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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 computationSat, 13 Dec 2008 09:21:20 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/13/t1229187102tz9rxh5k0d3jk9c.htm/, Retrieved Fri, 17 May 2024 04:19:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=33186, Retrieved Fri, 17 May 2024 04:19:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact199

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Cross Correlation Function] [Paper statistiek ...] [2008-12-11 10:45:48] [74be16979710d4c4e7c6647856088456]
-   PD  [Cross Correlation Function] [paper - crossrorr...] [2008-12-13 16:10:53] [b6c777429d07a05453509ef079833861]
- RMPD      [Multiple Regression] [paper - Multiple ...] [2008-12-13 16:21:20] [1828943283e41f5e3270e2e73d6433b4] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1.8	0.8	2.9
1.7	-0.1	2.9
1.4	-1.5	2.9
1.2	-4.4	1.4
1	-4.2	1.1
1.7	3.5	1.9
2.4	10	2.8
2	8.6	1.4
2.1	9.5	0.7
2	9.9	-0.8
1.8	10.4	-3.1
2.7	16	0.1
2.3	12.7	1
1.9	10.2	1.9
2	8.9	-0.5
2.3	12.6	1.5
2.8	13.6	3.9
2.4	14.8	1.9
2.3	9.5	2.6
2.7	13.7	1.7
2.7	17	1.4
2.9	14.7	2.8
3	17.4	0.5
2.2	9	1
2.3	9.1	1.5
2.8	12.2	1.8
2.8	15.9	2.7
2.8	12.9	3
2.2	10.9	-0.3
2.6	10.6	1.1
2.8	13.2	1.7
2.5	9.6	1.6
2.4	6.4	3
2.3	5.8	3.3
1.9	-1	6.7
1.7	-0.2	5.6
2	2.7	6
2.1	3.6	4.8
1.7	-0.9	5.9
1.8	0.3	4.3
1.8	-1.1	3.7
1.8	-2.5	5.6
1.3	-3.4	1.7
1.3	-3.5	3.2
1.3	-3.9	3.6
1.2	-4.6	1.7
1.4	-0.1	0.5
2.2	4.3	2.1
2.9	10.2	1.5
3.1	8.7	2.7
3.5	13.3	1.4
3.6	15	1.2
4.4	20.7	2.3
4.1	20.7	1.6
5.1	26.4	4.7
5.8	31.2	3.5
5.9	31.4	4.4
5.4	26.6	3.9
5.5	26.6	3.5
4.8	19.2	3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33186&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33186&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33186&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
totale_inflatie[t] = + 0.793045803171116 + 0.110299880437482inflatie_energiedragers[t] + 0.099961772311484inflatie_onbewerkte_levensmiddelen[t] + 0.0169049684459392t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
totale_inflatie[t] =  +  0.793045803171116 +  0.110299880437482inflatie_energiedragers[t] +  0.099961772311484inflatie_onbewerkte_levensmiddelen[t] +  0.0169049684459392t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33186&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]totale_inflatie[t] =  +  0.793045803171116 +  0.110299880437482inflatie_energiedragers[t] +  0.099961772311484inflatie_onbewerkte_levensmiddelen[t] +  0.0169049684459392t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33186&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33186&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
totale_inflatie[t] = + 0.793045803171116 + 0.110299880437482inflatie_energiedragers[t] + 0.099961772311484inflatie_onbewerkte_levensmiddelen[t] + 0.0169049684459392t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.7930458031711160.0809929.791700
inflatie_energiedragers0.1102998804374820.00434825.365800
inflatie_onbewerkte_levensmiddelen0.0999617723114840.0227844.38745.1e-052.6e-05
t0.01690496844593920.0024376.937100

\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) & 0.793045803171116 & 0.080992 & 9.7917 & 0 & 0 \tabularnewline
inflatie_energiedragers & 0.110299880437482 & 0.004348 & 25.3658 & 0 & 0 \tabularnewline
inflatie_onbewerkte_levensmiddelen & 0.099961772311484 & 0.022784 & 4.3874 & 5.1e-05 & 2.6e-05 \tabularnewline
t & 0.0169049684459392 & 0.002437 & 6.9371 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33186&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]0.793045803171116[/C][C]0.080992[/C][C]9.7917[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]inflatie_energiedragers[/C][C]0.110299880437482[/C][C]0.004348[/C][C]25.3658[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]inflatie_onbewerkte_levensmiddelen[/C][C]0.099961772311484[/C][C]0.022784[/C][C]4.3874[/C][C]5.1e-05[/C][C]2.6e-05[/C][/ROW]
[ROW][C]t[/C][C]0.0169049684459392[/C][C]0.002437[/C][C]6.9371[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33186&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33186&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)0.7930458031711160.0809929.791700
inflatie_energiedragers0.1102998804374820.00434825.365800
