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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 computationSat, 05 Dec 2009 08:17:52 -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/Dec/05/t1260026495e6u7oleieza9t73.htm/, Retrieved Thu, 31 Oct 2024 23:34:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=64281, Retrieved Thu, 31 Oct 2024 23:34:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact267
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 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [model 1] [2009-11-17 14:36:29] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D      [Multiple Regression] [multiple regression] [2009-11-19 21:38:11] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   P         [Multiple Regression] [monthly dummies] [2009-11-19 22:00:07] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   P           [Multiple Regression] [model3] [2009-11-20 08:47:44] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D            [Multiple Regression] [model 4] [2009-11-20 08:59:37] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D                [Multiple Regression] [Beste model] [2009-12-05 15:17:52] [307139c5e328127f586f26d5bcc435d8] [Current]
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Dataseries X:
6.3	3.1	6.3	6.1	6.1	6.3
6	3	6.3	6.3	6.1	6.1
6.2	2.8	6	6.3	6.3	6.1
6.4	2.5	6.2	6	6.3	6.3
6.8	1.9	6.4	6.2	6	6.3
7.5	1.9	6.8	6.4	6.2	6
7.5	1.8	7.5	6.8	6.4	6.2
7.6	2	7.5	7.5	6.8	6.4
7.6	2.6	7.6	7.5	7.5	6.8
7.4	2.5	7.6	7.6	7.5	7.5
7.3	2.5	7.4	7.6	7.6	7.5
7.1	1.6	7.3	7.4	7.6	7.6
6.9	1.4	7.1	7.3	7.4	7.6
6.8	0.8	6.9	7.1	7.3	7.4
7.5	1.1	6.8	6.9	7.1	7.3
7.6	1.3	7.5	6.8	6.9	7.1
7.8	1.2	7.6	7.5	6.8	6.9
8	1.3	7.8	7.6	7.5	6.8
8.1	1.1	8	7.8	7.6	7.5
8.2	1.3	8.1	8	7.8	7.6
8.3	1.2	8.2	8.1	8	7.8
8.2	1.6	8.3	8.2	8.1	8
8	1.7	8.2	8.3	8.2	8.1
7.9	1.5	8	8.2	8.3	8.2
7.6	0.9	7.9	8	8.2	8.3
7.6	1.5	7.6	7.9	8	8.2
8.3	1.4	7.6	7.6	7.9	8
8.4	1.6	8.3	7.6	7.6	7.9
8.4	1.7	8.4	8.3	7.6	7.6
8.4	1.4	8.4	8.4	8.3	7.6
8.4	1.8	8.4	8.4	8.4	8.3
8.6	1.7	8.4	8.4	8.4	8.4
8.9	1.4	8.6	8.4	8.4	8.4
8.8	1.2	8.9	8.6	8.4	8.4
8.3	1	8.8	8.9	8.6	8.4
7.5	1.7	8.3	8.8	8.9	8.6
7.2	2.4	7.5	8.3	8.8	8.9
7.4	2	7.2	7.5	8.3	8.8
8.8	2.1	7.4	7.2	7.5	8.3
9.3	2	8.8	7.4	7.2	7.5
9.3	1.8	9.3	8.8	7.4	7.2
8.7	2.7	9.3	9.3	8.8	7.4
8.2	2.3	8.7	9.3	9.3	8.8
8.3	1.9	8.2	8.7	9.3	9.3
8.5	2	8.3	8.2	8.7	9.3
8.6	2.3	8.5	8.3	8.2	8.7
8.5	2.8	8.6	8.5	8.3	8.2
8.2	2.4	8.5	8.6	8.5	8.3
8.1	2.3	8.2	8.5	8.6	8.5
7.9	2.7	8.1	8.2	8.5	8.6
8.6	2.7	7.9	8.1	8.2	8.5
8.7	2.9	8.6	7.9	8.1	8.2
8.7	3	8.7	8.6	7.9	8.1
8.5	2.2	8.7	8.7	8.6	7.9
8.4	2.3	8.5	8.7	8.7	8.6
8.5	2.8	8.4	8.5	8.7	8.7
8.7	2.8	8.5	8.4	8.5	8.7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64281&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]3 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=64281&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64281&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Werkl[t] = + 0.578720388686346 -0.0288976650552464Infl[t] + 1.30143214998577`Yt-1`[t] -0.500215742364641`Yt-2`[t] -0.406060070910419`Yt-3`[t] + 0.510605505838536`Yt-4`[t] -0.0090128947126465M1[t] + 0.00134048665666322M2[t] + 0.749204340383939M3[t] -0.00450670673461415M4[t] + 0.279500612908942M5[t] + 0.582882431043774M6[t] + 0.217843298407681M7[t] + 0.425664336820022M8[t] + 0.326808893226672M9[t] + 0.053531382937026M10[t] + 0.104634569514344M11[t] + 0.00124441197949185t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Werkl[t] =  +  0.578720388686346 -0.0288976650552464Infl[t] +  1.30143214998577`Yt-1`[t] -0.500215742364641`Yt-2`[t] -0.406060070910419`Yt-3`[t] +  0.510605505838536`Yt-4`[t] -0.0090128947126465M1[t] +  0.00134048665666322M2[t] +  0.749204340383939M3[t] -0.00450670673461415M4[t] +  0.279500612908942M5[t] +  0.582882431043774M6[t] +  0.217843298407681M7[t] +  0.425664336820022M8[t] +  