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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 computationWed, 23 Nov 2011 08:23:10 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/23/t13220546799ld1tvie2c3qmjy.htm/, Retrieved Fri, 29 Mar 2024 10:15:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146509, Retrieved Fri, 29 Mar 2024 10:15:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-23 13:23:10] [722cc7f94b3c1568a723b3c5e98a2726] [Current]
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Dataseries X:
1.464	1.487	1.232	0.6683	0.659
1.464	1.487	1.232	0.6683	0.659
1.464	1.487	1.268	0.7041	0.659
1.464	1.487	1.268	0.7041	0.659
1.464	1.487	1.268	0.7041	0.659
1.464	1.487	1.268	0.7041	0.659
1.498	1.523	1.268	0.7041	0.659
1.498	1.523	1.268	0.7041	0.659
1.498	1.523	1.268	0.7041	0.659
1.498	1.523	1.268	0.7041	0.732
1.498	1.523	1.268	0.7041	0.732
1.498	1.523	1.268	0.7041	0.732
1.498	1.523	1.298	0.7319	0.732
1.538	1.562	1.298	0.7319	0.732
1.538	1.562	1.298	0.7319	0.732
1.538	1.562	1.298	0.7319	0.732
1.538	1.562	1.298	0.7319	0.732
1.538	1.562	1.298	0.7319	0.732
1.538	1.562	1.298	0.7319	0.732
1.538	1.562	1.298	0.7319	0.725
1.538	1.562	1.298	0.7319	0.725
1.511	1.535	1.298	0.7319	0.725
1.511	1.535	1.298	0.7319	0.725
1.511	1.535	1.298	0.7319	0.725
1.511	1.535	1.298	0.7319	0.725
1.511	1.535	1.298	0.7319	0.725
1.511	1.535	1.298	0.7319	0.725
1.511	1.535	1.298	0.7319	0.725
1.511	1.535	1.298	0.7319	0.725
1.511	1.535	1.298	0.7319	0.725
1.544	1.569	1.324	0.7568	0.725
1.544	1.569	1.324	0.7568	0.725
1.544	1.569	1.324	0.7568	0.725
1.544	1.569	1.324	0.7568	0.725
1.544	1.569	1.324	0.7568	0.725
1.544	1.569	1.324	0.7568	0.725
1.544	1.569	1.324	0.7568	0.725
1.544	1.569	1.324	0.7568	0.725
1.544	1.569	1.324	0.7568	0.725
1.544	1.569	1.324	0.7568	0.725
1.544	1.569	1.31	0.7286	0.725
1.524	1.548	1.31	0.7286	0.725
1.524	1.548	1.31	0.7286	0.725
1.524	1.548	1.31	0.7286	0.725
1.524	1.548	1.31	0.7286	0.725
1.524	1.548	1.31	0.7286	0.725
1.524	1.548	1.31	0.7286	0.725
1.524	1.548	1.338	0.753	0.725
1.558	1.584	1.338	0.753	0.725




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146509&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146509&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146509&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'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Super95[t] = -0.00248381772087863 + 0.97947458025983Super98[t] + 0.0130008809431486Diesel[t] -0.0133885092431644Gasolie[t] + 0.00384030378759557LPG[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Super95[t] =  -0.00248381772087863 +  0.97947458025983Super98[t] +  0.0130008809431486Diesel[t] -0.0133885092431644Gasolie[t] +  0.00384030378759557LPG[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146509&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Super95[t] =  -0.00248381772087863 +  0.97947458025983Super98[t] +  0.0130008809431486Diesel[t] -0.0133885092431644Gasolie[t] +  0.00384030378759557LPG[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146509&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146509&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
Super95[t] = -0.00248381772087863 + 0.97947458025983Super98[t] + 0.0130008809431486Diesel[t] -0.0133885092431644Gasolie[t] + 0.00384030378759557LPG[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.002483817720878630.007972-0.31160.7568420.378421
Super980.979474580259830.006689146.424800
Diesel0.01300088094314860.0132810.97890.3329840.166492
Gasolie-0.01338850924316440.014757-0.90730.369210.184605
LPG0.003840303787595570.0047250.81280.4207160.210358

\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) & -0.00248381772087863 & 0.007972 & -0.3116 & 0.756842 & 0.378421 \tabularnewline
