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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 05:55:31 -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/2009/Nov/20/t1258722124ftm023dpnrhledg.htm/, Retrieved Fri, 29 Mar 2024 11:48:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58106, Retrieved Fri, 29 Mar 2024 11:48:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Model 2] [2009-11-20 12:55:31] [cf272a759dc2b193d9a85354803ede7b] [Current]
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Dataseries X:
108.5	98.71
112.3	98.54
116.6	98.2
115.5	96.92
120.1	99.06
132.9	99.65
128.1	99.82
129.3	99.99
132.5	100.33
131	99.31
124.9	101.1
120.8	101.1
122	100.93
122.1	100.85
127.4	100.93
135.2	99.6
137.3	101.88
135	101.81
136	102.38
138.4	102.74
134.7	102.82
138.4	101.72
133.9	103.47
133.6	102.98
141.2	102.68
151.8	102.9
155.4	103.03
156.6	101.29
161.6	103.69
160.7	103.68
156	104.2
159.5	104.08
168.7	104.16
169.9	103.05
169.9	104.66
185.9	104.46
190.8	104.95
195.8	105.85
211.9	106.23
227.1	104.86
251.3	107.44
256.7	108.23
251.9	108.45
251.2	109.39
270.3	110.15
267.2	109.13
243	110.28
229.9	110.17
187.2	109.99
178.2	109.26
175.2	109.11
192.4	107.06
187	109.53
184	108.92
194.1	109.24
212.7	109.12
217.5	109
200.5	107.23
205.9	109.49
196.5	109.04
206.3	109.02




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58106&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58106&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58106&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = -918.27639098955 + 10.3421732921795X[t] -1.90632391481678M1[t] + 0.108298714811628M2[t] + 5.16145524896801M3[t] + 29.293192545015M4[t] + 10.8408731493808M5[t] + 11.81365323506M6[t] + 7.45047084987543M7[t] + 9.90629621999926M8[t] + 14.0682807093823M9[t] + 23.1802573531665M10[t] -0.405543323044851M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -918.27639098955 +  10.3421732921795X[t] -1.90632391481678M1[t] +  0.108298714811628M2[t] +  5.16145524896801M3[t] +  29.293192545015M4[t] +  10.8408731493808M5[t] +  11.81365323506M6[t] +  7.45047084987543M7[t] +  9.90629621999926M8[t] +  14.0682807093823M9[t] +  23.1802573531665M10[t] -0.405543323044851M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58106&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -918.27639098955 +  10.3421732921795X[t] -1.90632391481678M1[t] +  0.108298714811628M2[t] +  5.16145524896801M3[t] +  29.293192545015M4[t] +  10.8408731493808M5[t] +  11.81365323506M6[t] +  7.45047084987543M7[t] +  9.90629621999926M8[t] +  14.0682807093823M9[t] +  23.1802573531665M10[t] -0.405543323044851M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58106&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58106&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = -918.27639098955 + 10.3421732921795X[t] -1.90632391481678M1[t] + 0.108298714811628M2[t] + 5.16145524896801M3[t] + 29.293192545015M4[t] + 10.8408731493808M5[t] + 11.81365323506M6[t] + 7.45047084987543M7[t] + 9.90629621999926M8[t] + 14.0682807093823M9[t] + 23.1802573531665M10[t] -0.405543323044851M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-918.2763909895582.359041-11.149700
X10.34217329217950.77449813.353400
M1-1.9063239148167813.586385-0.14030.8890010.444501
M20.10829871481162814.2493950.00760.9939670.496984
M35.1614552489680114.247660.36230.7187430.359371
M429.29319254501514.4314332.02980.0479380.023969
M510.840873149380814.1909290.76390.4486480.224324
M611.8136532350614.1841550.83290.4090390.20452
M77.4504708498754314.1702640.52580.601460.30073
M89.9062962199992614.1639210.69940.4876750.243838
M914.068280709382314.1603280.99350.3254490.162724
M1023.180257353166514.2041231.63190.1092370.054618
M11-0.40554332304485114.160242-0.02860.9772710.488635

\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) & -918.27639098955 & 82.359041 & -11.1497 & 0 & 0 \tabularnewline
X & 10.3421732921795 & 0.774498 & 13.3534 & 0 & 0 \tabularnewline
M1 & -1.90632391481678 & 13.586385 & -0.1403 & 0.889001 & 0.444501 \tabularnewline
M2 & 0.108298714811628 & 14.249395 & 0.0076 & 0.993967 & 0.496984 \tabularnewline
