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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 20 Dec 2012 12:26:59 -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/20/t1356024480shdiidn88ffdeki.htm/, Retrieved Thu, 25 Apr 2024 12:58:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=202940, Retrieved Thu, 25 Apr 2024 12:58:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact65

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	1	0	0	0	0	1
0	0	1	0	0	0	0
0	0	0	0	0	0	0
0	0	0	0	0	0	0
0	0	0	0	0	0	0
1	0	1	0	0	1	1
0	0	0	0	0	0	0
0	1	0	0	0	0	0
0	0	1	0	0	0	1
1	0	0	0	0	0	0
1	1	1	0	0	0	0
0	0	0	0	0	0	0
0	0	0	1	0	1	0
1	1	0	0	0	0	0
0	0	0	1	0	1	1
0	1	0	1	0	1	1
1	1	0	1	1	1	0
1	1	0	0	0	0	0
0	0	1	0	0	0	1
0	1	0	1	1	1	1
1	0	0	0	0	1	0
1	0	1	1	0	1	1
0	0	0	0	0	1	1
1	0	0	0	0	1	1
0	1	1	1	0	0	1
0	0	1	1	0	1	0
1	0	0	0	0	0	1
0	0	1	1	0	0	0
0	0	0	0	0	0	1
0	0	0	0	0	1	0
0	0	0	0	0	0	0
1	0	0	0	0	0	0
1	0	0	0	0	1	0
0	1	0	0	0	0	1
0	0	0	0	0	0	0
0	0	0	0	0	0	0
1	1	1	1	0	1	0
0	0	0	1	0	0	1
0	0	0	0	0	1	1
0	1	1	0	0	1	0
0	0	0	1	1	1	1
0	0	0	1	0	0	1
1	0	0	0	0	1	1
1	1	0	0	0	0	0
0	0	0	0	0	1	0
0	0	0	0	0	1	1
0	0	0	0	0	0	0
0	0	0	0	0	0	1
0	0	0	0	0	1	1
0	0	0	0	0	0	0
0	1	0	1	0	0	0
1	1	1	1	1	1	0
0	0	1	0	0	0	1
0	0	0	1	1	0	0
0	0	0	0	0	0	0
0	1	1	1	0	0	1
0	0	0	1	0	1	1
0	0	0	0	0	0	1
0	0	0	0	0	0	1
1	1	1	1	1	1	1
1	1	1	0	0	0	1
0	0	1	1	0	1	0
0	0	0	0	0	0	0
1	1	0	0	0	0	1
0	0	0	0	0	0	0
0	0	0	0	0	0	0
0	1	0	1	1	1	0
1	0	0	0	0	0	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202940&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202940&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202940&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.






Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = -0.0987694010088903 + 0.0456935068339825UseLimit[t] + 0.136486977088368T40[t] -0.10581292308639T20[t] + 0.27960795316419Used[t] + 0.110972976254381Useful[t] -0.0478061722217214`Outcome\r\r`[t] + 0.00191941881644583t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CorrectAnalysis[t] =  -0.0987694010088903 +  0.0456935068339825UseLimit[t] +  0.136486977088368T40[t] -0.10581292308639T20[t] +  0.27960795316419Used[t] +  0.110972976254381Useful[t] -0.0478061722217214`Outcome\r\r`[t] +  0.00191941881644583t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202940&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CorrectAnalysis[t] =  -0.0987694010088903 +  0.0456935068339825UseLimit[t] +  0.136486977088368T40[t] -0.10581292308639T20[t] +  0.27960795316419Used[t] +  0.110972976254381Useful[t] -0.0478061722217214`Outcome\r\r`[t] +  0.00191941881644583t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202940&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202940&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
CorrectAnalysis[t] = -0.0987694010088903 + 0.0456935068339825UseLimit[t] + 0.136486977088368T40[t] -0.10581292308639T20[t] + 0.27960795316419Used[t] + 0.110972976254381Useful[t] -0.0478061722217214`Outcome\r\r`[t] + 0.00191941881644583t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.09876940100889030.076434-1.29220.2012340.100617
UseLimit0.04569350683398250.0766090.59650.5531160.276558
T400.1364869770883680.0788461.7310.0885830.044291
T20-0.105812923086390.077196-1.37070.1755760.087788
Used0.279607953164190.0829843.36940.0013210.000661
Useful0.1109729762543810.0730631.51890.1340480.067024
`Outcome\r\r`-0.04780617222172140.064379-0.74260.4606390.23032
t0.001919418816445830.0016211.1840.24110.12055

\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) & -0.0987694010088903 & 0.076434 & -1.2922 & 0.201234 & 0.100617 \tabularnewline
UseLimit & 0.0456935068339825 & 0.076609 & 0.5965 & 0.553116 & 0.276558 \tabularnewline
T40 & 0.136486977088368 & 0.078846 & 1.731 & 0.088583 & 0.044291 \tabularnewline
T20 & -0.10581292308639 & 0.077196 & -1.3707 & 0.175576 & 0.087788 \tabularnewline
Used & 0.27960795316419 & 0.082984 & 3.3694 & 0.001321 & 0.000661 \tabularnewline
Useful & 0.110972976254381 & 0.073063 & 1.5189 & 0.134048 & 0.067024 \tabularnewline
`Outcome\r\r` & -0.0478061722217214 & 0.064379 & -0.7426 & 0.460639 & 0.23032 \tabularnewline
t & 0.00191941881644583 & 0.001621 & 1.184 & 0.2411 & 0.12055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202940&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]-0.0987694010088903[/C][C]0.076434[/C][C]-1.2922[/C][C]0.201234[/C][C]0.100617[/C][/ROW]
[ROW][C]UseLimit[/C][C]0.0456935068339825[/C][C]0.076609[/C][C]0.5965[/C][C]0.553116[/C][C]0.276558[/C][/ROW]
[ROW][C]T40[/C][C]0.136486977088368[/C][C]0.078846[/C][C]1.731[/C][C]0.088583[/C][C]0.044291[/C][/ROW]
[ROW][C]T20[/C][C]-0.10581292308639[/C][C]0.077196[/C][C]-1.3707[/C][C]0.175576[/C][C]0.087788[/C][/ROW]
