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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 05:32:59 -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/t1258720452nlzvlpo8wbg1gcp.htm/, Retrieved Fri, 29 Mar 2024 14:38:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58084, Retrieved Fri, 29 Mar 2024 14:38:24 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [] [2009-11-20 12:32:59] [fbab597368601c68e80be601720d8ff9] [Current]
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Dataseries X:
1.4	2
1.2	2
1	2
1.7	2
2.4	2
2	2
2.1	2
2	2
1.8	2
2.7	2
2.3	2
1.9	2
2	2
2.3	2
2.8	2
2.4	2
2.3	2
2.7	2
2.7	2
2.9	2
3	2
2.2	2
2.3	2
2.8	2.21
2.8	2.25
2.8	2.25
2.2	2.45
2.6	2.5
2.8	2.5
2.5	2.64
2.4	2.75
2.3	2.93
1.9	3
1.7	3.17
2	3.25
2.1	3.39
1.7	3.5
1.8	3.5
1.8	3.65
1.8	3.75
1.3	3.75
1.3	3.9
1.3	4
1.2	4
1.4	4
2.2	4
2.9	4
3.1	4
3.5	4
3.6	4
4.4	4
4.1	4
5.1	4
5.8	4
5.9	4.18
5.4	4.25
5.5	4.25
4.8	3.97
3.2	3.42
2.7	2.75




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=58084&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=58084&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58084&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
Inflatie[t] = + 0.94880597817748 + 0.547454363004362rente[t] -0.174305476439478M1[t] -0.114305476439477M2[t] -0.0526272818497814M3[t] + 0.0109490872600876M4[t] + 0.270949087260088M5[t] + 0.319196734205835M6[t] + 0.296495293891495M7[t] + 0.149122575741277M8[t] + 0.101458214659215M9[t] + 0.113502210645311M10[t] -0.0150370792322786M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Inflatie[t] =  +  0.94880597817748 +  0.547454363004362rente[t] -0.174305476439478M1[t] -0.114305476439477M2[t] -0.0526272818497814M3[t] +  0.0109490872600876M4[t] +  0.270949087260088M5[t] +  0.319196734205835M6[t] +  0.296495293891495M7[t] +  0.149122575741277M8[t] +  0.101458214659215M9[t] +  0.113502210645311M10[t] -0.0150370792322786M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58084&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Inflatie[t] =  +  0.94880597817748 +  0.547454363004362rente[t] -0.174305476439478M1[t] -0.114305476439477M2[t] -0.0526272818497814M3[t] +  0.0109490872600876M4[t] +  0.270949087260088M5[t] +  0.319196734205835M6[t] +  0.296495293891495M7[t] +  0.149122575741277M8[t] +  0.101458214659215M9[t] +  0.113502210645311M10[t] -0.0150370792322786M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58084&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58084&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] = + 0.94880597817748 + 0.547454363004362rente[t] -0.174305476439478M1[t] -0.114305476439477M2[t] -0.0526272818497814M3[t] + 0.0109490872600876M4[t] + 0.270949087260088M5[t] + 0.319196734205835M6[t] + 0.296495293891495M7[t] + 0.149122575741277M8[t] + 0.101458214659215M9[t] + 0.113502210645311M10[t] -0.0150370792322786M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.948805978177480.723871.31070.1963140.098157
rente0.5474543630043620.1739883.14650.0028660.001433
M1-0.1743054764394780.74143-0.23510.8151570.407579
M2-0.1143054764394770.74143-0.15420.8781370.439068
M3-0.05262728184978140.741187-0.0710.9436960.471848
M40.01094908726008760.7411440.01480.9882760.494138
M50.2709490872600880.7411440.36560.7163170.358158
M60.3191967342058350.7411650.43070.6686780.334339
M70.2964952938914950.741410.39990.6910370.345518
M80.1491225757412770.7416980.20110.8415230.420761