M3 & 5.16145524896801 & 14.24766 & 0.3623 & 0.718743 & 0.359371 \tabularnewline
M4 & 29.293192545015 & 14.431433 & 2.0298 & 0.047938 & 0.023969 \tabularnewline
M5 & 10.8408731493808 & 14.190929 & 0.7639 & 0.448648 & 0.224324 \tabularnewline
M6 & 11.81365323506 & 14.184155 & 0.8329 & 0.409039 & 0.20452 \tabularnewline
M7 & 7.45047084987543 & 14.170264 & 0.5258 & 0.60146 & 0.30073 \tabularnewline
M8 & 9.90629621999926 & 14.163921 & 0.6994 & 0.487675 & 0.243838 \tabularnewline
M9 & 14.0682807093823 & 14.160328 & 0.9935 & 0.325449 & 0.162724 \tabularnewline
M10 & 23.1802573531665 & 14.204123 & 1.6319 & 0.109237 & 0.054618 \tabularnewline
M11 & -0.405543323044851 & 14.160242 & -0.0286 & 0.977271 & 0.488635 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58106&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]-918.27639098955[/C][C]82.359041[/C][C]-11.1497[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]10.3421732921795[/C][C]0.774498[/C][C]13.3534[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-1.90632391481678[/C][C]13.586385[/C][C]-0.1403[/C][C]0.889001[/C][C]0.444501[/C][/ROW]
[ROW][C]M2[/C][C]0.108298714811628[/C][C]14.249395[/C][C]0.0076[/C][C]0.993967[/C][C]0.496984[/C][/ROW]
[ROW][C]M3[/C][C]5.16145524896801[/C][C]14.24766[/C][C]0.3623[/C][C]0.718743[/C][C]0.359371[/C][/ROW]
[ROW][C]M4[/C][C]29.293192545015[/C][C]14.431433[/C][C]2.0298[/C][C]0.047938[/C][C]0.023969[/C][/ROW]
[ROW][C]M5[/C][C]10.8408731493808[/C][C]14.190929[/C][C]0.7639[/C][C]0.448648[/C][C]0.224324[/C][/ROW]
[ROW][C]M6[/C][C]11.81365323506[/C][C]14.184155[/C][C]0.8329[/C][C]0.409039[/C][C]0.20452[/C][/ROW]
[ROW][C]M7[/C][C]7.45047084987543[/C][C]14.170264[/C][C]0.5258[/C][C]0.60146[/C][C]0.30073[/C][/ROW]
[ROW][C]M8[/C][C]9.90629621999926[/C][C]14.163921[/C][C]0.6994[/C][C]0.487675[/C][C]0.243838[/C][/ROW]
[ROW][C]M9[/C][C]14.0682807093823[/C][C]14.160328[/C][C]0.9935[/C][C]0.325449[/C][C]0.162724[/C][/ROW]
[ROW][C]M10[/C][C]23.1802573531665[/C][C]14.204123[/C][C]1.6319[/C][C]0.109237[/C][C]0.054618[/C][/ROW]
[ROW][C]M11[/C][C]-0.405543323044851[/C][C]14.160242[/C][C]-0.0286[/C][C]0.977271[/C][C]0.488635[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58106&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58106&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-918.2763909895582.359041-11.149700
X10.34217329217950.77449813.353400
M1-1.9063239148167813.586385-0.14030.8890010.444501
M20.10829871481162814.2493950.00760.9939670.496984
M35.1614552489680114.247660.36230.7187430.359371
M429.29319254501514.4314332.02980.0479380.023969
M510.840873149380814.1909290.76390.4486480.224324
M611.8136532350614.1841550.83290.4090390.20452
M77.4504708498754314.1702640.52580.601460.30073
M89.9062962199992614.1639210.69940.4876750.243838
M914.068280709382314.1603280.99350.3254490.162724
M1023.180257353166514.2041231.63190.1092370.054618
M11-0.40554332304485114.160242-0.02860.9772710.488635







Multiple Linear Regression - Regression Statistics
Multiple R0.893265188168467
R-squared0.797922696393646
Adjusted R-squared0.747403370492058
F-TEST (value)15.7944050549684
F-TEST (DF numerator)12
F-TEST (DF denominator)48
p-value8.69304628281498e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation22.3872153977978
Sum Squared Residuals24056.995836835

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.893265188168467 \tabularnewline
R-squared & 0.797922696393646 \tabularnewline
Adjusted R-squared & 0.747403370492058 \tabularnewline
F-TEST (value) & 15.7944050549684 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 8.69304628281498e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 22.3872153977978 \tabularnewline
Sum Squared Residuals & 24056.995836835 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58106&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.893265188168467[/C][/ROW]
[ROW][C]R-squared[/C][C]0.797922696393646[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.747403370492058[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.7944050549684[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/C][/ROW]