Super98 & 0.97947458025983 & 0.006689 & 146.4248 & 0 & 0 \tabularnewline
Diesel & 0.0130008809431486 & 0.013281 & 0.9789 & 0.332984 & 0.166492 \tabularnewline
Gasolie & -0.0133885092431644 & 0.014757 & -0.9073 & 0.36921 & 0.184605 \tabularnewline
LPG & 0.00384030378759557 & 0.004725 & 0.8128 & 0.420716 & 0.210358 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146509&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]-0.00248381772087863[/C][C]0.007972[/C][C]-0.3116[/C][C]0.756842[/C][C]0.378421[/C][/ROW]
[ROW][C]Super98[/C][C]0.97947458025983[/C][C]0.006689[/C][C]146.4248[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Diesel[/C][C]0.0130008809431486[/C][C]0.013281[/C][C]0.9789[/C][C]0.332984[/C][C]0.166492[/C][/ROW]
[ROW][C]Gasolie[/C][C]-0.0133885092431644[/C][C]0.014757[/C][C]-0.9073[/C][C]0.36921[/C][C]0.184605[/C][/ROW]
[ROW][C]LPG[/C][C]0.00384030378759557[/C][C]0.004725[/C][C]0.8128[/C][C]0.420716[/C][C]0.210358[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146509&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146509&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)-0.002483817720878630.007972-0.31160.7568420.378421
Super980.979474580259830.006689146.424800
Diesel0.01300088094314860.0132810.97890.3329840.166492
Gasolie-0.01338850924316440.014757-0.90730.369210.184605
LPG0.003840303787595570.0047250.81280.4207160.210358







Multiple Linear Regression - Regression Statistics
Multiple R0.999791225570011
R-squared0.999582494726785
Adjusted R-squared0.999544539701948
F-TEST (value)26335.9726149891
F-TEST (DF numerator)4
F-TEST (DF denominator)44
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.000561483509472919
Sum Squared Residuals1.38716041820411e-05

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999791225570011 \tabularnewline
R-squared & 0.999582494726785 \tabularnewline
Adjusted R-squared & 0.999544539701948 \tabularnewline
F-TEST (value) & 26335.9726149891 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 44 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.000561483509472919 \tabularnewline
Sum Squared Residuals & 1.38716041820411e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146509&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999791225570011[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999582494726785[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999544539701948[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]26335.9726149891[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]44[/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]0.000561483509472919[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.38716041820411e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146509&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146509&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 R0.999791225570011
R-squared0.999582494726785
Adjusted R-squared0.999544539701948
F-TEST (value)26335.9726149891
F-TEST (DF numerator)4
F-TEST (DF denominator)44
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.000561483509472919
Sum Squared Residuals1.38716041820411e-05







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.4641.463595187916270.000404812083733953
21.4641.463595187916270.000404812083733774
31.4641.463583910999310.000416089000685684
41.4641.463583910999310.000416089000685684
51.4641.463583910999310.000416089000685684
61.4641.463583910999310.000416089000685684
71.4981.49884499588867-0.00084499588866797
81.4981.49884499588867-0.00084499588866797
91.4981.49884499588867-0.00084499588866797
101.4981.49912533806516-0.00112533806516245
111.4981.49912533806516-0.00112533806516245
121.4981.49912533806516-0.00112533806516245
131.4981.4991431639365-0.00114316393649693
141.5381.537342672566630.000657327433369591