M90.1014582146592150.7417970.13680.8917940.445897
M100.1135022106453110.7416450.1530.8790210.439511
M11-0.01503707923227860.741219-0.02030.98390.49195

\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) & 0.94880597817748 & 0.72387 & 1.3107 & 0.196314 & 0.098157 \tabularnewline
rente & 0.547454363004362 & 0.173988 & 3.1465 & 0.002866 & 0.001433 \tabularnewline
M1 & -0.174305476439478 & 0.74143 & -0.2351 & 0.815157 & 0.407579 \tabularnewline
M2 & -0.114305476439477 & 0.74143 & -0.1542 & 0.878137 & 0.439068 \tabularnewline
M3 & -0.0526272818497814 & 0.741187 & -0.071 & 0.943696 & 0.471848 \tabularnewline
M4 & 0.0109490872600876 & 0.741144 & 0.0148 & 0.988276 & 0.494138 \tabularnewline
M5 & 0.270949087260088 & 0.741144 & 0.3656 & 0.716317 & 0.358158 \tabularnewline
M6 & 0.319196734205835 & 0.741165 & 0.4307 & 0.668678 & 0.334339 \tabularnewline
M7 & 0.296495293891495 & 0.74141 & 0.3999 & 0.691037 & 0.345518 \tabularnewline
M8 & 0.149122575741277 & 0.741698 & 0.2011 & 0.841523 & 0.420761 \tabularnewline
M9 & 0.101458214659215 & 0.741797 & 0.1368 & 0.891794 & 0.445897 \tabularnewline
M10 & 0.113502210645311 & 0.741645 & 0.153 & 0.879021 & 0.439511 \tabularnewline
M11 & -0.0150370792322786 & 0.741219 & -0.0203 & 0.9839 & 0.49195 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58084&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]0.94880597817748[/C][C]0.72387[/C][C]1.3107[/C][C]0.196314[/C][C]0.098157[/C][/ROW]
[ROW][C]rente[/C][C]0.547454363004362[/C][C]0.173988[/C][C]3.1465[/C][C]0.002866[/C][C]0.001433[/C][/ROW]
[ROW][C]M1[/C][C]-0.174305476439478[/C][C]0.74143[/C][C]-0.2351[/C][C]0.815157[/C][C]0.407579[/C][/ROW]
[ROW][C]M2[/C][C]-0.114305476439477[/C][C]0.74143[/C][C]-0.1542[/C][C]0.878137[/C][C]0.439068[/C][/ROW]
[ROW][C]M3[/C][C]-0.0526272818497814[/C][C]0.741187[/C][C]-0.071[/C][C]0.943696[/C][C]0.471848[/C][/ROW]
[ROW][C]M4[/C][C]0.0109490872600876[/C][C]0.741144[/C][C]0.0148[/C][C]0.988276[/C][C]0.494138[/C][/ROW]
[ROW][C]M5[/C][C]0.270949087260088[/C][C]0.741144[/C][C]0.3656[/C][C]0.716317[/C][C]0.358158[/C][/ROW]
[ROW][C]M6[/C][C]0.319196734205835[/C][C]0.741165[/C][C]0.4307[/C][C]0.668678[/C][C]0.334339[/C][/ROW]
[ROW][C]M7[/C][C]0.296495293891495[/C][C]0.74141[/C][C]0.3999[/C][C]0.691037[/C][C]0.345518[/C][/ROW]
[ROW][C]M8[/C][C]0.149122575741277[/C][C]0.741698[/C][C]0.2011[/C][C]0.841523[/C][C]0.420761[/C][/ROW]
[ROW][C]M9[/C][C]0.101458214659215[/C][C]0.741797[/C][C]0.1368[/C][C]0.891794[/C][C]0.445897[/C][/ROW]
[ROW][C]M10[/C][C]0.113502210645311[/C][C]0.741645[/C][C]0.153[/C][C]0.879021[/C][C]0.439511[/C][/ROW]
[ROW][C]M11[/C][C]-0.0150370792322786[/C][C]0.741219[/C][C]-0.0203[/C][C]0.9839[/C][C]0.49195[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58084&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58084&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)0.948805978177480.723871.31070.1963140.098157
rente0.5474543630043620.1739883.14650.0028660.001433
M1-0.1743054764394780.74143-0.23510.8151570.407579
M2-0.1143054764394770.74143-0.15420.8781370.439068
M3-0.05262728184978140.741187-0.0710.9436960.471848
M40.01094908726008760.7411440.01480.9882760.494138
M50.2709490872600880.7411440.36560.7163170.358158
M60.3191967342058350.7411650.43070.6686780.334339
M70.2964952938914950.741410.39990.6910370.345518
M80.1491225757412770.7416980.20110.8415230.420761
M90.1014582146592150.7417970.13680.8917940.445897
