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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 08:03:40 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t12587294988w2s60ky0l765yr.htm/, Retrieved Fri, 29 Mar 2024 11:19:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58247, Retrieved Fri, 29 Mar 2024 11:19:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWS7,MR4
Estimated Impact198
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2009-11-20 15:03:40] [30f5b608e5a1bbbae86b1702c0071566] [Current]
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Dataseries X:
1,1	2,1	1,2	1,3
1,4	2,5	1,1	1,2
1,2	2,2	1,4	1,1
1,5	2,3	1,2	1,4
1,1	2,3	1,5	1,2
1,3	2,2	1,1	1,5
1,5	2,2	1,3	1,1
1,1	1,6	1,5	1,3
1,4	1,8	1,1	1,5
1,3	1,7	1,4	1,1
1,5	1,9	1,3	1,4
1,6	1,8	1,5	1,3
1,7	1,9	1,6	1,5
1,1	1,5	1,7	1,6
1,6	1	1,1	1,7
1,3	0,8	1,6	1,1
1,7	1,1	1,3	1,6
1,6	1,5	1,7	1,3
1,7	1,7	1,6	1,7
1,9	2,3	1,7	1,6
1,8	2,4	1,9	1,7
1,9	3	1,8	1,9
1,6	3	1,9	1,8
1,5	3,2	1,6	1,9
1,6	3,2	1,5	1,6
1,6	3,2	1,6	1,5
1,7	3,5	1,6	1,6
2	4	1,7	1,6
2	4,3	2	1,7
1,9	4,1	2	2
1,7	4	1,9	2
1,8	4,1	1,7	1,9
1,9	4,2	1,8	1,7
1,7	4,5	1,9	1,8
2	5,6	1,7	1,9
2,1	6,5	2	1,7
2,4	7,6	2,1	2
2,5	8,5	2,4	2,1
2,5	8,7	2,5	2,4
2,6	8,3	2,5	2,5
2,2	8,3	2,6	2,5
2,5	8,5	2,2	2,6
2,8	8,7	2,5	2,2
2,8	8,7	2,8	2,5
2,9	8,5	2,8	2,8
3	7,9	2,9	2,8
3,1	7	3	2,9
2,9	5,8	3,1	3
2,7	4,5	2,9	3,1
2,2	3,7	2,7	2,9
2,5	3,1	2,2	2,7
2,3	2,7	2,5	2,2
2,6	2,3	2,3	2,5
2,3	1,8	2,6	2,3
2,2	1,5	2,3	2,6
1,8	1,2	2,2	2,3
1,8	1	1,8	2,2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58247&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]4 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=58247&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58247&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
inflatie[t] = + 0.427930689866057 + 0.0655500588117455inflatie_levensmiddelen[t] + 0.274321206698614`Y(t+1)`[t] + 0.300108421896122`Y(t+2)`[t] + 0.00523791030173268t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
inflatie[t] =  +  0.427930689866057 +  0.0655500588117455inflatie_levensmiddelen[t] +  0.274321206698614`Y(t+1)`[t] +  0.300108421896122`Y(t+2)`[t] +  0.00523791030173268t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58247&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]inflatie[t] =  +  0.427930689866057 +  0.0655500588117455inflatie_levensmiddelen[t] +  0.274321206698614`Y(t+1)`[t] +  0.300108421896122`Y(t+2)`[t] +  0.00523791030173268t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58247&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58247&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
inflatie[t] = + 0.427930689866057 + 0.0655500588117455inflatie_levensmiddelen[t] + 0.274321206698614`Y(t+1)`[t] + 0.300108421896122`Y(t+2)`[t] + 0.00523791030173268t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.4279306898660570.1349953.170.0025550.001278
inflatie_levensmiddelen0.06555005881174550.0144094.54923.3e-051.6e-05
`Y(t+1)`0.2743212066986140.1265092.16840.0347270.017363
`Y(t+2)`0.3001084218961220.1245822.40890.0195770.009789
t0.005237910301732680.0035531.47440.1463970.073198

\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) & 0.427930689866057 & 0.134995 & 3.17 & 0.002555 & 0.001278 \tabularnewline
inflatie_levensmiddelen & 0.0655500588117455 & 0.014409 & 4.5492 & 3.3e-05 & 1.6e-05 \tabularnewline
`Y(t+1)` & 0.274321206698614 & 0.126509 & 2.1684 & 0.034727 & 0.017363 \tabularnewline
`Y(t+2)` & 0.300108421896122 & 0.124582 & 2.4089 & 0.019577 & 0.009789 \tabularnewline
t & 0.00523791030173268 & 0.003553 & 1.4744 & 0.146397 & 0.073198 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58247&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]0.427930689866057[/C][C]0.134995[/C][C]3.17[/C][C]0.002555[/C][C]0.001278[/C][/ROW]
[ROW][C]inflatie_levensmiddelen[/C][C]0.0655500588117455[/C][C]0.014409[/C][C]4.5492[/C][C]3.3e-05[/C][C]1.6e-05[/C][/ROW]
[ROW][C]`Y(t+1)`[/C][C]0.274321206698614[/C][C]0.126509[/C][C]2.1684[/C][C]0.034727[/C][C]0.017363[/C][/ROW]