inflatie_onbewerkte_levensmiddelen0.0999617723114840.0227844.38745.1e-052.6e-05
t0.01690496844593920.0024376.937100






Multiple Linear Regression - Regression Statistics
Multiple R0.973276206517053
R-squared0.947266574172225
Adjusted R-squared0.944441569217166
F-TEST (value)335.315013333248
F-TEST (DF numerator)3
F-TEST (DF denominator)56
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.276856081743618
Sum Squared Residuals4.292360239912

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.973276206517053 \tabularnewline
R-squared & 0.947266574172225 \tabularnewline
Adjusted R-squared & 0.944441569217166 \tabularnewline
F-TEST (value) & 335.315013333248 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 56 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.276856081743618 \tabularnewline
Sum Squared Residuals & 4.292360239912 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33186&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.973276206517053[/C][/ROW]
[ROW][C]R-squared[/C][C]0.947266574172225[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.944441569217166[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]335.315013333248[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]56[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.276856081743618[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4.292360239912[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33186&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33186&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 R0.973276206517053
R-squared0.947266574172225
Adjusted R-squared0.944441569217166
F-TEST (value)335.315013333248
F-TEST (DF numerator)3
F-TEST (DF denominator)56
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.276856081743618
Sum Squared Residuals4.292360239912






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.81.188079815670350.611920184329654
21.71.105714891722550.59428510827745
31.40.9682000275560140.431799972443986
41.20.5152926842660280.684707315733972
510.5242690971060180.475730902893982
61.71.470452562769760.229547437230241
72.42.294272349139670.105727650860330
822.01681100373706-0.0168110037370560
92.12.063012623958690.0369873760413094
1021.974094886112400.0259051138876031
111.81.81623771846066-0.0162377184606642
122.72.77069968875325-0.0706996887532535
132.32.51358064683584-0.213580646835837
141.92.34470150926840-0.444701509268405
1521.978308379598060.0216916204019441
162.32.60324645028565-0.303246450285648
172.82.97035955271663-0.170359552716631
182.42.91970083306458-0.519700833064582
192.32.42198967580990-0.121989675809903
202.72.81218854701293-0.112188547012932
212.73.16309458920912-0.463094589209118
222.93.06625631388493-0.166256313884926
2333.15105888319565-0.151058883195654
242.22.29142574212248-0.0914257421224831
252.32.36934158476791-0.0693415847679128
262.82.758164714263490.0418352857365073
272.83.27314483540845-0.473144835408453
282.82.98913869423539-0.18913869423539
292.22.45557005317847-0.255570053178467
302.62.579331538729240.0206684612707611
312.82.94299325969952-0.142993259699523
322.52.55282248133938-0.052822481339377
332.42.356714313621450.0432856863785497
342.32.33742788549835-0.0374278854983453
351.91.94416369282845-0.0441636928284495
361.71.93935061608174-0.239350616081742
3722.31610994672097-0.316109946720974
382.12.31233068078687-0.212330680786867
391.71.94284413680677-0.242844136806768
401.81.93217012607931-0.132170126079311
411.81.734678198525880.0653218014741153
421.81.787090701751170.0129092982488319
431.31.31487486578859-0.0148748657885858
441.31.47069250465800-0.170692504658003
451.31.48346222985354-0.183462229853543
461.21.23322991460142-0.0332299146014250
471.41.62653021824225-0.226530218242254
482.22.28869349631149-0.08869349631149
492.92.896390695951680.00360930404831486
503.12.867799970515180.232200029484819
513.53.262134084968610.237865915031389
523.63.446556495695970.153443504304027
534.44.202128732178190.197871267821806
544.14.14906046000609-0.0490604600060954
555.15.10455624111128-0.00455624111128468
565.85.530946508883360.269053491116641
575.95.659877048497130.240122951502871
585.45.097361704687410.302638295312589
595.55.074281964208760.425718035791243
604.84.224986931261580.575013068738415

