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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 computationSun, 16 Dec 2012 13:09:28 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/16/t13556814019yxqfi9bwnrb4it.htm/, Retrieved Fri, 29 Mar 2024 00:33:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200535, Retrieved Fri, 29 Mar 2024 00:33:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact164
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2012-10-21 14:51:03] [235928acca9c96310100390b3cde8f3b]
-    D  [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [] [2012-12-09 16:20:41] [235928acca9c96310100390b3cde8f3b]
- RMPD    [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2012-12-12 12:23:23] [235928acca9c96310100390b3cde8f3b]
- RMPD      [Multiple Regression] [] [2012-12-12 13:15:40] [235928acca9c96310100390b3cde8f3b]
- R PD        [Multiple Regression] [] [2012-12-16 16:42:37] [235928acca9c96310100390b3cde8f3b]
- R PD          [Multiple Regression] [] [2012-12-16 17:13:55] [456f9f31a5baae2eb9a0b13ee35c0d42]
-   PD              [Multiple Regression] [] [2012-12-16 18:09:28] [c52127b355a401c4b5ab4a80e41e35a5] [Current]
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Dataseries X:
4	1	3	6	0	7	0
4	0	4	6	0	8	0
4	0	4	6	0	8	0
4	0	4	6	0	8	0
4	0	4	6	0	8	0
4	1	4	6	1	7	0
4	0	4	6	0	8	0
4	0	3	6	0	8	0
4	0	4	6	0	7	0
4	1	4	6	0	8	0
4	1	3	6	0	8	0
4	0	4	6	0	8	0
4	0	4	5	1	8	0
4	1	3	6	0	8	0
4	0	4	5	1	7	0
4	0	3	5	1	7	0
4	1	3	5	1	8	1
4	1	3	6	0	8	0
4	0	4	6	0	7	0
4	0	3	5	1	7	1
4	1	4	6	1	8	0
4	1	4	5	1	7	0
4	0	4	6	1	7	0
4	1	4	6	1	7	0
4	0	3	5	0	7	0
4	0	4	5	1	8	0
4	1	4	6	0	7	0
4	0	4	5	0	8	0
4	0	4	6	0	7	0
4	0	4	6	1	8	0
4	0	4	6	0	8	0
4	1	4	6	0	8	0
4	1	4	6	1	8	0
4	0	3	6	0	7	0
4	0	4	6	0	8	0
4	0	4	6	0	8	0
4	1	3	5	1	8	0
4	0	4	5	0	7	0
4	0	4	6	1	7	0
4	0	3	6	1	8	0
4	0	4	5	1	7	1
4	0	4	5	0	7	0
4	1	4	6	1	7	0
4	1	3	6	0	8	0
4	0	4	6	1	8	0
4	0	4	6	1	7	0
4	0	4	6	0	8	0
4	0	4	6	0	7	0
4	0	4	6	1	7	0
4	0	4	6	0	8	0
4	0	3	5	0	8	0
4	1	3	5	1	8	1
4	0	4	6	0	7	0
4	0	4	5	0	8	1
4	0	4	6	0	8	0
4	0	3	5	0	7	0
4	0	4	5	1	7	0
4	0	4	6	0	7	0
4	0	4	6	0	7	0
4	1	3	5	1	7	1
4	1	3	6	0	7	0
4	0	4	5	1	8	0
4	0	4	6	0	8	0
4	1	3	6	0	7	0
4	0	4	6	0	8	0
4	0	4	6	0	8	0
4	0	3	5	1	8	1
4	1	4	6	0	8	0
4	0	4	6	0	7	0
4	0	4	5	0	8	0
4	0	4	6	0	8	0
4	0	4	6	0	7	0
4	0	4	5	0	7	0
4	1	4	5	0	8	0
4	0	4	6	0	7	0
4	0	3	6	1	7	0
4	0	4	6	0	7	0
4	0	4	5	1	7	0
4	0	3	5	0	7	1
4	0	3	6	1	8	0
4	0	4	6	0	8	0
4	1	4	5	0	7	0
4	0	4	6	0	8	0
4	0	4	5	0	8	1
4	0	4	6	1	7	0
4	1	4	6	0	8	0
2	1	0	6	0	7	0
2	1	0	5	0	7	0
2	0	0	6	0	8	0
2	0	0	6	0	7	0
2	0	0	6	1	8	0
2	1	0	6	0	8	0
2	1	0	6	1	8	0
2	0	0	6	0	8	0
2	0	0	6	0	8	0
2	0	0	6	0	7	0
2	1	0	6	0	8	0
2	0	0	6	0	8	0
2	1	0	6	0	8	0
2	0	0	6	0	7	0
2	1	0	6	0	7	0
2	0	0	6	0	8	0
2	0	0	6	0	8	0
2	0	0	6	0	8	0
2	0	0	5	0	8	0
2	0	0	6	0	8	0
2	0	0	6	0	8	0
2	1	0	5	0	8	0
2	0	0	6	0	8	0
2	1	0	6	0	8	0
2	1	0	5	1	8	0
2	0	0	6	0	8	0
2	0	0	5	0	8	0
2	1	0	5	0	8	0
2	1	0	6	0	8	0
2	0	0	6	0	8	0
2	1	0	6	0	7	0
2	1	0	6	0	8	0
2	0	0	6	0	8	0
2	0	0	6	0	7	0
2	1	0	6	0	8	0
2	0	0	6	0	8	0
2	1	0	5	0	8	0
2	0	0	5	1	7	0
2	0	0	6	0	7	0
2	0	0	6	0	8	0
2	0	0	6	1	8	0
2	0	0	6	0	7	0
2	0	0	6	0	8	0
2	0	0	6	0	7	0
2	1	0	6	0	8	0
2	1	0	6	0	7	0
2	1	0	5	0	8	0
2	0	0	6	0	8	0
2	0	0	6	0	8	0
2	0	0	6	0	8	0
2	1	0	5	1	7	0
2	1	0	5	1	7	0
2	0	0	6	0	8	0
2	0	0	6	0	8	0
2	0	0	5	0	7	1
2	0	0	5	0	7	0
2	1	0	6	0	8	0
2	0	0	6	1	7	0
2	0	0	6	1	8	0
2	0	0	6	0	7	0
2	0	0	5	0	8	0
2	0	0	6	0	8	0
2	1	0	6	0	8	0
2	0	0	6	1	7	0
2	0	0	6	0	7	0
2	1	0	5	0	8	1
2	1	0	5	1	8	1
2	1	0	5	0	8	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200535&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200535&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200535&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 time9 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = + 0.53368885463191 + 0.320902431164541Weeks[t] -0.0139129710885322UsedLimit[t] -0.162197904481011T40[t] -0.237252262135127Used[t] + 0.0504952718491186Useful[t] + 0.02990124474082Outcome[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  +  0.53368885463191 +  0.320902431164541Weeks[t] -0.0139129710885322UsedLimit[t] -0.162197904481011T40[t] -0.237252262135127Used[t] +  0.0504952718491186Useful[t] +  0.02990124474082Outcome[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200535&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  +  0.53368885463191 +  0.320902431164541Weeks[t] -0.0139129710885322UsedLimit[t] -0.162197904481011T40[t] -0.237252262135127Used[t] +  0.0504952718491186Useful[t] +  0.02990124474082Outcome[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200535&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200535&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
CorrectAnalysis[t] = + 0.53368885463191 + 0.320902431164541Weeks[t] -0.0139129710885322UsedLimit[t] -0.162197904481011T40[t] -0.237252262135127Used[t] + 0.0504952718491186Useful[t] + 0.02990124474082Outcome[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.533688854631910.4706131.1340.2586280.129314
Weeks0.3209024311645410.1124552.85360.0049470.002473
UsedLimit-0.01391297108853220.042046-0.33090.7411910.370595
T40-0.1621979044810110.059289-2.73570.0069910.003496
Used-0.2372522621351270.043967-5.396200
Useful0.05049527184911860.0460231.09720.2743540.137177
Outcome0.029901244740820.0400420.74670.4564080.228204

\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) & 0.53368885463191 & 0.470613 & 1.134 & 0.258628 & 0.129314 \tabularnewline
Weeks & 0.320902431164541 & 0.112455 & 2.8536 & 0.004947 & 0.002473 \tabularnewline
UsedLimit & -0.0139129710885322 & 0.042046 & -0.3309 & 0.741191 & 0.370595 \tabularnewline
T40 & -0.162197904481011 & 0.059289 & -2.7357 & 0.006991 & 0.003496 \tabularnewline
Used & -0.237252262135127 & 0.043967 & -5.3962 & 0 & 0 \tabularnewline
Useful & 0.0504952718491186 & 0.046023 & 1.0972 & 0.274354 & 0.137177 \tabularnewline
Outcome & 0.02990124474082 & 0.040042 & 0.7467 & 0.456408 & 0.228204 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200535&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]0.53368885463191[/C][C]0.470613[/C][C]1.134[/C][C]0.258628[/C][C]0.129314[/C][/ROW]
[ROW][C]Weeks[/C][C]0.320902431164541[/C][C]0.112455[/C][C]2.8536[/C][C]0.004947[/C][C]0.002473[/C][/ROW]
[ROW][C]UsedLimit[/C][C]-0.0139129710885322[/C][C]0.042046[/C][C]-0.3309[/C][C]0.741191[/C][C]0.370595[/C][/ROW]
[ROW][C]T40[/C][C]-0.162197904481011[/C][C]0.059289[/C][C]-2.7357[/C][C]0.006991[/C][C]0.003496[/C][/ROW]
[ROW][C]Used[/C][C]-0.237252262135127[/C][C]0.043967[/C][C]-5.3962[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Useful[/C][C]0.0504952718491186[/C][C]0.046023[/C][C]1.0972[/C][C]0.274354[/C][C]0.137177[/C][/ROW]
[ROW][C]Outcome[/C][C]0.02990124474082[/C][C]0.040042[/C][C]0.7467[/C][C]0.456408[/C][C]0.228204[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200535&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200535&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)0.533688854631910.4706131.1340.2586280.129314
Weeks0.3209024311645410.1124552.85360.0049470.002473
UsedLimit-0.01391297108853220.042046-0.33090.7411910.370595
T40-0.1621979044810110.059289-2.73570.0069910.003496
Used-0.2372522621351270.043967-5.396200
Useful0.05049527184911860.0460231.09720.2743540.137177
Outcome0.029901244740820.0400420.74670.4564080.228204







Multiple Linear Regression - Regression Statistics
Multiple R0.507598175252901
R-squared0.257655907520075
Adjusted R-squared0.227356148643343
F-TEST (value)8.50356296788681
F-TEST (DF numerator)6
F-TEST (DF denominator)147
p-value6.22562983387809e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.236384173347197
Sum Squared Residuals8.21398917912852

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.507598175252901 \tabularnewline
R-squared & 0.257655907520075 \tabularnewline
Adjusted R-squared & 0.227356148643343 \tabularnewline
F-TEST (value) & 8.50356296788681 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 6.22562983387809e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.236384173347197 \tabularnewline
Sum Squared Residuals & 8.21398917912852 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200535&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.507598175252901[/C][/ROW]
[ROW][C]R-squared[/C][C]0.257655907520075[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.227356148643343[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.50356296788681[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]6.22562983387809e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.236384173347197[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8.21398917912852[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200535&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200535&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 R0.507598175252901
R-squared0.257655907520075
Adjusted R-squared0.227356148643343
F-TEST (value)8.50356296788681
F-TEST (DF numerator)6
F-TEST (DF denominator)147
p-value6.22562983387809e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.236384173347197
Sum Squared Residuals8.21398917912852







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.102587035133486-0.102587035133486
20-0.01579665351817230.0157966535181723
30-0.01579665351817210.0157966535181721
40-0.01579665351817210.0157966535181721
50-0.01579665351817230.0157966535181723
60-0.009115597498405560.00911559749840556
70-0.0157966535181720.015796653518172
800.146401250962839-0.146401250962839
90-0.0456978982589920.045697898258992
100-0.02970962460670420.0297096246067042
1100.132488279874306-0.132488279874306
120-0.0157966535181720.015796653518172
1300.271950880466074-0.271950880466074
1400.132488279874306-0.132488279874306
1500.242049635725254-0.242049635725254
1600.404247540206265-0.404247540206265
1710.4202358138585520.579764186141448
1800.132488279874306-0.132488279874306
190-0.0456978982589920.045697898258992
2010.4042475402062650.595752459793735
2100.0207856472424144-0.0207856472424144
2200.228136664636722-0.228136664636722
2300.00479737359012656-0.00479737359012656
240-0.00911559749840560.0091155974984056
2500.353752268357146-0.353752268357146
2600.271950880466074-0.271950880466074
270-0.05961086934752420.0596108693475242
2800.221455608616955-0.221455608616955
290-0.0456978982589920.045697898258992
3000.0346986183309466-0.0346986183309466
310-0.0157966535181720.015796653518172
320-0.02970962460670420.0297096246067042
3300.0207856472424144-0.0207856472424144