[ROW][C]`Y(t+2)`[/C][C]0.300108421896122[/C][C]0.124582[/C][C]2.4089[/C][C]0.019577[/C][C]0.009789[/C][/ROW]
[ROW][C]t[/C][C]0.00523791030173268[/C][C]0.003553[/C][C]1.4744[/C][C]0.146397[/C][C]0.073198[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58247&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58247&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)0.4279306898660570.1349953.170.0025550.001278
inflatie_levensmiddelen0.06555005881174550.0144094.54923.3e-051.6e-05
`Y(t+1)`0.2743212066986140.1265092.16840.0347270.017363
`Y(t+2)`0.3001084218961220.1245822.40890.0195770.009789
t0.005237910301732680.0035531.47440.1463970.073198







Multiple Linear Regression - Regression Statistics
Multiple R0.937641068115809
R-squared0.879170772617355
Adjusted R-squared0.869876216664844
F-TEST (value)94.5898628303838
F-TEST (DF numerator)4
F-TEST (DF denominator)52
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.194492190339018
Sum Squared Residuals1.96701502934919

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.937641068115809 \tabularnewline
R-squared & 0.879170772617355 \tabularnewline
Adjusted R-squared & 0.869876216664844 \tabularnewline
F-TEST (value) & 94.5898628303838 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.194492190339018 \tabularnewline
Sum Squared Residuals & 1.96701502934919 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58247&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.937641068115809[/C][/ROW]
[ROW][C]R-squared[/C][C]0.879170772617355[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.869876216664844[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]94.5898628303838[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/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]0.194492190339018[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.96701502934919[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58247&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58247&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 R0.937641068115809
R-squared0.879170772617355
Adjusted R-squared0.869876216664844
F-TEST (value)94.5898628303838
F-TEST (DF numerator)4
F-TEST (DF denominator)52
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.194492190339018
Sum Squared Residuals1.96701502934919







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.11.29015012017575-0.190150120175748
21.41.264165091142710.135834908857292
31.21.30202350362089-0.102023503620888
41.51.348984705032910.151015294967091
51.11.376497292965-0.276497292965001
61.31.35548424127495-0.0554842412749506
71.51.295543024157960.204456975842043
81.11.37633682489159-0.27633682489159
91.41.344977948655450.0550220513445495
101.31.30591384632714-0.00591384632714398
111.51.38686217429020.113137825709799
121.61.410398477860870.189601522139130
131.71.509645199092860.190354800907137
141.11.54610604872937-0.446106048729371
151.61.383987047795670.216012952204326
161.31.33321049654669-0.0332104965466920
171.71.425871273430430.274128726569575
181.61.477025163367460.122974836632535
191.71.587984333520130.112015666479866
201.91.629973557589160.270026442410837
211.81.726641557301410.0733584426985947
221.91.803799066599550.0962009334004516
231.61.80645825538153-0.20645825538153
241.51.77252065762564-0.27252065762564
251.61.66029392068867-0.0602939206886746
261.61.66295310947066-0.0629531094706565
271.71.71786687960552-0.0178668796055251
2821.783311939982990.216688060017008
2921.920522072127440.0794779278725556
301.92.00268249723566-0.102682497235665
311.71.97393328098636-0.273933280986361
321.81.90085111363993-0.100851113639934
331.91.880054466113480.0199455338865220
341.71.96240035691821-0.262400356918208
3522.01488993276275-0.0148899327627497
362.12.10139757362541-0.00139757362541303
372.42.296205195858760.103794804141236
382.52.472745363290260.0272546367097364
392.52.60855793259304-0.108557932593043
402.62.61758666155969-0.0175866615596900
412.22.65025669253128-0.450256692531284
422.52.58888697410553-0.0888869741055326
