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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 computationSun, 26 Dec 2010 10:00:42 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/26/t12933575862v7q7906bfuee2p.htm/, Retrieved Mon, 06 May 2024 23:29:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115494, Retrieved Mon, 06 May 2024 23:29:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact163

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [Workshop 10 (Mult...] [2010-12-26 10:00:42] [6427096bd21c899f2c90594929aeeec2] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	0	2	5	3	4
0	0	2	4	3	4
0	0	4	4	2	5
0	1	2	4	2	3
0	1	3	2	3	4
1	0	4	5	2	3
1	1	3	5	1	4
0	0	3	4	3	3
1	1	3	3	2	2
1	1	2	4	2	4
1	1	4	4	3	2
0	0	4	3	2	2
0	0	3	3	2	2
1	1	3	3	2	1
0	1	4	4	3	4
1	0	4	5	1	4
0	1	3	4	3	2
1	0	3	2	2	2
0	1	3	4	3	3
0	1	4	4	4	3
1	1	2	4	2	3
1	0	5	4	3	4
1	0	4	4	5	3
0	0	2	4	2	2
1	1	3	5	2	2
1	1	4	4	3	4
0	0	4	4	2	4
0	0	3	4	2	3
1	0	4	4	2	4
1	0	4	4	2	2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115494&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115494&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115494&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Pop[t] = -0.125547216990277 + 0.125002750324799Gender[t] + 0.131939818076267Standards[t] + 0.180852896225461Organization[t] -0.108880183613636Mistakes[t] -0.088318039357571Goals[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Pop[t] =  -0.125547216990277 +  0.125002750324799Gender[t] +  0.131939818076267Standards[t] +  0.180852896225461Organization[t] -0.108880183613636Mistakes[t] -0.088318039357571Goals[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115494&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Pop[t] =  -0.125547216990277 +  0.125002750324799Gender[t] +  0.131939818076267Standards[t] +  0.180852896225461Organization[t] -0.108880183613636Mistakes[t] -0.088318039357571Goals[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115494&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115494&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
Pop[t] = -0.125547216990277 + 0.125002750324799Gender[t] + 0.131939818076267Standards[t] + 0.180852896225461Organization[t] -0.108880183613636Mistakes[t] -0.088318039357571Goals[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.1255472169902770.700392-0.17930.8592440.429622
Gender0.1250027503247990.1965210.63610.5307450.265373
Standards0.1319398180762670.1221731.07990.29090.14545
Organization0.1808528962254610.1349681.340.1928060.096403
Mistakes-0.1088801836136360.121102-0.89910.3775420.188771
Goals-0.0883180393575710.105553-0.83670.4110060.205503

\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) & -0.125547216990277 & 0.700392 & -0.1793 & 0.859244 & 0.429622 \tabularnewline
Gender & 0.125002750324799 & 0.196521 & 0.6361 & 0.530745 & 0.265373 \tabularnewline
Standards & 0.131939818076267 & 0.122173 & 1.0799 & 0.2909 & 0.14545 \tabularnewline
Organization & 0.180852896225461 & 0.134968 & 1.34 & 0.192806 & 0.096403 \tabularnewline
Mistakes & -0.108880183613636 & 0.121102 & -0.8991 & 0.377542 & 0.188771 \tabularnewline
Goals & -0.088318039357571 & 0.105553 & -0.8367 & 0.411006 & 0.205503 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115494&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]-0.125547216990277[/C][C]0.700392[/C][C]-0.1793[/C][C]0.859244[/C][C]0.429622[/C][/ROW]
[ROW][C]Gender[/C][C]0.125002750324799[/C][C]0.196521[/C][C]0.6361[/C][C]0.530745[/C][C]0.265373[/C][/ROW]
[ROW][C]Standards[/C][C]0.131939818076267[/C][C]0.122173[/C][C]1.0799[/C][C]0.2909[/C][C]0.14545[/C][/ROW]
[ROW][C]Organization[/C][C]0.180852896225461[/C][C]0.134968[/C][C]1.34[/C][C]0.192806[/C][C]0.096403[/C][/ROW]
[ROW][C]Mistakes[/C][C]-0.108880183613636[/C][C]0.121102[/C][C]-0.8991[/C][C]0.377542[/C][C]0.188771[/C][/ROW]
[ROW][C]Goals[/C][C]-0.088318039357571[/C][C]0.105553[/C][C]-0.8367[/C][C]0.411006[/C][C]0.205503[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115494&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115494&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)-0.1255472169902770.700392-0.17930.8592440.429622
Gender0.1250027503247990.1965210.63610.5307450.265373
Standards0.1319398180762670.1221731.07990.29090.14545
Organization0.1808528962254610.1349681.340.1928060.096403
Mistakes-0.1088801836136360.121102-0.89910.3775420.188771
Goals-0.0883180393575710.105553-0.83670.4110060.205503