151.5381.537342672566630.000657327433369591
161.5381.537342672566630.000657327433369591
171.5381.537342672566630.000657327433369591
181.5381.537342672566630.000657327433369591
191.5381.537342672566630.000657327433369591
201.5381.537315790440120.00068420955988276
211.5381.537315790440120.00068420955988276
221.5111.51086997677310.000130023226898166
231.5111.51086997677310.000130023226898166
241.5111.51086997677310.000130023226898166
251.5111.51086997677310.000130023226898166
261.5111.51086997677310.000130023226898166
271.5111.51086997677310.000130023226898166
281.5111.51086997677310.000130023226898166
291.5111.51086997677310.000130023226898166
301.5111.51086997677310.000130023226898166
311.5441.5441767615263-0.000176761526303013
321.5441.5441767615263-0.000176761526303013
331.5441.5441767615263-0.000176761526303013
341.5441.5441767615263-0.000176761526303013
351.5441.5441767615263-0.000176761526303013
361.5441.5441767615263-0.000176761526303013
371.5441.5441767615263-0.000176761526303013
381.5441.5441767615263-0.000176761526303013
391.5441.5441767615263-0.000176761526303013
401.5441.5441767615263-0.000176761526303013
411.5441.54437230515376-0.000372305153756167
421.5241.52380333896830.000196661031700155
431.5241.52380333896830.000196661031700155
441.5241.52380333896830.000196661031700155
451.5241.52380333896830.000196661031700155
461.5241.52380333896830.000196661031700155
471.5241.52380333896830.000196661031700155
481.5241.523840684009170.000159315990825204
491.5581.55910176889853-0.00110176889852868

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.464 & 1.46359518791627 & 0.000404812083733953 \tabularnewline
2 & 1.464 & 1.46359518791627 & 0.000404812083733774 \tabularnewline
3 & 1.464 & 1.46358391099931 & 0.000416089000685684 \tabularnewline
4 & 1.464 & 1.46358391099931 & 0.000416089000685684 \tabularnewline
5 & 1.464 & 1.46358391099931 & 0.000416089000685684 \tabularnewline
6 & 1.464 & 1.46358391099931 & 0.000416089000685684 \tabularnewline
7 & 1.498 & 1.49884499588867 & -0.00084499588866797 \tabularnewline
8 & 1.498 & 1.49884499588867 & -0.00084499588866797 \tabularnewline
9 & 1.498 & 1.49884499588867 & -0.00084499588866797 \tabularnewline
10 & 1.498 & 1.49912533806516 & -0.00112533806516245 \tabularnewline
11 & 1.498 & 1.49912533806516 & -0.00112533806516245 \tabularnewline
12 & 1.498 & 1.49912533806516 & -0.00112533806516245 \tabularnewline
13 & 1.498 & 1.4991431639365 & -0.00114316393649693 \tabularnewline
14 & 1.538 & 1.53734267256663 & 0.000657327433369591 \tabularnewline
15 & 1.538 & 1.53734267256663 & 0.000657327433369591 \tabularnewline
16 & 1.538 & 1.53734267256663 & 0.000657327433369591 \tabularnewline
17 & 1.538 & 1.53734267256663 & 0.000657327433369591 \tabularnewline
18 & 1.538 & 1.53734267256663 & 0.000657327433369591 \tabularnewline
19 & 1.538 & 1.53734267256663 & 0.000657327433369591 \tabularnewline
20 & 1.538 & 1.53731579044012 & 0.00068420955988276 \tabularnewline
21 & 1.538 & 1.53731579044012 & 0.00068420955988276 \tabularnewline
22 & 1.511 & 1.5108699767731 & 0.000130023226898166 \tabularnewline
23 & 1.511 & 1.5108699767731 & 0.000130023226898166 \tabularnewline
24 & 1.511 & 1.5108699767731 & 0.000130023226898166 \tabularnewline
25 & 1.511 & 1.5108699767731 & 0.000130023226898166 \tabularnewline
26 & 1.511 & 1.5108699767731 & 0.000130023226898166 \tabularnewline
27 & 1.511 & 1.5108699767731 & 0.000130023226898166 \tabularnewline
28 & 1.511 & 1.5108699767731 & 0.000130023226898166 \tabularnewline
29 & 1.511 & 1.5108699767731 & 0.000130023226898166 \tabularnewline
30 & 1.511 & 1.5108699767731 & 0.000130023226898166 \tabularnewline
31 & 1.544 & 1.5441767615263 & -0.000176761526303013 \tabularnewline
32 & 1.544 & 1.5441767615263 & -0.000176761526303013 \tabularnewline