432.82.569487889420750.23051211057925
442.82.74705468830090.0529453116990967
452.92.829215113409120.0707848865908768
4632.822555109093670.17744489090633
473.12.826240929324310.273759070675694
482.92.810261731911420.0897382680885825
492.72.70543116660777-0.00543116660777018
502.22.54334310414116-0.343343104141159
512.52.312068691427310.187931308572686
522.32.223328729265870.0766712707341285
532.62.237514901272020.362485098727981
542.32.232252459798240.0677475402017604
552.22.2255615170157-0.0255615170157007
561.82.09366976243521-0.293669762435212
571.81.94605833610554-0.146058336105538

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.1 & 1.29015012017575 & -0.190150120175748 \tabularnewline
2 & 1.4 & 1.26416509114271 & 0.135834908857292 \tabularnewline
3 & 1.2 & 1.30202350362089 & -0.102023503620888 \tabularnewline
4 & 1.5 & 1.34898470503291 & 0.151015294967091 \tabularnewline
5 & 1.1 & 1.376497292965 & -0.276497292965001 \tabularnewline
6 & 1.3 & 1.35548424127495 & -0.0554842412749506 \tabularnewline
7 & 1.5 & 1.29554302415796 & 0.204456975842043 \tabularnewline
8 & 1.1 & 1.37633682489159 & -0.27633682489159 \tabularnewline
9 & 1.4 & 1.34497794865545 & 0.0550220513445495 \tabularnewline
10 & 1.3 & 1.30591384632714 & -0.00591384632714398 \tabularnewline
11 & 1.5 & 1.3868621742902 & 0.113137825709799 \tabularnewline
12 & 1.6 & 1.41039847786087 & 0.189601522139130 \tabularnewline
13 & 1.7 & 1.50964519909286 & 0.190354800907137 \tabularnewline
14 & 1.1 & 1.54610604872937 & -0.446106048729371 \tabularnewline
15 & 1.6 & 1.38398704779567 & 0.216012952204326 \tabularnewline
16 & 1.3 & 1.33321049654669 & -0.0332104965466920 \tabularnewline
17 & 1.7 & 1.42587127343043 & 0.274128726569575 \tabularnewline
18 & 1.6 & 1.47702516336746 & 0.122974836632535 \tabularnewline
19 & 1.7 & 1.58798433352013 & 0.112015666479866 \tabularnewline
20 & 1.9 & 1.62997355758916 & 0.270026442410837 \tabularnewline
21 & 1.8 & 1.72664155730141 & 0.0733584426985947 \tabularnewline
22 & 1.9 & 1.80379906659955 & 0.0962009334004516 \tabularnewline
23 & 1.6 & 1.80645825538153 & -0.20645825538153 \tabularnewline
24 & 1.5 & 1.77252065762564 & -0.27252065762564 \tabularnewline
25 & 1.6 & 1.66029392068867 & -0.0602939206886746 \tabularnewline
26 & 1.6 & 1.66295310947066 & -0.0629531094706565 \tabularnewline
27 & 1.7 & 1.71786687960552 & -0.0178668796055251 \tabularnewline
28 & 2 & 1.78331193998299 & 0.216688060017008 \tabularnewline
29 & 2 & 1.92052207212744 & 0.0794779278725556 \tabularnewline
30 & 1.9 & 2.00268249723566 & -0.102682497235665 \tabularnewline
31 & 1.7 & 1.97393328098636 & -0.273933280986361 \tabularnewline
32 & 1.8 & 1.90085111363993 & -0.100851113639934 \tabularnewline
33 & 1.9 & 1.88005446611348 & 0.0199455338865220 \tabularnewline
34 & 1.7 & 1.96240035691821 & -0.262400356918208 \tabularnewline
35 & 2 & 2.01488993276275 & -0.0148899327627497 \tabularnewline
36 & 2.1 & 2.10139757362541 & -0.00139757362541303 \tabularnewline
37 & 2.4 & 2.29620519585876 & 0.103794804141236 \tabularnewline
38 & 2.5 & 2.47274536329026 & 0.0272546367097364 \tabularnewline
39 & 2.5 & 2.60855793259304 & -0.108557932593043 \tabularnewline
40 & 2.6 & 2.61758666155969 & -0.0175866615596900 \tabularnewline
41 & 2.2 & 2.65025669253128 & -0.450256692531284 \tabularnewline
42 & 2.5 & 2.58888697410553 & -0.0888869741055326 \tabularnewline
43 & 2.8 & 2.56948788942075 & 0.23051211057925 \tabularnewline
44 & 2.8 & 2.7470546883009 & 0.0529453116990967 \tabularnewline
45 & 2.9 & 2.82921511340912 & 0.0707848865908768 \tabularnewline
46 & 3 & 2.82255510909367 & 0.17744489090633 \tabularnewline
47 & 3.1 & 2.82624092932431 & 0.273759070675694 \tabularnewline
48 & 2.9 & 2.81026173191142 & 0.0897382680885825 \tabularnewline
49 & 2.7 & 2.70543116660777 & -0.00543116660777018 \tabularnewline
50 & 2.2 & 2.54334310414116 & -0.343343104141159 \tabularnewline
51 & 2.5 & 2.31206869142731 & 0.187931308572686 \tabularnewline
52 & 2.3 & 2.22332872926587 & 0.0766712707341285 \tabularnewline