0.326808893226672M9[t] +  0.053531382937026M10[t] +  0.104634569514344M11[t] +  0.00124441197949185t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64281&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Werkl[t] =  +  0.578720388686346 -0.0288976650552464Infl[t] +  1.30143214998577`Yt-1`[t] -0.500215742364641`Yt-2`[t] -0.406060070910419`Yt-3`[t] +  0.510605505838536`Yt-4`[t] -0.0090128947126465M1[t] +  0.00134048665666322M2[t] +  0.749204340383939M3[t] -0.00450670673461415M4[t] +  0.279500612908942M5[t] +  0.582882431043774M6[t] +  0.217843298407681M7[t] +  0.425664336820022M8[t] +  0.326808893226672M9[t] +  0.053531382937026M10[t] +  0.104634569514344M11[t] +  0.00124441197949185t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64281&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64281&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
Werkl[t] = + 0.578720388686346 -0.0288976650552464Infl[t] + 1.30143214998577`Yt-1`[t] -0.500215742364641`Yt-2`[t] -0.406060070910419`Yt-3`[t] + 0.510605505838536`Yt-4`[t] -0.0090128947126465M1[t] + 0.00134048665666322M2[t] + 0.749204340383939M3[t] -0.00450670673461415M4[t] + 0.279500612908942M5[t] + 0.582882431043774M6[t] + 0.217843298407681M7[t] + 0.425664336820022M8[t] + 0.326808893226672M9[t] + 0.053531382937026M10[t] + 0.104634569514344M11[t] + 0.00124441197949185t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.5787203886863460.8384990.69020.4941640.247082
Infl-0.02889766505524640.053355-0.54160.5911650.295582
`Yt-1`1.301432149985770.1424539.135900
`Yt-2`-0.5002157423646410.239085-2.09220.0429780.021489
`Yt-3`-0.4060600709104190.239948-1.69230.0985680.049284
`Yt-4`0.5106055058385360.1501483.40070.0015640.000782
M1-0.00901289471264650.107011-0.08420.9333090.466655
M20.001340486656663220.1115480.0120.9904730.495237
M30.7492043403839390.1180826.344800
M4-0.004506706734614150.145622-0.03090.9754690.487734
M50.2795006129089420.1455531.92030.0621590.031079
M60.5828824310437740.1414074.1220.000199.5e-05
M70.2178432984076810.1051992.07080.0450440.022522
M80.4256643368200220.1030744.12970.0001859.3e-05
M90.3268088932266720.1105422.95640.005260.00263
M100.0535313829370260.1221960.43810.6637460.331873
M110.1046345695143440.1128270.92740.3594290.179714
t0.001244411979491850.0048740.25530.799830.399915

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 0.578720388686346 & 0.838499 & 0.6902 & 0.494164 & 0.247082 \tabularnewline
Infl & -0.0288976650552464 & 0.053355 & -0.5416 & 0.591165 & 0.295582 \tabularnewline
`Yt-1` & 1.30143214998577 & 0.142453 & 9.1359 & 0 & 0 \tabularnewline
`Yt-2` & -0.500215742364641 & 0.239085 & -2.0922 & 0.042978 & 0.021489 \tabularnewline
`Yt-3` & -0.406060070910419 & 0.239948 & -1.6923 & 0.098568 & 0.049284 \tabularnewline
`Yt-4` & 0.510605505838536 & 0.150148 & 3.4007 & 0.001564 & 0.000782 \tabularnewline
M1 & -0.0090128947126465 & 0.107011 & -0.0842 & 0.933309 & 0.466655 \tabularnewline
M2 & 0.00134048665666322 & 0.111548 & 0.012 & 0.990473 & 0.495237 \tabularnewline
M3 & 0.749204340383939 & 0.118082 & 6.3448 & 0 & 0 \tabularnewline
M4 & -0.00450670673461415 & 0.145622 & -0.0309 & 0.975469 & 0.487734 \tabularnewline
M5 & 0.279500612908942 & 0.145553 & 1.9203 & 0.062159 & 0.031079 \tabularnewline
M6 & 0.582882431043774 & 0.141407 & 4.122 & 0.00019 & 9.5e-05 \tabularnewline
M7 & 0.217843298407681 & 0.105199 & 2.0708 & 0.045044 & 0.022522 \tabularnewline
M8 & 0.425664336820022 & 0.103074 & 4.1297 & 0.000185 & 9.3e-05 \tabularnewline
M9 & 0.326808893226672 & 0.110542 & 2.9564 & 0.00526 & 0.00263 \tabularnewline
M10 & 0.053531382937026 & 0.122196 & 0.4381 & 0.663746 & 0.331873 \tabularnewline
M11 & 0.104634569514344 & 0.112827 & 0.9274 & 0.359429 & 0.179714 \tabularnewline