33 & 1.544 & 1.5441767615263 & -0.000176761526303013 \tabularnewline
34 & 1.544 & 1.5441767615263 & -0.000176761526303013 \tabularnewline
35 & 1.544 & 1.5441767615263 & -0.000176761526303013 \tabularnewline
36 & 1.544 & 1.5441767615263 & -0.000176761526303013 \tabularnewline
37 & 1.544 & 1.5441767615263 & -0.000176761526303013 \tabularnewline
38 & 1.544 & 1.5441767615263 & -0.000176761526303013 \tabularnewline
39 & 1.544 & 1.5441767615263 & -0.000176761526303013 \tabularnewline
40 & 1.544 & 1.5441767615263 & -0.000176761526303013 \tabularnewline
41 & 1.544 & 1.54437230515376 & -0.000372305153756167 \tabularnewline
42 & 1.524 & 1.5238033389683 & 0.000196661031700155 \tabularnewline
43 & 1.524 & 1.5238033389683 & 0.000196661031700155 \tabularnewline
44 & 1.524 & 1.5238033389683 & 0.000196661031700155 \tabularnewline
45 & 1.524 & 1.5238033389683 & 0.000196661031700155 \tabularnewline
46 & 1.524 & 1.5238033389683 & 0.000196661031700155 \tabularnewline
47 & 1.524 & 1.5238033389683 & 0.000196661031700155 \tabularnewline
48 & 1.524 & 1.52384068400917 & 0.000159315990825204 \tabularnewline
49 & 1.558 & 1.55910176889853 & -0.00110176889852868 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146509&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]1.464[/C][C]1.46359518791627[/C][C]0.000404812083733953[/C][/ROW]
[ROW][C]2[/C][C]1.464[/C][C]1.46359518791627[/C][C]0.000404812083733774[/C][/ROW]
[ROW][C]3[/C][C]1.464[/C][C]1.46358391099931[/C][C]0.000416089000685684[/C][/ROW]
[ROW][C]4[/C][C]1.464[/C][C]1.46358391099931[/C][C]0.000416089000685684[/C][/ROW]
[ROW][C]5[/C][C]1.464[/C][C]1.46358391099931[/C][C]0.000416089000685684[/C][/ROW]
[ROW][C]6[/C][C]1.464[/C][C]1.46358391099931[/C][C]0.000416089000685684[/C][/ROW]
[ROW][C]7[/C][C]1.498[/C][C]1.49884499588867[/C][C]-0.00084499588866797[/C][/ROW]
[ROW][C]8[/C][C]1.498[/C][C]1.49884499588867[/C][C]-0.00084499588866797[/C][/ROW]
[ROW][C]9[/C][C]1.498[/C][C]1.49884499588867[/C][C]-0.00084499588866797[/C][/ROW]
[ROW][C]10[/C][C]1.498[/C][C]1.49912533806516[/C][C]-0.00112533806516245[/C][/ROW]
[ROW][C]11[/C][C]1.498[/C][C]1.49912533806516[/C][C]-0.00112533806516245[/C][/ROW]
[ROW][C]12[/C][C]1.498[/C][C]1.49912533806516[/C][C]-0.00112533806516245[/C][/ROW]
[ROW][C]13[/C][C]1.498[/C][C]1.4991431639365[/C][C]-0.00114316393649693[/C][/ROW]
[ROW][C]14[/C][C]1.538[/C][C]1.53734267256663[/C][C]0.000657327433369591[/C][/ROW]
[ROW][C]15[/C][C]1.538[/C][C]1.53734267256663[/C][C]0.000657327433369591[/C][/ROW]
[ROW][C]16[/C][C]1.538[/C][C]1.53734267256663[/C][C]0.000657327433369591[/C][/ROW]
[ROW][C]17[/C][C]1.538[/C][C]1.53734267256663[/C][C]0.000657327433369591[/C][/ROW]
[ROW][C]18[/C][C]1.538[/C][C]1.53734267256663[/C][C]0.000657327433369591[/C][/ROW]
[ROW][C]19[/C][C]1.538[/C][C]1.53734267256663[/C][C]0.000657327433369591[/C][/ROW]
[ROW][C]20[/C][C]1.538[/C][C]1.53731579044012[/C][C]0.00068420955988276[/C][/ROW]
[ROW][C]21[/C][C]1.538[/C][C]1.53731579044012[/C][C]0.00068420955988276[/C][/ROW]
[ROW][C]22[/C][C]1.511[/C][C]1.5108699767731[/C][C]0.000130023226898166[/C][/ROW]
[ROW][C]23[/C][C]1.511[/C][C]1.5108699767731[/C][C]0.000130023226898166[/C][/ROW]
[ROW][C]24[/C][C]1.511[/C][C]1.5108699767731[/C][C]0.000130023226898166[/C][/ROW]
[ROW][C]25[/C][C]1.511[/C][C]1.5108699767731[/C][C]0.000130023226898166[/C][/ROW]
[ROW][C]26[/C][C]1.511[/C][C]1.5108699767731[/C][C]0.000130023226898166[/C][/ROW]
[ROW][C]27[/C][C]1.511[/C][C]1.5108699767731[/C][C]0.000130023226898166[/C][/ROW]
[ROW][C]28[/C][C]1.511[/C][C]1.5108699767731[/C][C]0.000130023226898166[/C][/ROW]
[ROW][C]29[/C][C]1.511[/C][C]1.5108699767731[/C][C]0.000130023226898166[/C][/ROW]
[ROW][C]30[/C][C]1.511[/C][C]1.5108699767731[/C][C]0.000130023226898166[/C][/ROW]