[ROW][C]Used[/C][C]0.27960795316419[/C][C]0.082984[/C][C]3.3694[/C][C]0.001321[/C][C]0.000661[/C][/ROW]
[ROW][C]Useful[/C][C]0.110972976254381[/C][C]0.073063[/C][C]1.5189[/C][C]0.134048[/C][C]0.067024[/C][/ROW]
[ROW][C]`Outcome\r\r`[/C][C]-0.0478061722217214[/C][C]0.064379[/C][C]-0.7426[/C][C]0.460639[/C][C]0.23032[/C][/ROW]
[ROW][C]t[/C][C]0.00191941881644583[/C][C]0.001621[/C][C]1.184[/C][C]0.2411[/C][C]0.12055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202940&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202940&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.09876940100889030.076434-1.29220.2012340.100617
UseLimit0.04569350683398250.0766090.59650.5531160.276558
T400.1364869770883680.0788461.7310.0885830.044291
T20-0.105812923086390.077196-1.37070.1755760.087788
Used0.279607953164190.0829843.36940.0013210.000661
Useful0.1109729762543810.0730631.51890.1340480.067024
`Outcome\r\r`-0.04780617222172140.064379-0.74260.4606390.23032
t0.001919418816445830.0016211.1840.24110.12055






Multiple Linear Regression - Regression Statistics
Multiple R0.601582661284846
R-squared0.361901698358558
Adjusted R-squared0.287456896500389
F-TEST (value)4.86134275765889
F-TEST (DF numerator)7
F-TEST (DF denominator)60
p-value0.00021774676359676
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.258420909318052
Sum Squared Residuals4.00688198236612

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.601582661284846 \tabularnewline
R-squared & 0.361901698358558 \tabularnewline
Adjusted R-squared & 0.287456896500389 \tabularnewline
F-TEST (value) & 4.86134275765889 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 60 \tabularnewline
p-value & 0.00021774676359676 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.258420909318052 \tabularnewline
Sum Squared Residuals & 4.00688198236612 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202940&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.601582661284846[/C][/ROW]
[ROW][C]R-squared[/C][C]0.361901698358558[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.287456896500389[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.86134275765889[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]60[/C][/ROW]
[ROW][C]p-value[/C][C]0.00021774676359676[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.258420909318052[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4.00688198236612[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202940&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202940&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.601582661284846
R-squared0.361901698358558
Adjusted R-squared0.287456896500389
F-TEST (value)4.86134275765889
F-TEST (DF numerator)7
F-TEST (DF denominator)60
p-value0.00021774676359676
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.258420909318052
Sum Squared Residuals4.00688198236612






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.0375243295081849-0.0375243295081849
20-0.2007434864623890.200743486462389
30-0.09301114455955270.0930111445595527
40-0.09109172574310710.0910917257431071
50-0.08917230692666110.0891723069266611
60-0.08420550032996360.0842055003299636
70-0.08533346929376950.0853334692937695
800.0530729266110446-0.0530729266110446
90-0.2351137269689890.235113726968989
100-0.03388170601044950.0338817060104495
110-0.001288233192025680.00128823319202568
120-0.07573637521154030.0757363752115403
1300.316763973023477-0.316763973023477
1400.110282946343702-0.110282946343702
1500.272796638434647-0.272796638434647
1600.411203034339461-0.411203034339461
1710.5066221322116110.493377867788389
1800.117960621609485-0.117960621609485
190-0.2159195388045310.215919538804531
2010.4188807096052440.581119290394756
2100.0982048772248355-0.0982048772248355
2200.22611315389736-0.22611315389736
2300.00854403580202322-0.00854403580202322
2400.0561569614524516-0.0561569614524516
2500.211691904346702-0.211691904346702
2600.235903494550882-0.235903494550882
270-0.04905775835259180.0490577583525918
2800.128769355929393-0.128769355929393
290-0.09091242755368260.0909124275536826
3000.0697861397388655-0.0697861397388655
310-0.03926741769906950.0392674176990695
3200.00834550795135879-0.00834550795135879
3300.121237903022185-0.121237903022185
3400.0551716436169147-0.0551716436169147
350-0.03158974243328620.0315897424332862
360-0.02967032361684040.0296703236168404
3700.439197585454137-0.439197585454137
3800.20597029495852-0.20597029495852
3900.0392547368651565-0.0392547368651565
4000.119654381905302-0.119654381905302
4110.3227015276622380.677298472337762
4200.213647970224304-0.213647970224304