[ROW][C]p-value[/C][C]8.69304628281498e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]22.3872153977978[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]24056.995836835[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58106&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58106&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.893265188168467
R-squared0.797922696393646
Adjusted R-squared0.747403370492058
F-TEST (value)15.7944050549684
F-TEST (DF numerator)12
F-TEST (DF denominator)48
p-value8.69304628281498e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation22.3872153977978
Sum Squared Residuals24056.995836835







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1108.5100.6932107666767.80678923332389
2112.3100.94966393663311.3503360633669
3116.6102.48648155144814.1135184485515
4115.5113.3802370335062.11976296649430
5120.1117.0601684831363.03983151686429
6132.9124.1348308112018.76516918879918
7128.1121.5298178856876.57018211431334
8129.3125.7438127154813.55618728451900
9132.5133.422136124205-0.922136124205162
10131131.985096009966-0.985096009966213
11124.9126.911785526756-2.01178552675617
12120.8127.317328849801-6.51732884980103
13122123.652835475314-1.65283547531385
14122.1124.840084241568-2.74008424156777
15127.4130.720614639099-3.32061463909864
16135.2141.097261456547-5.89726145654676
17137.3146.225097167082-8.9250971670819
18135146.473925122309-11.4739251223086
19136148.005781513666-12.0057815136663
20138.4154.184789268975-15.7847892689747
21134.7159.174147621732-24.4741476217321
22138.4156.909733644119-18.5097336441189
23133.9151.422736229222-17.5227362292217
24133.6146.760614639099-13.1606146390987
25141.2141.751638736628-0.55163873662805
26151.8146.0415394905365.75846050946409
27155.4152.4391785526762.9608214473244
28156.6158.575534320330-1.97553432033029
29161.6164.944430825927-3.34443082592689
30160.7165.813789178684-5.11378917868437
31156166.828536905433-10.8285369054331
32159.5168.043301480495-8.54330148049534
33168.7173.032659833253-4.33265983325275
34169.9170.664824122718-0.76482412271762
35169.9163.7299224469156.17007755308467
36185.9162.06703111152423.8329688884757
37190.8165.22837210987625.5716278901245
38195.8176.55095070246519.2490492975346
39211.9185.5341330876526.3658669123499
40227.1195.49709297341131.6029070265889
41251.3203.727580671647.5724193283999
42256.7212.87067765810143.8293223418988
43251.9210.78277339719641.1172266028039
44251.2222.96024166196928.2397583380313
45270.3234.98227785340835.3177221465918
46267.2233.54523773916933.6547622608308
47243221.85293634896421.1470636510357
48229.9221.1208406098698.77915939013054
49187.2217.352925502460-30.1529255024603
50178.2211.817761628798-33.6177616287978
51175.2215.319592169127-40.1195921691272
52192.4218.249874216206-25.8498742162061
53187225.342722852255-38.3427228522554
54184220.006777229705-36.0067772297050
55194.1218.953090298018-24.8530902980179
56212.7220.16785487308-7.46785487308026
57217.5223.088778567402-5.58877856740171
58200.5213.895108484028-13.3951084840281
59205.9213.682619448142-7.78261944814245
60196.5209.434184789707-12.9341847897066
61206.3207.321017409046-1.02101740904615

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 108.5 & 100.693210766676 & 7.80678923332389 \tabularnewline
2 & 112.3 & 100.949663936633 & 11.3503360633669 \tabularnewline
3 & 116.6 & 102.486481551448 & 14.1135184485515 \tabularnewline
4 & 115.5 & 113.380237033506 & 2.11976296649430 \tabularnewline
5 & 120.1 & 117.060168483136 & 3.03983151686429 \tabularnewline
6 & 132.9 & 124.134830811201 & 8.76516918879918 \tabularnewline
7 & 128.1 & 121.529817885687 & 6.57018211431334 \tabularnewline
8 & 129.3 & 125.743812715481 & 3.55618728451900 \tabularnewline
9 & 132.5 & 133.422136124205 & -0.922136124205162 \tabularnewline
10 & 131 & 131.985096009966 & -0.985096009966213 \tabularnewline
11 & 124.9 & 126.911785526756 & -2.01178552675617 \tabularnewline
12 & 120.8 & 127.317328849801 & -6.51732884980103 \tabularnewline
13 & 122 & 123.652835475314 & -1.65283547531385 \tabularnewline
14 & 122.1 & 124.840084241568 & -2.74008424156777 \tabularnewline
15 & 127.4 & 130.720614639099 & -3.32061463909864 \tabularnewline
16 & 135.2 & 141.097261456547 & -5.89726145654676 \tabularnewline