M100.1135022106453110.7416450.1530.8790210.439511
M11-0.01503707923227860.741219-0.02030.98390.49195







Multiple Linear Regression - Regression Statistics
Multiple R0.443564739811477
R-squared0.196749678404024
Adjusted R-squared-0.00833551008856603
F-TEST (value)0.959355864995257
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0.499354669494684
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.17183823442489
Sum Squared Residuals64.5406278400223

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.443564739811477 \tabularnewline
R-squared & 0.196749678404024 \tabularnewline
Adjusted R-squared & -0.00833551008856603 \tabularnewline
F-TEST (value) & 0.959355864995257 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0.499354669494684 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.17183823442489 \tabularnewline
Sum Squared Residuals & 64.5406278400223 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58084&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.443564739811477[/C][/ROW]
[ROW][C]R-squared[/C][C]0.196749678404024[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00833551008856603[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.959355864995257[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]0.499354669494684[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.17183823442489[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]64.5406278400223[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58084&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58084&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 R0.443564739811477
R-squared0.196749678404024
Adjusted R-squared-0.00833551008856603
F-TEST (value)0.959355864995257
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value0.499354669494684
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.17183823442489
Sum Squared Residuals64.5406278400223







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.41.86940922774674-0.469409227746737
21.21.92940922774673-0.729409227746731
311.99108742233642-0.991087422336423
41.72.05466379144629-0.354663791446293
52.42.314663791446290.085336208553708
622.36291143839204-0.36291143839204
72.12.3402099980777-0.2402099980777
822.19283727992748-0.192837279927482
91.82.14517291884542-0.34517291884542
102.72.157216914831520.542783085168484
112.32.028677624953930.271322375046073
121.92.04371470418620-0.143714704186205
1321.869409227746730.130590772253273
142.31.929409227746730.370590772253272
152.81.991087422336420.808912577663576
162.42.054663791446290.345336208553707
172.32.31466379144629-0.0146637914462926
182.72.362911438392040.33708856160796
192.72.34020999807770.3597900019223
202.92.192837279927480.707162720072518
2132.145172918845420.85482708115458
222.22.157216914831520.0427830851684838
232.32.028677624953930.271322375046073
242.82.158680120417120.641319879582879
252.82.006272818497820.793727181502182
262.82.066272818497820.733727181502181
272.22.23744188568839-0.0374418856883864
282.62.328390972948470.271609027051527
292.82.588390972948470.211609027051527
302.52.71328223071483-0.213282230714831
312.42.75080077033097-0.350800770330971
322.32.70196983752154-0.401969837521538
331.92.69262728184978-0.792627281849782
341.72.79773851954662-1.09773851954662
3522.71299557870938-0.712995578709378
362.12.80467626876227-0.704676268762268
371.72.69059077225327-0.99059077225327
381.82.75059077225327-0.95059077225327