Multiple Linear Regression - Regression Statistics
Multiple R0.378605691271687
R-squared0.143342269463312
Adjusted R-squared-0.035128091065165
F-TEST (value)0.803171288716258
F-TEST (DF numerator)5
F-TEST (DF denominator)24
p-value0.558525338414916
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.51625162313468
Sum Squared Residuals6.3963777213406

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.378605691271687 \tabularnewline
R-squared & 0.143342269463312 \tabularnewline
Adjusted R-squared & -0.035128091065165 \tabularnewline
F-TEST (value) & 0.803171288716258 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 24 \tabularnewline
p-value & 0.558525338414916 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.51625162313468 \tabularnewline
Sum Squared Residuals & 6.3963777213406 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115494&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.378605691271687[/C][/ROW]
[ROW][C]R-squared[/C][C]0.143342269463312[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.035128091065165[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.803171288716258[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]24[/C][/ROW]
[ROW][C]p-value[/C][C]0.558525338414916[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.51625162313468[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6.3963777213406[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115494&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115494&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 R0.378605691271687
R-squared0.143342269463312
Adjusted R-squared-0.035128091065165
F-TEST (value)0.803171288716258
F-TEST (DF numerator)5
F-TEST (DF denominator)24
p-value0.558525338414916
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.51625162313468
Sum Squared Residuals6.3963777213406






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110.3626841920183690.637315807981631
200.181831295792908-0.181831295792908
300.466273076201508-0.466273076201508
400.504032269088914-0.504032269088914
500.077068071743052-0.077068071743052
610.8237620511421110.176237948857889
710.8373871276467080.162612872353292
800.402089153226746-0.402089153226746
910.5434372302972910.456562769702709
1010.4157142297313430.584285770268657
1110.7473497609853830.252650239014617
1200.55037429804876-0.55037429804876
1300.418434479972493-0.418434479972493
1410.6317552696548620.368244730345138
1500.570713682270241-0.570713682270241
1610.8443241953981760.155675804601824
1700.615409942909116-0.615409942909116
1810.2375815837470320.762418416252968
1900.527091903551545-0.527091903551545
2000.550151538014176-0.550151538014176
2110.5040322690889140.495967730911086
2210.577650750021710.422349249978290
2310.316268604075740.68373139592426
2400.467347558121686-0.467347558121686
2510.9051430227482130.0948569772517865
2610.5707136822702410.429286317729759
2700.554591115559079-0.554591115559079
2800.510969336840383-0.510969336840383
2910.5545911155590790.445408884440921
3010.7312271942742210.268772805725779