t & 0.00124441197949185 & 0.004874 & 0.2553 & 0.79983 & 0.399915 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64281&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]0.578720388686346[/C][C]0.838499[/C][C]0.6902[/C][C]0.494164[/C][C]0.247082[/C][/ROW]
[ROW][C]Infl[/C][C]-0.0288976650552464[/C][C]0.053355[/C][C]-0.5416[/C][C]0.591165[/C][C]0.295582[/C][/ROW]
[ROW][C]`Yt-1`[/C][C]1.30143214998577[/C][C]0.142453[/C][C]9.1359[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Yt-2`[/C][C]-0.500215742364641[/C][C]0.239085[/C][C]-2.0922[/C][C]0.042978[/C][C]0.021489[/C][/ROW]
[ROW][C]`Yt-3`[/C][C]-0.406060070910419[/C][C]0.239948[/C][C]-1.6923[/C][C]0.098568[/C][C]0.049284[/C][/ROW]
[ROW][C]`Yt-4`[/C][C]0.510605505838536[/C][C]0.150148[/C][C]3.4007[/C][C]0.001564[/C][C]0.000782[/C][/ROW]
[ROW][C]M1[/C][C]-0.0090128947126465[/C][C]0.107011[/C][C]-0.0842[/C][C]0.933309[/C][C]0.466655[/C][/ROW]
[ROW][C]M2[/C][C]0.00134048665666322[/C][C]0.111548[/C][C]0.012[/C][C]0.990473[/C][C]0.495237[/C][/ROW]
[ROW][C]M3[/C][C]0.749204340383939[/C][C]0.118082[/C][C]6.3448[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M4[/C][C]-0.00450670673461415[/C][C]0.145622[/C][C]-0.0309[/C][C]0.975469[/C][C]0.487734[/C][/ROW]
[ROW][C]M5[/C][C]0.279500612908942[/C][C]0.145553[/C][C]1.9203[/C][C]0.062159[/C][C]0.031079[/C][/ROW]
[ROW][C]M6[/C][C]0.582882431043774[/C][C]0.141407[/C][C]4.122[/C][C]0.00019[/C][C]9.5e-05[/C][/ROW]
[ROW][C]M7[/C][C]0.217843298407681[/C][C]0.105199[/C][C]2.0708[/C][C]0.045044[/C][C]0.022522[/C][/ROW]
[ROW][C]M8[/C][C]0.425664336820022[/C][C]0.103074[/C][C]4.1297[/C][C]0.000185[/C][C]9.3e-05[/C][/ROW]
[ROW][C]M9[/C][C]0.326808893226672[/C][C]0.110542[/C][C]2.9564[/C][C]0.00526[/C][C]0.00263[/C][/ROW]
[ROW][C]M10[/C][C]0.053531382937026[/C][C]0.122196[/C][C]0.4381[/C][C]0.663746[/C][C]0.331873[/C][/ROW]
[ROW][C]M11[/C][C]0.104634569514344[/C][C]0.112827[/C][C]0.9274[/C][C]0.359429[/C][C]0.179714[/C][/ROW]
[ROW][C]t[/C][C]0.00124441197949185[/C][C]0.004874[/C][C]0.2553[/C][C]0.79983[/C][C]0.399915[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64281&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64281&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)0.5787203886863460.8384990.69020.4941640.247082
Infl-0.02889766505524640.053355-0.54160.5911650.295582
`Yt-1`1.301432149985770.1424539.135900
`Yt-2`-0.5002157423646410.239085-2.09220.0429780.021489
`Yt-3`-0.4060600709104190.239948-1.69230.0985680.049284
`Yt-4`0.5106055058385360.1501483.40070.0015640.000782
M1-0.00901289471264650.107011-0.08420.9333090.466655
M20.001340486656663220.1115480.0120.9904730.495237
M30.7492043403839390.1180826.344800
M4-0.004506706734614150.145622-0.03090.9754690.487734
M50.2795006129089420.1455531.92030.0621590.031079
M60.5828824310437740.1414074.1220.000199.5e-05
M70.2178432984076810.1051992.07080.0450440.022522
M80.4256643368200220.1030744.12970.0001859.3e-05
M90.3268088932266720.1105422.95640.005260.00263
M100.0535313829370260.1221960.43810.6637460.331873
M110.1046345695143440.1128270.92740.3594290.179714
t0.001244411979491850.0048740.25530.799830.399915







Multiple Linear Regression - Regression Statistics
Multiple R0.98576974984865
R-squared0.971741999716672
Adjusted R-squared0.95942440984958
F-TEST (value)78.8905954981365
F-TEST (DF numerator)17
F-TEST (DF denominator)39
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.152322251148176
Sum Squared Residuals0.904880659599072

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.98576974984865 \tabularnewline
R-squared & 0.971741999716672 \tabularnewline
Adjusted R-squared & 0.95942440984958 \tabularnewline
F-TEST (value) & 78.8905954981365 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 39 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.152322251148176 \tabularnewline
Sum Squared Residuals & 0.904880659599072 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64281&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.98576974984865[/C][/ROW]