391.82.89438712129362-1.09438712129362
401.83.01270892670393-1.21270892670393
411.33.27270892670392-1.97270892670392
421.33.40307472810033-2.10307472810033
431.33.43511872408642-2.13511872408642
441.23.28774600593620-2.08774600593620
451.43.24008164485414-1.84008164485414
462.23.25212564084024-1.05212564084024
472.93.12358635096265-0.223586350962649
483.13.13862343019493-0.0386234301949278
493.52.964317953755450.53568204624455
503.63.024317953755450.575682046244549
514.43.085996148345151.31400385165485
524.13.149572517455020.950427482544984
535.13.409572517455021.69042748254498
545.83.457820164400762.34217983559924
555.93.533660509427212.36633949057279
565.43.424609596687291.97539040331271
575.53.376945235605232.12305476439477
584.83.235702009950111.56429799004989
593.22.806062820420120.393937179579881
602.72.454305476439480.245694523560524

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.4 & 1.86940922774674 & -0.469409227746737 \tabularnewline
2 & 1.2 & 1.92940922774673 & -0.729409227746731 \tabularnewline
3 & 1 & 1.99108742233642 & -0.991087422336423 \tabularnewline
4 & 1.7 & 2.05466379144629 & -0.354663791446293 \tabularnewline
5 & 2.4 & 2.31466379144629 & 0.085336208553708 \tabularnewline
6 & 2 & 2.36291143839204 & -0.36291143839204 \tabularnewline
7 & 2.1 & 2.3402099980777 & -0.2402099980777 \tabularnewline
8 & 2 & 2.19283727992748 & -0.192837279927482 \tabularnewline
9 & 1.8 & 2.14517291884542 & -0.34517291884542 \tabularnewline
10 & 2.7 & 2.15721691483152 & 0.542783085168484 \tabularnewline
11 & 2.3 & 2.02867762495393 & 0.271322375046073 \tabularnewline
12 & 1.9 & 2.04371470418620 & -0.143714704186205 \tabularnewline
13 & 2 & 1.86940922774673 & 0.130590772253273 \tabularnewline
14 & 2.3 & 1.92940922774673 & 0.370590772253272 \tabularnewline
15 & 2.8 & 1.99108742233642 & 0.808912577663576 \tabularnewline
16 & 2.4 & 2.05466379144629 & 0.345336208553707 \tabularnewline
17 & 2.3 & 2.31466379144629 & -0.0146637914462926 \tabularnewline
18 & 2.7 & 2.36291143839204 & 0.33708856160796 \tabularnewline
19 & 2.7 & 2.3402099980777 & 0.3597900019223 \tabularnewline
20 & 2.9 & 2.19283727992748 & 0.707162720072518 \tabularnewline
21 & 3 & 2.14517291884542 & 0.85482708115458 \tabularnewline
22 & 2.2 & 2.15721691483152 & 0.0427830851684838 \tabularnewline
23 & 2.3 & 2.02867762495393 & 0.271322375046073 \tabularnewline
24 & 2.8 & 2.15868012041712 & 0.641319879582879 \tabularnewline
25 & 2.8 & 2.00627281849782 & 0.793727181502182 \tabularnewline
26 & 2.8 & 2.06627281849782 & 0.733727181502181 \tabularnewline
27 & 2.2 & 2.23744188568839 & -0.0374418856883864 \tabularnewline
28 & 2.6 & 2.32839097294847 & 0.271609027051527 \tabularnewline
29 & 2.8 & 2.58839097294847 & 0.211609027051527 \tabularnewline
30 & 2.5 & 2.71328223071483 & -0.213282230714831 \tabularnewline
31 & 2.4 & 2.75080077033097 & -0.350800770330971 \tabularnewline
32 & 2.3 & 2.70196983752154 & -0.401969837521538 \tabularnewline
33 & 1.9 & 2.69262728184978 & -0.792627281849782 \tabularnewline
34 & 1.7 & 2.79773851954662 & -1.09773851954662 \tabularnewline
35 & 2 & 2.71299557870938 & -0.712995578709378 \tabularnewline
36 & 2.1 & 2.80467626876227 & -0.704676268762268 \tabularnewline
37 & 1.7 & 2.69059077225327 & -0.99059077225327 \tabularnewline
38 & 1.8 & 2.75059077225327 & -0.95059077225327 \tabularnewline
39 & 1.8 & 2.89438712129362 & -1.09438712129362 \tabularnewline
40 & 1.8 & 3.01270892670393 & -1.21270892670393 \tabularnewline
