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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 12:35:22 -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/19/t1258659420khj3lb0e5nes0j8.htm/, Retrieved Thu, 28 Mar 2024 23:18:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57914, Retrieved Thu, 28 Mar 2024 23:18:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact134
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Multi lineair reg...] [2009-11-19 19:35:22] [244731fa3e7e6c85774b8c0902c58f85] [Current]
-   PD    [Multiple Regression] [multi ] [2009-11-20 16:46:48] [ba905ddf7cdf9ecb063c35348c4dab2e]
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Dataseries X:
8,9	6,3
8,2	6,2
7,6	6,1
7,7	6,3
8,1	6,5
8,3	6,6
8,3	6,5
7,9	6,2
7,8	6,2
8	5,9
8,5	6,1
8,6	6,1
8,5	6,1
8	6,1
7,8	6,1
8	6,4
8,2	6,7
8,3	6,9
8,2	7
8,1	7
8	6,8
7,8	6,4
7,8	5,9
7,7	5,5
7,6	5,5
7,6	5,6
7,6	5,8
7,8	5,9
8	6,1
8	6,1
7,9	6
7,7	6
7,4	5,9
6,9	5,5
6,7	5,6
6,5	5,4
6,4	5,2
6,7	5,2
6,8	5,2
6,9	5,5
6,9	5,8
6,7	5,8
6,4	5,5
6,2	5,3
5,9	5,1
6,1	5,2
6,7	5,8
6,8	5,8
6,6	5,5
6,4	5
6,4	4,9
6,7	5,3
7,1	6,1
7,1	6,5
6,9	6,8
6,4	6,6
6	6,4
6	6,4




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57914&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57914&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57914&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
wv[t] = + 2.12751659053694 + 0.88013875721796wm[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
wv[t] =  +  2.12751659053694 +  0.88013875721796wm[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57914&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]wv[t] =  +  2.12751659053694 +  0.88013875721796wm[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57914&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57914&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
wv[t] = + 2.12751659053694 + 0.88013875721796wm[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.127516590536940.9601472.21580.0307850.015393
wm0.880138757217960.1602375.49271e-061e-06

\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) & 2.12751659053694 & 0.960147 & 2.2158 & 0.030785 & 0.015393 \tabularnewline
wm & 0.88013875721796 & 0.160237 & 5.4927 & 1e-06 & 1e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57914&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]2.12751659053694[/C][C]0.960147[/C][C]2.2158[/C][C]0.030785[/C][C]0.015393[/C][/ROW]
[ROW][C]wm[/C][C]0.88013875721796[/C][C]0.160237[/C][C]5.4927[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57914&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57914&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)2.127516590536940.9601472.21580.0307850.015393
wm0.880138757217960.1602375.49271e-061e-06







Multiple Linear Regression - Regression Statistics
Multiple R0.591710957513192
R-squared0.350121857241178
Adjusted R-squared0.338516890406199
F-TEST (value)30.1700006746993
F-TEST (DF numerator)1
F-TEST (DF denominator)56
p-value1.00029949146041e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.641031880131442
Sum Squared Residuals23.0116247953117

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.591710957513192 \tabularnewline
R-squared & 0.350121857241178 \tabularnewline
Adjusted R-squared & 0.338516890406199 \tabularnewline
F-TEST (value) & 30.1700006746993 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 56 \tabularnewline
p-value & 1.00029949146041e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.641031880131442 \tabularnewline
Sum Squared Residuals & 23.0116247953117 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57914&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.591710957513192[/C][/ROW]
[ROW][C]R-squared[/C][C]0.350121857241178[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.338516890406199[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]30.1700006746993[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]56[/C][/ROW]