4300.0926259189649223-0.0926259189649223
4400.167865510837077-0.167865510837077
4500.0985774219855529-0.0985774219855529
4600.0526906685802773-0.0526906685802773
470-0.008556716635936240.00855671663593624
480-0.05444347004121180.0544434700412118
4900.0584489250296148-0.0584489250296148
500-0.002798460186598750.00279846018659875
5100.415215888882406-0.415215888882406
5210.4679888677008250.532011132299175
530-0.1506592990453730.150659299045373
5410.2844871682433750.715512831756625
5500.0067986338956304-0.0067986338956304
5600.271193887656523-0.271193887656523
5700.353412228725372-0.353412228725372
580-0.03524928187675350.0352492818767535
590-0.03332986306030770.0333298630603077
6010.435538046010670.56446195398933
6100.0468765354085444-0.0468765354085444
6200.305002571942932-0.305002571942932
6300.022153984427197-0.022153984427197
6400.158447714944272-0.158447714944272
6500.0259928220600887-0.0259928220600887
6600.0279122408765345-0.0279122408765345
6710.556899566199920.44310043380008
6800.0774445853434087-0.0774445853434087

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0 & 0.0375243295081849 & -0.0375243295081849 \tabularnewline
2 & 0 & -0.200743486462389 & 0.200743486462389 \tabularnewline
3 & 0 & -0.0930111445595527 & 0.0930111445595527 \tabularnewline
4 & 0 & -0.0910917257431071 & 0.0910917257431071 \tabularnewline
5 & 0 & -0.0891723069266611 & 0.0891723069266611 \tabularnewline
6 & 0 & -0.0842055003299636 & 0.0842055003299636 \tabularnewline
7 & 0 & -0.0853334692937695 & 0.0853334692937695 \tabularnewline
8 & 0 & 0.0530729266110446 & -0.0530729266110446 \tabularnewline
9 & 0 & -0.235113726968989 & 0.235113726968989 \tabularnewline
10 & 0 & -0.0338817060104495 & 0.0338817060104495 \tabularnewline
11 & 0 & -0.00128823319202568 & 0.00128823319202568 \tabularnewline
12 & 0 & -0.0757363752115403 & 0.0757363752115403 \tabularnewline
13 & 0 & 0.316763973023477 & -0.316763973023477 \tabularnewline
14 & 0 & 0.110282946343702 & -0.110282946343702 \tabularnewline
15 & 0 & 0.272796638434647 & -0.272796638434647 \tabularnewline
16 & 0 & 0.411203034339461 & -0.411203034339461 \tabularnewline
17 & 1 & 0.506622132211611 & 0.493377867788389 \tabularnewline
18 & 0 & 0.117960621609485 & -0.117960621609485 \tabularnewline
19 & 0 & -0.215919538804531 & 0.215919538804531 \tabularnewline
20 & 1 & 0.418880709605244 & 0.581119290394756 \tabularnewline
21 & 0 & 0.0982048772248355 & -0.0982048772248355 \tabularnewline
22 & 0 & 0.22611315389736 & -0.22611315389736 \tabularnewline
23 & 0 & 0.00854403580202322 & -0.00854403580202322 \tabularnewline
24 & 0 & 0.0561569614524516 & -0.0561569614524516 \tabularnewline
25 & 0 & 0.211691904346702 & -0.211691904346702 \tabularnewline
26 & 0 & 0.235903494550882 & -0.235903494550882 \tabularnewline
27 & 0 & -0.0490577583525918 & 0.0490577583525918 \tabularnewline
28 & 0 & 0.128769355929393 & -0.128769355929393 \tabularnewline
29 & 0 & -0.0909124275536826 & 0.0909124275536826 \tabularnewline
30 & 0 & 0.0697861397388655 & -0.0697861397388655 \tabularnewline
31 & 0 & -0.0392674176990695 & 0.0392674176990695 \tabularnewline
32 & 0 & 0.00834550795135879 & -0.00834550795135879 \tabularnewline
33 & 0 & 0.121237903022185 & -0.121237903022185 \tabularnewline
34 & 0 & 0.0551716436169147 & -0.0551716436169147 \tabularnewline
35 & 0 & -0.0315897424332862 & 0.0315897424332862 \tabularnewline
36 & 0 & -0.0296703236168404 & 0.0296703236168404 \tabularnewline
37 & 0 & 0.439197585454137 & -0.439197585454137 \tabularnewline
38 & 0 & 0.20597029495852 & -0.20597029495852 \tabularnewline
39 & 0 & 0.0392547368651565 & -0.0392547368651565 \tabularnewline
40 & 0 & 0.119654381905302 & -0.119654381905302 \tabularnewline
41 & 1 & 0.322701527662238 & 0.677298472337762 \tabularnewline
42 & 0 & 0.213647970224304 & -0.213647970224304 \tabularnewline
43 & 0 & 0.0926259189649223 & -0.0926259189649223 \tabularnewline
44 & 0 & 0.167865510837077 & -0.167865510837077 \tabularnewline
45 & 0 & 0.0985774219855529 & -0.0985774219855529 \tabularnewline
46 & 0 & 0.0526906685802773 & -0.0526906685802773 \tabularnewline
47 & 0 & -0.00855671663593624 & 0.00855671663593624 \tabularnewline
48 & 0 & -0.0544434700412118 & 0.0544434700412118 \tabularnewline
49 & 0 & 0.0584489250296148 & -0.0584489250296148 \tabularnewline
50 & 0 & -0.00279846018659875 & 0.00279846018659875 \tabularnewline
51 & 0 & 0.415215888882406 & -0.415215888882406 \tabularnewline
52 & 1 & 0.467988867700825 & 0.532011132299175 \tabularnewline
53 & 0 & -0.150659299045373 & 0.150659299045373 \tabularnewline
54 & 1 & 0.284487168243375 & 0.715512831756625 \tabularnewline
55 & 0 & 0.0067986338956304 & -0.0067986338956304 \tabularnewline