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.8 & 1.18807981567035 & 0.611920184329654 \tabularnewline
2 & 1.7 & 1.10571489172255 & 0.59428510827745 \tabularnewline
3 & 1.4 & 0.968200027556014 & 0.431799972443986 \tabularnewline
4 & 1.2 & 0.515292684266028 & 0.684707315733972 \tabularnewline
5 & 1 & 0.524269097106018 & 0.475730902893982 \tabularnewline
6 & 1.7 & 1.47045256276976 & 0.229547437230241 \tabularnewline
7 & 2.4 & 2.29427234913967 & 0.105727650860330 \tabularnewline
8 & 2 & 2.01681100373706 & -0.0168110037370560 \tabularnewline
9 & 2.1 & 2.06301262395869 & 0.0369873760413094 \tabularnewline
10 & 2 & 1.97409488611240 & 0.0259051138876031 \tabularnewline
11 & 1.8 & 1.81623771846066 & -0.0162377184606642 \tabularnewline
12 & 2.7 & 2.77069968875325 & -0.0706996887532535 \tabularnewline
13 & 2.3 & 2.51358064683584 & -0.213580646835837 \tabularnewline
14 & 1.9 & 2.34470150926840 & -0.444701509268405 \tabularnewline
15 & 2 & 1.97830837959806 & 0.0216916204019441 \tabularnewline
16 & 2.3 & 2.60324645028565 & -0.303246450285648 \tabularnewline
17 & 2.8 & 2.97035955271663 & -0.170359552716631 \tabularnewline
18 & 2.4 & 2.91970083306458 & -0.519700833064582 \tabularnewline
19 & 2.3 & 2.42198967580990 & -0.121989675809903 \tabularnewline
20 & 2.7 & 2.81218854701293 & -0.112188547012932 \tabularnewline
21 & 2.7 & 3.16309458920912 & -0.463094589209118 \tabularnewline
22 & 2.9 & 3.06625631388493 & -0.166256313884926 \tabularnewline
23 & 3 & 3.15105888319565 & -0.151058883195654 \tabularnewline
24 & 2.2 & 2.29142574212248 & -0.0914257421224831 \tabularnewline
25 & 2.3 & 2.36934158476791 & -0.0693415847679128 \tabularnewline
26 & 2.8 & 2.75816471426349 & 0.0418352857365073 \tabularnewline
27 & 2.8 & 3.27314483540845 & -0.473144835408453 \tabularnewline
28 & 2.8 & 2.98913869423539 & -0.18913869423539 \tabularnewline
29 & 2.2 & 2.45557005317847 & -0.255570053178467 \tabularnewline
30 & 2.6 & 2.57933153872924 & 0.0206684612707611 \tabularnewline
31 & 2.8 & 2.94299325969952 & -0.142993259699523 \tabularnewline
32 & 2.5 & 2.55282248133938 & -0.052822481339377 \tabularnewline
33 & 2.4 & 2.35671431362145 & 0.0432856863785497 \tabularnewline
34 & 2.3 & 2.33742788549835 & -0.0374278854983453 \tabularnewline
35 & 1.9 & 1.94416369282845 & -0.0441636928284495 \tabularnewline
36 & 1.7 & 1.93935061608174 & -0.239350616081742 \tabularnewline
37 & 2 & 2.31610994672097 & -0.316109946720974 \tabularnewline
38 & 2.1 & 2.31233068078687 & -0.212330680786867 \tabularnewline
39 & 1.7 & 1.94284413680677 & -0.242844136806768 \tabularnewline
40 & 1.8 & 1.93217012607931 & -0.132170126079311 \tabularnewline
41 & 1.8 & 1.73467819852588 & 0.0653218014741153 \tabularnewline
42 & 1.8 & 1.78709070175117 & 0.0129092982488319 \tabularnewline
43 & 1.3 & 1.31487486578859 & -0.0148748657885858 \tabularnewline
44 & 1.3 & 1.47069250465800 & -0.170692504658003 \tabularnewline
45 & 1.3 & 1.48346222985354 & -0.183462229853543 \tabularnewline
46 & 1.2 & 1.23322991460142 & -0.0332299146014250 \tabularnewline
47 & 1.4 & 1.62653021824225 & -0.226530218242254 \tabularnewline
48 & 2.2 & 2.28869349631149 & -0.08869349631149 \tabularnewline
49 & 2.9 & 2.89639069595168 & 0.00360930404831486 \tabularnewline
50 & 3.1 & 2.86779997051518 & 0.232200029484819 \tabularnewline
51 & 3.5 & 3.26213408496861 & 0.237865915031389 \tabularnewline
52 & 3.6 & 3.44655649569597 & 0.153443504304027 \tabularnewline
53 & 4.4 & 4.20212873217819 & 0.197871267821806 \tabularnewline
54 & 4.1 & 4.14906046000609 & -0.0490604600060954 \tabularnewline
55 & 5.1 & 5.10455624111128 & -0.00455624111128468 \tabularnewline
56 & 5.8 & 5.53094650888336 & 0.269053491116641 \tabularnewline
57 & 5.9 & 5.65987704849713 & 0.240122951502871 \tabularnewline
58 & 5.4 & 5.09736170468741 & 0.302638295312589 \tabularnewline
59 & 5.5 & 5.07428196420876 & 0.425718035791243 \tabularnewline
60 & 4.8 & 4.22498693126158 & 0.575013068738415 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33186&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]1.8[/C][C]1.18807981567035[/C][C]0.611920184329654[/C][/ROW]
[ROW][C]2[/C][C]1.7[/C][C]1.10571489172255[/C][C]0.59428510827745[/C][/ROW]
[ROW][C]3[/C][C]1.4[/C][C]0.968200027556014[/C][C]0.431799972443986[/C][/ROW]