[ROW][C]31[/C][C]1.544[/C][C]1.5441767615263[/C][C]-0.000176761526303013[/C][/ROW]
[ROW][C]32[/C][C]1.544[/C][C]1.5441767615263[/C][C]-0.000176761526303013[/C][/ROW]
[ROW][C]33[/C][C]1.544[/C][C]1.5441767615263[/C][C]-0.000176761526303013[/C][/ROW]
[ROW][C]34[/C][C]1.544[/C][C]1.5441767615263[/C][C]-0.000176761526303013[/C][/ROW]
[ROW][C]35[/C][C]1.544[/C][C]1.5441767615263[/C][C]-0.000176761526303013[/C][/ROW]
[ROW][C]36[/C][C]1.544[/C][C]1.5441767615263[/C][C]-0.000176761526303013[/C][/ROW]
[ROW][C]37[/C][C]1.544[/C][C]1.5441767615263[/C][C]-0.000176761526303013[/C][/ROW]
[ROW][C]38[/C][C]1.544[/C][C]1.5441767615263[/C][C]-0.000176761526303013[/C][/ROW]
[ROW][C]39[/C][C]1.544[/C][C]1.5441767615263[/C][C]-0.000176761526303013[/C][/ROW]
[ROW][C]40[/C][C]1.544[/C][C]1.5441767615263[/C][C]-0.000176761526303013[/C][/ROW]
[ROW][C]41[/C][C]1.544[/C][C]1.54437230515376[/C][C]-0.000372305153756167[/C][/ROW]
[ROW][C]42[/C][C]1.524[/C][C]1.5238033389683[/C][C]0.000196661031700155[/C][/ROW]
[ROW][C]43[/C][C]1.524[/C][C]1.5238033389683[/C][C]0.000196661031700155[/C][/ROW]
[ROW][C]44[/C][C]1.524[/C][C]1.5238033389683[/C][C]0.000196661031700155[/C][/ROW]
[ROW][C]45[/C][C]1.524[/C][C]1.5238033389683[/C][C]0.000196661031700155[/C][/ROW]
[ROW][C]46[/C][C]1.524[/C][C]1.5238033389683[/C][C]0.000196661031700155[/C][/ROW]
[ROW][C]47[/C][C]1.524[/C][C]1.5238033389683[/C][C]0.000196661031700155[/C][/ROW]
[ROW][C]48[/C][C]1.524[/C][C]1.52384068400917[/C][C]0.000159315990825204[/C][/ROW]
[ROW][C]49[/C][C]1.558[/C][C]1.55910176889853[/C][C]-0.00110176889852868[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146509&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146509&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
11.4641.463595187916270.000404812083733953
21.4641.463595187916270.000404812083733774
31.4641.463583910999310.000416089000685684
41.4641.463583910999310.000416089000685684
51.4641.463583910999310.000416089000685684
61.4641.463583910999310.000416089000685684
71.4981.49884499588867-0.00084499588866797
81.4981.49884499588867-0.00084499588866797
91.4981.49884499588867-0.00084499588866797
101.4981.49912533806516-0.00112533806516245
111.4981.49912533806516-0.00112533806516245
121.4981.49912533806516-0.00112533806516245
131.4981.4991431639365-0.00114316393649693
141.5381.537342672566630.000657327433369591
151.5381.537342672566630.000657327433369591
161.5381.537342672566630.000657327433369591
171.5381.537342672566630.000657327433369591
181.5381.537342672566630.000657327433369591
191.5381.537342672566630.000657327433369591
201.5381.537315790440120.00068420955988276
211.5381.537315790440120.00068420955988276
221.5111.51086997677310.000130023226898166
231.5111.51086997677310.000130023226898166
241.5111.51086997677310.000130023226898166
251.5111.51086997677310.000130023226898166
261.5111.51086997677310.000130023226898166
271.5111.51086997677310.000130023226898166
281.5111.51086997677310.000130023226898166
291.5111.51086997677310.000130023226898166
301.5111.51086997677310.000130023226898166
311.5441.5441767615263-0.000176761526303013
321.5441.5441767615263-0.000176761526303013
331.5441.5441767615263-0.000176761526303013
341.5441.5441767615263-0.000176761526303013
351.5441.5441767615263-0.000176761526303013
361.5441.5441767615263-0.000176761526303013
371.5441.5441767615263-0.000176761526303013
381.5441.5441767615263-0.000176761526303013
391.5441.5441767615263-0.000176761526303013
401.5441.5441767615263-0.000176761526303013
411.5441.54437230515376-0.000372305153756167
421.5241.52380333896830.000196661031700155
431.5241.52380333896830.000196661031700155
441.5241.52380333896830.000196661031700155
451.5241.52380333896830.000196661031700155
461.5241.52380333896830.000196661031700155
471.5241.52380333896830.000196661031700155