[ROW][C]p-value[/C][C]1.00029949146041e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.641031880131442[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]23.0116247953117[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57914&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57914&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 R0.591710957513192
R-squared0.350121857241178
Adjusted R-squared0.338516890406199
F-TEST (value)30.1700006746993
F-TEST (DF numerator)1
F-TEST (DF denominator)56
p-value1.00029949146041e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.641031880131442
Sum Squared Residuals23.0116247953117







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18.97.672390761010031.22760923898997
28.27.584376885288290.615623114711712
37.67.49636300956650.103636990433508
47.77.672390761010080.0276092389899161
58.17.848418512453680.251581487546323
68.37.936432388175470.363567611824529
78.37.848418512453680.451581487546325
87.97.584376885288290.315623114711712
97.87.584376885288290.215623114711711
1087.32033525812290.679664741877099
118.57.49636300956651.00363699043351
128.67.49636300956651.10363699043351
138.57.49636300956651.00363699043351
1487.49636300956650.503636990433508
157.87.49636300956650.303636990433508
1687.760404636731880.239595363268119
178.28.024446263897270.175553736102731
188.38.200474015340860.0995259846591403
198.28.28848789106266-0.0884878910626567
208.18.28848789106266-0.188487891062656
2188.11246013961906-0.112460139619064
227.87.760404636731880.0395953632681193
237.87.32033525812290.479664741877099
247.76.968279755235720.731720244764283
257.66.968279755235720.631720244764283
267.67.056293630957510.543706369042487
277.67.23232138240110.367678617598895
287.87.32033525812290.479664741877099
2987.49636300956650.503636990433508
3087.49636300956650.503636990433508
317.97.40834913384470.491650866155304
327.77.40834913384470.291650866155304
337.47.32033525812290.0796647418770996
346.96.96827975523572-0.0682797552357164
356.77.05629363095751-0.356293630957512
366.56.88026587951392-0.380265879513921
376.46.70423812807033-0.304238128070329
386.76.70423812807033-0.00423812807032881
396.86.704238128070330.0957618719296709
406.96.96827975523572-0.0682797552357164
416.97.2323213824011-0.332321382401104
426.77.2323213824011-0.532321382401104
436.46.96827975523572-0.568279755235716
446.26.79225200379212-0.592252003792125
455.96.61622425234853-0.716224252348532
466.16.70423812807033-0.604238128070329
476.77.2323213824011-0.532321382401104
486.87.2323213824011-0.432321382401104
496.66.96827975523572-0.368279755235717
506.46.52821037662674-0.128210376626736
516.46.44019650090494-0.0401965009049408
526.76.79225200379212-0.0922520037921245
537.17.4963630095665-0.396363009566492
547.17.84841851245368-0.748418512453677
556.98.11246013961906-1.21246013961906
566.47.93643238817547-1.53643238817547
5767.76040463673188-1.76040463673188
5867.76040463673188-1.76040463673188

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8.9 & 7.67239076101003 & 1.22760923898997 \tabularnewline
2 & 8.2 & 7.58437688528829 & 0.615623114711712 \tabularnewline
3 & 7.6 & 7.4963630095665 & 0.103636990433508 \tabularnewline
4 & 7.7 & 7.67239076101008 & 0.0276092389899161 \tabularnewline
5 & 8.1 & 7.84841851245368 & 0.251581487546323 \tabularnewline
6 & 8.3 & 7.93643238817547 & 0.363567611824529 \tabularnewline
7 & 8.3 & 7.84841851245368 & 0.451581487546325 \tabularnewline
8 & 7.9 & 7.58437688528829 & 0.315623114711712 \tabularnewline
9 & 7.8 & 7.58437688528829 & 0.215623114711711 \tabularnewline
10 & 8 & 7.3203352581229 & 0.679664741877099 \tabularnewline
11 & 8.5 & 7.4963630095665 & 1.00363699043351 \tabularnewline
12 & 8.6 & 7.4963630095665 & 1.10363699043351 \tabularnewline
13 & 8.5 & 7.4963630095665 & 1.00363699043351 \tabularnewline
14 & 8 & 7.4963630095665 & 0.503636990433508 \tabularnewline
15 & 7.8 & 7.4963630095665 & 0.303636990433508 \tabularnewline
16 & 8 & 7.76040463673188 & 0.239595363268119 \tabularnewline
17 & 8.2 & 8.02444626389727 & 0.175553736102731 \tabularnewline
18 & 8.3 & 8.20047401534086 & 0.0995259846591403 \tabularnewline
19 & 8.2 & 8.28848789106266 & -0.0884878910626567 \tabularnewline
20 & 8.1 & 8.28848789106266 & -0.188487891062656 \tabularnewline