[ROW][C]4[/C][C]1.2[/C][C]0.515292684266028[/C][C]0.684707315733972[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.524269097106018[/C][C]0.475730902893982[/C][/ROW]
[ROW][C]6[/C][C]1.7[/C][C]1.47045256276976[/C][C]0.229547437230241[/C][/ROW]
[ROW][C]7[/C][C]2.4[/C][C]2.29427234913967[/C][C]0.105727650860330[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]2.01681100373706[/C][C]-0.0168110037370560[/C][/ROW]
[ROW][C]9[/C][C]2.1[/C][C]2.06301262395869[/C][C]0.0369873760413094[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]1.97409488611240[/C][C]0.0259051138876031[/C][/ROW]
[ROW][C]11[/C][C]1.8[/C][C]1.81623771846066[/C][C]-0.0162377184606642[/C][/ROW]
[ROW][C]12[/C][C]2.7[/C][C]2.77069968875325[/C][C]-0.0706996887532535[/C][/ROW]
[ROW][C]13[/C][C]2.3[/C][C]2.51358064683584[/C][C]-0.213580646835837[/C][/ROW]
[ROW][C]14[/C][C]1.9[/C][C]2.34470150926840[/C][C]-0.444701509268405[/C][/ROW]
[ROW][C]15[/C][C]2[/C][C]1.97830837959806[/C][C]0.0216916204019441[/C][/ROW]
[ROW][C]16[/C][C]2.3[/C][C]2.60324645028565[/C][C]-0.303246450285648[/C][/ROW]
[ROW][C]17[/C][C]2.8[/C][C]2.97035955271663[/C][C]-0.170359552716631[/C][/ROW]
[ROW][C]18[/C][C]2.4[/C][C]2.91970083306458[/C][C]-0.519700833064582[/C][/ROW]
[ROW][C]19[/C][C]2.3[/C][C]2.42198967580990[/C][C]-0.121989675809903[/C][/ROW]
[ROW][C]20[/C][C]2.7[/C][C]2.81218854701293[/C][C]-0.112188547012932[/C][/ROW]
[ROW][C]21[/C][C]2.7[/C][C]3.16309458920912[/C][C]-0.463094589209118[/C][/ROW]
[ROW][C]22[/C][C]2.9[/C][C]3.06625631388493[/C][C]-0.166256313884926[/C][/ROW]
[ROW][C]23[/C][C]3[/C][C]3.15105888319565[/C][C]-0.151058883195654[/C][/ROW]
[ROW][C]24[/C][C]2.2[/C][C]2.29142574212248[/C][C]-0.0914257421224831[/C][/ROW]
[ROW][C]25[/C][C]2.3[/C][C]2.36934158476791[/C][C]-0.0693415847679128[/C][/ROW]
[ROW][C]26[/C][C]2.8[/C][C]2.75816471426349[/C][C]0.0418352857365073[/C][/ROW]
[ROW][C]27[/C][C]2.8[/C][C]3.27314483540845[/C][C]-0.473144835408453[/C][/ROW]
[ROW][C]28[/C][C]2.8[/C][C]2.98913869423539[/C][C]-0.18913869423539[/C][/ROW]
[ROW][C]29[/C][C]2.2[/C][C]2.45557005317847[/C][C]-0.255570053178467[/C][/ROW]
[ROW][C]30[/C][C]2.6[/C][C]2.57933153872924[/C][C]0.0206684612707611[/C][/ROW]
[ROW][C]31[/C][C]2.8[/C][C]2.94299325969952[/C][C]-0.142993259699523[/C][/ROW]
[ROW][C]32[/C][C]2.5[/C][C]2.55282248133938[/C][C]-0.052822481339377[/C][/ROW]
[ROW][C]33[/C][C]2.4[/C][C]2.35671431362145[/C][C]0.0432856863785497[/C][/ROW]
[ROW][C]34[/C][C]2.3[/C][C]2.33742788549835[/C][C]-0.0374278854983453[/C][/ROW]
[ROW][C]35[/C][C]1.9[/C][C]1.94416369282845[/C][C]-0.0441636928284495[/C][/ROW]
[ROW][C]36[/C][C]1.7[/C][C]1.93935061608174[/C][C]-0.239350616081742[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]2.31610994672097[/C][C]-0.316109946720974[/C][/ROW]
[ROW][C]38[/C][C]2.1[/C][C]2.31233068078687[/C][C]-0.212330680786867[/C][/ROW]
[ROW][C]39[/C][C]1.7[/C][C]1.94284413680677[/C][C]-0.242844136806768[/C][/ROW]
[ROW][C]40[/C][C]1.8[/C][C]1.93217012607931[/C][C]-0.132170126079311[/C][/ROW]
[ROW][C]41[/C][C]1.8[/C][C]1.73467819852588[/C][C]0.0653218014741153[/C][/ROW]
[ROW][C]42[/C][C]1.8[/C][C]1.78709070175117[/C][C]0.0129092982488319[/C][/ROW]
[ROW][C]43[/C][C]1.3[/C][C]1.31487486578859[/C][C]-0.0148748657885858[/C][/ROW]
[ROW][C]44[/C][C]1.3[/C][C]1.47069250465800[/C][C]-0.170692504658003[/C][/ROW]
[ROW][C]45[/C][C]1.3[/C][C]1.48346222985354[/C][C]-0.183462229853543[/C][/ROW]
[ROW][C]46[/C][C]1.2[/C][C]1.23322991460142[/C][C]-0.0332299146014250[/C][/ROW]
[ROW][C]47[/C][C]1.4[/C][C]1.62653021824225[/C][C]-0.226530218242254[/C][/ROW]
[ROW][C]48[/C][C]2.2[/C][C]2.28869349631149[/C][C]-0.08869349631149[/C][/ROW]
[ROW][C]49[/C][C]2.9[/C][C]2.89639069595168[/C][C]0.00360930404831486[/C][/ROW]
[ROW][C]50[/C][C]3.1[/C][C]2.86779997051518[/C][C]0.232200029484819[/C][/ROW]
[ROW][C]51[/C][C]3.5[/C][C]3.26213408496861[/C][C]0.237865915031389[/C][/ROW]
[ROW][C]52[/C][C]3.6[/C][C]3.44655649569597[/C][C]0.153443504304027[/C][/ROW]
[ROW][C]53[/C][C]4.4[/C][C]4.20212873217819[/C][C]0.197871267821806[/C][/ROW]
[ROW][C]54[/C][C]4.1[/C][C]4.14906046000609[/C][C]-0.0490604600060954[/C][/ROW]
[ROW][C]55[/C][C]5.1[/C][C]5.10455624111128[/C][C]-0.00455624111128468[/C][/ROW]
[ROW][C]56[/C][C]5.8[/C][C]5.53094650888336[/C][C]0.269053491116641[/C][/ROW]
[ROW][C]57[/C][C]5.9[/C][C]5.65987704849713[/C][C]0.240122951502871[/C][/ROW]