56 & 0 & 0.271193887656523 & -0.271193887656523 \tabularnewline
57 & 0 & 0.353412228725372 & -0.353412228725372 \tabularnewline
58 & 0 & -0.0352492818767535 & 0.0352492818767535 \tabularnewline
59 & 0 & -0.0333298630603077 & 0.0333298630603077 \tabularnewline
60 & 1 & 0.43553804601067 & 0.56446195398933 \tabularnewline
61 & 0 & 0.0468765354085444 & -0.0468765354085444 \tabularnewline
62 & 0 & 0.305002571942932 & -0.305002571942932 \tabularnewline
63 & 0 & 0.022153984427197 & -0.022153984427197 \tabularnewline
64 & 0 & 0.158447714944272 & -0.158447714944272 \tabularnewline
65 & 0 & 0.0259928220600887 & -0.0259928220600887 \tabularnewline
66 & 0 & 0.0279122408765345 & -0.0279122408765345 \tabularnewline
67 & 1 & 0.55689956619992 & 0.44310043380008 \tabularnewline
68 & 0 & 0.0774445853434087 & -0.0774445853434087 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202940&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]0.0375243295081849[/C][C]-0.0375243295081849[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]-0.200743486462389[/C][C]0.200743486462389[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]-0.0930111445595527[/C][C]0.0930111445595527[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]-0.0910917257431071[/C][C]0.0910917257431071[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]-0.0891723069266611[/C][C]0.0891723069266611[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]-0.0842055003299636[/C][C]0.0842055003299636[/C][/ROW]
[ROW][C]7[/C][C]0[/C][C]-0.0853334692937695[/C][C]0.0853334692937695[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.0530729266110446[/C][C]-0.0530729266110446[/C][/ROW]
[ROW][C]9[/C][C]0[/C][C]-0.235113726968989[/C][C]0.235113726968989[/C][/ROW]
[ROW][C]10[/C][C]0[/C][C]-0.0338817060104495[/C][C]0.0338817060104495[/C][/ROW]
[ROW][C]11[/C][C]0[/C][C]-0.00128823319202568[/C][C]0.00128823319202568[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]-0.0757363752115403[/C][C]0.0757363752115403[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.316763973023477[/C][C]-0.316763973023477[/C][/ROW]
[ROW][C]14[/C][C]0[/C][C]0.110282946343702[/C][C]-0.110282946343702[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.272796638434647[/C][C]-0.272796638434647[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0.411203034339461[/C][C]-0.411203034339461[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.506622132211611[/C][C]0.493377867788389[/C][/ROW]
[ROW][C]18[/C][C]0[/C][C]0.117960621609485[/C][C]-0.117960621609485[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]-0.215919538804531[/C][C]0.215919538804531[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]0.418880709605244[/C][C]0.581119290394756[/C][/ROW]
[ROW][C]21[/C][C]0[/C][C]0.0982048772248355[/C][C]-0.0982048772248355[/C][/ROW]
[ROW][C]22[/C][C]0[/C][C]0.22611315389736[/C][C]-0.22611315389736[/C][/ROW]
[ROW][C]23[/C][C]0[/C][C]0.00854403580202322[/C][C]-0.00854403580202322[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.0561569614524516[/C][C]-0.0561569614524516[/C][/ROW]
[ROW][C]25[/C][C]0[/C][C]0.211691904346702[/C][C]-0.211691904346702[/C][/ROW]
[ROW][C]26[/C][C]0[/C][C]0.235903494550882[/C][C]-0.235903494550882[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]-0.0490577583525918[/C][C]0.0490577583525918[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.128769355929393[/C][C]-0.128769355929393[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]-0.0909124275536826[/C][C]0.0909124275536826[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0.0697861397388655[/C][C]-0.0697861397388655[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]-0.0392674176990695[/C][C]0.0392674176990695[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0.00834550795135879[/C][C]-0.00834550795135879[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0.121237903022185[/C][C]-0.121237903022185[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0.0551716436169147[/C][C]-0.0551716436169147[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]-0.0315897424332862[/C][C]0.0315897424332862[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]-0.0296703236168404[/C][C]0.0296703236168404[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0.439197585454137[/C][C]-0.439197585454137[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0.20597029495852[/C][C]-0.20597029495852[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0.0392547368651565[/C][C]-0.0392547368651565[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0.119654381905302[/C][C]-0.119654381905302[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]0.322701527662