53 & 2.6 & 2.23751490127202 & 0.362485098727981 \tabularnewline
54 & 2.3 & 2.23225245979824 & 0.0677475402017604 \tabularnewline
55 & 2.2 & 2.2255615170157 & -0.0255615170157007 \tabularnewline
56 & 1.8 & 2.09366976243521 & -0.293669762435212 \tabularnewline
57 & 1.8 & 1.94605833610554 & -0.146058336105538 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58247&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]1.1[/C][C]1.29015012017575[/C][C]-0.190150120175748[/C][/ROW]
[ROW][C]2[/C][C]1.4[/C][C]1.26416509114271[/C][C]0.135834908857292[/C][/ROW]
[ROW][C]3[/C][C]1.2[/C][C]1.30202350362089[/C][C]-0.102023503620888[/C][/ROW]
[ROW][C]4[/C][C]1.5[/C][C]1.34898470503291[/C][C]0.151015294967091[/C][/ROW]
[ROW][C]5[/C][C]1.1[/C][C]1.376497292965[/C][C]-0.276497292965001[/C][/ROW]
[ROW][C]6[/C][C]1.3[/C][C]1.35548424127495[/C][C]-0.0554842412749506[/C][/ROW]
[ROW][C]7[/C][C]1.5[/C][C]1.29554302415796[/C][C]0.204456975842043[/C][/ROW]
[ROW][C]8[/C][C]1.1[/C][C]1.37633682489159[/C][C]-0.27633682489159[/C][/ROW]
[ROW][C]9[/C][C]1.4[/C][C]1.34497794865545[/C][C]0.0550220513445495[/C][/ROW]
[ROW][C]10[/C][C]1.3[/C][C]1.30591384632714[/C][C]-0.00591384632714398[/C][/ROW]
[ROW][C]11[/C][C]1.5[/C][C]1.3868621742902[/C][C]0.113137825709799[/C][/ROW]
[ROW][C]12[/C][C]1.6[/C][C]1.41039847786087[/C][C]0.189601522139130[/C][/ROW]
[ROW][C]13[/C][C]1.7[/C][C]1.50964519909286[/C][C]0.190354800907137[/C][/ROW]
[ROW][C]14[/C][C]1.1[/C][C]1.54610604872937[/C][C]-0.446106048729371[/C][/ROW]
[ROW][C]15[/C][C]1.6[/C][C]1.38398704779567[/C][C]0.216012952204326[/C][/ROW]
[ROW][C]16[/C][C]1.3[/C][C]1.33321049654669[/C][C]-0.0332104965466920[/C][/ROW]
[ROW][C]17[/C][C]1.7[/C][C]1.42587127343043[/C][C]0.274128726569575[/C][/ROW]
[ROW][C]18[/C][C]1.6[/C][C]1.47702516336746[/C][C]0.122974836632535[/C][/ROW]
[ROW][C]19[/C][C]1.7[/C][C]1.58798433352013[/C][C]0.112015666479866[/C][/ROW]
[ROW][C]20[/C][C]1.9[/C][C]1.62997355758916[/C][C]0.270026442410837[/C][/ROW]
[ROW][C]21[/C][C]1.8[/C][C]1.72664155730141[/C][C]0.0733584426985947[/C][/ROW]
[ROW][C]22[/C][C]1.9[/C][C]1.80379906659955[/C][C]0.0962009334004516[/C][/ROW]
[ROW][C]23[/C][C]1.6[/C][C]1.80645825538153[/C][C]-0.20645825538153[/C][/ROW]
[ROW][C]24[/C][C]1.5[/C][C]1.77252065762564[/C][C]-0.27252065762564[/C][/ROW]
[ROW][C]25[/C][C]1.6[/C][C]1.66029392068867[/C][C]-0.0602939206886746[/C][/ROW]
[ROW][C]26[/C][C]1.6[/C][C]1.66295310947066[/C][C]-0.0629531094706565[/C][/ROW]
[ROW][C]27[/C][C]1.7[/C][C]1.71786687960552[/C][C]-0.0178668796055251[/C][/ROW]
[ROW][C]28[/C][C]2[/C][C]1.78331193998299[/C][C]0.216688060017008[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]1.92052207212744[/C][C]0.0794779278725556[/C][/ROW]
[ROW][C]30[/C][C]1.9[/C][C]2.00268249723566[/C][C]-0.102682497235665[/C][/ROW]
[ROW][C]31[/C][C]1.7[/C][C]1.97393328098636[/C][C]-0.273933280986361[/C][/ROW]
[ROW][C]32[/C][C]1.8[/C][C]1.90085111363993[/C][C]-0.100851113639934[/C][/ROW]
[ROW][C]33[/C][C]1.9[/C][C]1.88005446611348[/C][C]0.0199455338865220[/C][/ROW]
[ROW][C]34[/C][C]1.7[/C][C]1.96240035691821[/C][C]-0.262400356918208[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]2.01488993276275[/C][C]-0.0148899327627497[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.10139757362541[/C][C]-0.00139757362541303[/C][/ROW]
[ROW][C]37[/C][C]2.4[/C][C]2.29620519585876[/C][C]0.103794804141236[/C][/ROW]
[ROW][C]38[/C][C]2.5[/C][C]2.47274536329026[/C][C]0.0272546367097364[/C][/ROW]
[ROW][C]39[/C][C]2.5[/C][C]2.60855793259304[/C][C]-0.108557932593043[/C][/ROW]
[ROW][C]40[/C][C]2.6[/C][C]2.61758666155969[/C][C]-0.0175866615596900[/C][/ROW]
[ROW][C]41[/C][C]2.2[/C][C]2.65025669253128[/C][C]-0.450256692531284[/C][/ROW]
[ROW][C]42[/C][C]2.5[/C][C]2.58888697410553[/C][C]-0.0888869741055326[/C][/ROW]
[ROW][C]43[/C][C]2.8[/C][C]2.56948788942075[/C][C]0.23051211057925[/C][/ROW]
[ROW][C]44[/C][C]2.8[/C][C]2.7470546883009[/C][C]0.0529453116990967[/C][/ROW]
[ROW][C]45[/C][C]2.9[/C][C]2.82921511340912[/C][C]0.0707848865908768[/C][/ROW]
