]126[/C][C]0.148415986340889[/C][C]0.296831972681778[/C][C]0.851584013659111[/C][/ROW]
[ROW][C]127[/C][C]0.107486237223407[/C][C]0.214972474446814[/C][C]0.892513762776593[/C][/ROW]
[ROW][C]128[/C][C]0.096171535467252[/C][C]0.192343070934504[/C][C]0.903828464532748[/C][/ROW]
[ROW][C]129[/C][C]0.0630616551263485[/C][C]0.126123310252697[/C][C]0.936938344873651[/C][/ROW]
[ROW][C]130[/C][C]0.0490195071451278[/C][C]0.0980390142902556[/C][C]0.950980492854872[/C][/ROW]
[ROW][C]131[/C][C]0.0351791673202444[/C][C]0.0703583346404888[/C][C]0.964820832679756[/C][/ROW]
[ROW][C]132[/C][C]0.0289185356943467[/C][C]0.0578370713886934[/C][C]0.971081464305653[/C][/ROW]
[ROW][C]133[/C][C]0.015359781853913[/C][C]0.030719563707826[/C][C]0.984640218146087[/C][/ROW]
[ROW][C]134[/C][C]0.00702237965933809[/C][C]0.0140447593186762[/C][C]0.992977620340662[/C][/ROW]
[ROW][C]135[/C][C]0.0124805011868111[/C][C]0.0249610023736221[/C][C]0.987519498813189[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98838&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98838&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.9252540898488040.1494918203023910.0747459101511956
160.858189477374650.2836210452506990.14181052262535
170.7843451536995940.4313096926008130.215654846300406
180.692137037253870.6157259254922610.307862962746131
190.5842199484757810.8315601030484370.415780051524219
200.4846348826363140.9692697652726290.515365117363686
210.6424200809153220.7151598381693560.357579919084678
220.6513534130455790.6972931739088420.348646586954421
230.5844319474111970.8311361051776050.415568052588803
240.5306806361614210.9386387276771580.469319363838579
250.475954927998610.951909855997220.52404507200139
260.4344944510614210.8689889021228410.56550554893858
270.3862629215546370.7725258431092750.613737078445363
280.5798258912323210.8403482175353590.420174108767679
290.6005436438770450.798912712245910.399456356122955
300.5508943156949960.8982113686100090.449105684305004
310.4932815943794010.9865631887588020.506718405620599
320.4331796289109590.8663592578219190.56682037108904
330.9675135736943580.06497285261128410.0324864263056421
340.9737402995410290.05251940091794270.0262597004589713
350.9630327360425120.07393452791497650.0369672639574882
360.9523227727542450.09535445449150920.0476772272457546
370.9513177261880060.09736454762398890.0486822738119944
380.9669707875586770.0660584248826460.033029212441323
390.9632267050996360.07354658980072760.0367732949003638
400.977288405857240.04542318828551850.0227115941427592
410.9682987219825850.06340255603482930.0317012780174147
420.9660046011118640.06799079777627150.0339953988881357
430.981518486049720.03696302790055960.0184815139502798
440.9744908202570120.05101835948597590.025509179742988
450.9662603585080430.06747928298391350.0337396414919567
460.9566165133128280.08676697337434360.0433834866871718
470.9476586333175030.1046827333649940.052341366682497
480.9356501014153850.1286997971692290.0643498985846147
490.9431536750398840.1136926499202330.0568463249601164
500.9279942902526750.1440114194946510.0720057097473255
510.9416906607719550.116618678456090.0583093392280449
520.926063447443730.1478731051125410.0739365525562703
530.9076434874818960.1847130250362080.0923565125181039
540.8925637841264740.2148724317470520.107436215873526
550.8707554193313860.2584891613372280.129244580668614
560.844736885690580.3105262286188410.155263114309421
570.817656868278910.3646862634421820.182343131721091
580.7837893885688910.4324212228622180.216210611431109
590.7449977317934080.5100045364131840.255002268206592
600.7552172653794250.489565469241150.244782734620575
610.7578651441201090.4842697117597820.242134855879891
620.7161777019927420.5676445960145160.283822298007258
630.764909132590240.4701817348195190.23509086740976
640.7607634459269610.4784731081460780.239236554073039
650.8101385429985310.3797229140029370.189861457001469
660.775950610782630.4480987784347410.224049389217371
670.8609571747742460.2780856504515080.139042825225754
680.8363407351803440.3273185296393110.163659264819656
690.840216976686320.3195660466273610.15978302331368
700.8131995475942630.3736009048114750.186800452405737
710.7838900916770190.4322198166459620.216109908322981
720.7466107474601510.5067785050796980.253389252539849