17 & 137.3 & 146.225097167082 & -8.9250971670819 \tabularnewline
18 & 135 & 146.473925122309 & -11.4739251223086 \tabularnewline
19 & 136 & 148.005781513666 & -12.0057815136663 \tabularnewline
20 & 138.4 & 154.184789268975 & -15.7847892689747 \tabularnewline
21 & 134.7 & 159.174147621732 & -24.4741476217321 \tabularnewline
22 & 138.4 & 156.909733644119 & -18.5097336441189 \tabularnewline
23 & 133.9 & 151.422736229222 & -17.5227362292217 \tabularnewline
24 & 133.6 & 146.760614639099 & -13.1606146390987 \tabularnewline
25 & 141.2 & 141.751638736628 & -0.55163873662805 \tabularnewline
26 & 151.8 & 146.041539490536 & 5.75846050946409 \tabularnewline
27 & 155.4 & 152.439178552676 & 2.9608214473244 \tabularnewline
28 & 156.6 & 158.575534320330 & -1.97553432033029 \tabularnewline
29 & 161.6 & 164.944430825927 & -3.34443082592689 \tabularnewline
30 & 160.7 & 165.813789178684 & -5.11378917868437 \tabularnewline
31 & 156 & 166.828536905433 & -10.8285369054331 \tabularnewline
32 & 159.5 & 168.043301480495 & -8.54330148049534 \tabularnewline
33 & 168.7 & 173.032659833253 & -4.33265983325275 \tabularnewline
34 & 169.9 & 170.664824122718 & -0.76482412271762 \tabularnewline
35 & 169.9 & 163.729922446915 & 6.17007755308467 \tabularnewline
36 & 185.9 & 162.067031111524 & 23.8329688884757 \tabularnewline
37 & 190.8 & 165.228372109876 & 25.5716278901245 \tabularnewline
38 & 195.8 & 176.550950702465 & 19.2490492975346 \tabularnewline
39 & 211.9 & 185.53413308765 & 26.3658669123499 \tabularnewline
40 & 227.1 & 195.497092973411 & 31.6029070265889 \tabularnewline
41 & 251.3 & 203.7275806716 & 47.5724193283999 \tabularnewline
42 & 256.7 & 212.870677658101 & 43.8293223418988 \tabularnewline
43 & 251.9 & 210.782773397196 & 41.1172266028039 \tabularnewline
44 & 251.2 & 222.960241661969 & 28.2397583380313 \tabularnewline
45 & 270.3 & 234.982277853408 & 35.3177221465918 \tabularnewline
46 & 267.2 & 233.545237739169 & 33.6547622608308 \tabularnewline
47 & 243 & 221.852936348964 & 21.1470636510357 \tabularnewline
48 & 229.9 & 221.120840609869 & 8.77915939013054 \tabularnewline
49 & 187.2 & 217.352925502460 & -30.1529255024603 \tabularnewline
50 & 178.2 & 211.817761628798 & -33.6177616287978 \tabularnewline
51 & 175.2 & 215.319592169127 & -40.1195921691272 \tabularnewline
52 & 192.4 & 218.249874216206 & -25.8498742162061 \tabularnewline
53 & 187 & 225.342722852255 & -38.3427228522554 \tabularnewline
54 & 184 & 220.006777229705 & -36.0067772297050 \tabularnewline
55 & 194.1 & 218.953090298018 & -24.8530902980179 \tabularnewline
56 & 212.7 & 220.16785487308 & -7.46785487308026 \tabularnewline
57 & 217.5 & 223.088778567402 & -5.58877856740171 \tabularnewline
58 & 200.5 & 213.895108484028 & -13.3951084840281 \tabularnewline
59 & 205.9 & 213.682619448142 & -7.78261944814245 \tabularnewline
60 & 196.5 & 209.434184789707 & -12.9341847897066 \tabularnewline
61 & 206.3 & 207.321017409046 & -1.02101740904615 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58106&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]108.5[/C][C]100.693210766676[/C][C]7.80678923332389[/C][/ROW]
[ROW][C]2[/C][C]112.3[/C][C]100.949663936633[/C][C]11.3503360633669[/C][/ROW]
[ROW][C]3[/C][C]116.6[/C][C]102.486481551448[/C][C]14.1135184485515[/C][/ROW]
[ROW][C]4[/C][C]115.5[/C][C]113.380237033506[/C][C]2.11976296649430[/C][/ROW]
[ROW][C]5[/C][C]120.1[/C][C]117.060168483136[/C][C]3.03983151686429[/C][/ROW]
[ROW][C]6[/C][C]132.9[/C][C]124.134830811201[/C][C]8.76516918879918[/C][/ROW]
[ROW][C]7[/C][C]128.1[/C][C]121.529817885687[/C][C]6.57018211431334[/C][/ROW]
[ROW][C]8[/C][C]129.3[/C][C]125.743812715481[/C][C]3.55618728451900[/C][/ROW]
[ROW][C]9[/C][C]132.5[/C][C]133.422136124205[/C][C]-0.922136124205162[/C][/ROW]
[ROW][C]10[/C][C]131[/C][C]131.985096009966[/C][C]-0.985096009966213[/C][/ROW]
[ROW][C]11[/C][C]124.9[/C][C]126.911785526756[/C][C]-2.01178552675617[/C][/ROW]
[ROW][C]12[/C][C]120.8[/C][C]127.317328849801[/C][C]-6.51732884980103[/C][/ROW]
[ROW][C]13[/C][C]122[/C][C]123.652835475314[/C][C]-1.65283547531385[/C][/ROW]
[ROW][C]14[/C][C]122.1[/C][C]124.840084241568[/C][C]-2.74008424156777[/C][/ROW]