[ROW][C]58[/C][C]5.4[/C][C]5.09736170468741[/C][C]0.302638295312589[/C][/ROW]
[ROW][C]59[/C][C]5.5[/C][C]5.07428196420876[/C][C]0.425718035791243[/C][/ROW]
[ROW][C]60[/C][C]4.8[/C][C]4.22498693126158[/C][C]0.575013068738415[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33186&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33186&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
11.81.188079815670350.611920184329654
21.71.105714891722550.59428510827745
31.40.9682000275560140.431799972443986
41.20.5152926842660280.684707315733972
510.5242690971060180.475730902893982
61.71.470452562769760.229547437230241
72.42.294272349139670.105727650860330
822.01681100373706-0.0168110037370560
92.12.063012623958690.0369873760413094
1021.974094886112400.0259051138876031
111.81.81623771846066-0.0162377184606642
122.72.77069968875325-0.0706996887532535
132.32.51358064683584-0.213580646835837
141.92.34470150926840-0.444701509268405
1521.978308379598060.0216916204019441
162.32.60324645028565-0.303246450285648
172.82.97035955271663-0.170359552716631
182.42.91970083306458-0.519700833064582
192.32.42198967580990-0.121989675809903
202.72.81218854701293-0.112188547012932
212.73.16309458920912-0.463094589209118
222.93.06625631388493-0.166256313884926
2333.15105888319565-0.151058883195654
242.22.29142574212248-0.0914257421224831
252.32.36934158476791-0.0693415847679128
262.82.758164714263490.0418352857365073
272.83.27314483540845-0.473144835408453
282.82.98913869423539-0.18913869423539
292.22.45557005317847-0.255570053178467
302.62.579331538729240.0206684612707611
312.82.94299325969952-0.142993259699523
322.52.55282248133938-0.052822481339377
332.42.356714313621450.0432856863785497
342.32.33742788549835-0.0374278854983453
351.91.94416369282845-0.0441636928284495
361.71.93935061608174-0.239350616081742
3722.31610994672097-0.316109946720974
382.12.31233068078687-0.212330680786867
391.71.94284413680677-0.242844136806768
401.81.93217012607931-0.132170126079311
411.81.734678198525880.0653218014741153
421.81.787090701751170.0129092982488319
431.31.31487486578859-0.0148748657885858
441.31.47069250465800-0.170692504658003
451.31.48346222985354-0.183462229853543
461.21.23322991460142-0.0332299146014250
471.41.62653021824225-0.226530218242254
482.22.28869349631149-0.08869349631149
492.92.896390695951680.00360930404831486
503.12.867799970515180.232200029484819
513.53.262134084968610.237865915031389
523.63.446556495695970.153443504304027
534.44.202128732178190.197871267821806
544.14.14906046000609-0.0490604600060954
555.15.10455624111128-0.00455624111128468
565.85.530946508883360.269053491116641
575.95.659877048497130.240122951502871
585.45.097361704687410.302638295312589
595.55.074281964208760.425718035791243
604.84.224986931261580.575013068738415






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.1586511673817720.3173023347635430.841348832618228
80.1262997310879890.2525994621759780.873700268912011
90.06988102779281770.1397620555856350.930118972207182
100.03500584965125370.07001169930250750.964994150348746
110.01992972895718940.03985945791437880.98007027104281
120.05728197671581210.1145639534316240.942718023284188
130.030111816881910.060223633763820.96988818311809
140.03178858070088890.06357716140177770.968211419299111
150.1803587787754920.3607175575509830.819641221224508
160.1357772467345150.2715544934690290.864222753265485
170.2476701564840440.4953403129680880.752329843515956
180.2591806987099170.5183613974198350.740819301290083
190.2931109180175020.5862218360350050.706889081982498
200.4197584073256580.8395168146513160.580241592674342
210.4164426779722490.8328853559444970.583557322027751
220.4693749595073360.9387499190146730.530625040492664
230.5878450675563230.8243098648873540.412154932443677
240.5259535448493340.9480929103013320.474046455150666
250.4789387529299720.9578775058599440.521061247070028
260.620070938478480.7598581230430410.379929061521521
270.6953205920826740.6093588158346520.304679407917326
280.6323068748090460.7353862503819090.367693125190954
290.6043522620445530.7912954759108940.395647737955447
300.6104225535235370.7791548929529250.389577446476463
310.5557958769538990.8884082460922020.444204123046101
320.4845109049431660.9690218098863330.515489095056833
330.5115912947004080.9768174105991840.488408705299592