[ROW][C]R-squared[/C][C]0.971741999716672[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.95942440984958[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]78.8905954981365[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]39[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.152322251148176[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.904880659599072[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64281&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64281&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 R0.98576974984865
R-squared0.971741999716672
Adjusted R-squared0.95942440984958
F-TEST (value)78.8905954981365
F-TEST (DF numerator)17
F-TEST (DF denominator)39
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.152322251148176
Sum Squared Residuals0.904880659599072







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16.36.36892391499723-0.068923914997231
266.18124722521091-0.181247225210907
36.26.4644933647509-0.264493364750907
46.46.233168283002670.166831716997327
56.86.81781991645622-0.0178199164562220
67.57.308582192158280.191417807841719
77.57.67950253303701-0.179502533037013
87.67.472334503566090.127665496433911
97.67.407528240615780.192471759384220
107.47.44578718866166-0.0457871886616623
117.37.197242350130280.102757649869723
127.17.14082057520335-0.04082057520335
136.97.00977878390264-0.109778783902636
146.86.8169568006837-0.0169568006836957
157.57.55744716394647-0.0574471639464694
167.67.73931598803724-0.139315988037242
177.87.745934587432480.054065412567522
1887.922633306580820.0773666934191797
198.18.041679247455430.0583207525445709
208.28.24491376776363-0.0449137677636313
218.38.251223230403030.048776769596967
228.28.109267800909560.0907321990904372
2387.989015387218620.0109846127813844
247.97.691594450426930.208405549573066
257.67.76273105787617-0.162731057876175
267.67.446733645030790.153266354969211
278.38.28728130587580.0127186941241927
288.48.51079511340501-0.110795113405013
298.48.4199676221143-0.0199676221143032
308.48.398999527871440.00100047212855698
318.48.340463588188680.059536411811323
328.68.60347935566989-0.00347935566988855
338.98.774824053569760.125175946430242
348.88.798956984793460.00104301520654187
358.38.49566416447126-0.195664164471265
367.57.7516542205359-0.251654220535898
377.27.126407182300370.073592817699626
387.47.311276475438610.0887235245613888
398.88.537391431155470.262608568844532
409.39.223110040631380.076889959368616
419.39.230161675014230.0698383249857733
428.78.79230913728963-0.0923091372896299
438.28.17103186538240.0289681346175983
448.38.29637250514150.00362749485850200
458.58.81975883574927-0.319758835749268
468.68.64598802563532-0.0459880256353168
478.58.418078098179840.0819219018201574
488.28.115930753833820.0840692461661811
498.17.832159060923580.267840939076416
507.97.943785853636-0.0437858536359974
518.68.553386734271350.0466132657286517
528.78.693610574923690.00638942507631222
538.78.78611619898277-0.08611619898277
548.58.67747583609983-0.177475836099826
558.48.367322765936480.0326772340635212
568.58.5828998678589-0.0828998678588934
578.78.74666563966216-0.0466656396621606

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.3 & 6.36892391499723 & -0.068923914997231 \tabularnewline
2 & 6 & 6.18124722521091 & -0.181247225210907 \tabularnewline
3 & 6.2 & 6.4644933647509 & -0.264493364750907 \tabularnewline
4 & 6.4 & 6.23316828300267 & 0.166831716997327 \tabularnewline
5 & 6.8 & 6.81781991645622 & -0.0178199164562220 \tabularnewline
6 & 7.5 & 7.30858219215828 & 0.191417807841719 \tabularnewline
7 & 7.5 & 7.67950253303701 & -0.179502533037013 \tabularnewline
8 & 7.6 & 7.47233450356609 & 0.127665496433911 \tabularnewline
9 & 7.6 & 7.40752824061578 & 0.192471759384220 \tabularnewline