[ROW][C]15[/C][C]127.4[/C][C]130.720614639099[/C][C]-3.32061463909864[/C][/ROW]
[ROW][C]16[/C][C]135.2[/C][C]141.097261456547[/C][C]-5.89726145654676[/C][/ROW]
[ROW][C]17[/C][C]137.3[/C][C]146.225097167082[/C][C]-8.9250971670819[/C][/ROW]
[ROW][C]18[/C][C]135[/C][C]146.473925122309[/C][C]-11.4739251223086[/C][/ROW]
[ROW][C]19[/C][C]136[/C][C]148.005781513666[/C][C]-12.0057815136663[/C][/ROW]
[ROW][C]20[/C][C]138.4[/C][C]154.184789268975[/C][C]-15.7847892689747[/C][/ROW]
[ROW][C]21[/C][C]134.7[/C][C]159.174147621732[/C][C]-24.4741476217321[/C][/ROW]
[ROW][C]22[/C][C]138.4[/C][C]156.909733644119[/C][C]-18.5097336441189[/C][/ROW]
[ROW][C]23[/C][C]133.9[/C][C]151.422736229222[/C][C]-17.5227362292217[/C][/ROW]
[ROW][C]24[/C][C]133.6[/C][C]146.760614639099[/C][C]-13.1606146390987[/C][/ROW]
[ROW][C]25[/C][C]141.2[/C][C]141.751638736628[/C][C]-0.55163873662805[/C][/ROW]
[ROW][C]26[/C][C]151.8[/C][C]146.041539490536[/C][C]5.75846050946409[/C][/ROW]
[ROW][C]27[/C][C]155.4[/C][C]152.439178552676[/C][C]2.9608214473244[/C][/ROW]
[ROW][C]28[/C][C]156.6[/C][C]158.575534320330[/C][C]-1.97553432033029[/C][/ROW]
[ROW][C]29[/C][C]161.6[/C][C]164.944430825927[/C][C]-3.34443082592689[/C][/ROW]
[ROW][C]30[/C][C]160.7[/C][C]165.813789178684[/C][C]-5.11378917868437[/C][/ROW]
[ROW][C]31[/C][C]156[/C][C]166.828536905433[/C][C]-10.8285369054331[/C][/ROW]
[ROW][C]32[/C][C]159.5[/C][C]168.043301480495[/C][C]-8.54330148049534[/C][/ROW]
[ROW][C]33[/C][C]168.7[/C][C]173.032659833253[/C][C]-4.33265983325275[/C][/ROW]
[ROW][C]34[/C][C]169.9[/C][C]170.664824122718[/C][C]-0.76482412271762[/C][/ROW]
[ROW][C]35[/C][C]169.9[/C][C]163.729922446915[/C][C]6.17007755308467[/C][/ROW]
[ROW][C]36[/C][C]185.9[/C][C]162.067031111524[/C][C]23.8329688884757[/C][/ROW]
[ROW][C]37[/C][C]190.8[/C][C]165.228372109876[/C][C]25.5716278901245[/C][/ROW]
[ROW][C]38[/C][C]195.8[/C][C]176.550950702465[/C][C]19.2490492975346[/C][/ROW]
[ROW][C]39[/C][C]211.9[/C][C]185.53413308765[/C][C]26.3658669123499[/C][/ROW]
[ROW][C]40[/C][C]227.1[/C][C]195.497092973411[/C][C]31.6029070265889[/C][/ROW]
[ROW][C]41[/C][C]251.3[/C][C]203.7275806716[/C][C]47.5724193283999[/C][/ROW]
[ROW][C]42[/C][C]256.7[/C][C]212.870677658101[/C][C]43.8293223418988[/C][/ROW]
[ROW][C]43[/C][C]251.9[/C][C]210.782773397196[/C][C]41.1172266028039[/C][/ROW]
[ROW][C]44[/C][C]251.2[/C][C]222.960241661969[/C][C]28.2397583380313[/C][/ROW]
[ROW][C]45[/C][C]270.3[/C][C]234.982277853408[/C][C]35.3177221465918[/C][/ROW]
[ROW][C]46[/C][C]267.2[/C][C]233.545237739169[/C][C]33.6547622608308[/C][/ROW]
[ROW][C]47[/C][C]243[/C][C]221.852936348964[/C][C]21.1470636510357[/C][/ROW]
[ROW][C]48[/C][C]229.9[/C][C]221.120840609869[/C][C]8.77915939013054[/C][/ROW]
[ROW][C]49[/C][C]187.2[/C][C]217.352925502460[/C][C]-30.1529255024603[/C][/ROW]
[ROW][C]50[/C][C]178.2[/C][C]211.817761628798[/C][C]-33.6177616287978[/C][/ROW]
[ROW][C]51[/C][C]175.2[/C][C]215.319592169127[/C][C]-40.1195921691272[/C][/ROW]
[ROW][C]52[/C][C]192.4[/C][C]218.249874216206[/C][C]-25.8498742162061[/C][/ROW]
[ROW][C]53[/C][C]187[/C][C]225.342722852255[/C][C]-38.3427228522554[/C][/ROW]
[ROW][C]54[/C][C]184[/C][C]220.006777229705[/C][C]-36.0067772297050[/C][/ROW]
[ROW][C]55[/C][C]194.1[/C][C]218.953090298018[/C][C]-24.8530902980179[/C][/ROW]
[ROW][C]56[/C][C]212.7[/C][C]220.16785487308[/C][C]-7.46785487308026[/C][/ROW]
[ROW][C]57[/C][C]217.5[/C][C]223.088778567402[/C][C]-5.58877856740171[/C][/ROW]
[ROW][C]58[/C][C]200.5[/C][C]213.895108484028[/C][C]-13.3951084840281[/C][/ROW]
[ROW][C]59[/C][C]205.9[/C][C]213.682619448142[/C][C]-7.78261944814245[/C][/ROW]
[ROW][C]60[/C][C]196.5[/C][C]209.434184789707[/C][C]-12.9341847897066[/C][/ROW]
[ROW][C]61[/C][C]206.3[/C][C]207.321017409046[/C][C]-1.02101740904615[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58106&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58106&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1108.5100.6932107666767.80678923332389
2112.3100.94966393663311.3503360633669
3116.6102.48648155144814.1135184485515
4115.5113.3802370335062.11976296649430
5120.1117.0601684831363.03983151686429
6132.9124.1348308112018.76516918879918