481.5241.523840684009170.000159315990825204
491.5581.55910176889853-0.00110176889852868







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
87.98365832036863e-401.59673166407373e-391
98.96017220922493e-501.79203444184499e-491
102.16880276269315e-724.33760552538629e-721
115.48947691168286e-751.09789538233657e-741
121.32917535700119e-852.65835071400238e-851
134.34746551731754e-1118.69493103463509e-1111
140.9999922615156791.54769686415272e-057.73848432076361e-06
150.9999967528228626.49435427518717e-063.24717713759359e-06
160.9999953512782549.29744349214836e-064.64872174607418e-06
170.9999906779587171.86440825667158e-059.32204128335792e-06
180.9999790230723114.19538553771305e-052.09769276885653e-05
190.9999511243686489.77512627042291e-054.88756313521145e-05
200.9999798912168064.02175663877038e-052.01087831938519e-05
210.9999997902948584.19410285032729e-072.09705142516365e-07
220.9999995289166679.42166666326614e-074.71083333163307e-07
230.9999988663583032.26728339456737e-061.13364169728368e-06
240.9999972698614815.46027703754306e-062.73013851877153e-06
250.9999937035350261.25929299477385e-056.29646497386926e-06
260.9999866999671052.66000657901359e-051.3300032895068e-05
270.9999760657081864.78685836271258e-052.39342918135629e-05
280.9999692459164546.15081670925856e-053.07540835462928e-05
290.999985908582942.81828341208685e-051.40914170604342e-05
300.9999999999999813.77350945840085e-141.88675472920042e-14
310.999999999999862.79161885076541e-131.39580942538271e-13
320.9999999999985412.91741145727446e-121.45870572863723e-12
330.9999999999825183.49646313586391e-111.74823156793196e-11
340.9999999997798814.40237263382954e-102.20118631691477e-10
350.9999999972158875.56822520163284e-092.78411260081642e-09
360.9999999655594066.88811876062237e-083.44405938031118e-08
370.9999995909592968.18081408837048e-074.09040704418524e-07
380.9999954048576669.190284668306e-064.595142334153e-06
390.9999518498073769.63003852484913e-054.81501926242456e-05
400.9995364414265070.0009271171469850570.000463558573492528
4112.67471392728967e-371.33735696364484e-37

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 7.98365832036863e-40 & 1.59673166407373e-39 & 1 \tabularnewline
9 & 8.96017220922493e-50 & 1.79203444184499e-49 & 1 \tabularnewline
10 & 2.16880276269315e-72 & 4.33760552538629e-72 & 1 \tabularnewline
11 & 5.48947691168286e-75 & 1.09789538233657e-74 & 1 \tabularnewline
12 & 1.32917535700119e-85 & 2.65835071400238e-85 & 1 \tabularnewline
13 & 4.34746551731754e-111 & 8.69493103463509e-111 & 1 \tabularnewline
14 & 0.999992261515679 & 1.54769686415272e-05 & 7.73848432076361e-06 \tabularnewline
15 & 0.999996752822862 & 6.49435427518717e-06 & 3.24717713759359e-06 \tabularnewline
16 & 0.999995351278254 & 9.29744349214836e-06 & 4.64872174607418e-06 \tabularnewline
17 & 0.999990677958717 & 1.86440825667158e-05 & 9.32204128335792e-06 \tabularnewline
18 & 0.999979023072311 & 4.19538553771305e-05 & 2.09769276885653e-05 \tabularnewline
19 & 0.999951124368648 & 9.77512627042291e-05 & 4.88756313521145e-05 \tabularnewline
20 & 0.999979891216806 & 4.02175663877038e-05 & 2.01087831938519e-05 \tabularnewline
21 & 0.999999790294858 & 4.19410285032729e-07 & 2.09705142516365e-07 \tabularnewline
22 & 0.999999528916667 & 9.42166666326614e-07 & 4.71083333163307e-07 \tabularnewline
23 & 0.999998866358303 & 2.26728339456737e-06 & 1.13364169728368e-06 \tabularnewline
24 & 0.999997269861481 & 5.46027703754306e-06 & 2.73013851877153e-06 \tabularnewline
25 & 0.999993703535026 & 1.25929299477385e-05 & 6.29646497386926e-06 \tabularnewline
26 & 0.999986699967105 & 2.66000657901359e-05 & 1.3300032895068e-05 \tabularnewline
27 & 0.999976065708186 & 4.78685836271258e-05 & 2.39342918135629e-05 \tabularnewline