21 & 8 & 8.11246013961906 & -0.112460139619064 \tabularnewline
22 & 7.8 & 7.76040463673188 & 0.0395953632681193 \tabularnewline
23 & 7.8 & 7.3203352581229 & 0.479664741877099 \tabularnewline
24 & 7.7 & 6.96827975523572 & 0.731720244764283 \tabularnewline
25 & 7.6 & 6.96827975523572 & 0.631720244764283 \tabularnewline
26 & 7.6 & 7.05629363095751 & 0.543706369042487 \tabularnewline
27 & 7.6 & 7.2323213824011 & 0.367678617598895 \tabularnewline
28 & 7.8 & 7.3203352581229 & 0.479664741877099 \tabularnewline
29 & 8 & 7.4963630095665 & 0.503636990433508 \tabularnewline
30 & 8 & 7.4963630095665 & 0.503636990433508 \tabularnewline
31 & 7.9 & 7.4083491338447 & 0.491650866155304 \tabularnewline
32 & 7.7 & 7.4083491338447 & 0.291650866155304 \tabularnewline
33 & 7.4 & 7.3203352581229 & 0.0796647418770996 \tabularnewline
34 & 6.9 & 6.96827975523572 & -0.0682797552357164 \tabularnewline
35 & 6.7 & 7.05629363095751 & -0.356293630957512 \tabularnewline
36 & 6.5 & 6.88026587951392 & -0.380265879513921 \tabularnewline
37 & 6.4 & 6.70423812807033 & -0.304238128070329 \tabularnewline
38 & 6.7 & 6.70423812807033 & -0.00423812807032881 \tabularnewline
39 & 6.8 & 6.70423812807033 & 0.0957618719296709 \tabularnewline
40 & 6.9 & 6.96827975523572 & -0.0682797552357164 \tabularnewline
41 & 6.9 & 7.2323213824011 & -0.332321382401104 \tabularnewline
42 & 6.7 & 7.2323213824011 & -0.532321382401104 \tabularnewline
43 & 6.4 & 6.96827975523572 & -0.568279755235716 \tabularnewline
44 & 6.2 & 6.79225200379212 & -0.592252003792125 \tabularnewline
45 & 5.9 & 6.61622425234853 & -0.716224252348532 \tabularnewline
46 & 6.1 & 6.70423812807033 & -0.604238128070329 \tabularnewline
47 & 6.7 & 7.2323213824011 & -0.532321382401104 \tabularnewline
48 & 6.8 & 7.2323213824011 & -0.432321382401104 \tabularnewline
49 & 6.6 & 6.96827975523572 & -0.368279755235717 \tabularnewline
50 & 6.4 & 6.52821037662674 & -0.128210376626736 \tabularnewline
51 & 6.4 & 6.44019650090494 & -0.0401965009049408 \tabularnewline
52 & 6.7 & 6.79225200379212 & -0.0922520037921245 \tabularnewline
53 & 7.1 & 7.4963630095665 & -0.396363009566492 \tabularnewline
54 & 7.1 & 7.84841851245368 & -0.748418512453677 \tabularnewline
55 & 6.9 & 8.11246013961906 & -1.21246013961906 \tabularnewline
56 & 6.4 & 7.93643238817547 & -1.53643238817547 \tabularnewline
57 & 6 & 7.76040463673188 & -1.76040463673188 \tabularnewline
58 & 6 & 7.76040463673188 & -1.76040463673188 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57914&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]8.9[/C][C]7.67239076101003[/C][C]1.22760923898997[/C][/ROW]
[ROW][C]2[/C][C]8.2[/C][C]7.58437688528829[/C][C]0.615623114711712[/C][/ROW]
[ROW][C]3[/C][C]7.6[/C][C]7.4963630095665[/C][C]0.103636990433508[/C][/ROW]
[ROW][C]4[/C][C]7.7[/C][C]7.67239076101008[/C][C]0.0276092389899161[/C][/ROW]
[ROW][C]5[/C][C]8.1[/C][C]7.84841851245368[/C][C]0.251581487546323[/C][/ROW]
[ROW][C]6[/C][C]8.3[/C][C]7.93643238817547[/C][C]0.363567611824529[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]7.84841851245368[/C][C]0.451581487546325[/C][/ROW]
[ROW][C]8[/C][C]7.9[/C][C]7.58437688528829[/C][C]0.315623114711712[/C][/ROW]
[ROW][C]9[/C][C]7.8[/C][C]7.58437688528829[/C][C]0.215623114711711[/C][/ROW]
[ROW][C]10[/C][C]8[/C][C]7.3203352581229[/C][C]0.679664741877099[/C][/ROW]
[ROW][C]11[/C][C]8.5[/C][C]7.4963630095665[/C][C]1.00363699043351[/C][/ROW]
[ROW][C]12[/C][C]8.6[/C][C]7.4963630095665[/C][C]1.10363699043351[/C][/ROW]
[ROW][C]13[/C][C]8.5[/C][C]7.4963630095665[/C][C]1.00363699043351[/C][/ROW]
[ROW][C]14[/C][C]8[/C][C]7.4963630095665[/C][C]0.503636990433508[/C][/ROW]
[ROW][C]15[/C][C]7.8[/C][C]7.4963630095665[/C][C]0.303636990433508[/C][/ROW]
[ROW][C]16[/C][C]8[/C][C]7.76040463673188[/C][C]0.239595363268119[/C][/ROW]
[ROW][C]17[/C][C]8.2[/C][C]8.02444626389727[/C][C]0.175553736102731[/C][/ROW]
[ROW][C]18[/C][C]8.3[/C][C]8.20047401534086[/C][C]0.0995259846591403[/C][/ROW]
[ROW][C]19[/C][C]8.2[/C][C]8.28848789106266[/C][C]-0.0884878910626567[/C][/ROW]
[ROW][C]20[/C][C]8.1[/C][C]8.28848789106266[/C][C]-0.188487891062656[/C][/ROW]
[ROW][C]21[/C][C]8[/C][C]8.11246013961906[/C][C]-0.112460139619064[/C][/ROW]