238[/C][C]0.677298472337762[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0.213647970224304[/C][C]-0.213647970224304[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0.0926259189649223[/C][C]-0.0926259189649223[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0.167865510837077[/C][C]-0.167865510837077[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0.0985774219855529[/C][C]-0.0985774219855529[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0.0526906685802773[/C][C]-0.0526906685802773[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]-0.00855671663593624[/C][C]0.00855671663593624[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]-0.0544434700412118[/C][C]0.0544434700412118[/C][/ROW]
[ROW][C]49[/C][C]0[/C][C]0.0584489250296148[/C][C]-0.0584489250296148[/C][/ROW]
[ROW][C]50[/C][C]0[/C][C]-0.00279846018659875[/C][C]0.00279846018659875[/C][/ROW]
[ROW][C]51[/C][C]0[/C][C]0.415215888882406[/C][C]-0.415215888882406[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]0.467988867700825[/C][C]0.532011132299175[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]-0.150659299045373[/C][C]0.150659299045373[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]0.284487168243375[/C][C]0.715512831756625[/C][/ROW]
[ROW][C]55[/C][C]0[/C][C]0.0067986338956304[/C][C]-0.0067986338956304[/C][/ROW]
[ROW][C]56[/C][C]0[/C][C]0.271193887656523[/C][C]-0.271193887656523[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]0.353412228725372[/C][C]-0.353412228725372[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]-0.0352492818767535[/C][C]0.0352492818767535[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]-0.0333298630603077[/C][C]0.0333298630603077[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]0.43553804601067[/C][C]0.56446195398933[/C][/ROW]
[ROW][C]61[/C][C]0[/C][C]0.0468765354085444[/C][C]-0.0468765354085444[/C][/ROW]
[ROW][C]62[/C][C]0[/C][C]0.305002571942932[/C][C]-0.305002571942932[/C][/ROW]
[ROW][C]63[/C][C]0[/C][C]0.022153984427197[/C][C]-0.022153984427197[/C][/ROW]
[ROW][C]64[/C][C]0[/C][C]0.158447714944272[/C][C]-0.158447714944272[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]0.0259928220600887[/C][C]-0.0259928220600887[/C][/ROW]
[ROW][C]66[/C][C]0[/C][C]0.0279122408765345[/C][C]-0.0279122408765345[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]0.55689956619992[/C][C]0.44310043380008[/C][/ROW]
[ROW][C]68[/C][C]0[/C][C]0.0774445853434087[/C][C]-0.0774445853434087[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202940&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202940&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
100.0375243295081849-0.0375243295081849
20-0.2007434864623890.200743486462389
30-0.09301114455955270.0930111445595527
40-0.09109172574310710.0910917257431071
50-0.08917230692666110.0891723069266611
60-0.08420550032996360.0842055003299636
70-0.08533346929376950.0853334692937695
800.0530729266110446-0.0530729266110446
90-0.2351137269689890.235113726968989
100-0.03388170601044950.0338817060104495
110-0.001288233192025680.00128823319202568
120-0.07573637521154030.0757363752115403
1300.316763973023477-0.316763973023477
1400.110282946343702-0.110282946343702
1500.272796638434647-0.272796638434647
1600.411203034339461-0.411203034339461
1710.5066221322116110.493377867788389
1800.117960621609485-0.117960621609485
190-0.2159195388045310.215919538804531
2010.4188807096052440.581119290394756
2100.0982048772248355-0.0982048772248355
2200.22611315389736-0.22611315389736
2300.00854403580202322-0.00854403580202322
2400.0561569614524516-0.0561569614524516
2500.211691904346702-0.211691904346702
2600.235903494550882-0.235903494550882
270-0.04905775835259180.0490577583525918
2800.128769355929393-0.128769355929393
290-0.09091242755368260.0909124275536826
3000.0697861397388655-0.0697861397388655
310-0.03926741769906950.0392674176990695
3200.00834550795135879-0.00834550795135879
3300.121237903022185-0.121237903022185
3400.0551716436169147-0.0551716436169147
350-0.03158974243328620.0315897424332862
360-0.02967032361684040.0296703236168404
3700.439197585454137-0.439197585454137
3800.20597029495852-0.20597029495852
3900.0392547368651565-0.0392547368651565
4000.119654381905302-0.119654381905302
4110.3227015276622380.677298472337762
4200.213647970224304-0.213647970224304
4300.0926259189649223-0.0926259189649223
4400.167865510837077-0.167865510837077
4500.0985774219855529-0.0985774219855529
4600.0526906685802773-0.0526906685802773
470-0.008556716635936240.00855671663593624
480-0.05444347004121180.0544434700412118
4900.0584489250296148-0.0584489250296148
500-0.002798460186598750.00279846018659875