340.5925928921959490.8148142156081030.407407107804051
350.7197898032270470.5604203935459070.280210196772953
360.7699630869446750.4600738261106500.230036913055325
370.754985512607160.4900289747856790.245014487392840
380.6927965279474590.6144069441050830.307203472052541
390.6679512159458540.6640975681082920.332048784054146
400.5920150462424730.8159699075150540.407984953757527
410.6793194604876740.6413610790246530.320680539512327
420.7220711360550380.5558577278899230.277928863944962
430.7868153893818680.4263692212362640.213184610618132
440.7390990451279080.5218019097441840.260900954872092
450.6760826399454460.6478347201091090.323917360054554
460.5841899249643240.8316201500713520.415810075035676
470.678757642670220.6424847146595610.321242357329780
480.701385989438380.5972280211232410.298614010561621
490.7081553816604790.5836892366790430.291844618339521
500.7515084814266490.4969830371467020.248491518573351
510.8316629000628880.3366741998742230.168337099937112
520.8003814698572270.3992370602855460.199618530142773
530.9727372345006020.05452553099879640.0272627654993982

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.158651167381772 & 0.317302334763543 & 0.841348832618228 \tabularnewline
8 & 0.126299731087989 & 0.252599462175978 & 0.873700268912011 \tabularnewline
9 & 0.0698810277928177 & 0.139762055585635 & 0.930118972207182 \tabularnewline
10 & 0.0350058496512537 & 0.0700116993025075 & 0.964994150348746 \tabularnewline
11 & 0.0199297289571894 & 0.0398594579143788 & 0.98007027104281 \tabularnewline
12 & 0.0572819767158121 & 0.114563953431624 & 0.942718023284188 \tabularnewline
13 & 0.03011181688191 & 0.06022363376382 & 0.96988818311809 \tabularnewline
14 & 0.0317885807008889 & 0.0635771614017777 & 0.968211419299111 \tabularnewline
15 & 0.180358778775492 & 0.360717557550983 & 0.819641221224508 \tabularnewline
16 & 0.135777246734515 & 0.271554493469029 & 0.864222753265485 \tabularnewline
17 & 0.247670156484044 & 0.495340312968088 & 0.752329843515956 \tabularnewline
18 & 0.259180698709917 & 0.518361397419835 & 0.740819301290083 \tabularnewline
19 & 0.293110918017502 & 0.586221836035005 & 0.706889081982498 \tabularnewline
20 & 0.419758407325658 & 0.839516814651316 & 0.580241592674342 \tabularnewline
21 & 0.416442677972249 & 0.832885355944497 & 0.583557322027751 \tabularnewline
22 & 0.469374959507336 & 0.938749919014673 & 0.530625040492664 \tabularnewline
23 & 0.587845067556323 & 0.824309864887354 & 0.412154932443677 \tabularnewline
24 & 0.525953544849334 & 0.948092910301332 & 0.474046455150666 \tabularnewline
25 & 0.478938752929972 & 0.957877505859944 & 0.521061247070028 \tabularnewline
26 & 0.62007093847848 & 0.759858123043041 & 0.379929061521521 \tabularnewline
27 & 0.695320592082674 & 0.609358815834652 & 0.304679407917326 \tabularnewline
28 & 0.632306874809046 & 0.735386250381909 & 0.367693125190954 \tabularnewline
29 & 0.604352262044553 & 0.791295475910894 & 0.395647737955447 \tabularnewline
30 & 0.610422553523537 & 0.779154892952925 & 0.389577446476463 \tabularnewline
31 & 0.555795876953899 & 0.888408246092202 & 0.444204123046101 \tabularnewline
32 & 0.484510904943166 & 0.969021809886333 & 0.515489095056833 \tabularnewline
33 & 0.511591294700408 & 0.976817410599184 & 0.488408705299592 \tabularnewline
34 & 0.592592892195949 & 0.814814215608103 & 0.407407107804051 \tabularnewline
35 & 0.719789803227047 & 0.560420393545907 & 0.280210196772953 \tabularnewline
36 & 0.769963086944675 & 0.460073826110650 & 0.230036913055325 \tabularnewline
37 & 0.75498551260716 & 0.490028974785679 & 0.245014487392840 \tabularnewline
38 & 0.692796527947459 & 0.614406944105083 & 0.307203472052541 \tabularnewline
39 & 0.667951215945854 & 0.664097568108292 & 0.332048784054146 \tabularnewline
40 & 0.592015046242473 & 0.815969907515054 & 0.407984953757527 \tabularnewline
41 & 0.679319460487674 & 0.641361079024653 & 0.320680539512327 \tabularnewline
42 & 0.722071136055038 & 0.555857727889923 & 0.277928863944962 \tabularnewline
43 & 0.786815389381868 & 0.426369221236264 & 0.213184610618132 \tabularnewline
44 & 0.739099045127908 & 0.521801909744184 & 0.260900954872092 \tabularnewline
45 & 0.676082639945446 & 0.647834720109109 & 0.323917360054554 \tabularnewline
46 & 0.584189924964324 & 0.831620150071352 & 0.415810075035676 \tabularnewline