[ROW][C]22[/C][C]7.8[/C][C]7.76040463673188[/C][C]0.0395953632681193[/C][/ROW]
[ROW][C]23[/C][C]7.8[/C][C]7.3203352581229[/C][C]0.479664741877099[/C][/ROW]
[ROW][C]24[/C][C]7.7[/C][C]6.96827975523572[/C][C]0.731720244764283[/C][/ROW]
[ROW][C]25[/C][C]7.6[/C][C]6.96827975523572[/C][C]0.631720244764283[/C][/ROW]
[ROW][C]26[/C][C]7.6[/C][C]7.05629363095751[/C][C]0.543706369042487[/C][/ROW]
[ROW][C]27[/C][C]7.6[/C][C]7.2323213824011[/C][C]0.367678617598895[/C][/ROW]
[ROW][C]28[/C][C]7.8[/C][C]7.3203352581229[/C][C]0.479664741877099[/C][/ROW]
[ROW][C]29[/C][C]8[/C][C]7.4963630095665[/C][C]0.503636990433508[/C][/ROW]
[ROW][C]30[/C][C]8[/C][C]7.4963630095665[/C][C]0.503636990433508[/C][/ROW]
[ROW][C]31[/C][C]7.9[/C][C]7.4083491338447[/C][C]0.491650866155304[/C][/ROW]
[ROW][C]32[/C][C]7.7[/C][C]7.4083491338447[/C][C]0.291650866155304[/C][/ROW]
[ROW][C]33[/C][C]7.4[/C][C]7.3203352581229[/C][C]0.0796647418770996[/C][/ROW]
[ROW][C]34[/C][C]6.9[/C][C]6.96827975523572[/C][C]-0.0682797552357164[/C][/ROW]
[ROW][C]35[/C][C]6.7[/C][C]7.05629363095751[/C][C]-0.356293630957512[/C][/ROW]
[ROW][C]36[/C][C]6.5[/C][C]6.88026587951392[/C][C]-0.380265879513921[/C][/ROW]
[ROW][C]37[/C][C]6.4[/C][C]6.70423812807033[/C][C]-0.304238128070329[/C][/ROW]
[ROW][C]38[/C][C]6.7[/C][C]6.70423812807033[/C][C]-0.00423812807032881[/C][/ROW]
[ROW][C]39[/C][C]6.8[/C][C]6.70423812807033[/C][C]0.0957618719296709[/C][/ROW]
[ROW][C]40[/C][C]6.9[/C][C]6.96827975523572[/C][C]-0.0682797552357164[/C][/ROW]
[ROW][C]41[/C][C]6.9[/C][C]7.2323213824011[/C][C]-0.332321382401104[/C][/ROW]
[ROW][C]42[/C][C]6.7[/C][C]7.2323213824011[/C][C]-0.532321382401104[/C][/ROW]
[ROW][C]43[/C][C]6.4[/C][C]6.96827975523572[/C][C]-0.568279755235716[/C][/ROW]
[ROW][C]44[/C][C]6.2[/C][C]6.79225200379212[/C][C]-0.592252003792125[/C][/ROW]
[ROW][C]45[/C][C]5.9[/C][C]6.61622425234853[/C][C]-0.716224252348532[/C][/ROW]
[ROW][C]46[/C][C]6.1[/C][C]6.70423812807033[/C][C]-0.604238128070329[/C][/ROW]
[ROW][C]47[/C][C]6.7[/C][C]7.2323213824011[/C][C]-0.532321382401104[/C][/ROW]
[ROW][C]48[/C][C]6.8[/C][C]7.2323213824011[/C][C]-0.432321382401104[/C][/ROW]
[ROW][C]49[/C][C]6.6[/C][C]6.96827975523572[/C][C]-0.368279755235717[/C][/ROW]
[ROW][C]50[/C][C]6.4[/C][C]6.52821037662674[/C][C]-0.128210376626736[/C][/ROW]
[ROW][C]51[/C][C]6.4[/C][C]6.44019650090494[/C][C]-0.0401965009049408[/C][/ROW]
[ROW][C]52[/C][C]6.7[/C][C]6.79225200379212[/C][C]-0.0922520037921245[/C][/ROW]
[ROW][C]53[/C][C]7.1[/C][C]7.4963630095665[/C][C]-0.396363009566492[/C][/ROW]
[ROW][C]54[/C][C]7.1[/C][C]7.84841851245368[/C][C]-0.748418512453677[/C][/ROW]
[ROW][C]55[/C][C]6.9[/C][C]8.11246013961906[/C][C]-1.21246013961906[/C][/ROW]
[ROW][C]56[/C][C]6.4[/C][C]7.93643238817547[/C][C]-1.53643238817547[/C][/ROW]
[ROW][C]57[/C][C]6[/C][C]7.76040463673188[/C][C]-1.76040463673188[/C][/ROW]
[ROW][C]58[/C][C]6[/C][C]7.76040463673188[/C][C]-1.76040463673188[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57914&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57914&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
18.97.672390761010031.22760923898997
28.27.584376885288290.615623114711712
37.67.49636300956650.103636990433508
47.77.672390761010080.0276092389899161
58.17.848418512453680.251581487546323
68.37.936432388175470.363567611824529
78.37.848418512453680.451581487546325
87.97.584376885288290.315623114711712
97.87.584376885288290.215623114711711
1087.32033525812290.679664741877099
118.57.49636300956651.00363699043351
128.67.49636300956651.10363699043351
138.57.49636300956651.00363699043351
1487.49636300956650.503636990433508
157.87.49636300956650.303636990433508
1687.760404636731880.239595363268119
178.28.024446263897270.175553736102731
188.38.200474015340860.0995259846591403
198.28.28848789106266-0.0884878910626567
208.18.28848789106266-0.188487891062656
2188.11246013961906-0.112460139619064
227.87.760404636731880.0395953632681193
237.87.32033525812290.479664741877099
247.76.968279755235720.731720244764283
257.66.968279755235720.631720244764283
267.67.056293630957510.543706369042487
277.67.23232138240110.367678617598895