[ROW][C]46[/C][C]3[/C][C]2.82255510909367[/C][C]0.17744489090633[/C][/ROW]
[ROW][C]47[/C][C]3.1[/C][C]2.82624092932431[/C][C]0.273759070675694[/C][/ROW]
[ROW][C]48[/C][C]2.9[/C][C]2.81026173191142[/C][C]0.0897382680885825[/C][/ROW]
[ROW][C]49[/C][C]2.7[/C][C]2.70543116660777[/C][C]-0.00543116660777018[/C][/ROW]
[ROW][C]50[/C][C]2.2[/C][C]2.54334310414116[/C][C]-0.343343104141159[/C][/ROW]
[ROW][C]51[/C][C]2.5[/C][C]2.31206869142731[/C][C]0.187931308572686[/C][/ROW]
[ROW][C]52[/C][C]2.3[/C][C]2.22332872926587[/C][C]0.0766712707341285[/C][/ROW]
[ROW][C]53[/C][C]2.6[/C][C]2.23751490127202[/C][C]0.362485098727981[/C][/ROW]
[ROW][C]54[/C][C]2.3[/C][C]2.23225245979824[/C][C]0.0677475402017604[/C][/ROW]
[ROW][C]55[/C][C]2.2[/C][C]2.2255615170157[/C][C]-0.0255615170157007[/C][/ROW]
[ROW][C]56[/C][C]1.8[/C][C]2.09366976243521[/C][C]-0.293669762435212[/C][/ROW]
[ROW][C]57[/C][C]1.8[/C][C]1.94605833610554[/C][C]-0.146058336105538[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58247&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58247&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
11.11.29015012017575-0.190150120175748
21.41.264165091142710.135834908857292
31.21.30202350362089-0.102023503620888
41.51.348984705032910.151015294967091
51.11.376497292965-0.276497292965001
61.31.35548424127495-0.0554842412749506
71.51.295543024157960.204456975842043
81.11.37633682489159-0.27633682489159
91.41.344977948655450.0550220513445495
101.31.30591384632714-0.00591384632714398
111.51.38686217429020.113137825709799
121.61.410398477860870.189601522139130
131.71.509645199092860.190354800907137
141.11.54610604872937-0.446106048729371
151.61.383987047795670.216012952204326
161.31.33321049654669-0.0332104965466920
171.71.425871273430430.274128726569575
181.61.477025163367460.122974836632535
191.71.587984333520130.112015666479866
201.91.629973557589160.270026442410837
211.81.726641557301410.0733584426985947
221.91.803799066599550.0962009334004516
231.61.80645825538153-0.20645825538153
241.51.77252065762564-0.27252065762564
251.61.66029392068867-0.0602939206886746
261.61.66295310947066-0.0629531094706565
271.71.71786687960552-0.0178668796055251
2821.783311939982990.216688060017008
2921.920522072127440.0794779278725556
301.92.00268249723566-0.102682497235665
311.71.97393328098636-0.273933280986361
321.81.90085111363993-0.100851113639934
331.91.880054466113480.0199455338865220
341.71.96240035691821-0.262400356918208
3522.01488993276275-0.0148899327627497
362.12.10139757362541-0.00139757362541303
372.42.296205195858760.103794804141236
382.52.472745363290260.0272546367097364
392.52.60855793259304-0.108557932593043
402.62.61758666155969-0.0175866615596900
412.22.65025669253128-0.450256692531284
422.52.58888697410553-0.0888869741055326
432.82.569487889420750.23051211057925
442.82.74705468830090.0529453116990967
452.92.829215113409120.0707848865908768
4632.822555109093670.17744489090633
473.12.826240929324310.273759070675694
482.92.810261731911420.0897382680885825
492.72.70543116660777-0.00543116660777018
502.22.54334310414116-0.343343104141159
512.52.312068691427310.187931308572686
522.32.223328729265870.0766712707341285
532.62.237514901272020.362485098727981
542.32.232252459798240.0677475402017604
552.22.2255615170157-0.0255615170157007
561.82.09366976243521-0.293669762435212
571.81.94605833610554-0.146058336105538







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.3559279540937030.7118559081874060.644072045906297
90.2044739220607580.4089478441215160.795526077939242
100.1125969830334330.2251939660668660.887403016966567
110.06418418715372020.1283683743074400.93581581284628
120.09595600601392520.1919120120278500.904043993986075
130.08850506305679510.1770101261135900.911494936943205
140.1873486527927740.3746973055855470.812651347207226
150.2001488020985200.4002976041970390.79985119790148
160.1399275786797110.2798551573594230.860072421320289
170.1342385077871490.2684770155742980.865761492212851
180.08945328484456830.1789065696891370.910546715155432