41 & 1.3 & 3.27270892670392 & -1.97270892670392 \tabularnewline
42 & 1.3 & 3.40307472810033 & -2.10307472810033 \tabularnewline
43 & 1.3 & 3.43511872408642 & -2.13511872408642 \tabularnewline
44 & 1.2 & 3.28774600593620 & -2.08774600593620 \tabularnewline
45 & 1.4 & 3.24008164485414 & -1.84008164485414 \tabularnewline
46 & 2.2 & 3.25212564084024 & -1.05212564084024 \tabularnewline
47 & 2.9 & 3.12358635096265 & -0.223586350962649 \tabularnewline
48 & 3.1 & 3.13862343019493 & -0.0386234301949278 \tabularnewline
49 & 3.5 & 2.96431795375545 & 0.53568204624455 \tabularnewline
50 & 3.6 & 3.02431795375545 & 0.575682046244549 \tabularnewline
51 & 4.4 & 3.08599614834515 & 1.31400385165485 \tabularnewline
52 & 4.1 & 3.14957251745502 & 0.950427482544984 \tabularnewline
53 & 5.1 & 3.40957251745502 & 1.69042748254498 \tabularnewline
54 & 5.8 & 3.45782016440076 & 2.34217983559924 \tabularnewline
55 & 5.9 & 3.53366050942721 & 2.36633949057279 \tabularnewline
56 & 5.4 & 3.42460959668729 & 1.97539040331271 \tabularnewline
57 & 5.5 & 3.37694523560523 & 2.12305476439477 \tabularnewline
58 & 4.8 & 3.23570200995011 & 1.56429799004989 \tabularnewline
59 & 3.2 & 2.80606282042012 & 0.393937179579881 \tabularnewline
60 & 2.7 & 2.45430547643948 & 0.245694523560524 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58084&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]1.4[/C][C]1.86940922774674[/C][C]-0.469409227746737[/C][/ROW]
[ROW][C]2[/C][C]1.2[/C][C]1.92940922774673[/C][C]-0.729409227746731[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.99108742233642[/C][C]-0.991087422336423[/C][/ROW]
[ROW][C]4[/C][C]1.7[/C][C]2.05466379144629[/C][C]-0.354663791446293[/C][/ROW]
[ROW][C]5[/C][C]2.4[/C][C]2.31466379144629[/C][C]0.085336208553708[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]2.36291143839204[/C][C]-0.36291143839204[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.3402099980777[/C][C]-0.2402099980777[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]2.19283727992748[/C][C]-0.192837279927482[/C][/ROW]
[ROW][C]9[/C][C]1.8[/C][C]2.14517291884542[/C][C]-0.34517291884542[/C][/ROW]
[ROW][C]10[/C][C]2.7[/C][C]2.15721691483152[/C][C]0.542783085168484[/C][/ROW]
[ROW][C]11[/C][C]2.3[/C][C]2.02867762495393[/C][C]0.271322375046073[/C][/ROW]
[ROW][C]12[/C][C]1.9[/C][C]2.04371470418620[/C][C]-0.143714704186205[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]1.86940922774673[/C][C]0.130590772253273[/C][/ROW]
[ROW][C]14[/C][C]2.3[/C][C]1.92940922774673[/C][C]0.370590772253272[/C][/ROW]
[ROW][C]15[/C][C]2.8[/C][C]1.99108742233642[/C][C]0.808912577663576[/C][/ROW]
[ROW][C]16[/C][C]2.4[/C][C]2.05466379144629[/C][C]0.345336208553707[/C][/ROW]
[ROW][C]17[/C][C]2.3[/C][C]2.31466379144629[/C][C]-0.0146637914462926[/C][/ROW]
[ROW][C]18[/C][C]2.7[/C][C]2.36291143839204[/C][C]0.33708856160796[/C][/ROW]
[ROW][C]19[/C][C]2.7[/C][C]2.3402099980777[/C][C]0.3597900019223[/C][/ROW]
[ROW][C]20[/C][C]2.9[/C][C]2.19283727992748[/C][C]0.707162720072518[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]2.14517291884542[/C][C]0.85482708115458[/C][/ROW]
[ROW][C]22[/C][C]2.2[/C][C]2.15721691483152[/C][C]0.0427830851684838[/C][/ROW]
[ROW][C]23[/C][C]2.3[/C][C]2.02867762495393[/C][C]0.271322375046073[/C][/ROW]
[ROW][C]24[/C][C]2.8[/C][C]2.15868012041712[/C][C]0.641319879582879[/C][/ROW]
[ROW][C]25[/C][C]2.8[/C][C]2.00627281849782[/C][C]0.793727181502182[/C][/ROW]