287.87.32033525812290.479664741877099
2987.49636300956650.503636990433508
3087.49636300956650.503636990433508
317.97.40834913384470.491650866155304
327.77.40834913384470.291650866155304
337.47.32033525812290.0796647418770996
346.96.96827975523572-0.0682797552357164
356.77.05629363095751-0.356293630957512
366.56.88026587951392-0.380265879513921
376.46.70423812807033-0.304238128070329
386.76.70423812807033-0.00423812807032881
396.86.704238128070330.0957618719296709
406.96.96827975523572-0.0682797552357164
416.97.2323213824011-0.332321382401104
426.77.2323213824011-0.532321382401104
436.46.96827975523572-0.568279755235716
446.26.79225200379212-0.592252003792125
455.96.61622425234853-0.716224252348532
466.16.70423812807033-0.604238128070329
476.77.2323213824011-0.532321382401104
486.87.2323213824011-0.432321382401104
496.66.96827975523572-0.368279755235717
506.46.52821037662674-0.128210376626736
516.46.44019650090494-0.0401965009049408
526.76.79225200379212-0.0922520037921245
537.17.4963630095665-0.396363009566492
547.17.84841851245368-0.748418512453677
556.98.11246013961906-1.21246013961906
566.47.93643238817547-1.53643238817547
5767.76040463673188-1.76040463673188
5867.76040463673188-1.76040463673188







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.4930603169576850.986120633915370.506939683042315
60.3272977670393380.6545955340786770.672702232960662
70.2020495001395880.4040990002791760.797950499860412
80.1183085331098680.2366170662197370.881691466890131
90.06859676193115090.1371935238623020.93140323806885
100.04498778283509310.08997556567018610.955012217164907
110.05418521236946890.1083704247389380.945814787630531
120.07235534342820050.1447106868564010.9276446565718
130.07355273083636640.1471054616727330.926447269163634
140.05143519232450280.1028703846490060.948564807675497
150.04040602416556750.0808120483311350.959593975834433
160.02733800091372640.05467600182745290.972661999086273
170.01701512823438550.0340302564687710.982984871765614
180.01046556271340750.02093112542681490.989534437286593
190.00623070587937820.01246141175875640.993769294120622
200.003776526946422080.007553053892844170.996223473053578
210.002467685227765510.004935370455531020.997532314772234
220.002113002193906930.004226004387813870.997886997806093
230.001916783205138560.003833566410277130.998083216794861
240.001902938649722630.003805877299445250.998097061350277
250.001936373061612270.003872746123224530.998063626938388
260.001955893755098270.003911787510196550.998044106244902
270.002036356150495010.004072712300990010.997963643849505
280.002349420558615520.004698841117231050.997650579441384
290.004105063298967850.00821012659793570.995894936701032
300.01043999523153750.02087999046307510.989560004768463
310.03417486542949970.06834973085899940.9658251345705
320.1040759490898180.2081518981796360.895924050910182
330.2423064347179840.4846128694359680.757693565282016
340.3884058337202090.7768116674404180.611594166279791
350.5455930577025560.9088138845948890.454406942297444
360.6372570109961940.7254859780076120.362742989003806
370.6574684024579320.6850631950841360.342531597542068
380.6178056312404880.7643887375190240.382194368759512
390.5906076534052240.8187846931895530.409392346594776
400.6004988511014610.7990022977970780.399501148898539
410.6345318815828040.7309362368343910.365468118417196
420.648103714268840.7037925714623210.351896285731160
430.6296115919438640.7407768161122730.370388408056136
440.6101559325661090.7796881348677820.389844067433891
450.6824391351577570.6351217296844860.317560864842243
460.6937758899086870.6124482201826260.306224110091313
470.6350241080845080.7299517838309840.364975891915492
480.5759513630178190.8480972739643620.424048636982181
490.4724401829482980.9448803658965960.527559817051702
500.3584888315846430.7169776631692850.641511168415357
510.2627375518093510.5254751036187010.73726244819065
520.1625100097318900.3250200194637810.83748999026811
530.3864058285907460.7728116571814920.613594171409254

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.493060316957685 & 0.98612063391537 & 0.506939683042315 \tabularnewline
6 & 0.327297767039338 & 0.654595534078677 & 0.672702232960662 \tabularnewline