28 & 0.999969245916454 & 6.15081670925856e-05 & 3.07540835462928e-05 \tabularnewline
29 & 0.99998590858294 & 2.81828341208685e-05 & 1.40914170604342e-05 \tabularnewline
30 & 0.999999999999981 & 3.77350945840085e-14 & 1.88675472920042e-14 \tabularnewline
31 & 0.99999999999986 & 2.79161885076541e-13 & 1.39580942538271e-13 \tabularnewline
32 & 0.999999999998541 & 2.91741145727446e-12 & 1.45870572863723e-12 \tabularnewline
33 & 0.999999999982518 & 3.49646313586391e-11 & 1.74823156793196e-11 \tabularnewline
34 & 0.999999999779881 & 4.40237263382954e-10 & 2.20118631691477e-10 \tabularnewline
35 & 0.999999997215887 & 5.56822520163284e-09 & 2.78411260081642e-09 \tabularnewline
36 & 0.999999965559406 & 6.88811876062237e-08 & 3.44405938031118e-08 \tabularnewline
37 & 0.999999590959296 & 8.18081408837048e-07 & 4.09040704418524e-07 \tabularnewline
38 & 0.999995404857666 & 9.190284668306e-06 & 4.595142334153e-06 \tabularnewline
39 & 0.999951849807376 & 9.63003852484913e-05 & 4.81501926242456e-05 \tabularnewline
40 & 0.999536441426507 & 0.000927117146985057 & 0.000463558573492528 \tabularnewline
41 & 1 & 2.67471392728967e-37 & 1.33735696364484e-37 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146509&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]8[/C][C]7.98365832036863e-40[/C][C]1.59673166407373e-39[/C][C]1[/C][/ROW]
[ROW][C]9[/C][C]8.96017220922493e-50[/C][C]1.79203444184499e-49[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]2.16880276269315e-72[/C][C]4.33760552538629e-72[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]5.48947691168286e-75[/C][C]1.09789538233657e-74[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]1.32917535700119e-85[/C][C]2.65835071400238e-85[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]4.34746551731754e-111[/C][C]8.69493103463509e-111[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]0.999992261515679[/C][C]1.54769686415272e-05[/C][C]7.73848432076361e-06[/C][/ROW]
[ROW][C]15[/C][C]0.999996752822862[/C][C]6.49435427518717e-06[/C][C]3.24717713759359e-06[/C][/ROW]
[ROW][C]16[/C][C]0.999995351278254[/C][C]9.29744349214836e-06[/C][C]4.64872174607418e-06[/C][/ROW]
[ROW][C]17[/C][C]0.999990677958717[/C][C]1.86440825667158e-05[/C][C]9.32204128335792e-06[/C][/ROW]
[ROW][C]18[/C][C]0.999979023072311[/C][C]4.19538553771305e-05[/C][C]2.09769276885653e-05[/C][/ROW]
[ROW][C]19[/C][C]0.999951124368648[/C][C]9.77512627042291e-05[/C][C]4.88756313521145e-05[/C][/ROW]
[ROW][C]20[/C][C]0.999979891216806[/C][C]4.02175663877038e-05[/C][C]2.01087831938519e-05[/C][/ROW]
[ROW][C]21[/C][C]0.999999790294858[/C][C]4.19410285032729e-07[/C][C]2.09705142516365e-07[/C][/ROW]
[ROW][C]22[/C][C]0.999999528916667[/C][C]9.42166666326614e-07[/C][C]4.71083333163307e-07[/C][/ROW]
[ROW][C]23[/C][C]0.999998866358303[/C][C]2.26728339456737e-06[/C][C]1.13364169728368e-06[/C][/ROW]
[ROW][C]24[/C][C]0.999997269861481[/C][C]5.46027703754306e-06[/C][C]2.73013851877153e-06[/C][/ROW]
[ROW][C]25[/C][C]0.999993703535026[/C][C]1.25929299477385e-05[/C][C]6.29646497386926e-06[/C][/ROW]
[ROW][C]26[/C][C]0.999986699967105[/C][C]2.66000657901359e-05[/C][C]1.3300032895068e-05[/C][/ROW]
[ROW][C]27[/C][C]0.999976065708186[/C][C]4.78685836271258e-05[/C][C]2.39342918135629e-05[/C][/ROW]
[ROW][C]28[/C][C]0.999969245916454[/C][C]6.15081670925856e-05[/C][C]3.07540835462928e-05[/C][/ROW]
[ROW][C]29[/C][C]0.99998590858294[/C][C]2.81828341208685e-05[/C][C]1.40914170604342e-05[/C][/ROW]
[ROW][C]30[/C][C]0.999999999999981[/C][C]3.77350945840085e-14[/C][C]1.88675472920042e-14[/C][/ROW]
[ROW][C]31[/C][C]0.99999999999986[/C][C]2.79161885076541e-13[/C][C]1.39580942538271e-13[/C][/ROW]
[ROW][C]32[/C][C]0.999999999998541[/C][C]2.91741145727446e-12[/C][C]1.45870572863723e-12[/C][/ROW]
[ROW][C]33[/C][C]0.999999999982518[/C][C]3.49646313586391e-11[/C][C]1.74823156793196e-11[/C][/ROW]