5100.415215888882406-0.415215888882406
5210.4679888677008250.532011132299175
530-0.1506592990453730.150659299045373
5410.2844871682433750.715512831756625
5500.0067986338956304-0.0067986338956304
5600.271193887656523-0.271193887656523
5700.353412228725372-0.353412228725372
580-0.03524928187675350.0352492818767535
590-0.03332986306030770.0333298630603077
6010.435538046010670.56446195398933
6100.0468765354085444-0.0468765354085444
6200.305002571942932-0.305002571942932
6300.022153984427197-0.022153984427197
6400.158447714944272-0.158447714944272
6500.0259928220600887-0.0259928220600887
6600.0279122408765345-0.0279122408765345
6710.556899566199920.44310043380008
6800.0774445853434087-0.0774445853434087






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
11001
12001
13001
14001
15001
16001
170.3449004585325250.6898009170650510.655099541467475
180.2617936400255710.5235872800511430.738206359974429
190.2581202961894560.5162405923789110.741879703810544
200.7314505894475340.5370988211049310.268549410552465
210.6539132638878490.6921734722243010.346086736112151
220.6308656976803040.7382686046393920.369134302319696
230.5458446519191480.9083106961617050.454155348080852
240.4600075067075390.9200150134150780.539992493292461
250.4194397762336160.8388795524672320.580560223766384
260.3777902278335130.7555804556670260.622209772166487
270.318835107325980.637670214651960.68116489267402
280.251033017110980.502066034221960.74896698288902
290.2033150322461130.4066300644922270.796684967753887
300.1521101262477120.3042202524954240.847889873752288
310.114063629678090.228127259356180.88593637032191
320.081096915408070.162193830816140.91890308459193
330.05678106298536540.1135621259707310.943218937014635
340.04013007516814670.08026015033629350.959869924831853
350.02759137987935210.05518275975870430.972408620120648
360.01891115770227240.03782231540454470.981088842297728
370.03116368747031640.06232737494063280.968836312529684
380.0239208163403490.04784163268069790.976079183659651
390.01466584912226510.02933169824453010.985334150877735
400.008771044318755520.0175420886375110.991228955681244
410.1260783443029870.2521566886059740.873921655697013
420.1047082518073210.2094165036146430.895291748192679
430.07946198339559270.1589239667911850.920538016604407
440.06558581990411690.1311716398082340.934414180095883
450.0471091075759530.09421821515190590.952890892424047
460.03034698123674860.06069396247349710.969653018763251
470.01877576271402310.03755152542804620.981224237285977
480.01131563874668580.02263127749337170.988684361253314
490.006485154450953070.01297030890190610.993514845549047
500.003522519451998080.007045038903996160.996477480548002
510.029483205890410.058966411780820.97051679410959
520.0580178604850620.1160357209701240.941982139514938
530.06498295808360540.1299659161672110.935017041916395
540.3187134868988970.6374269737977930.681286513101103
550.2147657112680810.4295314225361610.785234288731919
560.1344581145158790.2689162290317570.865541885484121
570.252536903228330.5050738064566590.74746309677167

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \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 & 0.344900458532525 & 0.689800917065051 & 0.655099541467475 \tabularnewline
18 & 0.261793640025571 & 0.523587280051143 & 0.738206359974429 \tabularnewline
19 & 0.258120296189456 & 0.516240592378911 & 0.741879703810544 \tabularnewline
20 & 0.731450589447534 & 0.537098821104931 & 0.268549410552465 \tabularnewline
21 & 0.653913263887849 & 0.692173472224301 & 0.346086736112151 \tabularnewline
22 & 0.630865697680304 & 0.738268604639392 & 0.369134302319696 \tabularnewline
23 & 0.545844651919148 & 0.908310696161705 & 0.454155348080852 \tabularnewline
24 & 0.460007506707539 & 0.920015013415078 & 0.539992493292461 \tabularnewline
25 & 0.419439776233616 & 0.838879552467232 & 0.580560223766384 \tabularnewline
26 & 0.377790227833513 & 0.755580455667026 & 0.622209772166487 \tabularnewline
27 & 0.31883510732598 & 0.63767021465196 & 0.68116489267402 \tabularnewline
28 & 0.25103301711098 & 0.50206603422196 & 0.74896698288902 \tabularnewline
29 & 0.203315032246113 & 0.406630064492227 & 0.796684967753887 \tabularnewline
30 & 0.152110126247712 & 0.304220252495424 & 0.847889873752288 \tabularnewline
31 & 0.11406362967809 & 0.22812725935618 & 0.88593637032191 \tabularnewline
32 & 0.08109691540807 & 0.16219383081614 & 0.91890308459193 \tabularnewline
33 & 0.0567810629853654 & 0.113562125970731 & 0.943218937014635 \tabularnewline
34 & 0.0401300751681467 & 0.0802601503362935 & 0.959869924831853 \tabularnewline
35 & 0.0275913798793521 & 0.0551827597587043 & 0.972408620120648 \tabularnewline