7128.1121.5298178856876.57018211431334
8129.3125.7438127154813.55618728451900
9132.5133.422136124205-0.922136124205162
10131131.985096009966-0.985096009966213
11124.9126.911785526756-2.01178552675617
12120.8127.317328849801-6.51732884980103
13122123.652835475314-1.65283547531385
14122.1124.840084241568-2.74008424156777
15127.4130.720614639099-3.32061463909864
16135.2141.097261456547-5.89726145654676
17137.3146.225097167082-8.9250971670819
18135146.473925122309-11.4739251223086
19136148.005781513666-12.0057815136663
20138.4154.184789268975-15.7847892689747
21134.7159.174147621732-24.4741476217321
22138.4156.909733644119-18.5097336441189
23133.9151.422736229222-17.5227362292217
24133.6146.760614639099-13.1606146390987
25141.2141.751638736628-0.55163873662805
26151.8146.0415394905365.75846050946409
27155.4152.4391785526762.9608214473244
28156.6158.575534320330-1.97553432033029
29161.6164.944430825927-3.34443082592689
30160.7165.813789178684-5.11378917868437
31156166.828536905433-10.8285369054331
32159.5168.043301480495-8.54330148049534
33168.7173.032659833253-4.33265983325275
34169.9170.664824122718-0.76482412271762
35169.9163.7299224469156.17007755308467
36185.9162.06703111152423.8329688884757
37190.8165.22837210987625.5716278901245
38195.8176.55095070246519.2490492975346
39211.9185.5341330876526.3658669123499
40227.1195.49709297341131.6029070265889
41251.3203.727580671647.5724193283999
42256.7212.87067765810143.8293223418988
43251.9210.78277339719641.1172266028039
44251.2222.96024166196928.2397583380313
45270.3234.98227785340835.3177221465918
46267.2233.54523773916933.6547622608308
47243221.85293634896421.1470636510357
48229.9221.1208406098698.77915939013054
49187.2217.352925502460-30.1529255024603
50178.2211.817761628798-33.6177616287978
51175.2215.319592169127-40.1195921691272
52192.4218.249874216206-25.8498742162061
53187225.342722852255-38.3427228522554
54184220.006777229705-36.0067772297050
55194.1218.953090298018-24.8530902980179
56212.7220.16785487308-7.46785487308026
57217.5223.088778567402-5.58877856740171
58200.5213.895108484028-13.3951084840281
59205.9213.682619448142-7.78261944814245
60196.5209.434184789707-12.9341847897066
61206.3207.321017409046-1.02101740904615







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.001856415526913440.003712831053826880.998143584473087
170.0001803184623873880.0003606369247747770.999819681537613
180.0001507653985188170.0003015307970376340.99984923460148
192.64464834021305e-055.2892966804261e-050.999973553516598
204.06840388595918e-068.13680777191835e-060.999995931596114
212.02377698747084e-064.04755397494169e-060.999997976223012
223.08705348193656e-076.17410696387312e-070.999999691294652
234.16837259290224e-088.33674518580449e-080.999999958316274
248.97461230218056e-091.79492246043611e-080.999999991025388
253.35877216383567e-086.71754432767134e-080.999999966412278
262.09094509567139e-074.18189019134278e-070.99999979090549
271.53477869209684e-073.06955738419368e-070.99999984652213
288.56829075858595e-081.71365815171719e-070.999999914317092
294.11781909114705e-088.2356381822941e-080.99999995882181
301.11496889292279e-082.22993778584558e-080.999999988850311
312.78039505371385e-095.56079010742769e-090.999999997219605
329.90702611598446e-101.98140522319689e-090.999999999009297
332.02092387577511e-094.04184775155021e-090.999999997979076
343.59866706434258e-097.19733412868516e-090.999999996401333
351.90463266338430e-083.80926532676861e-080.999999980953673
369.89511362508275e-071.97902272501655e-060.999999010488637
372.88159849590332e-065.76319699180665e-060.999997118401504
381.57821656654409e-063.15643313308818e-060.999998421783433
391.38550378502142e-062.77100757004283e-060.999998614496215
402.79154236492112e-065.58308472984223e-060.999997208457635
410.0001369761919930310.0002739523839860630.999863023808007
420.004980371597879680.009960743195759360.99501962840212
430.1623888434894800.3247776869789610.83761115651052
440.1835780271501050.3671560543002110.816421972849895
450.1454655005562880.2909310011125760.854534499443712

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.00185641552691344 & 0.00371283105382688 & 0.998143584473087 \tabularnewline