[ROW][C]26[/C][C]2.8[/C][C]2.06627281849782[/C][C]0.733727181502181[/C][/ROW]
[ROW][C]27[/C][C]2.2[/C][C]2.23744188568839[/C][C]-0.0374418856883864[/C][/ROW]
[ROW][C]28[/C][C]2.6[/C][C]2.32839097294847[/C][C]0.271609027051527[/C][/ROW]
[ROW][C]29[/C][C]2.8[/C][C]2.58839097294847[/C][C]0.211609027051527[/C][/ROW]
[ROW][C]30[/C][C]2.5[/C][C]2.71328223071483[/C][C]-0.213282230714831[/C][/ROW]
[ROW][C]31[/C][C]2.4[/C][C]2.75080077033097[/C][C]-0.350800770330971[/C][/ROW]
[ROW][C]32[/C][C]2.3[/C][C]2.70196983752154[/C][C]-0.401969837521538[/C][/ROW]
[ROW][C]33[/C][C]1.9[/C][C]2.69262728184978[/C][C]-0.792627281849782[/C][/ROW]
[ROW][C]34[/C][C]1.7[/C][C]2.79773851954662[/C][C]-1.09773851954662[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]2.71299557870938[/C][C]-0.712995578709378[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.80467626876227[/C][C]-0.704676268762268[/C][/ROW]
[ROW][C]37[/C][C]1.7[/C][C]2.69059077225327[/C][C]-0.99059077225327[/C][/ROW]
[ROW][C]38[/C][C]1.8[/C][C]2.75059077225327[/C][C]-0.95059077225327[/C][/ROW]
[ROW][C]39[/C][C]1.8[/C][C]2.89438712129362[/C][C]-1.09438712129362[/C][/ROW]
[ROW][C]40[/C][C]1.8[/C][C]3.01270892670393[/C][C]-1.21270892670393[/C][/ROW]
[ROW][C]41[/C][C]1.3[/C][C]3.27270892670392[/C][C]-1.97270892670392[/C][/ROW]
[ROW][C]42[/C][C]1.3[/C][C]3.40307472810033[/C][C]-2.10307472810033[/C][/ROW]
[ROW][C]43[/C][C]1.3[/C][C]3.43511872408642[/C][C]-2.13511872408642[/C][/ROW]
[ROW][C]44[/C][C]1.2[/C][C]3.28774600593620[/C][C]-2.08774600593620[/C][/ROW]
[ROW][C]45[/C][C]1.4[/C][C]3.24008164485414[/C][C]-1.84008164485414[/C][/ROW]
[ROW][C]46[/C][C]2.2[/C][C]3.25212564084024[/C][C]-1.05212564084024[/C][/ROW]
[ROW][C]47[/C][C]2.9[/C][C]3.12358635096265[/C][C]-0.223586350962649[/C][/ROW]
[ROW][C]48[/C][C]3.1[/C][C]3.13862343019493[/C][C]-0.0386234301949278[/C][/ROW]
[ROW][C]49[/C][C]3.5[/C][C]2.96431795375545[/C][C]0.53568204624455[/C][/ROW]
[ROW][C]50[/C][C]3.6[/C][C]3.02431795375545[/C][C]0.575682046244549[/C][/ROW]
[ROW][C]51[/C][C]4.4[/C][C]3.08599614834515[/C][C]1.31400385165485[/C][/ROW]
[ROW][C]52[/C][C]4.1[/C][C]3.14957251745502[/C][C]0.950427482544984[/C][/ROW]
[ROW][C]53[/C][C]5.1[/C][C]3.40957251745502[/C][C]1.69042748254498[/C][/ROW]
[ROW][C]54[/C][C]5.8[/C][C]3.45782016440076[/C][C]2.34217983559924[/C][/ROW]
[ROW][C]55[/C][C]5.9[/C][C]3.53366050942721[/C][C]2.36633949057279[/C][/ROW]
[ROW][C]56[/C][C]5.4[/C][C]3.42460959668729[/C][C]1.97539040331271[/C][/ROW]
[ROW][C]57[/C][C]5.5[/C][C]3.37694523560523[/C][C]2.12305476439477[/C][/ROW]
[ROW][C]58[/C][C]4.8[/C][C]3.23570200995011[/C][C]1.56429799004989[/C][/ROW]
[ROW][C]59[/C][C]3.2[/C][C]2.80606282042012[/C][C]0.393937179579881[/C][/ROW]
[ROW][C]60[/C][C]2.7[/C][C]2.45430547643948[/C][C]0.245694523560524[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58084&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58084&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
11.41.86940922774674-0.469409227746737
21.21.92940922774673-0.729409227746731
311.99108742233642-0.991087422336423
41.72.05466379144629-0.354663791446293
52.42.314663791446290.085336208553708
622.36291143839204-0.36291143839204
72.12.3402099980777-0.2402099980777
822.19283727992748-0.192837279927482
91.82.14517291884542-0.34517291884542
102.72.157216914831520.542783085168484
112.32.028677624953930.271322375046073
121.92.04371470418620-0.143714704186205