7 & 0.202049500139588 & 0.404099000279176 & 0.797950499860412 \tabularnewline
8 & 0.118308533109868 & 0.236617066219737 & 0.881691466890131 \tabularnewline
9 & 0.0685967619311509 & 0.137193523862302 & 0.93140323806885 \tabularnewline
10 & 0.0449877828350931 & 0.0899755656701861 & 0.955012217164907 \tabularnewline
11 & 0.0541852123694689 & 0.108370424738938 & 0.945814787630531 \tabularnewline
12 & 0.0723553434282005 & 0.144710686856401 & 0.9276446565718 \tabularnewline
13 & 0.0735527308363664 & 0.147105461672733 & 0.926447269163634 \tabularnewline
14 & 0.0514351923245028 & 0.102870384649006 & 0.948564807675497 \tabularnewline
15 & 0.0404060241655675 & 0.080812048331135 & 0.959593975834433 \tabularnewline
16 & 0.0273380009137264 & 0.0546760018274529 & 0.972661999086273 \tabularnewline
17 & 0.0170151282343855 & 0.034030256468771 & 0.982984871765614 \tabularnewline
18 & 0.0104655627134075 & 0.0209311254268149 & 0.989534437286593 \tabularnewline
19 & 0.0062307058793782 & 0.0124614117587564 & 0.993769294120622 \tabularnewline
20 & 0.00377652694642208 & 0.00755305389284417 & 0.996223473053578 \tabularnewline
21 & 0.00246768522776551 & 0.00493537045553102 & 0.997532314772234 \tabularnewline
22 & 0.00211300219390693 & 0.00422600438781387 & 0.997886997806093 \tabularnewline
23 & 0.00191678320513856 & 0.00383356641027713 & 0.998083216794861 \tabularnewline
24 & 0.00190293864972263 & 0.00380587729944525 & 0.998097061350277 \tabularnewline
25 & 0.00193637306161227 & 0.00387274612322453 & 0.998063626938388 \tabularnewline
26 & 0.00195589375509827 & 0.00391178751019655 & 0.998044106244902 \tabularnewline
27 & 0.00203635615049501 & 0.00407271230099001 & 0.997963643849505 \tabularnewline
28 & 0.00234942055861552 & 0.00469884111723105 & 0.997650579441384 \tabularnewline
29 & 0.00410506329896785 & 0.0082101265979357 & 0.995894936701032 \tabularnewline
30 & 0.0104399952315375 & 0.0208799904630751 & 0.989560004768463 \tabularnewline
31 & 0.0341748654294997 & 0.0683497308589994 & 0.9658251345705 \tabularnewline
32 & 0.104075949089818 & 0.208151898179636 & 0.895924050910182 \tabularnewline
33 & 0.242306434717984 & 0.484612869435968 & 0.757693565282016 \tabularnewline
34 & 0.388405833720209 & 0.776811667440418 & 0.611594166279791 \tabularnewline
35 & 0.545593057702556 & 0.908813884594889 & 0.454406942297444 \tabularnewline
36 & 0.637257010996194 & 0.725485978007612 & 0.362742989003806 \tabularnewline
37 & 0.657468402457932 & 0.685063195084136 & 0.342531597542068 \tabularnewline
38 & 0.617805631240488 & 0.764388737519024 & 0.382194368759512 \tabularnewline
39 & 0.590607653405224 & 0.818784693189553 & 0.409392346594776 \tabularnewline
40 & 0.600498851101461 & 0.799002297797078 & 0.399501148898539 \tabularnewline
41 & 0.634531881582804 & 0.730936236834391 & 0.365468118417196 \tabularnewline
42 & 0.64810371426884 & 0.703792571462321 & 0.351896285731160 \tabularnewline
43 & 0.629611591943864 & 0.740776816112273 & 0.370388408056136 \tabularnewline
44 & 0.610155932566109 & 0.779688134867782 & 0.389844067433891 \tabularnewline
45 & 0.682439135157757 & 0.635121729684486 & 0.317560864842243 \tabularnewline
46 & 0.693775889908687 & 0.612448220182626 & 0.306224110091313 \tabularnewline
47 & 0.635024108084508 & 0.729951783830984 & 0.364975891915492 \tabularnewline
48 & 0.575951363017819 & 0.848097273964362 & 0.424048636982181 \tabularnewline
49 & 0.472440182948298 & 0.944880365896596 & 0.527559817051702 \tabularnewline
50 & 0.358488831584643 & 0.716977663169285 & 0.641511168415357 \tabularnewline
51 & 0.262737551809351 & 0.525475103618701 & 0.73726244819065 \tabularnewline
52 & 0.162510009731890 & 0.325020019463781 & 0.83748999026811 \tabularnewline
53 & 0.386405828590746 & 0.772811657181492 & 0.613594171409254 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57914&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]5[/C][C]0.493060316957685[/C][C]0.98612063391537[/C][C]0.506939683042315[/C][/ROW]
[ROW][C]6[/C][C]0.327297767039338[/C][C]0.654595534078677[/C][C]0.672702232960662[/C][/ROW]
[ROW][C]7[/C][C]0.202049500139588[/C][C]0.404099000279176[/C][C]0.797950499860412[/C][/ROW]