3400.116500006222019-0.116500006222019
350-0.0157966535181720.015796653518172
360-0.0157966535181720.015796653518172
3700.420235813858552-0.420235813858552
3800.191554363876135-0.191554363876135
3900.00479737359012656-0.00479737359012656
4000.196896522811957-0.196896522811957
4110.2420496357252540.757950364274746
4200.191554363876135-0.191554363876135
430-0.00911559749840560.0091155974984056
4400.132488279874306-0.132488279874306
4500.0346986183309466-0.0346986183309466
4600.00479737359012656-0.00479737359012656
470-0.0157966535181720.015796653518172
480-0.0456978982589920.045697898258992
4900.00479737359012656-0.00479737359012656
500-0.0157966535181720.015796653518172
5100.383653513097966-0.383653513097966
5210.4202358138585520.579764186141448
530-0.0456978982589920.045697898258992
5410.2214556086169550.778544391383045
550-0.0157966535181720.015796653518172
5600.353752268357146-0.353752268357146
5700.242049635725254-0.242049635725254
580-0.0456978982589920.045697898258992
590-0.0456978982589920.045697898258992
6010.3903345691177320.609665430882268
6100.102587035133486-0.102587035133486
6200.271950880466074-0.271950880466074
630-0.0157966535181720.015796653518172
6400.102587035133486-0.102587035133486
650-0.0157966535181720.015796653518172
660-0.0157966535181720.015796653518172
6710.4341487849470850.565851215052915
680-0.02970962460670420.0297096246067042
690-0.0456978982589920.045697898258992
7000.221455608616955-0.221455608616955
710-0.0157966535181720.015796653518172
720-0.0456978982589920.045697898258992
7300.191554363876135-0.191554363876135
7400.207542637528423-0.207542637528423
750-0.0456978982589920.045697898258992
7600.166995278071137-0.166995278071137
770-0.0456978982589920.045697898258992
7800.242049635725254-0.242049635725254
7910.3537522683571460.646247731642854
8000.196896522811957-0.196896522811957
810-0.0157966535181720.015796653518172
8200.177641392787603-0.177641392787603
830-0.0157966535181720.015796653518172
8410.2214556086169550.778544391383045
8500.00479737359012656-0.00479737359012656
860-0.02970962460670420.0297096246067042
870-0.05262411375256410.0526241137525641
8800.184628148382563-0.184628148382563
890-0.008809897923211960.00880989792321196
900-0.0387111426640320.038711142664032
9100.0416853739259066-0.0416853739259066
920-0.02272286901174410.0227228690117441
9300.0277724028373745-0.0277724028373745
940-0.008809897923211960.00880989792321196
950-0.008809897923211960.00880989792321196
960-0.0387111426640320.038711142664032
970-0.02272286901174410.0227228690117441
980-0.008809897923211960.00880989792321196
990-0.02272286901174410.0227228690117441
1000-0.0387111426640320.038711142664032
1010-0.05262411375256410.0526241137525641
1020-0.008809897923211960.00880989792321196
1030-0.008809897923211960.00880989792321196
1040-0.008809897923211960.00880989792321196
10500.228442364211915-0.228442364211915
1060-0.008809897923211960.00880989792321196
1070-0.008809897923211960.00880989792321196
10800.214529393123383-0.214529393123383
1090-0.008809897923211960.00880989792321196
1100-0.02272286901174410.0227228690117441
11100.265024664972502-0.265024664972502
1120-0.008809897923211960.00880989792321196
11300.228442364211915-0.228442364211915
11400.214529393123383-0.214529393123383
1150-0.02272286901174410.0227228690117441
1160-0.008809897923211960.00880989792321196
1170-0.05262411375256410.0526241137525641
1180-0.02272286901174410.0227228690117441
1190-0.008809897923211960.00880989792321196
1200-0.0387111426640320.038711142664032
1210-0.02272286901174410.0227228690117441
1220-0.008809897923211960.00880989792321196
12300.214529393123383-0.214529393123383
12400.249036391320214-0.249036391320214
1250-0.0387111426640320.038711142664032
1260-0.008809897923211960.00880989792321196
12700.0416853739259066-0.0416853739259066
1280-0.0387111426640320.038711142664032
1290-0.008809897923211960.00880989792321196
1300-0.0387111426640320.038711142664032
1310-0.02272286901174410.0227228690117441
1320-0.05262411375256410.0526241137525641
13300.214529393123383-0.214529393123383
1340-0.008809897923211960.00880989792321196
1350-0.008809897923211960.00880989792321196
1360-0.008809897923211960.00880989792321196
13700.235123420231682-0.235123420231682
13800.235123420231682-0.235123420231682
1390-0.008809897923211960.00880989792321196
1400-0.008809897923211960.00880989792321196
14110.1985411194710950.801458880528905
14200.198541119471095-0.198541119471095
1430-0.02272286901174410.0227228690117441
14400.0117841291850866-0.0117841291850866
14500.0416853739259066-0.0416853739259066
1460-0.0387111426640320.038711142664032
14700.228442364211915-0.228442364211915
1480-0.008809897923211960.00880989792321196
1490-0.02272286901174410.0227228690117441
15000.0117841291850866-0.0117841291850866
1510-0.0387111426640320.038711142664032
15210.2145293931233830.785470606876617
15310.2650246649725020.734975335027498
15400.214529393123383-0.214529393123383

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.102587035133486 & -0.102587035133486 \tabularnewline
2 & 0 & -0.0157966535181723 & 0.0157966535181723 \tabularnewline
3 & 0 & -0.0157966535181721 & 0.0157966535181721 \tabularnewline
4 & 0 & -0.0157966535181721 & 0.0157966535181721 \tabularnewline
5 & 0 & -0.0157966535181723 & 0.0157966535181723 \tabularnewline
6 & 0 & -0.00911559749840556 & 0.00911559749840556 \tabularnewline
7 & 0 & -0.015796653518172 & 0.015796653518172 \tabularnewline
8 & 0 & 0.146401250962839 & -0.146401250962839 \tabularnewline
9 & 0 & -0.045697898258992 & 0.045697898258992 \tabularnewline
10 & 0 & -0.0297096246067042 & 0.0297096246067042 \tabularnewline
11 & 0 & 0.132488279874306 & -0.132488279874306 \tabularnewline
12 & 0 & -0.015796653518172 & 0.015796653518172 \tabularnewline
13 & 0 & 0.271950880466074 & -0.271950880466074 \tabularnewline
14 & 0 & 0.132488279874306 & -0.132488279874306 \tabularnewline
15 & 0 & 0.242049635725254 & -0.242049635725254 \tabularnewline
16 & 0 & 0.404247540206265 & -0.404247540206265 \tabularnewline
17 & 1 & 0.420235813858552 & 0.579764186141448 \tabularnewline
18 & 0 & 0.132488279874306 & -0.132488279874306 \tabularnewline
19 & 0 & -0.045697898258992 & 0.045697898258992 \tabularnewline
20 & 1 & 0.404247540206265 & 0.595752459793735 \tabularnewline
21 & 0 & 0.0207856472424144 & -0.0207856472424144 \tabularnewline
22 & 0 & 0.228136664636722 & -0.228136664636722 \tabularnewline
23 & 0 & 0.00479737359012656 & -0.00479737359012656 \tabularnewline
24 & 0 & -0.0091155974984056 & 0.0091155974984056 \tabularnewline
25 & 0 & 0.353752268357146 & -0.353752268357146 \tabularnewline
26 & 0 & 0.271950880466074 & -0.271950880466074 \tabularnewline
27 & 0 & -0.0596108693475242 & 0.0596108693475242 \tabularnewline
28 & 0 & 0.221455608616955 & -0.221455608616955 \tabularnewline
29 & 0 & -0.045697898258992 & 0.045697898258992 \tabularnewline
30 & 0 & 0.0346986183309466 & -0.0346986183309466 \tabularnewline
31 & 0 & -0.015796653518172 & 0.015796653518172 \tabularnewline
32 & 0 & -0.0297096246067042 & 0.0297096246067042 \tabularnewline
33 & 0 & 0.0207856472424144 & -0.0207856472424144 \tabularnewline
34 & 0 & 0.116500006222019 & -0.116500006222019 \tabularnewline
35 & 0 & -0.015796653518172 & 0.015796653518172 \tabularnewline
36 & 0 & -0.015796653518172 & 0.015796653518172 \tabularnewline
37 & 0 & 0.420235813858552 & -0.420235813858552 \tabularnewline
38 & 0 & 0.191554363876135 & -0.191554363876135 \tabularnewline
39 & 0 & 0.00479737359012656 & -0.00479737359012656 \tabularnewline
40 & 0 & 0.196896522811957 & -0.196896522811957 \tabularnewline
41 & 1 & 0.242049635725254 & 0.757950364274746 \tabularnewline
42 & 0 & 0.191554363876135 & -0.191554363876135 \tabularnewline
43 & 0 & -0.0091155974984056 & 0.0091155974984056 \tabularnewline
44 & 0 & 0.132488279874306 & -0.132488279874306 \tabularnewline
45 & 0 & 0.0346986183309466 & -0.0346986183309466 \tabularnewline
46 & 0 & 0.00479737359012656 & -0.00479737359012656 \tabularnewline
47 & 0 & -0.015796653518172 & 0.015796653518172 \tabularnewline
48 & 0 & -0.045697898258992 & 0.045697898258992 \tabularnewline
49 & 0 & 0.00479737359012656 & -0.00479737359012656 \tabularnewline
50 & 0 & -0.015796653518172 & 0.015796653518172 \tabularnewline
51 & 0 & 0.383653513097966 & -0.383653513097966 \tabularnewline
52 & 1 & 0.420235813858552 & 0.579764186141448 \tabularnewline
53 & 0 & -0.045697898258992 & 0.045697898258992 \tabularnewline
54 & 1 & 0.221455608616955 & 0.778544391383045 \tabularnewline
55 & 0 & -0.015796653518172 & 0.015796653518172 \tabularnewline
56 & 0 & 0.353752268357146 & -0.353752268357146 \tabularnewline
57 & 0 & 0.242049635725254 & -0.242049635725254 \tabularnewline
58 & 0 & -0.045697898258992 & 0.045697898258992 \tabularnewline
59 & 0 & -0.045697898258992 & 0.045697898258992 \tabularnewline
60 & 1 & 0.390334569117732 & 0.609665430882268 \tabularnewline
61 & 0 & 0.102587035133486 & -0.102587035133486 \tabularnewline
62 & 0 & 0.271950880466074 & -0.271950880466074 \tabularnewline
63 & 0 & -0.015796653518172 & 0.015796653518172 \tabularnewline
64 & 0 & 0.102587035133486 & -0.102587035133486 \tabularnewline
65 & 0 & -0.015796653518172 & 0.015796653518172 \tabularnewline
66 & 0 & -0.015796653518172 & 0.015796653518172 \tabularnewline
67 & 1 & 0.434148784947085 & 0.565851215052915 \tabularnewline
68 & 0 & -0.0297096246067042 & 0.0297096246067042 \tabularnewline
69 & 0 & -0.045697898258992 & 0.045697898258992 \tabularnewline
70 & 0 & 0.221455608616955 & -0.221455608616955 \tabularnewline
71 & 0 & -0.015796653518172 & 0.015796653518172 \tabularnewline
72 & 0 & -0.045697898258992 & 0.045697898258992 \tabularnewline
73 & 0 & 0.191554363876135 & -0.191554363876135 \tabularnewline
74 & 0 & 0.207542637528423 & -0.207542637528423 \tabularnewline
75 & 0 & -0.045697898258992 & 0.045697898258992 \tabularnewline
76 & 0 & 0.166995278071137 & -0.166995278071137 \tabularnewline
77 & 0 & -0.045697898258992 & 0.045697898258992 \tabularnewline
78 & 0 & 0.242049635725254 & -0.242049635725254 \tabularnewline
79 & 1 & 0.353752268357146 & 0.646247731642854 \tabularnewline
80 & 0 & 0.196896522811957 & -0.196896522811957 \tabularnewline
81 & 0 & -0.015796653518172 & 0.015796653518172 \tabularnewline
82 & 0 & 0.177641392787603 & -0.177641392787603 \tabularnewline
83 & 0 & -0.015796653518172 & 0.015796653518172 \tabularnewline