190.0638774153365930.1277548306731860.936122584663407
200.0604618839568040.1209237679136080.939538116043196
210.04221636661311950.0844327332262390.95778363338688
220.05611324223848920.1122264844769780.943886757761511
230.1766103815196660.3532207630393310.823389618480334
240.5324291199848750.935141760030250.467570880015125
250.5466318543307170.9067362913385650.453368145669283
260.4878643388935220.9757286777870440.512135661106478
270.4102194419820360.8204388839640710.589780558017964
280.4776081964929440.9552163929858870.522391803507056
290.4531425897180710.9062851794361430.546857410281929
300.3761655733006140.7523311466012280.623834426699386
310.3679638178635390.7359276357270770.632036182136462
320.3086910109724140.6173820219448270.691308989027586
330.2613147777602570.5226295555205140.738685222239743
340.2518054649930360.5036109299860730.748194535006964
350.1985143506016040.3970287012032080.801485649398396
360.1557584523029720.3115169046059440.844241547697028
370.1875933553141120.3751867106282240.812406644685888
380.1731426569916530.3462853139833060.826857343008347
390.1232505090515380.2465010181030760.876749490948462
400.1011373432202040.2022746864404070.898862656779796
410.247259325765060.494518651530120.75274067423494
420.2309486811242500.4618973622485010.76905131887575
430.2078956935287970.4157913870575940.792104306471203
440.2062884777901660.4125769555803310.793711522209834
450.2013511774313730.4027023548627450.798648822568627
460.1744382869375760.3488765738751530.825561713062424
470.1330195262558860.2660390525117730.866980473744114
480.1146382423234330.2292764846468670.885361757676567
490.423522753708670.847045507417340.57647724629133

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.355927954093703 & 0.711855908187406 & 0.644072045906297 \tabularnewline
9 & 0.204473922060758 & 0.408947844121516 & 0.795526077939242 \tabularnewline
10 & 0.112596983033433 & 0.225193966066866 & 0.887403016966567 \tabularnewline
11 & 0.0641841871537202 & 0.128368374307440 & 0.93581581284628 \tabularnewline
12 & 0.0959560060139252 & 0.191912012027850 & 0.904043993986075 \tabularnewline
13 & 0.0885050630567951 & 0.177010126113590 & 0.911494936943205 \tabularnewline
14 & 0.187348652792774 & 0.374697305585547 & 0.812651347207226 \tabularnewline
15 & 0.200148802098520 & 0.400297604197039 & 0.79985119790148 \tabularnewline
16 & 0.139927578679711 & 0.279855157359423 & 0.860072421320289 \tabularnewline
17 & 0.134238507787149 & 0.268477015574298 & 0.865761492212851 \tabularnewline
18 & 0.0894532848445683 & 0.178906569689137 & 0.910546715155432 \tabularnewline
19 & 0.063877415336593 & 0.127754830673186 & 0.936122584663407 \tabularnewline
20 & 0.060461883956804 & 0.120923767913608 & 0.939538116043196 \tabularnewline
21 & 0.0422163666131195 & 0.084432733226239 & 0.95778363338688 \tabularnewline
22 & 0.0561132422384892 & 0.112226484476978 & 0.943886757761511 \tabularnewline
23 & 0.176610381519666 & 0.353220763039331 & 0.823389618480334 \tabularnewline
24 & 0.532429119984875 & 0.93514176003025 & 0.467570880015125 \tabularnewline
25 & 0.546631854330717 & 0.906736291338565 & 0.453368145669283 \tabularnewline
26 & 0.487864338893522 & 0.975728677787044 & 0.512135661106478 \tabularnewline
27 & 0.410219441982036 & 0.820438883964071 & 0.589780558017964 \tabularnewline
28 & 0.477608196492944 & 0.955216392985887 & 0.522391803507056 \tabularnewline
29 & 0.453142589718071 & 0.906285179436143 & 0.546857410281929 \tabularnewline
30 & 0.376165573300614 & 0.752331146601228 & 0.623834426699386 \tabularnewline
31 & 0.367963817863539 & 0.735927635727077 & 0.632036182136462 \tabularnewline
32 & 0.308691010972414 & 0.617382021944827 & 0.691308989027586 \tabularnewline
33 & 0.261314777760257 & 0.522629555520514 & 0.738685222239743 \tabularnewline
34 & 0.251805464993036 & 0.503610929986073 & 0.748194535006964 \tabularnewline
35 & 0.198514350601604 & 0.397028701203208 & 0.801485649398396 \tabularnewline
36 & 0.155758452302972 & 0.311516904605944 & 0.844241547697028 \tabularnewline