47 & 0.67875764267022 & 0.642484714659561 & 0.321242357329780 \tabularnewline
48 & 0.70138598943838 & 0.597228021123241 & 0.298614010561621 \tabularnewline
49 & 0.708155381660479 & 0.583689236679043 & 0.291844618339521 \tabularnewline
50 & 0.751508481426649 & 0.496983037146702 & 0.248491518573351 \tabularnewline
51 & 0.831662900062888 & 0.336674199874223 & 0.168337099937112 \tabularnewline
52 & 0.800381469857227 & 0.399237060285546 & 0.199618530142773 \tabularnewline
53 & 0.972737234500602 & 0.0545255309987964 & 0.0272627654993982 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33186&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]7[/C][C]0.158651167381772[/C][C]0.317302334763543[/C][C]0.841348832618228[/C][/ROW]
[ROW][C]8[/C][C]0.126299731087989[/C][C]0.252599462175978[/C][C]0.873700268912011[/C][/ROW]
[ROW][C]9[/C][C]0.0698810277928177[/C][C]0.139762055585635[/C][C]0.930118972207182[/C][/ROW]
[ROW][C]10[/C][C]0.0350058496512537[/C][C]0.0700116993025075[/C][C]0.964994150348746[/C][/ROW]
[ROW][C]11[/C][C]0.0199297289571894[/C][C]0.0398594579143788[/C][C]0.98007027104281[/C][/ROW]
[ROW][C]12[/C][C]0.0572819767158121[/C][C]0.114563953431624[/C][C]0.942718023284188[/C][/ROW]
[ROW][C]13[/C][C]0.03011181688191[/C][C]0.06022363376382[/C][C]0.96988818311809[/C][/ROW]
[ROW][C]14[/C][C]0.0317885807008889[/C][C]0.0635771614017777[/C][C]0.968211419299111[/C][/ROW]
[ROW][C]15[/C][C]0.180358778775492[/C][C]0.360717557550983[/C][C]0.819641221224508[/C][/ROW]
[ROW][C]16[/C][C]0.135777246734515[/C][C]0.271554493469029[/C][C]0.864222753265485[/C][/ROW]
[ROW][C]17[/C][C]0.247670156484044[/C][C]0.495340312968088[/C][C]0.752329843515956[/C][/ROW]
[ROW][C]18[/C][C]0.259180698709917[/C][C]0.518361397419835[/C][C]0.740819301290083[/C][/ROW]
[ROW][C]19[/C][C]0.293110918017502[/C][C]0.586221836035005[/C][C]0.706889081982498[/C][/ROW]
[ROW][C]20[/C][C]0.419758407325658[/C][C]0.839516814651316[/C][C]0.580241592674342[/C][/ROW]
[ROW][C]21[/C][C]0.416442677972249[/C][C]0.832885355944497[/C][C]0.583557322027751[/C][/ROW]
[ROW][C]22[/C][C]0.469374959507336[/C][C]0.938749919014673[/C][C]0.530625040492664[/C][/ROW]
[ROW][C]23[/C][C]0.587845067556323[/C][C]0.824309864887354[/C][C]0.412154932443677[/C][/ROW]
[ROW][C]24[/C][C]0.525953544849334[/C][C]0.948092910301332[/C][C]0.474046455150666[/C][/ROW]
[ROW][C]25[/C][C]0.478938752929972[/C][C]0.957877505859944[/C][C]0.521061247070028[/C][/ROW]
[ROW][C]26[/C][C]0.62007093847848[/C][C]0.759858123043041[/C][C]0.379929061521521[/C][/ROW]
[ROW][C]27[/C][C]0.695320592082674[/C][C]0.609358815834652[/C][C]0.304679407917326[/C][/ROW]
[ROW][C]28[/C][C]0.632306874809046[/C][C]0.735386250381909[/C][C]0.367693125190954[/C][/ROW]
[ROW][C]29[/C][C]0.604352262044553[/C][C]0.791295475910894[/C][C]0.395647737955447[/C][/ROW]
[ROW][C]30[/C][C]0.610422553523537[/C][C]0.779154892952925[/C][C]0.389577446476463[/C][/ROW]
[ROW][C]31[/C][C]0.555795876953899[/C][C]0.888408246092202[/C][C]0.444204123046101[/C][/ROW]
[ROW][C]32[/C][C]0.484510904943166[/C][C]0.969021809886333[/C][C]0.515489095056833[/C][/ROW]
[ROW][C]33[/C][C]0.511591294700408[/C][C]0.976817410599184[/C][C]0.488408705299592[/C][/ROW]
[ROW][C]34[/C][C]0.592592892195949[/C][C]0.814814215608103[/C][C]0.407407107804051[/C][/ROW]
[ROW][C]35[/C][C]0.719789803227047[/C][C]0.560420393545907[/C][C]0.280210196772953[/C][/ROW]
[ROW][C]36[/C][C]0.769963086944675[/C][C]0.460073826110650[/C][C]0.230036913055325[/C][/ROW]
[ROW][C]37[/C][C]0.75498551260716[/C][C]0.490028974785679[/C][C]0.245014487392840[/C][/ROW]
[ROW][C]38[/C][C]0.692796527947459[/C][C]0.614406944105083[/C][C]0.307203472052541[/C][/ROW]
[ROW][C]39[/C][C]0.667951215945854[/C][C]0.664097568108292[/C][C]0.332048784054146[/C][/ROW]
[ROW][C]40[/C][C]0.592015046242473[/C][C]0.815969907515054[/C][C]0.407984953757527[/C][/ROW]
[ROW][C]41[/C][C]0.679319460487674[/C][C]0.641361079024653[/C][C]0.320680539512327[/C][/ROW]
[ROW][C]42[/C][C]0.722071136055038[/C][C]0.555857727889923[/C][C]0.277928863944962[/C][/ROW]
[ROW][C]43[/C][C]0.786815389381868[/C][C]0.426369221236264[/C][C]0.213184610618132[/C][/ROW]
[ROW][C]44[/C][C]0.739099045127908[/C][C]0.521801909744184[/C][C]0.260900954872092[/C][/ROW]
[ROW][C]45[/C][C]0.676082639945446[/C][C]0.647834720109109[/C][C]0.323917360054554[/C][/ROW]