84 & 1 & 0.221455608616955 & 0.778544391383045 \tabularnewline
85 & 0 & 0.00479737359012656 & -0.00479737359012656 \tabularnewline
86 & 0 & -0.0297096246067042 & 0.0297096246067042 \tabularnewline
87 & 0 & -0.0526241137525641 & 0.0526241137525641 \tabularnewline
88 & 0 & 0.184628148382563 & -0.184628148382563 \tabularnewline
89 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
90 & 0 & -0.038711142664032 & 0.038711142664032 \tabularnewline
91 & 0 & 0.0416853739259066 & -0.0416853739259066 \tabularnewline
92 & 0 & -0.0227228690117441 & 0.0227228690117441 \tabularnewline
93 & 0 & 0.0277724028373745 & -0.0277724028373745 \tabularnewline
94 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
95 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
96 & 0 & -0.038711142664032 & 0.038711142664032 \tabularnewline
97 & 0 & -0.0227228690117441 & 0.0227228690117441 \tabularnewline
98 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
99 & 0 & -0.0227228690117441 & 0.0227228690117441 \tabularnewline
100 & 0 & -0.038711142664032 & 0.038711142664032 \tabularnewline
101 & 0 & -0.0526241137525641 & 0.0526241137525641 \tabularnewline
102 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
103 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
104 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
105 & 0 & 0.228442364211915 & -0.228442364211915 \tabularnewline
106 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
107 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
108 & 0 & 0.214529393123383 & -0.214529393123383 \tabularnewline
109 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
110 & 0 & -0.0227228690117441 & 0.0227228690117441 \tabularnewline
111 & 0 & 0.265024664972502 & -0.265024664972502 \tabularnewline
112 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
113 & 0 & 0.228442364211915 & -0.228442364211915 \tabularnewline
114 & 0 & 0.214529393123383 & -0.214529393123383 \tabularnewline
115 & 0 & -0.0227228690117441 & 0.0227228690117441 \tabularnewline
116 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
117 & 0 & -0.0526241137525641 & 0.0526241137525641 \tabularnewline
118 & 0 & -0.0227228690117441 & 0.0227228690117441 \tabularnewline
119 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
120 & 0 & -0.038711142664032 & 0.038711142664032 \tabularnewline
121 & 0 & -0.0227228690117441 & 0.0227228690117441 \tabularnewline
122 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
123 & 0 & 0.214529393123383 & -0.214529393123383 \tabularnewline
124 & 0 & 0.249036391320214 & -0.249036391320214 \tabularnewline
125 & 0 & -0.038711142664032 & 0.038711142664032 \tabularnewline
126 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
127 & 0 & 0.0416853739259066 & -0.0416853739259066 \tabularnewline
128 & 0 & -0.038711142664032 & 0.038711142664032 \tabularnewline
129 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
130 & 0 & -0.038711142664032 & 0.038711142664032 \tabularnewline
131 & 0 & -0.0227228690117441 & 0.0227228690117441 \tabularnewline
132 & 0 & -0.0526241137525641 & 0.0526241137525641 \tabularnewline
133 & 0 & 0.214529393123383 & -0.214529393123383 \tabularnewline
134 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
135 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
136 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
137 & 0 & 0.235123420231682 & -0.235123420231682 \tabularnewline
138 & 0 & 0.235123420231682 & -0.235123420231682 \tabularnewline
139 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
140 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
141 & 1 & 0.198541119471095 & 0.801458880528905 \tabularnewline
142 & 0 & 0.198541119471095 & -0.198541119471095 \tabularnewline
143 & 0 & -0.0227228690117441 & 0.0227228690117441 \tabularnewline
144 & 0 & 0.0117841291850866 & -0.0117841291850866 \tabularnewline
145 & 0 & 0.0416853739259066 & -0.0416853739259066 \tabularnewline
146 & 0 & -0.038711142664032 & 0.038711142664032 \tabularnewline
147 & 0 & 0.228442364211915 & -0.228442364211915 \tabularnewline
148 & 0 & -0.00880989792321196 & 0.00880989792321196 \tabularnewline
149 & 0 & -0.0227228690117441 & 0.0227228690117441 \tabularnewline
150 & 0 & 0.0117841291850866 & -0.0117841291850866 \tabularnewline
151 & 0 & -0.038711142664032 & 0.038711142664032 \tabularnewline
152 & 1 & 0.214529393123383 & 0.785470606876617 \tabularnewline
153 & 1 & 0.265024664972502 & 0.734975335027498 \tabularnewline
154 & 0 & 0.214529393123383 & -0.214529393123383 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200535&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]0[/C][C]0.102587035133486[/C][C]-0.102587035133486[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]-0.0157966535181723[/C][C]0.0157966535181723[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.0157966535181721[/C][C]0.0157966535181721[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]-0.0157966535181721[/C][C]0.0157966535181721[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.0157966535181723[/C][C]0.0157966535181723[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]-0.00911559749840556[/C][C]0.00911559749840556[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-0.015796653518172[/C][C]0.015796653518172[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.146401250962839[/C][C]-0.146401250962839[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]-0.045697898258992[/C][C]0.045697898258992[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]-0.0297096246067042[/C][C]0.0297096246067042[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0.132488279874306[/C][C]-0.132488279874306[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]-0.015796653518172[/C][C]0.015796653518172[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.271950880466074[/C][C]-0.271950880466074[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.132488279874306[/C][C]-0.132488279874306[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.242049635725254[/C][C]-0.242049635725254[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.404247540206265[/C][C]-0.404247540206265[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.420235813858552[/C][C]0.579764186141448[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.132488279874306[/C][C]-0.132488279874306[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-0.045697898258992[/C][C]0.045697898258992[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.404247540206265[/C][C]0.595752459793735[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.0207856472424144[/C][C]-0.0207856472424144[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.228136664636722[/C][C]-0.228136664636722[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.00479737359012656[/C][C]-0.00479737359012656[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]-0.0091155974984056[/C][C]0.0091155974984056[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.353752268357146[/C][C]-0.353752268357146[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.271950880466074[/C][C]-0.271950880466074[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]-0.0596108693475242[/C][C]0.0596108693475242[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.221455608616955[/C][C]-0.221455608616955[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]-0.045697898258992[/C][C]0.045697898258992[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0346986183309466[/C][C]-0.0346986183309466[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]-0.015796653518172[/C][C]0.015796653518172[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]-0.0297096246067042[/C][C]0.0297096246067042[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.0207856472424144[/C][C]-0.0207856472424144[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.116500006222019[/C][C]-0.116500006222019[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]-0.015796653518172[/C][C]0.015796653518172[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]-0.015796653518172[/C][C]0.015796653518172[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.420235813858552[/C][C]-0.420235813858552[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.191554363876135[/C][C]-0.191554363876135[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.00479737359012656[/C][C]-0.00479737359012656[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.196896522811957[/C][C]-0.196896522811957[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.242049635725254[/C][C]0.757950364274746[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.191554363876135[/C][C]-0.191554363876135[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]-0.0091155974984056[/C][C]0.0091155974984056[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.132488279874306[/C][C]-0.132488279874306[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.0346986183309466[/C][C]-0.0346986183309466[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.00479737359012656[/C][C]-0.00479737359012656[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]-0.015796653518172[/C][C]0.015796653518172[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]-0.045697898258992[/C][C]0.045697898258992[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.00479737359012656[/C][C]-0.00479737359012656[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]-0.015796653518172[/C][C]0.015796653518172[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.383653513097966[/C][C]-0.383653513097966[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.420235813858552[/C][C]0.579764186141448[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]-0.045697898258992[/C][C]0.045697898258992[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.221455608616955[/C][C]0.778544391383045[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]-0.015796653518172[/C][C]0.015796653518172[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.353752268357146[/C][C]-0.353752268357146[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.242049635725254[/C][C]-0.242049635725254[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]-0.045697898258992[/C][C]0.045697898258992[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]-0.045697898258992[/C][C]0.045697898258992[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.390334569117732[/C][C]0.609665430882268[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.102587035133486[/C][C]-0.102587035133486[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.271950880466074[/C][C]-0.271950880466074[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]-0.015796653518172[/C][C]0.015796653518172[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.102587035133486[/C][C]-0.102587035133486[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]-0.015796653518172[/C][C]0.015796653518172[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]-0.015796653518172[/C][C]0.015796653518172[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.434148784947085[/C][C]0.565851215052915[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]-0.0297096246067042[/C][C]0.0297096246067042[/C][/ROW]