37 & 0.187593355314112 & 0.375186710628224 & 0.812406644685888 \tabularnewline
38 & 0.173142656991653 & 0.346285313983306 & 0.826857343008347 \tabularnewline
39 & 0.123250509051538 & 0.246501018103076 & 0.876749490948462 \tabularnewline
40 & 0.101137343220204 & 0.202274686440407 & 0.898862656779796 \tabularnewline
41 & 0.24725932576506 & 0.49451865153012 & 0.75274067423494 \tabularnewline
42 & 0.230948681124250 & 0.461897362248501 & 0.76905131887575 \tabularnewline
43 & 0.207895693528797 & 0.415791387057594 & 0.792104306471203 \tabularnewline
44 & 0.206288477790166 & 0.412576955580331 & 0.793711522209834 \tabularnewline
45 & 0.201351177431373 & 0.402702354862745 & 0.798648822568627 \tabularnewline
46 & 0.174438286937576 & 0.348876573875153 & 0.825561713062424 \tabularnewline
47 & 0.133019526255886 & 0.266039052511773 & 0.866980473744114 \tabularnewline
48 & 0.114638242323433 & 0.229276484646867 & 0.885361757676567 \tabularnewline
49 & 0.42352275370867 & 0.84704550741734 & 0.57647724629133 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58247&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]0.355927954093703[/C][C]0.711855908187406[/C][C]0.644072045906297[/C][/ROW]
[ROW][C]9[/C][C]0.204473922060758[/C][C]0.408947844121516[/C][C]0.795526077939242[/C][/ROW]
[ROW][C]10[/C][C]0.112596983033433[/C][C]0.225193966066866[/C][C]0.887403016966567[/C][/ROW]
[ROW][C]11[/C][C]0.0641841871537202[/C][C]0.128368374307440[/C][C]0.93581581284628[/C][/ROW]
[ROW][C]12[/C][C]0.0959560060139252[/C][C]0.191912012027850[/C][C]0.904043993986075[/C][/ROW]
[ROW][C]13[/C][C]0.0885050630567951[/C][C]0.177010126113590[/C][C]0.911494936943205[/C][/ROW]
[ROW][C]14[/C][C]0.187348652792774[/C][C]0.374697305585547[/C][C]0.812651347207226[/C][/ROW]
[ROW][C]15[/C][C]0.200148802098520[/C][C]0.400297604197039[/C][C]0.79985119790148[/C][/ROW]
[ROW][C]16[/C][C]0.139927578679711[/C][C]0.279855157359423[/C][C]0.860072421320289[/C][/ROW]
[ROW][C]17[/C][C]0.134238507787149[/C][C]0.268477015574298[/C][C]0.865761492212851[/C][/ROW]
[ROW][C]18[/C][C]0.0894532848445683[/C][C]0.178906569689137[/C][C]0.910546715155432[/C][/ROW]
[ROW][C]19[/C][C]0.063877415336593[/C][C]0.127754830673186[/C][C]0.936122584663407[/C][/ROW]
[ROW][C]20[/C][C]0.060461883956804[/C][C]0.120923767913608[/C][C]0.939538116043196[/C][/ROW]
[ROW][C]21[/C][C]0.0422163666131195[/C][C]0.084432733226239[/C][C]0.95778363338688[/C][/ROW]
[ROW][C]22[/C][C]0.0561132422384892[/C][C]0.112226484476978[/C][C]0.943886757761511[/C][/ROW]
[ROW][C]23[/C][C]0.176610381519666[/C][C]0.353220763039331[/C][C]0.823389618480334[/C][/ROW]
[ROW][C]24[/C][C]0.532429119984875[/C][C]0.93514176003025[/C][C]0.467570880015125[/C][/ROW]
[ROW][C]25[/C][C]0.546631854330717[/C][C]0.906736291338565[/C][C]0.453368145669283[/C][/ROW]
[ROW][C]26[/C][C]0.487864338893522[/C][C]0.975728677787044[/C][C]0.512135661106478[/C][/ROW]
[ROW][C]27[/C][C]0.410219441982036[/C][C]0.820438883964071[/C][C]0.589780558017964[/C][/ROW]
[ROW][C]28[/C][C]0.477608196492944[/C][C]0.955216392985887[/C][C]0.522391803507056[/C][/ROW]
[ROW][C]29[/C][C]0.453142589718071[/C][C]0.906285179436143[/C][C]0.546857410281929[/C][/ROW]
[ROW][C]30[/C][C]0.376165573300614[/C][C]0.752331146601228[/C][C]0.623834426699386[/C][/ROW]
[ROW][C]31[/C][C]0.367963817863539[/C][C]0.735927635727077[/C][C]0.632036182136462[/C][/ROW]
[ROW][C]32[/C][C]0.308691010972414[/C][C]0.617382021944827[/C][C]0.691308989027586[/C][/ROW]
[ROW][C]33[/C][C]0.261314777760257[/C][C]0.522629555520514[/C][C]0.738685222239743[/C][/ROW]
[ROW][C]34[/C][C]0.251805464993036[/C][C]0.503610929986073[/C][C]0.748194535006964[/C][/ROW]
[ROW][C]35[/C][C]0.198514350601604[/C][C]0.397028701203208[/C][C]0.801485649398396[/C][/ROW]
[ROW][C]36[/C][C]0.155758452302972[/C][C]0.311516904605944[/C][C]0.844241547697028[/C][/ROW]
[ROW][C]37[/C][C]0.187593355314112[/C][C]0.375186710628224[/C][C]0.812406644685888[/C][/ROW]
[ROW][C]38[/C][C]0.173142656991653[/C][C]0.346285313983306[/C][C]0.826857343008347[/C][/ROW]