1321.869409227746730.130590772253273
142.31.929409227746730.370590772253272
152.81.991087422336420.808912577663576
162.42.054663791446290.345336208553707
172.32.31466379144629-0.0146637914462926
182.72.362911438392040.33708856160796
192.72.34020999807770.3597900019223
202.92.192837279927480.707162720072518
2132.145172918845420.85482708115458
222.22.157216914831520.0427830851684838
232.32.028677624953930.271322375046073
242.82.158680120417120.641319879582879
252.82.006272818497820.793727181502182
262.82.066272818497820.733727181502181
272.22.23744188568839-0.0374418856883864
282.62.328390972948470.271609027051527
292.82.588390972948470.211609027051527
302.52.71328223071483-0.213282230714831
312.42.75080077033097-0.350800770330971
322.32.70196983752154-0.401969837521538
331.92.69262728184978-0.792627281849782
341.72.79773851954662-1.09773851954662
3522.71299557870938-0.712995578709378
362.12.80467626876227-0.704676268762268
371.72.69059077225327-0.99059077225327
381.82.75059077225327-0.95059077225327
391.82.89438712129362-1.09438712129362
401.83.01270892670393-1.21270892670393
411.33.27270892670392-1.97270892670392
421.33.40307472810033-2.10307472810033
431.33.43511872408642-2.13511872408642
441.23.28774600593620-2.08774600593620
451.43.24008164485414-1.84008164485414
462.23.25212564084024-1.05212564084024
472.93.12358635096265-0.223586350962649
483.13.13862343019493-0.0386234301949278
493.52.964317953755450.53568204624455
503.63.024317953755450.575682046244549
514.43.085996148345151.31400385165485
524.13.149572517455020.950427482544984
535.13.409572517455021.69042748254498
545.83.457820164400762.34217983559924
555.93.533660509427212.36633949057279
565.43.424609596687291.97539040331271
575.53.376945235605232.12305476439477
584.83.235702009950111.56429799004989
593.22.806062820420120.393937179579881
602.72.454305476439480.245694523560524







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.2819873552833300.5639747105666610.71801264471667
170.143329447930890.286658895861780.85667055206911
180.07836366467583060.1567273293516610.92163633532417
190.03963603114802160.07927206229604320.960363968851978
200.02386642541687930.04773285083375860.97613357458312
210.01867206372291320.03734412744582640.981327936277087
220.008753010450838620.01750602090167720.991246989549161
230.003567578058374510.007135156116749020.996432421941626
240.001425443168783950.002850886337567890.998574556831216
250.0005751304774900160.001150260954980030.99942486952251
260.0002298369350563870.0004596738701127740.999770163064944
270.0002003960965352880.0004007921930705770.999799603903465
288.88327004843554e-050.0001776654009687110.999911167299516
293.86730824985778e-057.73461649971555e-050.999961326917501
301.95028617443509e-053.90057234887018e-050.999980497138256
311.01399014324411e-052.02798028648822e-050.999989860098567
325.7716199531706e-061.15432399063412e-050.999994228380047
333.46090948963644e-066.92181897927289e-060.99999653909051
341.79815219201476e-063.59630438402952e-060.999998201847808
355.24234058123672e-071.04846811624734e-060.999999475765942
361.52038077527634e-073.04076155055268e-070.999999847961923
373.93388972215717e-087.86777944431433e-080.999999960661103
389.23729786479997e-091.84745957295999e-080.999999990762702
392.75422225580247e-095.50844451160495e-090.999999997245778
408.18741184863495e-101.63748236972699e-090.999999999181259
411.62840713312882e-093.25681426625765e-090.999999998371593