[ROW][C]8[/C][C]0.118308533109868[/C][C]0.236617066219737[/C][C]0.881691466890131[/C][/ROW]
[ROW][C]9[/C][C]0.0685967619311509[/C][C]0.137193523862302[/C][C]0.93140323806885[/C][/ROW]
[ROW][C]10[/C][C]0.0449877828350931[/C][C]0.0899755656701861[/C][C]0.955012217164907[/C][/ROW]
[ROW][C]11[/C][C]0.0541852123694689[/C][C]0.108370424738938[/C][C]0.945814787630531[/C][/ROW]
[ROW][C]12[/C][C]0.0723553434282005[/C][C]0.144710686856401[/C][C]0.9276446565718[/C][/ROW]
[ROW][C]13[/C][C]0.0735527308363664[/C][C]0.147105461672733[/C][C]0.926447269163634[/C][/ROW]
[ROW][C]14[/C][C]0.0514351923245028[/C][C]0.102870384649006[/C][C]0.948564807675497[/C][/ROW]
[ROW][C]15[/C][C]0.0404060241655675[/C][C]0.080812048331135[/C][C]0.959593975834433[/C][/ROW]
[ROW][C]16[/C][C]0.0273380009137264[/C][C]0.0546760018274529[/C][C]0.972661999086273[/C][/ROW]
[ROW][C]17[/C][C]0.0170151282343855[/C][C]0.034030256468771[/C][C]0.982984871765614[/C][/ROW]
[ROW][C]18[/C][C]0.0104655627134075[/C][C]0.0209311254268149[/C][C]0.989534437286593[/C][/ROW]
[ROW][C]19[/C][C]0.0062307058793782[/C][C]0.0124614117587564[/C][C]0.993769294120622[/C][/ROW]
[ROW][C]20[/C][C]0.00377652694642208[/C][C]0.00755305389284417[/C][C]0.996223473053578[/C][/ROW]
[ROW][C]21[/C][C]0.00246768522776551[/C][C]0.00493537045553102[/C][C]0.997532314772234[/C][/ROW]
[ROW][C]22[/C][C]0.00211300219390693[/C][C]0.00422600438781387[/C][C]0.997886997806093[/C][/ROW]
[ROW][C]23[/C][C]0.00191678320513856[/C][C]0.00383356641027713[/C][C]0.998083216794861[/C][/ROW]
[ROW][C]24[/C][C]0.00190293864972263[/C][C]0.00380587729944525[/C][C]0.998097061350277[/C][/ROW]
[ROW][C]25[/C][C]0.00193637306161227[/C][C]0.00387274612322453[/C][C]0.998063626938388[/C][/ROW]
[ROW][C]26[/C][C]0.00195589375509827[/C][C]0.00391178751019655[/C][C]0.998044106244902[/C][/ROW]
[ROW][C]27[/C][C]0.00203635615049501[/C][C]0.00407271230099001[/C][C]0.997963643849505[/C][/ROW]
[ROW][C]28[/C][C]0.00234942055861552[/C][C]0.00469884111723105[/C][C]0.997650579441384[/C][/ROW]
[ROW][C]29[/C][C]0.00410506329896785[/C][C]0.0082101265979357[/C][C]0.995894936701032[/C][/ROW]
[ROW][C]30[/C][C]0.0104399952315375[/C][C]0.0208799904630751[/C][C]0.989560004768463[/C][/ROW]
[ROW][C]31[/C][C]0.0341748654294997[/C][C]0.0683497308589994[/C][C]0.9658251345705[/C][/ROW]
[ROW][C]32[/C][C]0.104075949089818[/C][C]0.208151898179636[/C][C]0.895924050910182[/C][/ROW]
[ROW][C]33[/C][C]0.242306434717984[/C][C]0.484612869435968[/C][C]0.757693565282016[/C][/ROW]
[ROW][C]34[/C][C]0.388405833720209[/C][C]0.776811667440418[/C][C]0.611594166279791[/C][/ROW]
[ROW][C]35[/C][C]0.545593057702556[/C][C]0.908813884594889[/C][C]0.454406942297444[/C][/ROW]
[ROW][C]36[/C][C]0.637257010996194[/C][C]0.725485978007612[/C][C]0.362742989003806[/C][/ROW]
[ROW][C]37[/C][C]0.657468402457932[/C][C]0.685063195084136[/C][C]0.342531597542068[/C][/ROW]
[ROW][C]38[/C][C]0.617805631240488[/C][C]0.764388737519024[/C][C]0.382194368759512[/C][/ROW]
[ROW][C]39[/C][C]0.590607653405224[/C][C]0.818784693189553[/C][C]0.409392346594776[/C][/ROW]
[ROW][C]40[/C][C]0.600498851101461[/C][C]0.799002297797078[/C][C]0.399501148898539[/C][/ROW]
[ROW][C]41[/C][C]0.634531881582804[/C][C]0.730936236834391[/C][C]0.365468118417196[/C][/ROW]
[ROW][C]42[/C][C]0.64810371426884[/C][C]0.703792571462321[/C][C]0.351896285731160[/C][/ROW]
[ROW][C]43[/C][C]0.629611591943864[/C][C]0.740776816112273[/C][C]0.370388408056136[/C][/ROW]
[ROW][C]44[/C][C]0.610155932566109[/C][C]0.779688134867782[/C][C]0.389844067433891[/C][/ROW]
[ROW][C]45[/C][C]0.682439135157757[/C][C]0.635121729684486[/C][C]0.317560864842243[/C][/ROW]
[ROW][C]46[/C][C]0.693775889908687[/C][C]0.612448220182626[/C][C]0.306224110091313[/C][/ROW]
[ROW][C]47[/C][C]0.635024108084508[/C][C]0.729951783830984[/C][C]0.364975891915492[/C][/ROW]
[ROW][C]48[/C][C]0.575951363017819[/C][C]0.848097273964362[/C][C]0.424048636982181[/C][/ROW]
[ROW][C]49[/C][C]0.472440182948298[/C][C]0.944880365896596[/C][C]0.527559817051702[/C][/ROW]
[ROW][C]50[/C][C]0.358488831584643[/C][C]0.716977663169285[/C][C]0.641511168415357[/C][/ROW]