17 & 0.000180318462387388 & 0.000360636924774777 & 0.999819681537613 \tabularnewline
18 & 0.000150765398518817 & 0.000301530797037634 & 0.99984923460148 \tabularnewline
19 & 2.64464834021305e-05 & 5.2892966804261e-05 & 0.999973553516598 \tabularnewline
20 & 4.06840388595918e-06 & 8.13680777191835e-06 & 0.999995931596114 \tabularnewline
21 & 2.02377698747084e-06 & 4.04755397494169e-06 & 0.999997976223012 \tabularnewline
22 & 3.08705348193656e-07 & 6.17410696387312e-07 & 0.999999691294652 \tabularnewline
23 & 4.16837259290224e-08 & 8.33674518580449e-08 & 0.999999958316274 \tabularnewline
24 & 8.97461230218056e-09 & 1.79492246043611e-08 & 0.999999991025388 \tabularnewline
25 & 3.35877216383567e-08 & 6.71754432767134e-08 & 0.999999966412278 \tabularnewline
26 & 2.09094509567139e-07 & 4.18189019134278e-07 & 0.99999979090549 \tabularnewline
27 & 1.53477869209684e-07 & 3.06955738419368e-07 & 0.99999984652213 \tabularnewline
28 & 8.56829075858595e-08 & 1.71365815171719e-07 & 0.999999914317092 \tabularnewline
29 & 4.11781909114705e-08 & 8.2356381822941e-08 & 0.99999995882181 \tabularnewline
30 & 1.11496889292279e-08 & 2.22993778584558e-08 & 0.999999988850311 \tabularnewline
31 & 2.78039505371385e-09 & 5.56079010742769e-09 & 0.999999997219605 \tabularnewline
32 & 9.90702611598446e-10 & 1.98140522319689e-09 & 0.999999999009297 \tabularnewline
33 & 2.02092387577511e-09 & 4.04184775155021e-09 & 0.999999997979076 \tabularnewline
34 & 3.59866706434258e-09 & 7.19733412868516e-09 & 0.999999996401333 \tabularnewline
35 & 1.90463266338430e-08 & 3.80926532676861e-08 & 0.999999980953673 \tabularnewline
36 & 9.89511362508275e-07 & 1.97902272501655e-06 & 0.999999010488637 \tabularnewline
37 & 2.88159849590332e-06 & 5.76319699180665e-06 & 0.999997118401504 \tabularnewline
38 & 1.57821656654409e-06 & 3.15643313308818e-06 & 0.999998421783433 \tabularnewline
39 & 1.38550378502142e-06 & 2.77100757004283e-06 & 0.999998614496215 \tabularnewline
40 & 2.79154236492112e-06 & 5.58308472984223e-06 & 0.999997208457635 \tabularnewline
41 & 0.000136976191993031 & 0.000273952383986063 & 0.999863023808007 \tabularnewline
42 & 0.00498037159787968 & 0.00996074319575936 & 0.99501962840212 \tabularnewline
43 & 0.162388843489480 & 0.324777686978961 & 0.83761115651052 \tabularnewline
44 & 0.183578027150105 & 0.367156054300211 & 0.816421972849895 \tabularnewline
45 & 0.145465500556288 & 0.290931001112576 & 0.854534499443712 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58106&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]16[/C][C]0.00185641552691344[/C][C]0.00371283105382688[/C][C]0.998143584473087[/C][/ROW]
[ROW][C]17[/C][C]0.000180318462387388[/C][C]0.000360636924774777[/C][C]0.999819681537613[/C][/ROW]
[ROW][C]18[/C][C]0.000150765398518817[/C][C]0.000301530797037634[/C][C]0.99984923460148[/C][/ROW]
[ROW][C]19[/C][C]2.64464834021305e-05[/C][C]5.2892966804261e-05[/C][C]0.999973553516598[/C][/ROW]
[ROW][C]20[/C][C]4.06840388595918e-06[/C][C]8.13680777191835e-06[/C][C]0.999995931596114[/C][/ROW]
[ROW][C]21[/C][C]2.02377698747084e-06[/C][C]4.04755397494169e-06[/C][C]0.999997976223012[/C][/ROW]
[ROW][C]22[/C][C]3.08705348193656e-07[/C][C]6.17410696387312e-07[/C][C]0.999999691294652[/C][/ROW]
[ROW][C]23[/C][C]4.16837259290224e-08[/C][C]8.33674518580449e-08[/C][C]0.999999958316274[/C][/ROW]
[ROW][C]24[/C][C]8.97461230218056e-09[/C][C]1.79492246043611e-08[/C][C]0.999999991025388[/C][/ROW]
[ROW][C]25[/C][C]3.35877216383567e-08[/C][C]6.71754432767134e-08[/C][C]0.999999966412278[/C][/ROW]
[ROW][C]26[/C][C]2.09094509567139e-07[/C][C]4.18189019134278e-07[/C][C]0.99999979090549[/C][/ROW]
[ROW][C]27[/C][C]1.53477869209684e-07[/C][C]3.06955738419368e-07[/C][C]0.99999984652213[/C][/ROW]
[ROW][C]28[/C][C]8.56829075858595e-08[/C][C]1.71365815171719e-07[/C][C]0.999999914317092[/C][/ROW]
[ROW][C]29[/C][C]4.11781909114705e-08[/C][C]8.2356381822941e-08[/C][C]0.99999995882181[/C][/ROW]
[ROW][C]30[/C][C]1.11496889292279e-08[/C][C]2.22993778584558e-08[/C][C]0.999999988850311[/C][/ROW]
[ROW][C]31[/C][C]2.78039505371385e-09[/C][C]5.56079010742769e-09[/C][C]0.999999997219605[/C][/ROW]