36 & 0.0189111577022724 & 0.0378223154045447 & 0.981088842297728 \tabularnewline
37 & 0.0311636874703164 & 0.0623273749406328 & 0.968836312529684 \tabularnewline
38 & 0.023920816340349 & 0.0478416326806979 & 0.976079183659651 \tabularnewline
39 & 0.0146658491222651 & 0.0293316982445301 & 0.985334150877735 \tabularnewline
40 & 0.00877104431875552 & 0.017542088637511 & 0.991228955681244 \tabularnewline
41 & 0.126078344302987 & 0.252156688605974 & 0.873921655697013 \tabularnewline
42 & 0.104708251807321 & 0.209416503614643 & 0.895291748192679 \tabularnewline
43 & 0.0794619833955927 & 0.158923966791185 & 0.920538016604407 \tabularnewline
44 & 0.0655858199041169 & 0.131171639808234 & 0.934414180095883 \tabularnewline
45 & 0.047109107575953 & 0.0942182151519059 & 0.952890892424047 \tabularnewline
46 & 0.0303469812367486 & 0.0606939624734971 & 0.969653018763251 \tabularnewline
47 & 0.0187757627140231 & 0.0375515254280462 & 0.981224237285977 \tabularnewline
48 & 0.0113156387466858 & 0.0226312774933717 & 0.988684361253314 \tabularnewline
49 & 0.00648515445095307 & 0.0129703089019061 & 0.993514845549047 \tabularnewline
50 & 0.00352251945199808 & 0.00704503890399616 & 0.996477480548002 \tabularnewline
51 & 0.02948320589041 & 0.05896641178082 & 0.97051679410959 \tabularnewline
52 & 0.058017860485062 & 0.116035720970124 & 0.941982139514938 \tabularnewline
53 & 0.0649829580836054 & 0.129965916167211 & 0.935017041916395 \tabularnewline
54 & 0.318713486898897 & 0.637426973797793 & 0.681286513101103 \tabularnewline
55 & 0.214765711268081 & 0.429531422536161 & 0.785234288731919 \tabularnewline
56 & 0.134458114515879 & 0.268916229031757 & 0.865541885484121 \tabularnewline
57 & 0.25253690322833 & 0.505073806456659 & 0.74746309677167 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202940&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]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]0.344900458532525[/C][C]0.689800917065051[/C][C]0.655099541467475[/C][/ROW]
[ROW][C]18[/C][C]0.261793640025571[/C][C]0.523587280051143[/C][C]0.738206359974429[/C][/ROW]
[ROW][C]19[/C][C]0.258120296189456[/C][C]0.516240592378911[/C][C]0.741879703810544[/C][/ROW]
[ROW][C]20[/C][C]0.731450589447534[/C][C]0.537098821104931[/C][C]0.268549410552465[/C][/ROW]
[ROW][C]21[/C][C]0.653913263887849[/C][C]0.692173472224301[/C][C]0.346086736112151[/C][/ROW]
[ROW][C]22[/C][C]0.630865697680304[/C][C]0.738268604639392[/C][C]0.369134302319696[/C][/ROW]
[ROW][C]23[/C][C]0.545844651919148[/C][C]0.908310696161705[/C][C]0.454155348080852[/C][/ROW]
[ROW][C]24[/C][C]0.460007506707539[/C][C]0.920015013415078[/C][C]0.539992493292461[/C][/ROW]
[ROW][C]25[/C][C]0.419439776233616[/C][C]0.838879552467232[/C][C]0.580560223766384[/C][/ROW]
[ROW][C]26[/C][C]0.377790227833513[/C][C]0.755580455667026[/C][C]0.622209772166487[/C][/ROW]
[ROW][C]27[/C][C]0.31883510732598[/C][C]0.63767021465196[/C][C]0.68116489267402[/C][/ROW]
[ROW][C]28[/C][C]0.25103301711098[/C][C]0.50206603422196[/C][C]0.74896698288902[/C][/ROW]
[ROW][C]29[/C][C]0.203315032246113[/C][C]0.406630064492227[/C][C]0.796684967753887[/C][/ROW]
[ROW][C]30[/C][C]0.152110126247712[/C][C]0.304220252495424[/C][C]0.847889873752288[/C][/ROW]
[ROW][C]31[/C][C]0.11406362967809[/C][C]0.22812725935618[/C][C]0.88593637032191[/C][/ROW]
[ROW][C]32[/C][C]0.08109691540807[/C][C]0.16219383081614[/C][C]0.91890308459193[/C][/ROW]
[ROW][C]33[/C][C]0.0567810629853654[/C][C]0.113562125970731[/C][C]0.943218937014635[/C][/ROW]
[ROW][C]34[/C][C]0.0401300751681467[/C][C]0.0802601503362935[/C][C]0.959869924831853[/C][/ROW]
[ROW][C]35[/C][C]0.0275913798793521[/C][C]0.0551827597587043[/C][C]0.972408620120648[/C][/ROW]
[ROW][C]36[/C][C]0.0189111577022724[/C][C]0.0378223154045447[/C][C]0.981088842297728[/C][/ROW]
[ROW][C]37[/C][C]0.0311636874703164[/C][C]0.0623273749406328[/C][C]0.968836312529684[/C][/ROW]
[ROW][C]38[/C][C]0.023920816340349[/C][C]0.0478416326806979[/C][C]0.976079183659651[/C][/ROW]
[ROW][C]39[/C][C]0.0146658491222651[/C][C]0.0293316982445301[/C][C]0.985334150877735[/C][/ROW]
[ROW][C]40[/C][C]0.00877104431875552[/C][C]0.017542088637511[/C][C]0.991228955681244[/C][/ROW]
[ROW][C]41[/C][C]0.126078344302987[/C][C]0.252156688605974[/C][C]0.873921655697013[/C][/ROW]
[ROW][C]42[/C][C]0.104708251807321[/C][C]0.209416503614643[/C][C]0.895291748192679[/C][/ROW]
[ROW][C]43[/C][C]0.0794619833955927[/C][C]0.158923966791185[/C][C]0.920538016604407[/C][/ROW]
[ROW][C]44[/C][C]0.0655858199041169[/C][C]0.131171639808234[/C][C]0.934414180095883[/C][/ROW]
[ROW][C]45[/C][C]0.047109107575953[/C][C]0.0942182151519059[/C][C]0.952890892424047[/C][/ROW]
[ROW][C]46[/C][C]0.0303469812367486[/C][C]0.0606939624734971[/C][C]0.969653018763251[/C][/ROW]