[ROW][C]69[/C][C]0[/C][C]-0.045697898258992[/C][C]0.045697898258992[/C][/ROW]
[ROW][C]70[/C][C]0[/C][C]0.221455608616955[/C][C]-0.221455608616955[/C][/ROW]
[ROW][C]71[/C][C]0[/C][C]-0.015796653518172[/C][C]0.015796653518172[/C][/ROW]
[ROW][C]72[/C][C]0[/C][C]-0.045697898258992[/C][C]0.045697898258992[/C][/ROW]
[ROW][C]73[/C][C]0[/C][C]0.191554363876135[/C][C]-0.191554363876135[/C][/ROW]
[ROW][C]74[/C][C]0[/C][C]0.207542637528423[/C][C]-0.207542637528423[/C][/ROW]
[ROW][C]75[/C][C]0[/C][C]-0.045697898258992[/C][C]0.045697898258992[/C][/ROW]
[ROW][C]76[/C][C]0[/C][C]0.166995278071137[/C][C]-0.166995278071137[/C][/ROW]
[ROW][C]77[/C][C]0[/C][C]-0.045697898258992[/C][C]0.045697898258992[/C][/ROW]
[ROW][C]78[/C][C]0[/C][C]0.242049635725254[/C][C]-0.242049635725254[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]0.353752268357146[/C][C]0.646247731642854[/C][/ROW]
[ROW][C]80[/C][C]0[/C][C]0.196896522811957[/C][C]-0.196896522811957[/C][/ROW]
[ROW][C]81[/C][C]0[/C][C]-0.015796653518172[/C][C]0.015796653518172[/C][/ROW]
[ROW][C]82[/C][C]0[/C][C]0.177641392787603[/C][C]-0.177641392787603[/C][/ROW]
[ROW][C]83[/C][C]0[/C][C]-0.015796653518172[/C][C]0.015796653518172[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]0.221455608616955[/C][C]0.778544391383045[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]0.00479737359012656[/C][C]-0.00479737359012656[/C][/ROW]
[ROW][C]86[/C][C]0[/C][C]-0.0297096246067042[/C][C]0.0297096246067042[/C][/ROW]
[ROW][C]87[/C][C]0[/C][C]-0.0526241137525641[/C][C]0.0526241137525641[/C][/ROW]
[ROW][C]88[/C][C]0[/C][C]0.184628148382563[/C][C]-0.184628148382563[/C][/ROW]
[ROW][C]89[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]90[/C][C]0[/C][C]-0.038711142664032[/C][C]0.038711142664032[/C][/ROW]
[ROW][C]91[/C][C]0[/C][C]0.0416853739259066[/C][C]-0.0416853739259066[/C][/ROW]
[ROW][C]92[/C][C]0[/C][C]-0.0227228690117441[/C][C]0.0227228690117441[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]0.0277724028373745[/C][C]-0.0277724028373745[/C][/ROW]
[ROW][C]94[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]95[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]96[/C][C]0[/C][C]-0.038711142664032[/C][C]0.038711142664032[/C][/ROW]
[ROW][C]97[/C][C]0[/C][C]-0.0227228690117441[/C][C]0.0227228690117441[/C][/ROW]
[ROW][C]98[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]99[/C][C]0[/C][C]-0.0227228690117441[/C][C]0.0227228690117441[/C][/ROW]
[ROW][C]100[/C][C]0[/C][C]-0.038711142664032[/C][C]0.038711142664032[/C][/ROW]
[ROW][C]101[/C][C]0[/C][C]-0.0526241137525641[/C][C]0.0526241137525641[/C][/ROW]
[ROW][C]102[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]103[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]104[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]105[/C][C]0[/C][C]0.228442364211915[/C][C]-0.228442364211915[/C][/ROW]
[ROW][C]106[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]107[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]108[/C][C]0[/C][C]0.214529393123383[/C][C]-0.214529393123383[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]110[/C][C]0[/C][C]-0.0227228690117441[/C][C]0.0227228690117441[/C][/ROW]
[ROW][C]111[/C][C]0[/C][C]0.265024664972502[/C][C]-0.265024664972502[/C][/ROW]
[ROW][C]112[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]113[/C][C]0[/C][C]0.228442364211915[/C][C]-0.228442364211915[/C][/ROW]
[ROW][C]114[/C][C]0[/C][C]0.214529393123383[/C][C]-0.214529393123383[/C][/ROW]
[ROW][C]115[/C][C]0[/C][C]-0.0227228690117441[/C][C]0.0227228690117441[/C][/ROW]
[ROW][C]116[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]117[/C][C]0[/C][C]-0.0526241137525641[/C][C]0.0526241137525641[/C][/ROW]
[ROW][C]118[/C][C]0[/C][C]-0.0227228690117441[/C][C]0.0227228690117441[/C][/ROW]
[ROW][C]119[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]120[/C][C]0[/C][C]-0.038711142664032[/C][C]0.038711142664032[/C][/ROW]
[ROW][C]121[/C][C]0[/C][C]-0.0227228690117441[/C][C]0.0227228690117441[/C][/ROW]
[ROW][C]122[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]123[/C][C]0[/C][C]0.214529393123383[/C][C]-0.214529393123383[/C][/ROW]
[ROW][C]124[/C][C]0[/C][C]0.249036391320214[/C][C]-0.249036391320214[/C][/ROW]
[ROW][C]125[/C][C]0[/C][C]-0.038711142664032[/C][C]0.038711142664032[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]127[/C][C]0[/C][C]0.0416853739259066[/C][C]-0.0416853739259066[/C][/ROW]
[ROW][C]128[/C][C]0[/C][C]-0.038711142664032[/C][C]0.038711142664032[/C][/ROW]
[ROW][C]129[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]130[/C][C]0[/C][C]-0.038711142664032[/C][C]0.038711142664032[/C][/ROW]
[ROW][C]131[/C][C]0[/C][C]-0.0227228690117441[/C][C]0.0227228690117441[/C][/ROW]
[ROW][C]132[/C][C]0[/C][C]-0.0526241137525641[/C][C]0.0526241137525641[/C][/ROW]
[ROW][C]133[/C][C]0[/C][C]0.214529393123383[/C][C]-0.214529393123383[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]135[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]136[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]137[/C][C]0[/C][C]0.235123420231682[/C][C]-0.235123420231682[/C][/ROW]
[ROW][C]138[/C][C]0[/C][C]0.235123420231682[/C][C]-0.235123420231682[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]140[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]0.198541119471095[/C][C]0.801458880528905[/C][/ROW]
[ROW][C]142[/C][C]0[/C][C]0.198541119471095[/C][C]-0.198541119471095[/C][/ROW]
[ROW][C]143[/C][C]0[/C][C]-0.0227228690117441[/C][C]0.0227228690117441[/C][/ROW]
[ROW][C]144[/C][C]0[/C][C]0.0117841291850866[/C][C]-0.0117841291850866[/C][/ROW]
[ROW][C]145[/C][C]0[/C][C]0.0416853739259066[/C][C]-0.0416853739259066[/C][/ROW]
[ROW][C]146[/C][C]0[/C][C]-0.038711142664032[/C][C]0.038711142664032[/C][/ROW]
[ROW][C]147[/C][C]0[/C][C]0.228442364211915[/C][C]-0.228442364211915[/C][/ROW]
[ROW][C]148[/C][C]0[/C][C]-0.00880989792321196[/C][C]0.00880989792321196[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]-0.0227228690117441[/C][C]0.0227228690117441[/C][/ROW]
[ROW][C]150[/C][C]0[/C][C]0.0117841291850866[/C][C]-0.0117841291850866[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]-0.038711142664032[/C][C]0.038711142664032[/C][/ROW]
[ROW][C]152[/C][C]1[/C][C]0.214529393123383[/C][C]0.785470606876617[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]0.265024664972502[/C][C]0.734975335027498[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]0.214529393123383[/C][C]-0.214529393123383[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200535&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200535&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
100.102587035133486-0.102587035133486
20-0.01579665351817230.0157966535181723
30-0.01579665351817210.0157966535181721
40-0.01579665351817210.0157966535181721
50-0.01579665351817230.0157966535181723
60-0.009115597498405560.00911559749840556
70-0.0157966535181720.015796653518172
800.146401250962839-0.146401250962839
90-0.0456978982589920.045697898258992
100-0.02970962460670420.0297096246067042
1100.132488279874306-0.132488279874306
120-0.0157966535181720.015796653518172
1300.271950880466074-0.271950880466074
1400.132488279874306-0.132488279874306
1500.242049635725254-0.242049635725254
1600.404247540206265-0.404247540206265
1710.4202358138585520.579764186141448
1800.132488279874306-0.132488279874306
190-0.0456978982589920.045697898258992
2010.4042475402062650.595752459793735
2100.0207856472424144-0.0207856472424144
2200.228136664636722-0.228136664636722
2300.00479737359012656-0.00479737359012656
240-0.00911559749840560.0091155974984056
2500.353752268357146-0.353752268357146
2600.271950880466074-0.271950880466074
270-0.05961086934752420.0596108693475242
2800.221455608616955-0.221455608616955
290-0.0456978982589920.045697898258992
3000.0346986183309466-0.0346986183309466
310-0.0157966535181720.015796653518172
320-0.02970962460670420.0297096246067042
3300.0207856472424144-0.0207856472424144
3400.116500006222019-0.116500006222019
350-0.0157966535181720.015796653518172
360-0.0157966535181720.015796653518172
3700.420235813858552-0.420235813858552
3800.191554363876135-0.191554363876135
3900.00479737359012656-0.00479737359012656
4000.196896522811957-0.196896522811957
4110.2420496357252540.757950364274746
4200.191554363876135-0.191554363876135
430-0.00911559749840560.0091155974984056
4400.132488279874306-0.132488279874306
4500.0346986183309466-0.0346986183309466
4600.00479737359012656-0.00479737359012656
470-0.0157966535181720.015796653518172
480-0.0456978982589920.045697898258992
4900.00479737359012656-0.00479737359012656
500-0.0157966535181720.015796653518172
5100.383653513097966-0.383653513097966
5210.4202358138585520.579764186141448
530-0.0456978982589920.045697898258992
5410.2214556086169550.778544391383045
550-0.0157966535181720.015796653518172
5600.353752268357146-0.353752268357146
5700.242049635725254-0.242049635725254
580-0.0456978982589920.045697898258992
590-0.0456978982589920.045697898258992
6010.3903345691177320.609665430882268
6100.102587035133486-0.102587035133486
6200.271950880466074-0.271950880466074
630-0.0157966535181720.015796653518172
6400.102587035133486-0.102587035133486
650-0.0157966535181720.015796653518172
660-0.0157966535181720.015796653518172
6710.4341487849470850.565851215052915
680-0.02970962460670420.0297096246067042
690-0.0456978982589920.045697898258992
7000.221455608616955-0.221455608616955
710-0.0157966535181720.015796653518172
720-0.0456978982589920.045697898258992
7300.191554363876135-0.191554363876135
7400.207542637528423-0.207542637528423
750-0.0456978982589920.045697898258992
7600.166995278071137-0.166995278071137
770-0.0456978982589920.045697898258992