10 & 7.4 & 7.44578718866166 & -0.0457871886616623 \tabularnewline
11 & 7.3 & 7.19724235013028 & 0.102757649869723 \tabularnewline
12 & 7.1 & 7.14082057520335 & -0.04082057520335 \tabularnewline
13 & 6.9 & 7.00977878390264 & -0.109778783902636 \tabularnewline
14 & 6.8 & 6.8169568006837 & -0.0169568006836957 \tabularnewline
15 & 7.5 & 7.55744716394647 & -0.0574471639464694 \tabularnewline
16 & 7.6 & 7.73931598803724 & -0.139315988037242 \tabularnewline
17 & 7.8 & 7.74593458743248 & 0.054065412567522 \tabularnewline
18 & 8 & 7.92263330658082 & 0.0773666934191797 \tabularnewline
19 & 8.1 & 8.04167924745543 & 0.0583207525445709 \tabularnewline
20 & 8.2 & 8.24491376776363 & -0.0449137677636313 \tabularnewline
21 & 8.3 & 8.25122323040303 & 0.048776769596967 \tabularnewline
22 & 8.2 & 8.10926780090956 & 0.0907321990904372 \tabularnewline
23 & 8 & 7.98901538721862 & 0.0109846127813844 \tabularnewline
24 & 7.9 & 7.69159445042693 & 0.208405549573066 \tabularnewline
25 & 7.6 & 7.76273105787617 & -0.162731057876175 \tabularnewline
26 & 7.6 & 7.44673364503079 & 0.153266354969211 \tabularnewline
27 & 8.3 & 8.2872813058758 & 0.0127186941241927 \tabularnewline
28 & 8.4 & 8.51079511340501 & -0.110795113405013 \tabularnewline
29 & 8.4 & 8.4199676221143 & -0.0199676221143032 \tabularnewline
30 & 8.4 & 8.39899952787144 & 0.00100047212855698 \tabularnewline
31 & 8.4 & 8.34046358818868 & 0.059536411811323 \tabularnewline
32 & 8.6 & 8.60347935566989 & -0.00347935566988855 \tabularnewline
33 & 8.9 & 8.77482405356976 & 0.125175946430242 \tabularnewline
34 & 8.8 & 8.79895698479346 & 0.00104301520654187 \tabularnewline
35 & 8.3 & 8.49566416447126 & -0.195664164471265 \tabularnewline
36 & 7.5 & 7.7516542205359 & -0.251654220535898 \tabularnewline
37 & 7.2 & 7.12640718230037 & 0.073592817699626 \tabularnewline
38 & 7.4 & 7.31127647543861 & 0.0887235245613888 \tabularnewline
39 & 8.8 & 8.53739143115547 & 0.262608568844532 \tabularnewline
40 & 9.3 & 9.22311004063138 & 0.076889959368616 \tabularnewline
41 & 9.3 & 9.23016167501423 & 0.0698383249857733 \tabularnewline
42 & 8.7 & 8.79230913728963 & -0.0923091372896299 \tabularnewline
43 & 8.2 & 8.1710318653824 & 0.0289681346175983 \tabularnewline
44 & 8.3 & 8.2963725051415 & 0.00362749485850200 \tabularnewline
45 & 8.5 & 8.81975883574927 & -0.319758835749268 \tabularnewline
46 & 8.6 & 8.64598802563532 & -0.0459880256353168 \tabularnewline
47 & 8.5 & 8.41807809817984 & 0.0819219018201574 \tabularnewline
48 & 8.2 & 8.11593075383382 & 0.0840692461661811 \tabularnewline
49 & 8.1 & 7.83215906092358 & 0.267840939076416 \tabularnewline
50 & 7.9 & 7.943785853636 & -0.0437858536359974 \tabularnewline
51 & 8.6 & 8.55338673427135 & 0.0466132657286517 \tabularnewline
52 & 8.7 & 8.69361057492369 & 0.00638942507631222 \tabularnewline
53 & 8.7 & 8.78611619898277 & -0.08611619898277 \tabularnewline
54 & 8.5 & 8.67747583609983 & -0.177475836099826 \tabularnewline
55 & 8.4 & 8.36732276593648 & 0.0326772340635212 \tabularnewline
56 & 8.5 & 8.5828998678589 & -0.0828998678588934 \tabularnewline
57 & 8.7 & 8.74666563966216 & -0.0466656396621606 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64281&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]6.3[/C][C]6.36892391499723[/C][C]-0.068923914997231[/C][/ROW]
[ROW][C]2[/C][C]6[/C][C]6.18124722521091[/C][C]-0.181247225210907[/C][/ROW]
[ROW][C]3[/C][C]6.2[/C][C]6.4644933647509[/C][C]-0.264493364750907[/C][/ROW]
[ROW][C]4[/C][C]6.4[/C][C]6.23316828300267[/C][C]0.166831716997327[/C][/ROW]
[ROW][C]5[/C][C]6.8[/C][C]6.81781991645622[/C][C]-0.0178199164562220[/C][/ROW]
[ROW][C]6[/C][C]7.5[/C][C]7.30858219215828[/C][C]0.191417807841719[/C][/ROW]
[ROW][C]7[/C][C]7.5[/C][C]7.67950253303701[/C][C]-0.179502533037013[/C][/ROW]
[ROW][C]8[/C][C]7.6[/C][C]7.47233450356609[/C][C]0.127665496433911[/C][/ROW]