[ROW][C]51[/C][C]0.262737551809351[/C][C]0.525475103618701[/C][C]0.73726244819065[/C][/ROW]
[ROW][C]52[/C][C]0.162510009731890[/C][C]0.325020019463781[/C][C]0.83748999026811[/C][/ROW]
[ROW][C]53[/C][C]0.386405828590746[/C][C]0.772811657181492[/C][C]0.613594171409254[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57914&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57914&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
50.4930603169576850.986120633915370.506939683042315
60.3272977670393380.6545955340786770.672702232960662
70.2020495001395880.4040990002791760.797950499860412
80.1183085331098680.2366170662197370.881691466890131
90.06859676193115090.1371935238623020.93140323806885
100.04498778283509310.08997556567018610.955012217164907
110.05418521236946890.1083704247389380.945814787630531
120.07235534342820050.1447106868564010.9276446565718
130.07355273083636640.1471054616727330.926447269163634
140.05143519232450280.1028703846490060.948564807675497
150.04040602416556750.0808120483311350.959593975834433
160.02733800091372640.05467600182745290.972661999086273
170.01701512823438550.0340302564687710.982984871765614
180.01046556271340750.02093112542681490.989534437286593
190.00623070587937820.01246141175875640.993769294120622
200.003776526946422080.007553053892844170.996223473053578
210.002467685227765510.004935370455531020.997532314772234
220.002113002193906930.004226004387813870.997886997806093
230.001916783205138560.003833566410277130.998083216794861
240.001902938649722630.003805877299445250.998097061350277
250.001936373061612270.003872746123224530.998063626938388
260.001955893755098270.003911787510196550.998044106244902
270.002036356150495010.004072712300990010.997963643849505
280.002349420558615520.004698841117231050.997650579441384
290.004105063298967850.00821012659793570.995894936701032
300.01043999523153750.02087999046307510.989560004768463
310.03417486542949970.06834973085899940.9658251345705
320.1040759490898180.2081518981796360.895924050910182
330.2423064347179840.4846128694359680.757693565282016
340.3884058337202090.7768116674404180.611594166279791
350.5455930577025560.9088138845948890.454406942297444
360.6372570109961940.7254859780076120.362742989003806
370.6574684024579320.6850631950841360.342531597542068
380.6178056312404880.7643887375190240.382194368759512
390.5906076534052240.8187846931895530.409392346594776
400.6004988511014610.7990022977970780.399501148898539
410.6345318815828040.7309362368343910.365468118417196
420.648103714268840.7037925714623210.351896285731160
430.6296115919438640.7407768161122730.370388408056136
440.6101559325661090.7796881348677820.389844067433891
450.6824391351577570.6351217296844860.317560864842243
460.6937758899086870.6124482201826260.306224110091313
470.6350241080845080.7299517838309840.364975891915492
480.5759513630178190.8480972739643620.424048636982181
490.4724401829482980.9448803658965960.527559817051702
500.3584888315846430.7169776631692850.641511168415357
510.2627375518093510.5254751036187010.73726244819065
520.1625100097318900.3250200194637810.83748999026811
530.3864058285907460.7728116571814920.613594171409254







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.204081632653061NOK
5% type I error level140.285714285714286NOK
10% type I error level180.36734693877551NOK

\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 & 10 & 0.204081632653061 & NOK \tabularnewline
5% type I error level & 14 & 0.285714285714286 & NOK \tabularnewline
10% type I error level & 18 & 0.36734693877551 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57914&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]10[/C][C]0.204081632653061[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]0.285714285714286[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]18[/C][C]0.36734693877551[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57914&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57914&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 level100.204081632653061NOK
5% type I error level140.285714285714286NOK
10% type I error level180.36734693877551NOK



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