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1 & 0.362684192018369 & 0.637315807981631 \tabularnewline
2 & 0 & 0.181831295792908 & -0.181831295792908 \tabularnewline
3 & 0 & 0.466273076201508 & -0.466273076201508 \tabularnewline
4 & 0 & 0.504032269088914 & -0.504032269088914 \tabularnewline
5 & 0 & 0.077068071743052 & -0.077068071743052 \tabularnewline
6 & 1 & 0.823762051142111 & 0.176237948857889 \tabularnewline
7 & 1 & 0.837387127646708 & 0.162612872353292 \tabularnewline
8 & 0 & 0.402089153226746 & -0.402089153226746 \tabularnewline
9 & 1 & 0.543437230297291 & 0.456562769702709 \tabularnewline
10 & 1 & 0.415714229731343 & 0.584285770268657 \tabularnewline
11 & 1 & 0.747349760985383 & 0.252650239014617 \tabularnewline
12 & 0 & 0.55037429804876 & -0.55037429804876 \tabularnewline
13 & 0 & 0.418434479972493 & -0.418434479972493 \tabularnewline
14 & 1 & 0.631755269654862 & 0.368244730345138 \tabularnewline
15 & 0 & 0.570713682270241 & -0.570713682270241 \tabularnewline
16 & 1 & 0.844324195398176 & 0.155675804601824 \tabularnewline
17 & 0 & 0.615409942909116 & -0.615409942909116 \tabularnewline
18 & 1 & 0.237581583747032 & 0.762418416252968 \tabularnewline
19 & 0 & 0.527091903551545 & -0.527091903551545 \tabularnewline
20 & 0 & 0.550151538014176 & -0.550151538014176 \tabularnewline
21 & 1 & 0.504032269088914 & 0.495967730911086 \tabularnewline
22 & 1 & 0.57765075002171 & 0.422349249978290 \tabularnewline
23 & 1 & 0.31626860407574 & 0.68373139592426 \tabularnewline
24 & 0 & 0.467347558121686 & -0.467347558121686 \tabularnewline
25 & 1 & 0.905143022748213 & 0.0948569772517865 \tabularnewline
26 & 1 & 0.570713682270241 & 0.429286317729759 \tabularnewline
27 & 0 & 0.554591115559079 & -0.554591115559079 \tabularnewline
28 & 0 & 0.510969336840383 & -0.510969336840383 \tabularnewline
29 & 1 & 0.554591115559079 & 0.445408884440921 \tabularnewline
30 & 1 & 0.731227194274221 & 0.268772805725779 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115494&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]1[/C][C]0.362684192018369[/C][C]0.637315807981631[/C][/ROW]
[ROW][C]2[/C][C]0[/C][C]0.181831295792908[/C][C]-0.181831295792908[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]0.466273076201508[/C][C]-0.466273076201508[/C][/ROW]
[ROW][C]4[/C][C]0[/C][C]0.504032269088914[/C][C]-0.504032269088914[/C][/ROW]
[ROW][C]5[/C][C]0[/C][C]0.077068071743052[/C][C]-0.077068071743052[/C][/ROW]
[ROW][C]6[/C][C]1[/C][C]0.823762051142111[/C][C]0.176237948857889[/C][/ROW]
[ROW][C]7[/C][C]1[/C][C]0.837387127646708[/C][C]0.162612872353292[/C][/ROW]
[ROW][C]8[/C][C]0[/C][C]0.402089153226746[/C][C]-0.402089153226746[/C][/ROW]
[ROW][C]9[/C][C]1[/C][C]0.543437230297291[/C][C]0.456562769702709[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]0.415714229731343[/C][C]0.584285770268657[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.747349760985383[/C][C]0.252650239014617[/C][/ROW]
[ROW][C]12[/C][C]0[/C][C]0.55037429804876[/C][C]-0.55037429804876[/C][/ROW]
[ROW][C]13[/C][C]0[/C][C]0.418434479972493[/C][C]-0.418434479972493[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]0.631755269654862[/C][C]0.368244730345138[/C][/ROW]
[ROW][C]15[/C][C]0[/C][C]0.570713682270241[/C][C]-0.570713682270241[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.844324195398176[/C][C]0.155675804601824[/C][/ROW]
[ROW][C]17[/C][C]0[/C][C]0.615409942909116[/C][C]-0.615409942909116[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]0.237581583747032[/C][C]0.762418416252968[/C][/ROW]
[ROW][C]19[/C][C]0[/C][C]0.527091903551545[/C][C]-0.527091903551545[/C][/ROW]
[ROW][C]20[/C][C]0[/C][C]0.550151538014176[/C][C]-0.550151538014176[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]0.504032269088914[/C][C]0.495967730911086[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]0.57765075002171[/C][C]0.422349249978290[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]0.31626860407574[/C][C]0.68373139592426[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.467347558121686[/C][C]-0.467347558121686[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]0.905143022748213[/C][C]0.0948569772517865[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]0.570713682270241[/C][C]0.429286317729759[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0.554591115559079[/C][C]-0.554591115559079[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0.510969336840383[/C][C]-0.510969336840383[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]0.554591115559079[/C][C]0.445408884440921[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]0.731227194274221[/C][C]0.268772805725779[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115494&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115494&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
110.3626841920183690.637315807981631
200.181831295792908-0.181831295792908
300.466273076201508-0.466273076201508
400.504032269088914-0.504032269088914
500.077068071743052-0.077068071743052
610.8237620511421110.176237948857889
710.8373871276467080.162612872353292
800.402089153226746-0.402089153226746
910.5434372302972910.456562769702709
1010.4157142297313430.584285770268657
1110.7473497609853830.252650239014617
1200.55037429804876-0.55037429804876
1300.418434479972493-0.418434479972493
1410.6317552696548620.368244730345138
1500.570713682270241-0.570713682270241
1610.8443241953981760.155675804601824
1700.615409942909116-0.615409942909116
1810.2375815837470320.762418416252968
1900.527091903551545-0.527091903551545
2000.550151538014176-0.550151538014176
2110.5040322690889140.495967730911086
2210.577650750021710.422349249978290
2310.316268604075740.68373139592426
2400.467347558121686-0.467347558121686
2510.9051430227482130.0948569772517865
2610.5707136822702410.429286317729759
2700.554591115559079-0.554591115559079
2800.510969336840383-0.510969336840383
2910.5545911155590790.445408884440921
3010.7312271942742210.268772805725779