7800.242049635725254-0.242049635725254
7910.3537522683571460.646247731642854
8000.196896522811957-0.196896522811957
810-0.0157966535181720.015796653518172
8200.177641392787603-0.177641392787603
830-0.0157966535181720.015796653518172
8410.2214556086169550.778544391383045
8500.00479737359012656-0.00479737359012656
860-0.02970962460670420.0297096246067042
870-0.05262411375256410.0526241137525641
8800.184628148382563-0.184628148382563
890-0.008809897923211960.00880989792321196
900-0.0387111426640320.038711142664032
9100.0416853739259066-0.0416853739259066
920-0.02272286901174410.0227228690117441
9300.0277724028373745-0.0277724028373745
940-0.008809897923211960.00880989792321196
950-0.008809897923211960.00880989792321196
960-0.0387111426640320.038711142664032
970-0.02272286901174410.0227228690117441
980-0.008809897923211960.00880989792321196
990-0.02272286901174410.0227228690117441
1000-0.0387111426640320.038711142664032
1010-0.05262411375256410.0526241137525641
1020-0.008809897923211960.00880989792321196
1030-0.008809897923211960.00880989792321196
1040-0.008809897923211960.00880989792321196
10500.228442364211915-0.228442364211915
1060-0.008809897923211960.00880989792321196
1070-0.008809897923211960.00880989792321196
10800.214529393123383-0.214529393123383
1090-0.008809897923211960.00880989792321196
1100-0.02272286901174410.0227228690117441
11100.265024664972502-0.265024664972502
1120-0.008809897923211960.00880989792321196
11300.228442364211915-0.228442364211915
11400.214529393123383-0.214529393123383
1150-0.02272286901174410.0227228690117441
1160-0.008809897923211960.00880989792321196
1170-0.05262411375256410.0526241137525641
1180-0.02272286901174410.0227228690117441
1190-0.008809897923211960.00880989792321196
1200-0.0387111426640320.038711142664032
1210-0.02272286901174410.0227228690117441
1220-0.008809897923211960.00880989792321196
12300.214529393123383-0.214529393123383
12400.249036391320214-0.249036391320214
1250-0.0387111426640320.038711142664032
1260-0.008809897923211960.00880989792321196
12700.0416853739259066-0.0416853739259066
1280-0.0387111426640320.038711142664032
1290-0.008809897923211960.00880989792321196
1300-0.0387111426640320.038711142664032
1310-0.02272286901174410.0227228690117441
1320-0.05262411375256410.0526241137525641
13300.214529393123383-0.214529393123383
1340-0.008809897923211960.00880989792321196
1350-0.008809897923211960.00880989792321196
1360-0.008809897923211960.00880989792321196
13700.235123420231682-0.235123420231682
13800.235123420231682-0.235123420231682
1390-0.008809897923211960.00880989792321196
1400-0.008809897923211960.00880989792321196
14110.1985411194710950.801458880528905
14200.198541119471095-0.198541119471095
1430-0.02272286901174410.0227228690117441
14400.0117841291850866-0.0117841291850866
14500.0416853739259066-0.0416853739259066
1460-0.0387111426640320.038711142664032
14700.228442364211915-0.228442364211915
1480-0.008809897923211960.00880989792321196
1490-0.02272286901174410.0227228690117441
15000.0117841291850866-0.0117841291850866
1510-0.0387111426640320.038711142664032
15210.2145293931233830.785470606876617
15310.2650246649725020.734975335027498
15400.214529393123383-0.214529393123383







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
10001
11001
12001
13001
14001
15001
16001
170.4349212397831150.869842479566230.565078760216885
180.3860876607659540.7721753215319070.613912339234046
190.3512631842966010.7025263685932020.648736815703399
200.842640537001820.3147189259963590.15735946299818
210.7912499187248950.417500162550210.208750081275105
220.7775542658615050.444891468276990.222445734138495
230.7174996445870510.5650007108258980.282500355412949
240.6530403334382240.6939193331235510.346959666561776
250.655192123065040.6896157538699210.34480787693496
260.6564633291239420.6870733417521160.343536670876058
270.614782013086750.7704359738265010.38521798691325
280.5602022219344970.8795955561310050.439797778065503
290.5065322492764190.9869355014471610.493467750723581
300.448466049891120.8969320997822410.55153395010888
310.388882666345160.7777653326903210.61111733365484
320.3326229799469530.6652459598939060.667377020053047
330.2808026990130250.561605398026050.719197300986975
340.240221882190560.480443764381120.75977811780944
350.1969491893068710.3938983786137420.803050810693129
360.1588149992886290.3176299985772590.84118500071137
370.2218880778038910.4437761556077810.778111922196109
380.1874762889910610.3749525779821220.812523711008939
390.1510425749011080.3020851498022160.848957425098892
400.1465410345260490.2930820690520980.853458965473951
410.664694599514990.670610800970020.33530540048501
420.6335846805439760.7328306389120470.366415319456024
430.5825625048854220.8348749902291570.417437495114578
440.5447477857785110.9105044284429780.455252214221489
450.4929928383563550.985985676712710.507007161643645
460.4416757546068170.8833515092136340.558324245393183
470.3915561900871090.7831123801742180.608443809912891
480.3429487484534420.6858974969068850.657051251546558
490.2973150306623420.5946300613246840.702684969337658
500.2547866363036850.509573272607370.745213363696315
510.3109201250043350.6218402500086690.689079874995665
520.5842462235668490.8315075528663030.415753776433151
530.5379345232297460.9241309535405080.462065476770254
540.9068913542378580.1862172915242850.0931086457621423
550.8845356418551310.2309287162897380.115464358144869
560.9213881932321210.1572236135357570.0786118067678787
570.9200770523190640.1598458953618720.0799229476809359
580.9019011667133620.1961976665732770.0980988332866384
590.8807678627182290.2384642745635420.119232137281771
600.9596842699840080.08063146003198370.0403157300159919
610.9544453563747220.09110928725055650.0455546436252782
620.9564002348130950.08719953037380910.0435997651869046
630.9442901419817860.1114197160364290.0557098580182143
640.942549375862380.114901248275240.0574506241376199
650.9275878346759190.1448243306481630.0724121653240815
660.9097956255939010.1804087488121980.0902043744060988
670.9627613188089520.07447736238209530.0372386811910476
680.9519671731260610.09606565374787780.0480328268739389
690.9396885452247520.1206229095504960.0603114547752479
700.9377293666173390.1245412667653230.0622706333826613
710.9219104193509560.1561791612980890.0780895806490443
720.9040464438049520.1919071123900960.095953556195048
730.899096711515870.201806576968260.10090328848413
740.8978907138692870.2042185722614270.102109286130713
750.8766394736934680.2467210526130640.123360526306532
760.8769671780250850.2460656439498310.123032821974916
770.8531102070236140.2937795859527720.146889792976386
780.8596615389030370.2806769221939250.140338461096963
790.9597576002907790.08048479941844290.0402423997092215
800.9516226537114280.09675469257714420.0483773462885721
810.9410101423178070.1179797153643860.0589898576821928
820.9478226260305970.1043547479388060.052177373969403
830.9435363740689470.1129272518621070.0564636259310535
840.9943282737132990.01134345257340240.00567172628670118
850.9919984147624510.01600317047509850.00800158523754923
860.9888811652639020.02223766947219530.0111188347360976
870.9847168807255020.03056623854899550.0152831192744978
880.9829227760621780.03415444787564450.0170772239378222
890.9770792096323630.04584158073527420.0229207903676371
900.9697515117115740.0604969765768520.030248488288426
910.9602184430530310.07956311389393890.0397815569469694
920.9483797969562910.1032404060874190.0516202030437094
930.9337657654792490.1324684690415020.0662342345207508
940.916068312744860.1678633745102810.0839316872551403
950.8949450442419080.2101099115161830.105054955758092
960.8706492641858090.2587014716283820.129350735814191
970.8420774991385850.3158450017228310.157922500861416
980.8093975255162590.3812049489674810.190602474483741
990.7728383148114980.4543233703770050.227161685188502
1000.7329317014083620.5341365971832770.267068298591638
1010.6899397091861190.6201205816277620.310060290813881
1020.642932153884620.714135692230760.35706784611538
1030.5934399632456770.8131200735086450.406560036754323
1040.5421858986068620.9156282027862760.457814101393138
1050.5287621954627790.9424756090744420.471237804537221
1060.4762373877703750.952474775540750.523762612229625
1070.423860754098980.847721508197960.57613924590102
1080.4110561008703890.8221122017407780.588943899129611
1090.3598919188153970.7197838376307950.640108081184603
1100.311124131686690.6222482633733790.68887586831331
1110.3089696569145240.6179393138290490.691030343085476
1120.262618149820860.525236299641720.73738185017914
1130.2568994620543980.5137989241087960.743100537945602
1140.2574435082032360.5148870164064730.742556491796764
1150.2150520227887230.4301040455774460.784947977211277
1160.1763104563529360.3526209127058720.823689543647064
1170.1430750341320280.2861500682640550.856924965867972
1180.1134800908713950.2269601817427890.886519909128605
1190.08814995841458310.1762999168291660.911850041585417
1200.06741705374288130.1348341074857630.932582946257119
1210.05041192351797520.100823847035950.949588076482025
1220.03680950478120570.07361900956241140.963190495218794
1230.03959512630157470.07919025260314940.960404873698425
1240.04029589654843410.08059179309686810.959704103451566
1250.0288117823777050.057623564755410.971188217622295
1260.01995999842265530.03991999684531050.980040001577345
1270.01352373313107770.02704746626215540.986476266868922
1280.008951118420485110.01790223684097020.991048881579515
1290.005706084865149070.01141216973029810.994293915134851
1300.003564330868767140.007128661737534280.996435669131233
1310.002132581409922170.004265162819844350.997867418590078
1320.001283279819957990.002566559639915980.998716720180042
1330.001713052097405190.003426104194810370.998286947902595
1340.0009532549293622470.001906509858724490.999046745070638
1350.0005088941262380070.001017788252476010.999491105873762
1360.0002599680167615770.0005199360335231550.999740031983238
1370.0003423959050912450.0006847918101824910.999657604094909
1380.002031058959096340.004062117918192690.997968941040904
1390.001162797215261550.00232559443052310.998837202784738
1400.0007154459650391530.001430891930078310.999284554034961
1410.02386478780650960.04772957561301920.97613521219349
1420.01774222318998530.03548444637997050.982257776810015
1430.009409336631560250.01881867326312050.99059066336844
1440.004368220558058890.008736441116117780.995631779441941