[ROW][C]47[/C][C]0.0187757627140231[/C][C]0.0375515254280462[/C][C]0.981224237285977[/C][/ROW]
[ROW][C]48[/C][C]0.0113156387466858[/C][C]0.0226312774933717[/C][C]0.988684361253314[/C][/ROW]
[ROW][C]49[/C][C]0.00648515445095307[/C][C]0.0129703089019061[/C][C]0.993514845549047[/C][/ROW]
[ROW][C]50[/C][C]0.00352251945199808[/C][C]0.00704503890399616[/C][C]0.996477480548002[/C][/ROW]
[ROW][C]51[/C][C]0.02948320589041[/C][C]0.05896641178082[/C][C]0.97051679410959[/C][/ROW]
[ROW][C]52[/C][C]0.058017860485062[/C][C]0.116035720970124[/C][C]0.941982139514938[/C][/ROW]
[ROW][C]53[/C][C]0.0649829580836054[/C][C]0.129965916167211[/C][C]0.935017041916395[/C][/ROW]
[ROW][C]54[/C][C]0.318713486898897[/C][C]0.637426973797793[/C][C]0.681286513101103[/C][/ROW]
[ROW][C]55[/C][C]0.214765711268081[/C][C]0.429531422536161[/C][C]0.785234288731919[/C][/ROW]
[ROW][C]56[/C][C]0.134458114515879[/C][C]0.268916229031757[/C][C]0.865541885484121[/C][/ROW]
[ROW][C]57[/C][C]0.25253690322833[/C][C]0.505073806456659[/C][C]0.74746309677167[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202940&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202940&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
11001
12001
13001
14001
15001
16001
170.3449004585325250.6898009170650510.655099541467475
180.2617936400255710.5235872800511430.738206359974429
190.2581202961894560.5162405923789110.741879703810544
200.7314505894475340.5370988211049310.268549410552465
210.6539132638878490.6921734722243010.346086736112151
220.6308656976803040.7382686046393920.369134302319696
230.5458446519191480.9083106961617050.454155348080852
240.4600075067075390.9200150134150780.539992493292461
250.4194397762336160.8388795524672320.580560223766384
260.3777902278335130.7555804556670260.622209772166487
270.318835107325980.637670214651960.68116489267402
280.251033017110980.502066034221960.74896698288902
290.2033150322461130.4066300644922270.796684967753887
300.1521101262477120.3042202524954240.847889873752288
310.114063629678090.228127259356180.88593637032191
320.081096915408070.162193830816140.91890308459193
330.05678106298536540.1135621259707310.943218937014635
340.04013007516814670.08026015033629350.959869924831853
350.02759137987935210.05518275975870430.972408620120648
360.01891115770227240.03782231540454470.981088842297728
370.03116368747031640.06232737494063280.968836312529684
380.0239208163403490.04784163268069790.976079183659651
390.01466584912226510.02933169824453010.985334150877735
400.008771044318755520.0175420886375110.991228955681244
410.1260783443029870.2521566886059740.873921655697013
420.1047082518073210.2094165036146430.895291748192679
430.07946198339559270.1589239667911850.920538016604407
440.06558581990411690.1311716398082340.934414180095883
450.0471091075759530.09421821515190590.952890892424047
460.03034698123674860.06069396247349710.969653018763251
470.01877576271402310.03755152542804620.981224237285977
480.01131563874668580.02263127749337170.988684361253314
490.006485154450953070.01297030890190610.993514845549047
500.003522519451998080.007045038903996160.996477480548002
510.029483205890410.058966411780820.97051679410959
520.0580178604850620.1160357209701240.941982139514938
530.06498295808360540.1299659161672110.935017041916395
540.3187134868988970.6374269737977930.681286513101103
550.2147657112680810.4295314225361610.785234288731919
560.1344581145158790.2689162290317570.865541885484121
570.252536903228330.5050738064566590.74746309677167






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.148936170212766NOK
5% type I error level140.297872340425532NOK
10% type I error level200.425531914893617NOK

\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 & 7 & 0.148936170212766 & NOK \tabularnewline
5% type I error level & 14 & 0.297872340425532 & NOK \tabularnewline
10% type I error level & 20 & 0.425531914893617 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=202940&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]7[/C][C]0.148936170212766[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]0.297872340425532[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.425531914893617[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=202940&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=202940&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.148936170212766NOK
5% type I error level140.297872340425532NOK
10% type I error level200.425531914893617NOK


Figures (Output of Computation)

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Figure 6
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Figure 7
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}