[ROW][C]39[/C][C]0.123250509051538[/C][C]0.246501018103076[/C][C]0.876749490948462[/C][/ROW]
[ROW][C]40[/C][C]0.101137343220204[/C][C]0.202274686440407[/C][C]0.898862656779796[/C][/ROW]
[ROW][C]41[/C][C]0.24725932576506[/C][C]0.49451865153012[/C][C]0.75274067423494[/C][/ROW]
[ROW][C]42[/C][C]0.230948681124250[/C][C]0.461897362248501[/C][C]0.76905131887575[/C][/ROW]
[ROW][C]43[/C][C]0.207895693528797[/C][C]0.415791387057594[/C][C]0.792104306471203[/C][/ROW]
[ROW][C]44[/C][C]0.206288477790166[/C][C]0.412576955580331[/C][C]0.793711522209834[/C][/ROW]
[ROW][C]45[/C][C]0.201351177431373[/C][C]0.402702354862745[/C][C]0.798648822568627[/C][/ROW]
[ROW][C]46[/C][C]0.174438286937576[/C][C]0.348876573875153[/C][C]0.825561713062424[/C][/ROW]
[ROW][C]47[/C][C]0.133019526255886[/C][C]0.266039052511773[/C][C]0.866980473744114[/C][/ROW]
[ROW][C]48[/C][C]0.114638242323433[/C][C]0.229276484646867[/C][C]0.885361757676567[/C][/ROW]
[ROW][C]49[/C][C]0.42352275370867[/C][C]0.84704550741734[/C][C]0.57647724629133[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58247&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58247&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
80.3559279540937030.7118559081874060.644072045906297
90.2044739220607580.4089478441215160.795526077939242
100.1125969830334330.2251939660668660.887403016966567
110.06418418715372020.1283683743074400.93581581284628
120.09595600601392520.1919120120278500.904043993986075
130.08850506305679510.1770101261135900.911494936943205
140.1873486527927740.3746973055855470.812651347207226
150.2001488020985200.4002976041970390.79985119790148
160.1399275786797110.2798551573594230.860072421320289
170.1342385077871490.2684770155742980.865761492212851
180.08945328484456830.1789065696891370.910546715155432
190.0638774153365930.1277548306731860.936122584663407
200.0604618839568040.1209237679136080.939538116043196
210.04221636661311950.0844327332262390.95778363338688
220.05611324223848920.1122264844769780.943886757761511
230.1766103815196660.3532207630393310.823389618480334
240.5324291199848750.935141760030250.467570880015125
250.5466318543307170.9067362913385650.453368145669283
260.4878643388935220.9757286777870440.512135661106478
270.4102194419820360.8204388839640710.589780558017964
280.4776081964929440.9552163929858870.522391803507056
290.4531425897180710.9062851794361430.546857410281929
300.3761655733006140.7523311466012280.623834426699386
310.3679638178635390.7359276357270770.632036182136462
320.3086910109724140.6173820219448270.691308989027586
330.2613147777602570.5226295555205140.738685222239743
340.2518054649930360.5036109299860730.748194535006964
350.1985143506016040.3970287012032080.801485649398396
360.1557584523029720.3115169046059440.844241547697028
370.1875933553141120.3751867106282240.812406644685888
380.1731426569916530.3462853139833060.826857343008347
390.1232505090515380.2465010181030760.876749490948462
400.1011373432202040.2022746864404070.898862656779796
410.247259325765060.494518651530120.75274067423494
420.2309486811242500.4618973622485010.76905131887575
430.2078956935287970.4157913870575940.792104306471203
440.2062884777901660.4125769555803310.793711522209834
450.2013511774313730.4027023548627450.798648822568627
460.1744382869375760.3488765738751530.825561713062424
470.1330195262558860.2660390525117730.866980473744114
480.1146382423234330.2292764846468670.885361757676567
490.423522753708670.847045507417340.57647724629133







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 level10.0238095238095238OK

\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 & 1 & 0.0238095238095238 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58247&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]1[/C][C]0.0238095238095238[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58247&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58247&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 level10.0238095238095238OK



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