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0 & 0 & 1 \tabularnewline
11 & 0 & 0 & 1 \tabularnewline
12 & 0 & 0 & 1 \tabularnewline
13 & 0 & 0 & 1 \tabularnewline
14 & 0 & 0 & 1 \tabularnewline
15 & 0 & 0 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 0.434921239783115 & 0.86984247956623 & 0.565078760216885 \tabularnewline
18 & 0.386087660765954 & 0.772175321531907 & 0.613912339234046 \tabularnewline
19 & 0.351263184296601 & 0.702526368593202 & 0.648736815703399 \tabularnewline
20 & 0.84264053700182 & 0.314718925996359 & 0.15735946299818 \tabularnewline
21 & 0.791249918724895 & 0.41750016255021 & 0.208750081275105 \tabularnewline
22 & 0.777554265861505 & 0.44489146827699 & 0.222445734138495 \tabularnewline
23 & 0.717499644587051 & 0.565000710825898 & 0.282500355412949 \tabularnewline
24 & 0.653040333438224 & 0.693919333123551 & 0.346959666561776 \tabularnewline
25 & 0.65519212306504 & 0.689615753869921 & 0.34480787693496 \tabularnewline
26 & 0.656463329123942 & 0.687073341752116 & 0.343536670876058 \tabularnewline
27 & 0.61478201308675 & 0.770435973826501 & 0.38521798691325 \tabularnewline
28 & 0.560202221934497 & 0.879595556131005 & 0.439797778065503 \tabularnewline
29 & 0.506532249276419 & 0.986935501447161 & 0.493467750723581 \tabularnewline
30 & 0.44846604989112 & 0.896932099782241 & 0.55153395010888 \tabularnewline
31 & 0.38888266634516 & 0.777765332690321 & 0.61111733365484 \tabularnewline
32 & 0.332622979946953 & 0.665245959893906 & 0.667377020053047 \tabularnewline
33 & 0.280802699013025 & 0.56160539802605 & 0.719197300986975 \tabularnewline
34 & 0.24022188219056 & 0.48044376438112 & 0.75977811780944 \tabularnewline
35 & 0.196949189306871 & 0.393898378613742 & 0.803050810693129 \tabularnewline
36 & 0.158814999288629 & 0.317629998577259 & 0.84118500071137 \tabularnewline
37 & 0.221888077803891 & 0.443776155607781 & 0.778111922196109 \tabularnewline
38 & 0.187476288991061 & 0.374952577982122 & 0.812523711008939 \tabularnewline
39 & 0.151042574901108 & 0.302085149802216 & 0.848957425098892 \tabularnewline
40 & 0.146541034526049 & 0.293082069052098 & 0.853458965473951 \tabularnewline
41 & 0.66469459951499 & 0.67061080097002 & 0.33530540048501 \tabularnewline
42 & 0.633584680543976 & 0.732830638912047 & 0.366415319456024 \tabularnewline
43 & 0.582562504885422 & 0.834874990229157 & 0.417437495114578 \tabularnewline
44 & 0.544747785778511 & 0.910504428442978 & 0.455252214221489 \tabularnewline
45 & 0.492992838356355 & 0.98598567671271 & 0.507007161643645 \tabularnewline
46 & 0.441675754606817 & 0.883351509213634 & 0.558324245393183 \tabularnewline
47 & 0.391556190087109 & 0.783112380174218 & 0.608443809912891 \tabularnewline
48 & 0.342948748453442 & 0.685897496906885 & 0.657051251546558 \tabularnewline
49 & 0.297315030662342 & 0.594630061324684 & 0.702684969337658 \tabularnewline
50 & 0.254786636303685 & 0.50957327260737 & 0.745213363696315 \tabularnewline
51 & 0.310920125004335 & 0.621840250008669 & 0.689079874995665 \tabularnewline
52 & 0.584246223566849 & 0.831507552866303 & 0.415753776433151 \tabularnewline
53 & 0.537934523229746 & 0.924130953540508 & 0.462065476770254 \tabularnewline
54 & 0.906891354237858 & 0.186217291524285 & 0.0931086457621423 \tabularnewline
55 & 0.884535641855131 & 0.230928716289738 & 0.115464358144869 \tabularnewline
56 & 0.921388193232121 & 0.157223613535757 & 0.0786118067678787 \tabularnewline
57 & 0.920077052319064 & 0.159845895361872 & 0.0799229476809359 \tabularnewline
58 & 0.901901166713362 & 0.196197666573277 & 0.0980988332866384 \tabularnewline
59 & 0.880767862718229 & 0.238464274563542 & 0.119232137281771 \tabularnewline
60 & 0.959684269984008 & 0.0806314600319837 & 0.0403157300159919 \tabularnewline
61 & 0.954445356374722 & 0.0911092872505565 & 0.0455546436252782 \tabularnewline
62 & 0.956400234813095 & 0.0871995303738091 & 0.0435997651869046 \tabularnewline
63 & 0.944290141981786 & 0.111419716036429 & 0.0557098580182143 \tabularnewline
64 & 0.94254937586238 & 0.11490124827524 & 0.0574506241376199 \tabularnewline
65 & 0.927587834675919 & 0.144824330648163 & 0.0724121653240815 \tabularnewline
66 & 0.909795625593901 & 0.180408748812198 & 0.0902043744060988 \tabularnewline
67 & 0.962761318808952 & 0.0744773623820953 & 0.0372386811910476 \tabularnewline
68 & 0.951967173126061 & 0.0960656537478778 & 0.0480328268739389 \tabularnewline
69 & 0.939688545224752 & 0.120622909550496 & 0.0603114547752479 \tabularnewline
70 & 0.937729366617339 & 0.124541266765323 & 0.0622706333826613 \tabularnewline
71 & 0.921910419350956 & 0.156179161298089 & 0.0780895806490443 \tabularnewline
72 & 0.904046443804952 & 0.191907112390096 & 0.095953556195048 \tabularnewline
73 & 0.89909671151587 & 0.20180657696826 & 0.10090328848413 \tabularnewline
74 & 0.897890713869287 & 0.204218572261427 & 0.102109286130713 \tabularnewline
75 & 0.876639473693468 & 0.246721052613064 & 0.123360526306532 \tabularnewline
76 & 0.876967178025085 & 0.246065643949831 & 0.123032821974916 \tabularnewline
77 & 0.853110207023614 & 0.293779585952772 & 0.146889792976386 \tabularnewline
78 & 0.859661538903037 & 0.280676922193925 & 0.140338461096963 \tabularnewline
79 & 0.959757600290779 & 0.0804847994184429 & 0.0402423997092215 \tabularnewline
80 & 0.951622653711428 & 0.0967546925771442 & 0.0483773462885721 \tabularnewline
81 & 0.941010142317807 & 0.117979715364386 & 0.0589898576821928 \tabularnewline
82 & 0.947822626030597 & 0.104354747938806 & 0.052177373969403 \tabularnewline
83 & 0.943536374068947 & 0.112927251862107 & 0.0564636259310535 \tabularnewline
84 & 0.994328273713299 & 0.0113434525734024 & 0.00567172628670118 \tabularnewline
85 & 0.991998414762451 & 0.0160031704750985 & 0.00800158523754923 \tabularnewline
86 & 0.988881165263902 & 0.0222376694721953 & 0.0111188347360976 \tabularnewline
87 & 0.984716880725502 & 0.0305662385489955 & 0.0152831192744978 \tabularnewline
88 & 0.982922776062178 & 0.0341544478756445 & 0.0170772239378222 \tabularnewline
89 & 0.977079209632363 & 0.0458415807352742 & 0.0229207903676371 \tabularnewline
90 & 0.969751511711574 & 0.060496976576852 & 0.030248488288426 \tabularnewline
91 & 0.960218443053031 & 0.0795631138939389 & 0.0397815569469694 \tabularnewline
92 & 0.948379796956291 & 0.103240406087419 & 0.0516202030437094 \tabularnewline
93 & 0.933765765479249 & 0.132468469041502 & 0.0662342345207508 \tabularnewline
94 & 0.91606831274486 & 0.167863374510281 & 0.0839316872551403 \tabularnewline
95 & 0.894945044241908 & 0.210109911516183 & 0.105054955758092 \tabularnewline
96 & 0.870649264185809 & 0.258701471628382 & 0.129350735814191 \tabularnewline
97 & 0.842077499138585 & 0.315845001722831 & 0.157922500861416 \tabularnewline
98 & 0.809397525516259 & 0.381204948967481 & 0.190602474483741 \tabularnewline
99 & 0.772838314811498 & 0.454323370377005 & 0.227161685188502 \tabularnewline
100 & 0.732931701408362 & 0.534136597183277 & 0.267068298591638 \tabularnewline
101 & 0.689939709186119 & 0.620120581627762 & 0.310060290813881 \tabularnewline
102 & 0.64293215388462 & 0.71413569223076 & 0.35706784611538 \tabularnewline
103 & 0.593439963245677 & 0.813120073508645 & 0.406560036754323 \tabularnewline
104 & 0.542185898606862 & 0.915628202786276 & 0.457814101393138 \tabularnewline
105 & 0.528762195462779 & 0.942475609074442 & 0.471237804537221 \tabularnewline
106 & 0.476237387770375 & 0.95247477554075 & 0.523762612229625 \tabularnewline
107 & 0.42386075409898 & 0.84772150819796 & 0.57613924590102 \tabularnewline
108 & 0.411056100870389 & 0.822112201740778 & 0.588943899129611 \tabularnewline
109 & 0.359891918815397 & 0.719783837630795 & 0.640108081184603 \tabularnewline
110 & 0.31112413168669 & 0.622248263373379 & 0.68887586831331 \tabularnewline
111 & 0.308969656914524 & 0.617939313829049 & 0.691030343085476 \tabularnewline
112 & 0.26261814982086 & 0.52523629964172 & 0.73738185017914 \tabularnewline
113 & 0.256899462054398 & 0.513798924108796 & 0.743100537945602 \tabularnewline
114 & 0.257443508203236 & 0.514887016406473 & 0.742556491796764 \tabularnewline
115 & 0.215052022788723 & 0.430104045577446 & 0.784947977211277 \tabularnewline
116 & 0.176310456352936 & 0.352620912705872 & 0.823689543647064 \tabularnewline
117 & 0.143075034132028 & 0.286150068264055 & 0.856924965867972 \tabularnewline
118 & 0.113480090871395 & 0.226960181742789 & 0.886519909128605 \tabularnewline
119 & 0.0881499584145831 & 0.176299916829166 & 0.911850041585417 \tabularnewline
120 & 0.0674170537428813 & 0.134834107485763 & 0.932582946257119 \tabularnewline
121 & 0.0504119235179752 & 0.10082384703595 & 0.949588076482025 \tabularnewline
122 & 0.0368095047812057 & 0.0736190095624114 & 0.963190495218794 \tabularnewline
123 & 0.0395951263015747 & 0.0791902526031494 & 0.960404873698425 \tabularnewline
124 & 0.0402958965484341 & 0.0805917930968681 & 0.959704103451566 \tabularnewline
125 & 0.028811782377705 & 0.05762356475541 & 0.971188217622295 \tabularnewline
126 & 0.0199599984226553 & 0.0399199968453105 & 0.980040001577345 \tabularnewline
127 & 0.0135237331310777 & 0.0270474662621554 & 0.986476266868922 \tabularnewline
128 & 0.00895111842048511 & 0.0179022368409702 & 0.991048881579515 \tabularnewline
129 & 0.00570608486514907 & 0.0114121697302981 & 0.994293915134851 \tabularnewline
130 & 0.00356433086876714 & 0.00712866173753428 & 0.996435669131233 \tabularnewline
131 & 0.00213258140992217 & 0.00426516281984435 & 0.997867418590078 \tabularnewline
132 & 0.00128327981995799 & 0.00256655963991598 & 0.998716720180042 \tabularnewline
133 & 0.00171305209740519 & 0.00342610419481037 & 0.998286947902595 \tabularnewline
134 & 0.000953254929362247 & 0.00190650985872449 & 0.999046745070638 \tabularnewline
135 & 0.000508894126238007 & 0.00101778825247601 & 0.999491105873762 \tabularnewline
136 & 0.000259968016761577 & 0.000519936033523155 & 0.999740031983238 \tabularnewline
137 & 0.000342395905091245 & 0.000684791810182491 & 0.999657604094909 \tabularnewline
138 & 0.00203105895909634 & 0.00406211791819269 & 0.997968941040904 \tabularnewline
139 & 0.00116279721526155 & 0.0023255944305231 & 0.998837202784738 \tabularnewline
140 & 0.000715445965039153 & 0.00143089193007831 & 0.999284554034961 \tabularnewline
141 & 0.0238647878065096 & 0.0477295756130192 & 0.97613521219349 \tabularnewline
142 & 0.0177422231899853 & 0.0354844463799705 & 0.982257776810015 \tabularnewline