[ROW][C]46[/C][C]0.584189924964324[/C][C]0.831620150071352[/C][C]0.415810075035676[/C][/ROW]
[ROW][C]47[/C][C]0.67875764267022[/C][C]0.642484714659561[/C][C]0.321242357329780[/C][/ROW]
[ROW][C]48[/C][C]0.70138598943838[/C][C]0.597228021123241[/C][C]0.298614010561621[/C][/ROW]
[ROW][C]49[/C][C]0.708155381660479[/C][C]0.583689236679043[/C][C]0.291844618339521[/C][/ROW]
[ROW][C]50[/C][C]0.751508481426649[/C][C]0.496983037146702[/C][C]0.248491518573351[/C][/ROW]
[ROW][C]51[/C][C]0.831662900062888[/C][C]0.336674199874223[/C][C]0.168337099937112[/C][/ROW]
[ROW][C]52[/C][C]0.800381469857227[/C][C]0.399237060285546[/C][C]0.199618530142773[/C][/ROW]
[ROW][C]53[/C][C]0.972737234500602[/C][C]0.0545255309987964[/C][C]0.0272627654993982[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33186&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33186&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
70.1586511673817720.3173023347635430.841348832618228
80.1262997310879890.2525994621759780.873700268912011
90.06988102779281770.1397620555856350.930118972207182
100.03500584965125370.07001169930250750.964994150348746
110.01992972895718940.03985945791437880.98007027104281
120.05728197671581210.1145639534316240.942718023284188
130.030111816881910.060223633763820.96988818311809
140.03178858070088890.06357716140177770.968211419299111
150.1803587787754920.3607175575509830.819641221224508
160.1357772467345150.2715544934690290.864222753265485
170.2476701564840440.4953403129680880.752329843515956
180.2591806987099170.5183613974198350.740819301290083
190.2931109180175020.5862218360350050.706889081982498
200.4197584073256580.8395168146513160.580241592674342
210.4164426779722490.8328853559444970.583557322027751
220.4693749595073360.9387499190146730.530625040492664
230.5878450675563230.8243098648873540.412154932443677
240.5259535448493340.9480929103013320.474046455150666
250.4789387529299720.9578775058599440.521061247070028
260.620070938478480.7598581230430410.379929061521521
270.6953205920826740.6093588158346520.304679407917326
280.6323068748090460.7353862503819090.367693125190954
290.6043522620445530.7912954759108940.395647737955447
300.6104225535235370.7791548929529250.389577446476463
310.5557958769538990.8884082460922020.444204123046101
320.4845109049431660.9690218098863330.515489095056833
330.5115912947004080.9768174105991840.488408705299592
340.5925928921959490.8148142156081030.407407107804051
350.7197898032270470.5604203935459070.280210196772953
360.7699630869446750.4600738261106500.230036913055325
370.754985512607160.4900289747856790.245014487392840
380.6927965279474590.6144069441050830.307203472052541
390.6679512159458540.6640975681082920.332048784054146
400.5920150462424730.8159699075150540.407984953757527
410.6793194604876740.6413610790246530.320680539512327
420.7220711360550380.5558577278899230.277928863944962
430.7868153893818680.4263692212362640.213184610618132
440.7390990451279080.5218019097441840.260900954872092
450.6760826399454460.6478347201091090.323917360054554
460.5841899249643240.8316201500713520.415810075035676
470.678757642670220.6424847146595610.321242357329780
480.701385989438380.5972280211232410.298614010561621
490.7081553816604790.5836892366790430.291844618339521
500.7515084814266490.4969830371467020.248491518573351
510.8316629000628880.3366741998742230.168337099937112
520.8003814698572270.3992370602855460.199618530142773
530.9727372345006020.05452553099879640.0272627654993982






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0212765957446809OK
10% type I error level50.106382978723404NOK

\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 & 1 & 0.0212765957446809 & OK \tabularnewline
10% type I error level & 5 & 0.106382978723404 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33186&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]1[/C][C]0.0212765957446809[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.106382978723404[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33186&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33186&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 level10.0212765957446809OK
10% type I error level50.106382978723404NOK


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 = Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}