[ROW][C]9[/C][C]7.6[/C][C]7.40752824061578[/C][C]0.192471759384220[/C][/ROW]
[ROW][C]10[/C][C]7.4[/C][C]7.44578718866166[/C][C]-0.0457871886616623[/C][/ROW]
[ROW][C]11[/C][C]7.3[/C][C]7.19724235013028[/C][C]0.102757649869723[/C][/ROW]
[ROW][C]12[/C][C]7.1[/C][C]7.14082057520335[/C][C]-0.04082057520335[/C][/ROW]
[ROW][C]13[/C][C]6.9[/C][C]7.00977878390264[/C][C]-0.109778783902636[/C][/ROW]
[ROW][C]14[/C][C]6.8[/C][C]6.8169568006837[/C][C]-0.0169568006836957[/C][/ROW]
[ROW][C]15[/C][C]7.5[/C][C]7.55744716394647[/C][C]-0.0574471639464694[/C][/ROW]
[ROW][C]16[/C][C]7.6[/C][C]7.73931598803724[/C][C]-0.139315988037242[/C][/ROW]
[ROW][C]17[/C][C]7.8[/C][C]7.74593458743248[/C][C]0.054065412567522[/C][/ROW]
[ROW][C]18[/C][C]8[/C][C]7.92263330658082[/C][C]0.0773666934191797[/C][/ROW]
[ROW][C]19[/C][C]8.1[/C][C]8.04167924745543[/C][C]0.0583207525445709[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]8.24491376776363[/C][C]-0.0449137677636313[/C][/ROW]
[ROW][C]21[/C][C]8.3[/C][C]8.25122323040303[/C][C]0.048776769596967[/C][/ROW]
[ROW][C]22[/C][C]8.2[/C][C]8.10926780090956[/C][C]0.0907321990904372[/C][/ROW]
[ROW][C]23[/C][C]8[/C][C]7.98901538721862[/C][C]0.0109846127813844[/C][/ROW]
[ROW][C]24[/C][C]7.9[/C][C]7.69159445042693[/C][C]0.208405549573066[/C][/ROW]
[ROW][C]25[/C][C]7.6[/C][C]7.76273105787617[/C][C]-0.162731057876175[/C][/ROW]
[ROW][C]26[/C][C]7.6[/C][C]7.44673364503079[/C][C]0.153266354969211[/C][/ROW]
[ROW][C]27[/C][C]8.3[/C][C]8.2872813058758[/C][C]0.0127186941241927[/C][/ROW]
[ROW][C]28[/C][C]8.4[/C][C]8.51079511340501[/C][C]-0.110795113405013[/C][/ROW]
[ROW][C]29[/C][C]8.4[/C][C]8.4199676221143[/C][C]-0.0199676221143032[/C][/ROW]
[ROW][C]30[/C][C]8.4[/C][C]8.39899952787144[/C][C]0.00100047212855698[/C][/ROW]
[ROW][C]31[/C][C]8.4[/C][C]8.34046358818868[/C][C]0.059536411811323[/C][/ROW]
[ROW][C]32[/C][C]8.6[/C][C]8.60347935566989[/C][C]-0.00347935566988855[/C][/ROW]
[ROW][C]33[/C][C]8.9[/C][C]8.77482405356976[/C][C]0.125175946430242[/C][/ROW]
[ROW][C]34[/C][C]8.8[/C][C]8.79895698479346[/C][C]0.00104301520654187[/C][/ROW]
[ROW][C]35[/C][C]8.3[/C][C]8.49566416447126[/C][C]-0.195664164471265[/C][/ROW]
[ROW][C]36[/C][C]7.5[/C][C]7.7516542205359[/C][C]-0.251654220535898[/C][/ROW]
[ROW][C]37[/C][C]7.2[/C][C]7.12640718230037[/C][C]0.073592817699626[/C][/ROW]
[ROW][C]38[/C][C]7.4[/C][C]7.31127647543861[/C][C]0.0887235245613888[/C][/ROW]
[ROW][C]39[/C][C]8.8[/C][C]8.53739143115547[/C][C]0.262608568844532[/C][/ROW]
[ROW][C]40[/C][C]9.3[/C][C]9.22311004063138[/C][C]0.076889959368616[/C][/ROW]
[ROW][C]41[/C][C]9.3[/C][C]9.23016167501423[/C][C]0.0698383249857733[/C][/ROW]
[ROW][C]42[/C][C]8.7[/C][C]8.79230913728963[/C][C]-0.0923091372896299[/C][/ROW]
[ROW][C]43[/C][C]8.2[/C][C]8.1710318653824[/C][C]0.0289681346175983[/C][/ROW]
[ROW][C]44[/C][C]8.3[/C][C]8.2963725051415[/C][C]0.00362749485850200[/C][/ROW]
[ROW][C]45[/C][C]8.5[/C][C]8.81975883574927[/C][C]-0.319758835749268[/C][/ROW]
[ROW][C]46[/C][C]8.6[/C][C]8.64598802563532[/C][C]-0.0459880256353168[/C][/ROW]
[ROW][C]47[/C][C]8.5[/C][C]8.41807809817984[/C][C]0.0819219018201574[/C][/ROW]
[ROW][C]48[/C][C]8.2[/C][C]8.11593075383382[/C][C]0.0840692461661811[/C][/ROW]
[ROW][C]49[/C][C]8.1[/C][C]7.83215906092358[/C][C]0.267840939076416[/C][/ROW]
[ROW][C]50[/C][C]7.9[/C][C]7.943785853636[/C][C]-0.0437858536359974[/C][/ROW]
[ROW][C]51[/C][C]8.6[/C][C]8.55338673427135[/C][C]0.0466132657286517[/C][/ROW]
[ROW][C]52[/C][C]8.7[/C][C]8.69361057492369[/C][C]0.00638942507631222[/C][/ROW]
[ROW][C]53[/C][C]8.7[/C][C]8.78611619898277[/C][C]-0.08611619898277[/C][/ROW]
[ROW][C]54[/C][C]8.5[/C][C]8.67747583609983[/C][C]-0.177475836099826[/C][/ROW]
[ROW][C]55[/C][C]8.4[/C][C]8.36732276593648[/C][C]0.0326772340635212[/C][/ROW]
[ROW][C]56[/C][C]8.5[/C][C]8.5828998678589[/C][C]-0.0828998678588934[/C][/ROW]