143 & 0.00940933663156025 & 0.0188186732631205 & 0.99059066336844 \tabularnewline
144 & 0.00436822055805889 & 0.00873644111611778 & 0.995631779441941 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200535&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]10[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]0.434921239783115[/C][C]0.86984247956623[/C][C]0.565078760216885[/C][/ROW]
[ROW][C]18[/C][C]0.386087660765954[/C][C]0.772175321531907[/C][C]0.613912339234046[/C][/ROW]
[ROW][C]19[/C][C]0.351263184296601[/C][C]0.702526368593202[/C][C]0.648736815703399[/C][/ROW]
[ROW][C]20[/C][C]0.84264053700182[/C][C]0.314718925996359[/C][C]0.15735946299818[/C][/ROW]
[ROW][C]21[/C][C]0.791249918724895[/C][C]0.41750016255021[/C][C]0.208750081275105[/C][/ROW]
[ROW][C]22[/C][C]0.777554265861505[/C][C]0.44489146827699[/C][C]0.222445734138495[/C][/ROW]
[ROW][C]23[/C][C]0.717499644587051[/C][C]0.565000710825898[/C][C]0.282500355412949[/C][/ROW]
[ROW][C]24[/C][C]0.653040333438224[/C][C]0.693919333123551[/C][C]0.346959666561776[/C][/ROW]
[ROW][C]25[/C][C]0.65519212306504[/C][C]0.689615753869921[/C][C]0.34480787693496[/C][/ROW]
[ROW][C]26[/C][C]0.656463329123942[/C][C]0.687073341752116[/C][C]0.343536670876058[/C][/ROW]
[ROW][C]27[/C][C]0.61478201308675[/C][C]0.770435973826501[/C][C]0.38521798691325[/C][/ROW]
[ROW][C]28[/C][C]0.560202221934497[/C][C]0.879595556131005[/C][C]0.439797778065503[/C][/ROW]
[ROW][C]29[/C][C]0.506532249276419[/C][C]0.986935501447161[/C][C]0.493467750723581[/C][/ROW]
[ROW][C]30[/C][C]0.44846604989112[/C][C]0.896932099782241[/C][C]0.55153395010888[/C][/ROW]
[ROW][C]31[/C][C]0.38888266634516[/C][C]0.777765332690321[/C][C]0.61111733365484[/C][/ROW]
[ROW][C]32[/C][C]0.332622979946953[/C][C]0.665245959893906[/C][C]0.667377020053047[/C][/ROW]
[ROW][C]33[/C][C]0.280802699013025[/C][C]0.56160539802605[/C][C]0.719197300986975[/C][/ROW]
[ROW][C]34[/C][C]0.24022188219056[/C][C]0.48044376438112[/C][C]0.75977811780944[/C][/ROW]
[ROW][C]35[/C][C]0.196949189306871[/C][C]0.393898378613742[/C][C]0.803050810693129[/C][/ROW]
[ROW][C]36[/C][C]0.158814999288629[/C][C]0.317629998577259[/C][C]0.84118500071137[/C][/ROW]
[ROW][C]37[/C][C]0.221888077803891[/C][C]0.443776155607781[/C][C]0.778111922196109[/C][/ROW]
[ROW][C]38[/C][C]0.187476288991061[/C][C]0.374952577982122[/C][C]0.812523711008939[/C][/ROW]
[ROW][C]39[/C][C]0.151042574901108[/C][C]0.302085149802216[/C][C]0.848957425098892[/C][/ROW]
[ROW][C]40[/C][C]0.146541034526049[/C][C]0.293082069052098[/C][C]0.853458965473951[/C][/ROW]
[ROW][C]41[/C][C]0.66469459951499[/C][C]0.67061080097002[/C][C]0.33530540048501[/C][/ROW]
[ROW][C]42[/C][C]0.633584680543976[/C][C]0.732830638912047[/C][C]0.366415319456024[/C][/ROW]
[ROW][C]43[/C][C]0.582562504885422[/C][C]0.834874990229157[/C][C]0.417437495114578[/C][/ROW]
[ROW][C]44[/C][C]0.544747785778511[/C][C]0.910504428442978[/C][C]0.455252214221489[/C][/ROW]
[ROW][C]45[/C][C]0.492992838356355[/C][C]0.98598567671271[/C][C]0.507007161643645[/C][/ROW]
[ROW][C]46[/C][C]0.441675754606817[/C][C]0.883351509213634[/C][C]0.558324245393183[/C][/ROW]
[ROW][C]47[/C][C]0.391556190087109[/C][C]0.783112380174218[/C][C]0.608443809912891[/C][/ROW]
[ROW][C]48[/C][C]0.342948748453442[/C][C]0.685897496906885[/C][C]0.657051251546558[/C][/ROW]
[ROW][C]49[/C][C]0.297315030662342[/C][C]0.594630061324684[/C][C]0.702684969337658[/C][/ROW]
[ROW][C]50[/C][C]0.254786636303685[/C][C]0.50957327260737[/C][C]0.745213363696315[/C][/ROW]
[ROW][C]51[/C][C]0.310920125004335[/C][C]0.621840250008669[/C][C]0.689079874995665[/C][/ROW]
[ROW][C]52[/C][C]0.584246223566849[/C][C]0.831507552866303[/C][C]0.415753776433151[/C][/ROW]
[ROW][C]53[/C][C]0.537934523229746[/C][C]0.924130953540508[/C][C]0.462065476770254[/C][/ROW]
[ROW][C]54[/C][C]0.906891354237858[/C][C]0.186217291524285[/C][C]0.0931086457621423[/C][/ROW]
[ROW][C]55[/C][C]0.884535641855131[/C][C]0.230928716289738[/C][C]0.115464358144869[/C][/ROW]
[ROW][C]56[/C][C]0.921388193232121[/C][C]0.157223613535757[/C][C]0.0786118067678787[/C][/ROW]
[ROW][C]57[/C][C]0.920077052319064[/C][C]0.159845895361872[/C][C]0.0799229476809359[/C][/ROW]
[ROW][C]58[/C][C]0.901901166713362[/C][C]0.196197666573277[/C][C]0.0980988332866384[/C][/ROW]
[ROW][C]59[/C][C]0.880767862718229[/C][C]0.238464274563542[/C][C]0.119232137281771[/C][/ROW]
[ROW][C]60[/C][C]0.959684269984008[/C][C]0.0806314600319837[/C][C]0.0403157300159919[/C][/ROW]
[ROW][C]61[/C][C]0.954445356374722[/C][C]0.0911092872505565[/C][C]0.0455546436252782[/C][/ROW]
[ROW][C]62[/C][C]0.956400234813095[/C][C]0.0871995303738091[/C][C]0.0435997651869046[/C][/ROW]
[ROW][C]63[/C][C]0.944290141981786[/C][C]0.111419716036429[/C][C]0.0557098580182143[/C][/ROW]
[ROW][C]64[/C][C]0.94254937586238[/C][C]0.11490124827524[/C][C]0.0574506241376199[/C][/ROW]
[ROW][C]65[/C][C]0.927587834675919[/C][C]0.144824330648163[/C][C]0.0724121653240815[/C][/ROW]
[ROW][C]66[/C][C]0.909795625593901[/C][C]0.180408748812198[/C][C]0.0902043744060988[/C][/ROW]
[ROW][C]67[/C][C]0.962761318808952[/C][C]0.0744773623820953[/C][C]0.0372386811910476[/C][/ROW]
[ROW][C]68[/C][C]0.951967173126061[/C][C]0.0960656537478778[/C][C]0.0480328268739389[/C][/ROW]
[ROW][C]69[/C][C]0.939688545224752[/C][C]0.120622909550496[/C][C]0.0603114547752479[/C][/ROW]
[ROW][C]70[/C][C]0.937729366617339[/C][C]0.124541266765323[/C][C]0.0622706333826613[/C][/ROW]
[ROW][C]71[/C][C]0.921910419350956[/C][C]0.156179161298089[/C][C]0.0780895806490443[/C][/ROW]
[ROW][C]72[/C][C]0.904046443804952[/C][C]0.191907112390096[/C][C]0.095953556195048[/C][/ROW]
[ROW][C]73[/C][C]0.89909671151587[/C][C]0.20180657696826[/C][C]0.10090328848413[/C][/ROW]
[ROW][C]74[/C][C]0.897890713869287[/C][C]0.204218572261427[/C][C]0.102109286130713[/C][/ROW]
[ROW][C]75[/C][C]0.876639473693468[/C][C]0.246721052613064[/C][C]0.123360526306532[/C][/ROW]
[ROW][C]76[/C][C]0.876967178025085[/C][C]0.246065643949831[/C][C]0.123032821974916[/C][/ROW]
[ROW][C]77[/C][C]0.853110207023614[/C][C]0.293779585952772[/C][C]0.146889792976386[/C][/ROW]
[ROW][C]78[/C][C]0.859661538903037[/C][C]0.280676922193925[/C][C]0.140338461096963[/C][/ROW]
[ROW][C]79[/C][C]0.959757600290779[/C][C]0.0804847994184429[/C][C]0.0402423997092215[/C][/ROW]
[ROW][C]80[/C][C]0.951622653711428[/C][C]0.0967546925771442[/C][C]0.0483773462885721[/C][/ROW]
[ROW][C]81[/C][C]0.941010142317807[/C][C]0.117979715364386[/C][C]0.0589898576821928[/C][/ROW]
[ROW][C]82[/C][C]0.947822626030597[/C][C]0.104354747938806[/C][C]0.052177373969403[/C][/ROW]
[ROW][C]83[/C][C]0.943536374068947[/C][C]0.112927251862107[/C][C]0.0564636259310535[/C][/ROW]
[ROW][C]84[/C][C]0.994328273713299[/C][C]0.0113434525734024[/C][C]0.00567172628670118[/C][/ROW]
[ROW][C]85[/C][C]0.991998414762451[/C][C]0.0160031704750985[/C][C]0.00800158523754923[/C][/ROW]
[ROW][C]86[/C][C]0.988881165263902[/C][C]0.0222376694721953[/C][C]0.0111188347360976[/C][/ROW]
[ROW][C]87[/C][C]0.984716880725502[/C][C]0.0305662385489955[/C][C]0.0152831192744978[/C][/ROW]
[ROW][C]88[/C][C]0.982922776062178[/C][C]0.0341544478756445[/C][C]0.0170772239378222[/C][/ROW]
[ROW][C]89[/C][C]0.977079209632363[/C][C]0.0458415807352742[/C][C]0.0229207903676371[/C][/ROW]
[ROW][C]90[/C][C]0.969751511711574[/C][C]0.060496976576852[/C][C]0.030248488288426[/C][/ROW]
[ROW][C]91[/C][C]0.960218443053031[/C][C]0.0795631138939389[/C][C]0.0397815569469694[/C][/ROW]
[ROW][C]92[/C][C]0.948379796956291[/C][C]0.103240406087419[/C][C]0.0516202030437094[/C][/ROW]
[ROW][C]93[/C][C]0.933765765479249[/C][C]0.132468469041502[/C][C]0.0662342345207508[/C][/ROW]
[ROW][C]94[/C][C]0.91606831274486[/C][C]0.167863374510281[/C][C]0.0839316872551403[/C][/ROW]
[ROW][C]95[/C][C]0.894945044241908[/C][C]0.210109911516183[/C][C]0.105054955758092[/C][/ROW]
[ROW][C]96[/C][C]0.870649264185809[/C][C]0.258701471628382[/C][C]0.129350735814191[/C][/ROW]
[ROW][C]97[/C][C]0.842077499138585[/C][C]0.315845001722831[/C][C]0.157922500861416[/C][/ROW]
[ROW][C]98[/C][C]0.809397525516259[/C][C]0.381204948967481[/C][C]0.190602474483741[/C][/ROW]
[ROW][C]99[/C][C]0.772838314811498[/C][C]0.454323370377005[/C][C]0.227161685188502[/C][/ROW]
[ROW][C]100[/C][C]0.732931701408362[/C][C]0.534136597183277[/C][C]0.267068298591638[/C][/ROW]
[ROW][C]101[/C][C]0.689939709186119[/C][C]0.620120581627762[/C][C]0.310060290813881[/C][/ROW]
[ROW][C]102[/C][C]0.64293215388462[/C][C]0.71413569223076[/C][C]0.35706784611538[/C][/ROW]
[ROW][C]103[/C][C]0.593439963245677[/C][C]0.813120073508645[/C][C]0.406560036754323[/C][/ROW]
[ROW][C]104[/C][C]0.542185898606862[/C][C]0.915628202786276[/C][C]0.457814101393138[/C][/ROW]
[ROW][C]105[/C][C]0.528762195462779[/C][C]0.942475609074442[/C][C]0.471237804537221[/C][/ROW]
[ROW][C]106[/C][C]0.476237387770375[/C][C]0.95247477554075[/C][C]0.523762612229625[/C][/ROW]
[ROW][C]107[/C][C]0.42386075409898[/C][C]0.84772150819796[/C][C]0.57613924590102[/C][/ROW]
[ROW][C]108[/C][C]0.411056100870389[/C][C]0.822112201740778[/C][C]0.588943899129611[/C][/ROW]
[ROW][C]109[/C][C]0.359891918815397[/C][C]0.719783837630795[/C][C]0.640108081184603[/C][/ROW]
[ROW][C]110[/C][C]0.31112413168669[/C][C]0.622248263373379[/C][C]0.68887586831331[/C][/ROW]
[ROW][C]111[/C][C]0.308969656914524[/C][C]0.617939313829049[/C][C]0.691030343085476[/C][/ROW]
[ROW][C]112[/C][C]0.26261814982086[/C][C]0.52523629964172[/C][C]0.73738185017914[/C][/ROW]
[ROW][C]113[/C][C]0.256899462054398[/C][C]0.513798924108796[/C][C]0.743100537945602[/C][/ROW]
[ROW][C]114[/C][C]0.257443508203236[/C][C]0.514887016406473[/C][C]0.742556491796764[/C][/ROW]
[ROW][C]115[/C][C]0.215052022788723[/C][C]0.430104045577446[/C][C]0.784947977211277[/C][/ROW]
[ROW][C]116[/C][C]0.176310456352936[/C][C]0.352620912705872[/C][C]0.823689543647064[/C][/ROW]
[ROW][C]117[/C][C]0.143075034132028[/C][C]0.286150068264055[/C][C]0.856924965867972[/C][/ROW]
[ROW][C]118[/C][C]0.113480090871395[/C][C]0.226960181742789[/C][C]0.886519909128605[/C][/ROW]
[ROW][C]119[/C][C]0.0881499584145831[/C][C]0.176299916829166[/C][C]0.911850041585417[/C][/ROW]
[ROW][C]120[/C][C]0.0674170537428813[/C][C]0.134834107485763[/C][C]0.932582946257119[/C][/ROW]
[ROW][C]121[/C][C]0.0504119235179752[/C][C]0.10082384703595[/C][C]0.949588076482025[/C][/ROW]
[ROW][C]122[/C][C]0.0368095047812057[/C][C]0.0736190095624114[/C][C]0.963190495218794[/C][/ROW]
[ROW][C]123[/C][C]0.0395951263015747[/C][C]0.0791902526031494[/C][C]0.960404873698425[/C][/ROW]
[ROW][C]124[/C][C]0.0402958965484341[/C][C]0.0805917930968681[/C][C]0.959704103451566[/C][/ROW]