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.5979394849457760.8041210301084470.402060515054224
100.5471992094677080.9056015810645850.452800790532292
110.4181558926122560.8363117852245130.581844107387744
120.3369351312114020.6738702624228050.663064868788598
130.2609741952825270.5219483905650530.739025804717473
140.1843354229503060.3686708459006120.815664577049694
150.2194217669185460.4388435338370910.780578233081454
160.1525638286996170.3051276573992350.847436171300383
170.1987800055395270.3975600110790530.801219994460473
180.2468352601551200.4936705203102410.75316473984488
190.2030643850416120.4061287700832250.796935614958388
200.4940026395323410.9880052790646820.505997360467659
210.4941139425956240.9882278851912490.505886057404376

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.597939484945776 & 0.804121030108447 & 0.402060515054224 \tabularnewline
10 & 0.547199209467708 & 0.905601581064585 & 0.452800790532292 \tabularnewline
11 & 0.418155892612256 & 0.836311785224513 & 0.581844107387744 \tabularnewline
12 & 0.336935131211402 & 0.673870262422805 & 0.663064868788598 \tabularnewline
13 & 0.260974195282527 & 0.521948390565053 & 0.739025804717473 \tabularnewline
14 & 0.184335422950306 & 0.368670845900612 & 0.815664577049694 \tabularnewline
15 & 0.219421766918546 & 0.438843533837091 & 0.780578233081454 \tabularnewline
16 & 0.152563828699617 & 0.305127657399235 & 0.847436171300383 \tabularnewline
17 & 0.198780005539527 & 0.397560011079053 & 0.801219994460473 \tabularnewline
18 & 0.246835260155120 & 0.493670520310241 & 0.75316473984488 \tabularnewline
19 & 0.203064385041612 & 0.406128770083225 & 0.796935614958388 \tabularnewline
20 & 0.494002639532341 & 0.988005279064682 & 0.505997360467659 \tabularnewline
21 & 0.494113942595624 & 0.988227885191249 & 0.505886057404376 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115494&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]9[/C][C]0.597939484945776[/C][C]0.804121030108447[/C][C]0.402060515054224[/C][/ROW]
[ROW][C]10[/C][C]0.547199209467708[/C][C]0.905601581064585[/C][C]0.452800790532292[/C][/ROW]
[ROW][C]11[/C][C]0.418155892612256[/C][C]0.836311785224513[/C][C]0.581844107387744[/C][/ROW]
[ROW][C]12[/C][C]0.336935131211402[/C][C]0.673870262422805[/C][C]0.663064868788598[/C][/ROW]
[ROW][C]13[/C][C]0.260974195282527[/C][C]0.521948390565053[/C][C]0.739025804717473[/C][/ROW]
[ROW][C]14[/C][C]0.184335422950306[/C][C]0.368670845900612[/C][C]0.815664577049694[/C][/ROW]
[ROW][C]15[/C][C]0.219421766918546[/C][C]0.438843533837091[/C][C]0.780578233081454[/C][/ROW]
[ROW][C]16[/C][C]0.152563828699617[/C][C]0.305127657399235[/C][C]0.847436171300383[/C][/ROW]
[ROW][C]17[/C][C]0.198780005539527[/C][C]0.397560011079053[/C][C]0.801219994460473[/C][/ROW]
[ROW][C]18[/C][C]0.246835260155120[/C][C]0.493670520310241[/C][C]0.75316473984488[/C][/ROW]
[ROW][C]19[/C][C]0.203064385041612[/C][C]0.406128770083225[/C][C]0.796935614958388[/C][/ROW]
[ROW][C]20[/C][C]0.494002639532341[/C][C]0.988005279064682[/C][C]0.505997360467659[/C][/ROW]
[ROW][C]21[/C][C]0.494113942595624[/C][C]0.988227885191249[/C][C]0.505886057404376[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115494&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115494&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
90.5979394849457760.8041210301084470.402060515054224
100.5471992094677080.9056015810645850.452800790532292
110.4181558926122560.8363117852245130.581844107387744
120.3369351312114020.6738702624228050.663064868788598
130.2609741952825270.5219483905650530.739025804717473
140.1843354229503060.3686708459006120.815664577049694
150.2194217669185460.4388435338370910.780578233081454
160.1525638286996170.3051276573992350.847436171300383
170.1987800055395270.3975600110790530.801219994460473
180.2468352601551200.4936705203102410.75316473984488
190.2030643850416120.4061287700832250.796935614958388
200.4940026395323410.9880052790646820.505997360467659
210.4941139425956240.9882278851912490.505886057404376






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115494&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115494&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115494&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 level00OK
5% type I error level00OK
10% type I error level00OK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

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