730.7215568101169080.5568863797661850.278443189883092
740.7059966949705940.5880066100588130.294003305029406
750.6862681601305710.6274636797388570.313731839869429
760.6558022452548930.6883955094902140.344197754745107
770.7207792049237390.5584415901525230.279220795076261
780.683709962268520.632580075462960.31629003773148
790.784504448469260.430991103061480.21549555153074
800.7487720519062740.5024558961874530.251227948093726
810.7071985673735380.5856028652529240.292801432626462
820.708155357168050.5836892856638990.29184464283195
830.6802954053479570.6394091893040860.319704594652043
840.7353281211845290.5293437576309420.264671878815471
850.6956199063443790.6087601873112420.304380093655621
860.6528734897835730.6942530204328540.347126510216427
870.626429806222220.747140387555560.37357019377778
880.6006769636214780.7986460727570440.399323036378522
890.5533318223396620.8933363553206760.446668177660338
900.5080263859283060.9839472281433880.491973614071694
910.4559820189620080.9119640379240160.544017981037992
920.4250340651914440.8500681303828870.574965934808556
930.4401375233585350.8802750467170690.559862476641465
940.4411058003969440.8822116007938880.558894199603056
950.4374157166474150.874831433294830.562584283352585
960.5907513660414950.818497267917010.409248633958505
970.5497690780591260.9004618438817480.450230921940874
980.5566153547609420.8867692904781160.443384645239058
990.5066399965993450.986720006801310.493360003400655
1000.4777576050296460.955515210059290.522242394970354
1010.4734849405900070.9469698811800140.526515059409993
1020.447597245682680.8951944913653610.55240275431732
1030.4136361801623160.8272723603246330.586363819837683
1040.3922962952702740.7845925905405490.607703704729726
1050.4066805079447090.8133610158894170.593319492055291
1060.4308567632743460.8617135265486920.569143236725654
1070.547581681343850.90483663731230.45241831865615
1080.5981874196438460.8036251607123080.401812580356154
1090.5754793752850980.8490412494298050.424520624714902
1100.5206273267819220.9587453464361560.479372673218078
1110.469863875666690.939727751333380.53013612433331
1120.4167620118793550.833524023758710.583237988120645
1130.3719357139185180.7438714278370360.628064286081482
1140.361924754328270.7238495086565390.63807524567173
1150.3494050622105590.6988101244211170.650594937789441
1160.2908370498446120.5816740996892240.709162950155388
1170.2678673266195250.535734653239050.732132673380475
1180.2635285689087910.5270571378175810.73647143109121
1190.2549358840650910.5098717681301820.745064115934909
1200.2312800604364790.4625601208729590.76871993956352
1210.1875062337622980.3750124675245950.812493766237703
1220.1647702653084060.3295405306168110.835229734691594
1230.1739643371093540.3479286742187080.826035662890646
1240.2520150730300270.5040301460600540.747984926969973
1250.1962594283955440.3925188567910890.803740571604456
1260.1484159863408890.2968319726817780.851584013659111
1270.1074862372234070.2149724744468140.892513762776593
1280.0961715354672520.1923430709345040.903828464532748
1290.06306165512634850.1261233102526970.936938344873651
1300.04901950714512780.09803901429025560.950980492854872
1310.03517916732024440.07035833464048880.964820832679756
1320.02891853569434670.05783707138869340.971081464305653
1330.0153597818539130.0307195637078260.984640218146087
1340.007022379659338090.01404475931867620.992977620340662
1350.01248050118681110.02496100237362210.987519498813189







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.0413223140495868OK
10% type I error level200.165289256198347NOK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 5 & 0.0413223140495868 & OK \tabularnewline
10% type I error level & 20 & 0.165289256198347 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=98838&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]5[/C][C]0.0413223140495868[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.165289256198347[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=98838&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=98838&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level50.0413223140495868OK
10% type I error level200.165289256198347NOK



Parameters (Session):
par1 = 0 ; par2 = 36 ;
Parameters (R input):
par1 = 2 ; 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')
}