[ROW][C]125[/C][C]0.028811782377705[/C][C]0.05762356475541[/C][C]0.971188217622295[/C][/ROW]
[ROW][C]126[/C][C]0.0199599984226553[/C][C]0.0399199968453105[/C][C]0.980040001577345[/C][/ROW]
[ROW][C]127[/C][C]0.0135237331310777[/C][C]0.0270474662621554[/C][C]0.986476266868922[/C][/ROW]
[ROW][C]128[/C][C]0.00895111842048511[/C][C]0.0179022368409702[/C][C]0.991048881579515[/C][/ROW]
[ROW][C]129[/C][C]0.00570608486514907[/C][C]0.0114121697302981[/C][C]0.994293915134851[/C][/ROW]
[ROW][C]130[/C][C]0.00356433086876714[/C][C]0.00712866173753428[/C][C]0.996435669131233[/C][/ROW]
[ROW][C]131[/C][C]0.00213258140992217[/C][C]0.00426516281984435[/C][C]0.997867418590078[/C][/ROW]
[ROW][C]132[/C][C]0.00128327981995799[/C][C]0.00256655963991598[/C][C]0.998716720180042[/C][/ROW]
[ROW][C]133[/C][C]0.00171305209740519[/C][C]0.00342610419481037[/C][C]0.998286947902595[/C][/ROW]
[ROW][C]134[/C][C]0.000953254929362247[/C][C]0.00190650985872449[/C][C]0.999046745070638[/C][/ROW]
[ROW][C]135[/C][C]0.000508894126238007[/C][C]0.00101778825247601[/C][C]0.999491105873762[/C][/ROW]
[ROW][C]136[/C][C]0.000259968016761577[/C][C]0.000519936033523155[/C][C]0.999740031983238[/C][/ROW]
[ROW][C]137[/C][C]0.000342395905091245[/C][C]0.000684791810182491[/C][C]0.999657604094909[/C][/ROW]
[ROW][C]138[/C][C]0.00203105895909634[/C][C]0.00406211791819269[/C][C]0.997968941040904[/C][/ROW]
[ROW][C]139[/C][C]0.00116279721526155[/C][C]0.0023255944305231[/C][C]0.998837202784738[/C][/ROW]
[ROW][C]140[/C][C]0.000715445965039153[/C][C]0.00143089193007831[/C][C]0.999284554034961[/C][/ROW]
[ROW][C]141[/C][C]0.0238647878065096[/C][C]0.0477295756130192[/C][C]0.97613521219349[/C][/ROW]
[ROW][C]142[/C][C]0.0177422231899853[/C][C]0.0354844463799705[/C][C]0.982257776810015[/C][/ROW]
[ROW][C]143[/C][C]0.00940933663156025[/C][C]0.0188186732631205[/C][C]0.99059066336844[/C][/ROW]
[ROW][C]144[/C][C]0.00436822055805889[/C][C]0.00873644111611778[/C][C]0.995631779441941[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200535&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200535&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
10001
11001
12001
13001
14001
15001
16001
170.4349212397831150.869842479566230.565078760216885
180.3860876607659540.7721753215319070.613912339234046
190.3512631842966010.7025263685932020.648736815703399
200.842640537001820.3147189259963590.15735946299818
210.7912499187248950.417500162550210.208750081275105
220.7775542658615050.444891468276990.222445734138495
230.7174996445870510.5650007108258980.282500355412949
240.6530403334382240.6939193331235510.346959666561776
250.655192123065040.6896157538699210.34480787693496
260.6564633291239420.6870733417521160.343536670876058
270.614782013086750.7704359738265010.38521798691325
280.5602022219344970.8795955561310050.439797778065503
290.5065322492764190.9869355014471610.493467750723581
300.448466049891120.8969320997822410.55153395010888
310.388882666345160.7777653326903210.61111733365484
320.3326229799469530.6652459598939060.667377020053047
330.2808026990130250.561605398026050.719197300986975
340.240221882190560.480443764381120.75977811780944
350.1969491893068710.3938983786137420.803050810693129
360.1588149992886290.3176299985772590.84118500071137
370.2218880778038910.4437761556077810.778111922196109
380.1874762889910610.3749525779821220.812523711008939
390.1510425749011080.3020851498022160.848957425098892
400.1465410345260490.2930820690520980.853458965473951
410.664694599514990.670610800970020.33530540048501
420.6335846805439760.7328306389120470.366415319456024
430.5825625048854220.8348749902291570.417437495114578
440.5447477857785110.9105044284429780.455252214221489
450.4929928383563550.985985676712710.507007161643645
460.4416757546068170.8833515092136340.558324245393183
470.3915561900871090.7831123801742180.608443809912891
480.3429487484534420.6858974969068850.657051251546558
490.2973150306623420.5946300613246840.702684969337658
500.2547866363036850.509573272607370.745213363696315
510.3109201250043350.6218402500086690.689079874995665
520.5842462235668490.8315075528663030.415753776433151
530.5379345232297460.9241309535405080.462065476770254
540.9068913542378580.1862172915242850.0931086457621423
550.8845356418551310.2309287162897380.115464358144869
560.9213881932321210.1572236135357570.0786118067678787
570.9200770523190640.1598458953618720.0799229476809359
580.9019011667133620.1961976665732770.0980988332866384
590.8807678627182290.2384642745635420.119232137281771
600.9596842699840080.08063146003198370.0403157300159919
610.9544453563747220.09110928725055650.0455546436252782
620.9564002348130950.08719953037380910.0435997651869046
630.9442901419817860.1114197160364290.0557098580182143
640.942549375862380.114901248275240.0574506241376199
650.9275878346759190.1448243306481630.0724121653240815
660.9097956255939010.1804087488121980.0902043744060988
670.9627613188089520.07447736238209530.0372386811910476
680.9519671731260610.09606565374787780.0480328268739389
690.9396885452247520.1206229095504960.0603114547752479
700.9377293666173390.1245412667653230.0622706333826613
710.9219104193509560.1561791612980890.0780895806490443
720.9040464438049520.1919071123900960.095953556195048
730.899096711515870.201806576968260.10090328848413
740.8978907138692870.2042185722614270.102109286130713
750.8766394736934680.2467210526130640.123360526306532
760.8769671780250850.2460656439498310.123032821974916
770.8531102070236140.2937795859527720.146889792976386
780.8596615389030370.2806769221939250.140338461096963
790.9597576002907790.08048479941844290.0402423997092215
800.9516226537114280.09675469257714420.0483773462885721
810.9410101423178070.1179797153643860.0589898576821928
820.9478226260305970.1043547479388060.052177373969403
830.9435363740689470.1129272518621070.0564636259310535
840.9943282737132990.01134345257340240.00567172628670118
850.9919984147624510.01600317047509850.00800158523754923
860.9888811652639020.02223766947219530.0111188347360976
870.9847168807255020.03056623854899550.0152831192744978
880.9829227760621780.03415444787564450.0170772239378222
890.9770792096323630.04584158073527420.0229207903676371
900.9697515117115740.0604969765768520.030248488288426
910.9602184430530310.07956311389393890.0397815569469694
920.9483797969562910.1032404060874190.0516202030437094
930.9337657654792490.1324684690415020.0662342345207508
940.916068312744860.1678633745102810.0839316872551403
950.8949450442419080.2101099115161830.105054955758092
960.8706492641858090.2587014716283820.129350735814191
970.8420774991385850.3158450017228310.157922500861416
980.8093975255162590.3812049489674810.190602474483741
990.7728383148114980.4543233703770050.227161685188502
1000.7329317014083620.5341365971832770.267068298591638
1010.6899397091861190.6201205816277620.310060290813881
1020.642932153884620.714135692230760.35706784611538
1030.5934399632456770.8131200735086450.406560036754323
1040.5421858986068620.9156282027862760.457814101393138
1050.5287621954627790.9424756090744420.471237804537221
1060.4762373877703750.952474775540750.523762612229625
1070.423860754098980.847721508197960.57613924590102
1080.4110561008703890.8221122017407780.588943899129611
1090.3598919188153970.7197838376307950.640108081184603
1100.311124131686690.6222482633733790.68887586831331
1110.3089696569145240.6179393138290490.691030343085476
1120.262618149820860.525236299641720.73738185017914
1130.2568994620543980.5137989241087960.743100537945602
1140.2574435082032360.5148870164064730.742556491796764
1150.2150520227887230.4301040455774460.784947977211277
1160.1763104563529360.3526209127058720.823689543647064
1170.1430750341320280.2861500682640550.856924965867972
1180.1134800908713950.2269601817427890.886519909128605
1190.08814995841458310.1762999168291660.911850041585417
1200.06741705374288130.1348341074857630.932582946257119
1210.05041192351797520.100823847035950.949588076482025
1220.03680950478120570.07361900956241140.963190495218794
1230.03959512630157470.07919025260314940.960404873698425
1240.04029589654843410.08059179309686810.959704103451566
1250.0288117823777050.057623564755410.971188217622295
1260.01995999842265530.03991999684531050.980040001577345
1270.01352373313107770.02704746626215540.986476266868922
1280.008951118420485110.01790223684097020.991048881579515
1290.005706084865149070.01141216973029810.994293915134851
1300.003564330868767140.007128661737534280.996435669131233
1310.002132581409922170.004265162819844350.997867418590078
1320.001283279819957990.002566559639915980.998716720180042
1330.001713052097405190.003426104194810370.998286947902595
1340.0009532549293622470.001906509858724490.999046745070638
1350.0005088941262380070.001017788252476010.999491105873762
1360.0002599680167615770.0005199360335231550.999740031983238
1370.0003423959050912450.0006847918101824910.999657604094909
1380.002031058959096340.004062117918192690.997968941040904
1390.001162797215261550.00232559443052310.998837202784738
1400.0007154459650391530.001430891930078310.999284554034961
1410.02386478780650960.04772957561301920.97613521219349
1420.01774222318998530.03548444637997050.982257776810015
1430.009409336631560250.01881867326312050.99059066336844
1440.004368220558058890.008736441116117780.995631779441941







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level190.140740740740741NOK
5% type I error level320.237037037037037NOK
10% type I error level450.333333333333333NOK

\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 & 19 & 0.140740740740741 & NOK \tabularnewline
5% type I error level & 32 & 0.237037037037037 & NOK \tabularnewline
10% type I error level & 45 & 0.333333333333333 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200535&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]19[/C][C]0.140740740740741[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.237037037037037[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]45[/C][C]0.333333333333333[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200535&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200535&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 level190.140740740740741NOK
5% type I error level320.237037037037037NOK
10% type I error level450.333333333333333NOK



Parameters (Session):
par1 = 8 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 7 ; 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')
}