[ROW][C]34[/C][C]0.999999999779881[/C][C]4.40237263382954e-10[/C][C]2.20118631691477e-10[/C][/ROW]
[ROW][C]35[/C][C]0.999999997215887[/C][C]5.56822520163284e-09[/C][C]2.78411260081642e-09[/C][/ROW]
[ROW][C]36[/C][C]0.999999965559406[/C][C]6.88811876062237e-08[/C][C]3.44405938031118e-08[/C][/ROW]
[ROW][C]37[/C][C]0.999999590959296[/C][C]8.18081408837048e-07[/C][C]4.09040704418524e-07[/C][/ROW]
[ROW][C]38[/C][C]0.999995404857666[/C][C]9.190284668306e-06[/C][C]4.595142334153e-06[/C][/ROW]
[ROW][C]39[/C][C]0.999951849807376[/C][C]9.63003852484913e-05[/C][C]4.81501926242456e-05[/C][/ROW]
[ROW][C]40[/C][C]0.999536441426507[/C][C]0.000927117146985057[/C][C]0.000463558573492528[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]2.67471392728967e-37[/C][C]1.33735696364484e-37[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146509&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146509&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
87.98365832036863e-401.59673166407373e-391
98.96017220922493e-501.79203444184499e-491
102.16880276269315e-724.33760552538629e-721
115.48947691168286e-751.09789538233657e-741
121.32917535700119e-852.65835071400238e-851
134.34746551731754e-1118.69493103463509e-1111
140.9999922615156791.54769686415272e-057.73848432076361e-06
150.9999967528228626.49435427518717e-063.24717713759359e-06
160.9999953512782549.29744349214836e-064.64872174607418e-06
170.9999906779587171.86440825667158e-059.32204128335792e-06
180.9999790230723114.19538553771305e-052.09769276885653e-05
190.9999511243686489.77512627042291e-054.88756313521145e-05
200.9999798912168064.02175663877038e-052.01087831938519e-05
210.9999997902948584.19410285032729e-072.09705142516365e-07
220.9999995289166679.42166666326614e-074.71083333163307e-07
230.9999988663583032.26728339456737e-061.13364169728368e-06
240.9999972698614815.46027703754306e-062.73013851877153e-06
250.9999937035350261.25929299477385e-056.29646497386926e-06
260.9999866999671052.66000657901359e-051.3300032895068e-05
270.9999760657081864.78685836271258e-052.39342918135629e-05
280.9999692459164546.15081670925856e-053.07540835462928e-05
290.999985908582942.81828341208685e-051.40914170604342e-05
300.9999999999999813.77350945840085e-141.88675472920042e-14
310.999999999999862.79161885076541e-131.39580942538271e-13
320.9999999999985412.91741145727446e-121.45870572863723e-12
330.9999999999825183.49646313586391e-111.74823156793196e-11
340.9999999997798814.40237263382954e-102.20118631691477e-10
350.9999999972158875.56822520163284e-092.78411260081642e-09
360.9999999655594066.88811876062237e-083.44405938031118e-08
370.9999995909592968.18081408837048e-074.09040704418524e-07
380.9999954048576669.190284668306e-064.595142334153e-06
390.9999518498073769.63003852484913e-054.81501926242456e-05
400.9995364414265070.0009271171469850570.000463558573492528
4112.67471392728967e-371.33735696364484e-37







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level341NOK
5% type I error level341NOK
10% type I error level341NOK

\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 & 34 & 1 & NOK \tabularnewline
5% type I error level & 34 & 1 & NOK \tabularnewline
10% type I error level & 34 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146509&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]34[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]34[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]34[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146509&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146509&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 level341NOK
5% type I error level341NOK
10% type I error level341NOK



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')
}