[ROW][C]32[/C][C]9.90702611598446e-10[/C][C]1.98140522319689e-09[/C][C]0.999999999009297[/C][/ROW]
[ROW][C]33[/C][C]2.02092387577511e-09[/C][C]4.04184775155021e-09[/C][C]0.999999997979076[/C][/ROW]
[ROW][C]34[/C][C]3.59866706434258e-09[/C][C]7.19733412868516e-09[/C][C]0.999999996401333[/C][/ROW]
[ROW][C]35[/C][C]1.90463266338430e-08[/C][C]3.80926532676861e-08[/C][C]0.999999980953673[/C][/ROW]
[ROW][C]36[/C][C]9.89511362508275e-07[/C][C]1.97902272501655e-06[/C][C]0.999999010488637[/C][/ROW]
[ROW][C]37[/C][C]2.88159849590332e-06[/C][C]5.76319699180665e-06[/C][C]0.999997118401504[/C][/ROW]
[ROW][C]38[/C][C]1.57821656654409e-06[/C][C]3.15643313308818e-06[/C][C]0.999998421783433[/C][/ROW]
[ROW][C]39[/C][C]1.38550378502142e-06[/C][C]2.77100757004283e-06[/C][C]0.999998614496215[/C][/ROW]
[ROW][C]40[/C][C]2.79154236492112e-06[/C][C]5.58308472984223e-06[/C][C]0.999997208457635[/C][/ROW]
[ROW][C]41[/C][C]0.000136976191993031[/C][C]0.000273952383986063[/C][C]0.999863023808007[/C][/ROW]
[ROW][C]42[/C][C]0.00498037159787968[/C][C]0.00996074319575936[/C][C]0.99501962840212[/C][/ROW]
[ROW][C]43[/C][C]0.162388843489480[/C][C]0.324777686978961[/C][C]0.83761115651052[/C][/ROW]
[ROW][C]44[/C][C]0.183578027150105[/C][C]0.367156054300211[/C][C]0.816421972849895[/C][/ROW]
[ROW][C]45[/C][C]0.145465500556288[/C][C]0.290931001112576[/C][C]0.854534499443712[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58106&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58106&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.001856415526913440.003712831053826880.998143584473087
170.0001803184623873880.0003606369247747770.999819681537613
180.0001507653985188170.0003015307970376340.99984923460148
192.64464834021305e-055.2892966804261e-050.999973553516598
204.06840388595918e-068.13680777191835e-060.999995931596114
212.02377698747084e-064.04755397494169e-060.999997976223012
223.08705348193656e-076.17410696387312e-070.999999691294652
234.16837259290224e-088.33674518580449e-080.999999958316274
248.97461230218056e-091.79492246043611e-080.999999991025388
253.35877216383567e-086.71754432767134e-080.999999966412278
262.09094509567139e-074.18189019134278e-070.99999979090549
271.53477869209684e-073.06955738419368e-070.99999984652213
288.56829075858595e-081.71365815171719e-070.999999914317092
294.11781909114705e-088.2356381822941e-080.99999995882181
301.11496889292279e-082.22993778584558e-080.999999988850311
312.78039505371385e-095.56079010742769e-090.999999997219605
329.90702611598446e-101.98140522319689e-090.999999999009297
332.02092387577511e-094.04184775155021e-090.999999997979076
343.59866706434258e-097.19733412868516e-090.999999996401333
351.90463266338430e-083.80926532676861e-080.999999980953673
369.89511362508275e-071.97902272501655e-060.999999010488637
372.88159849590332e-065.76319699180665e-060.999997118401504
381.57821656654409e-063.15643313308818e-060.999998421783433
391.38550378502142e-062.77100757004283e-060.999998614496215
402.79154236492112e-065.58308472984223e-060.999997208457635
410.0001369761919930310.0002739523839860630.999863023808007
420.004980371597879680.009960743195759360.99501962840212
430.1623888434894800.3247776869789610.83761115651052
440.1835780271501050.3671560543002110.816421972849895
450.1454655005562880.2909310011125760.854534499443712







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level270.9NOK
5% type I error level270.9NOK
10% type I error level270.9NOK

\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 & 27 & 0.9 & NOK \tabularnewline
5% type I error level & 27 & 0.9 & NOK \tabularnewline
10% type I error level & 27 & 0.9 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58106&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]27[/C][C]0.9[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]27[/C][C]0.9[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]27[/C][C]0.9[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58106&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58106&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level270.9NOK
5% type I error level270.9NOK
10% type I error level270.9NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}