[ROW][C]57[/C][C]8.7[/C][C]8.74666563966216[/C][C]-0.0466656396621606[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64281&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64281&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
16.36.36892391499723-0.068923914997231
266.18124722521091-0.181247225210907
36.26.4644933647509-0.264493364750907
46.46.233168283002670.166831716997327
56.86.81781991645622-0.0178199164562220
67.57.308582192158280.191417807841719
77.57.67950253303701-0.179502533037013
87.67.472334503566090.127665496433911
97.67.407528240615780.192471759384220
107.47.44578718866166-0.0457871886616623
117.37.197242350130280.102757649869723
127.17.14082057520335-0.04082057520335
136.97.00977878390264-0.109778783902636
146.86.8169568006837-0.0169568006836957
157.57.55744716394647-0.0574471639464694
167.67.73931598803724-0.139315988037242
177.87.745934587432480.054065412567522
1887.922633306580820.0773666934191797
198.18.041679247455430.0583207525445709
208.28.24491376776363-0.0449137677636313
218.38.251223230403030.048776769596967
228.28.109267800909560.0907321990904372
2387.989015387218620.0109846127813844
247.97.691594450426930.208405549573066
257.67.76273105787617-0.162731057876175
267.67.446733645030790.153266354969211
278.38.28728130587580.0127186941241927
288.48.51079511340501-0.110795113405013
298.48.4199676221143-0.0199676221143032
308.48.398999527871440.00100047212855698
318.48.340463588188680.059536411811323
328.68.60347935566989-0.00347935566988855
338.98.774824053569760.125175946430242
348.88.798956984793460.00104301520654187
358.38.49566416447126-0.195664164471265
367.57.7516542205359-0.251654220535898
377.27.126407182300370.073592817699626
387.47.311276475438610.0887235245613888
398.88.537391431155470.262608568844532
409.39.223110040631380.076889959368616
419.39.230161675014230.0698383249857733
428.78.79230913728963-0.0923091372896299
438.28.17103186538240.0289681346175983
448.38.29637250514150.00362749485850200
458.58.81975883574927-0.319758835749268
468.68.64598802563532-0.0459880256353168
478.58.418078098179840.0819219018201574
488.28.115930753833820.0840692461661811
498.17.832159060923580.267840939076416
507.97.943785853636-0.0437858536359974
518.68.553386734271350.0466132657286517
528.78.693610574923690.00638942507631222
538.78.78611619898277-0.08611619898277
548.58.67747583609983-0.177475836099826
558.48.367322765936480.0326772340635212
568.58.5828998678589-0.0828998678588934
578.78.74666563966216-0.0466656396621606







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.7890892833439860.4218214333120290.210910716656015
220.6973423210081580.6053153579836840.302657678991842
230.5719718891054350.856056221789130.428028110894565
240.596300456658690.807399086682620.40369954334131
250.6653030613698860.6693938772602290.334696938630114
260.6135986863323590.7728026273352830.386401313667641
270.757047027501720.4859059449965610.242952972498281
280.6734682439174350.653063512165130.326531756082565
290.6689183455128740.6621633089742530.331081654487126
300.7021279144421730.5957441711156550.297872085557827
310.6546975071384780.6906049857230450.345302492861522
320.5704624068822620.8590751862354760.429537593117738
330.5221234368943980.9557531262112040.477876563105602
340.5641892957850250.871621408429950.435810704214975
350.4644785373457760.9289570746915510.535521462654224
360.8279396375665080.3441207248669850.172060362433492

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.789089283343986 & 0.421821433312029 & 0.210910716656015 \tabularnewline
22 & 0.697342321008158 & 0.605315357983684 & 0.302657678991842 \tabularnewline
23 & 0.571971889105435 & 0.85605622178913 & 0.428028110894565 \tabularnewline
24 & 0.59630045665869 & 0.80739908668262 & 0.40369954334131 \tabularnewline
25 & 0.665303061369886 & 0.669393877260229 & 0.334696938630114 \tabularnewline
26 & 0.613598686332359 & 0.772802627335283 & 0.386401313667641 \tabularnewline
27 & 0.75704702750172 & 0.485905944996561 & 0.242952972498281 \tabularnewline
28 & 0.673468243917435 & 0.65306351216513 & 0.326531756082565 \tabularnewline
29 & 0.668918345512874 & 0.662163308974253 & 0.331081654487126 \tabularnewline
30 & 0.702127914442173 & 0.595744171115655 & 0.297872085557827 \tabularnewline
31 & 0.654697507138478 & 0.690604985723045 & 0.345302492861522 \tabularnewline
32 & 0.570462406882262 & 0.859075186235476 & 0.429537593117738 \tabularnewline
33 & 0.522123436894398 & 0.955753126211204 & 0.477876563105602 \tabularnewline
34 & 0.564189295785025 & 0.87162140842995 & 0.435810704214975 \tabularnewline
35 & 0.464478537345776 & 0.928957074691551 & 0.535521462654224 \tabularnewline
36 & 0.827939637566508 & 0.344120724866985 & 0.172060362433492 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64281&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]21[/C][C]0.789089283343986[/C][C]0.421821433312029[/C][C]0.210910716656015[/C][/ROW]
[ROW][C]22[/C][C]0.697342321008158[/C][C]0.605315357983684[/C][C]0.302657678991842[/C][/ROW]
[ROW][C]23[/C][C]0.571971889105435[/C][C]0.85605622178913[/C][C]0.428028110894565[/C][/ROW]
[ROW][C]24[/C][C]0.59630045665869[/C][C]0.80739908668262[/C][C]0.40369954334131[/C][/ROW]
[ROW][C]25[/C][C]0.665303061369886[/C][C]0.669393877260229[/C][C]0.334696938630114[/C][/ROW]
[ROW][C]26[/C][C]0.613598686332359[/C][C]0.772802627335283[/C][C]0.386401313667641[/C][/ROW]
[ROW][C]27[/C][C]0.75704702750172[/C][C]0.485905944996561[/C][C]0.242952972498281[/C][/ROW]
[ROW][C]28[/C][C]0.673468243917435[/C][C]0.65306351216513[/C][C]0.326531756082565[/C][/ROW]
[ROW][C]29[/C][C]0.668918345512874[/C][C]0.662163308974253[/C][C]0.331081654487126[/C][/ROW]
[ROW][C]30[/C][C]0.702127914442173[/C][C]0.595744171115655[/C][C]0.297872085557827[/C][/ROW]
[ROW][C]31[/C][C]0.654697507138478[/C][C]0.690604985723045[/C][C]0.345302492861522[/C][/ROW]
[ROW][C]32[/C][C]0.570462406882262[/C][C]0.859075186235476[/C][C]0.429537593117738[/C][/ROW]
[ROW][C]33[/C][C]0.522123436894398[/C][C]0.955753126211204[/C][C]0.477876563105602[/C][/ROW]
[ROW][C]34[/C][C]0.564189295785025[/C][C]0.87162140842995[/C][C]0.435810704214975[/C][/ROW]
[ROW][C]35[/C][C]0.464478537345776[/C][C]0.928957074691551[/C][C]0.535521462654224[/C][/ROW]
[ROW][C]36[/C][C]0.827939637566508[/C][C]0.344120724866985[/C][C]0.172060362433492[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64281&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64281&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
210.7890892833439860.4218214333120290.210910716656015
220.6973423210081580.6053153579836840.302657678991842
230.5719718891054350.856056221789130.428028110894565
240.596300456658690.807399086682620.40369954334131
250.6653030613698860.6693938772602290.334696938630114
260.6135986863323590.7728026273352830.386401313667641
270.757047027501720.4859059449965610.242952972498281
280.6734682439174350.653063512165130.326531756082565
290.6689183455128740.6621633089742530.331081654487126
300.7021279144421730.5957441711156550.297872085557827
310.6546975071384780.6906049857230450.345302492861522
320.5704624068822620.8590751862354760.429537593117738
330.5221234368943980.9557531262112040.477876563105602
340.5641892957850250.871621408429950.435810704214975
350.4644785373457760.9289570746915510.535521462654224
360.8279396375665080.3441207248669850.172060362433492







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=64281&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=64281&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=64281&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 level00OK
5% type I error level00OK
10% type I error level00OK



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