421.10011936209979e-082.20023872419958e-080.999999988998806
431.98048392775034e-073.96096785550067e-070.999999801951607
447.71802307836185e-061.54360461567237e-050.999992281976922

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.281987355283330 & 0.563974710566661 & 0.71801264471667 \tabularnewline
17 & 0.14332944793089 & 0.28665889586178 & 0.85667055206911 \tabularnewline
18 & 0.0783636646758306 & 0.156727329351661 & 0.92163633532417 \tabularnewline
19 & 0.0396360311480216 & 0.0792720622960432 & 0.960363968851978 \tabularnewline
20 & 0.0238664254168793 & 0.0477328508337586 & 0.97613357458312 \tabularnewline
21 & 0.0186720637229132 & 0.0373441274458264 & 0.981327936277087 \tabularnewline
22 & 0.00875301045083862 & 0.0175060209016772 & 0.991246989549161 \tabularnewline
23 & 0.00356757805837451 & 0.00713515611674902 & 0.996432421941626 \tabularnewline
24 & 0.00142544316878395 & 0.00285088633756789 & 0.998574556831216 \tabularnewline
25 & 0.000575130477490016 & 0.00115026095498003 & 0.99942486952251 \tabularnewline
26 & 0.000229836935056387 & 0.000459673870112774 & 0.999770163064944 \tabularnewline
27 & 0.000200396096535288 & 0.000400792193070577 & 0.999799603903465 \tabularnewline
28 & 8.88327004843554e-05 & 0.000177665400968711 & 0.999911167299516 \tabularnewline
29 & 3.86730824985778e-05 & 7.73461649971555e-05 & 0.999961326917501 \tabularnewline
30 & 1.95028617443509e-05 & 3.90057234887018e-05 & 0.999980497138256 \tabularnewline
31 & 1.01399014324411e-05 & 2.02798028648822e-05 & 0.999989860098567 \tabularnewline
32 & 5.7716199531706e-06 & 1.15432399063412e-05 & 0.999994228380047 \tabularnewline
33 & 3.46090948963644e-06 & 6.92181897927289e-06 & 0.99999653909051 \tabularnewline
34 & 1.79815219201476e-06 & 3.59630438402952e-06 & 0.999998201847808 \tabularnewline
35 & 5.24234058123672e-07 & 1.04846811624734e-06 & 0.999999475765942 \tabularnewline
36 & 1.52038077527634e-07 & 3.04076155055268e-07 & 0.999999847961923 \tabularnewline
37 & 3.93388972215717e-08 & 7.86777944431433e-08 & 0.999999960661103 \tabularnewline
38 & 9.23729786479997e-09 & 1.84745957295999e-08 & 0.999999990762702 \tabularnewline
39 & 2.75422225580247e-09 & 5.50844451160495e-09 & 0.999999997245778 \tabularnewline
40 & 8.18741184863495e-10 & 1.63748236972699e-09 & 0.999999999181259 \tabularnewline
41 & 1.62840713312882e-09 & 3.25681426625765e-09 & 0.999999998371593 \tabularnewline
42 & 1.10011936209979e-08 & 2.20023872419958e-08 & 0.999999988998806 \tabularnewline
43 & 1.98048392775034e-07 & 3.96096785550067e-07 & 0.999999801951607 \tabularnewline
44 & 7.71802307836185e-06 & 1.54360461567237e-05 & 0.999992281976922 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58084&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.281987355283330[/C][C]0.563974710566661[/C][C]0.71801264471667[/C][/ROW]
[ROW][C]17[/C][C]0.14332944793089[/C][C]0.28665889586178[/C][C]0.85667055206911[/C][/ROW]
[ROW][C]18[/C][C]0.0783636646758306[/C][C]0.156727329351661[/C][C]0.92163633532417[/C][/ROW]
[ROW][C]19[/C][C]0.0396360311480216[/C][C]0.0792720622960432[/C][C]0.960363968851978[/C][/ROW]
[ROW][C]20[/C][C]0.0238664254168793[/C][C]0.0477328508337586[/C][C]0.97613357458312[/C][/ROW]
[ROW][C]21[/C][C]0.0186720637229132[/C][C]0.0373441274458264[/C][C]0.981327936277087[/C][/ROW]
[ROW][C]22[/C][C]0.00875301045083862[/C][C]0.0175060209016772[/C][C]0.991246989549161[/C][/ROW]
[ROW][C]23[/C][C]0.00356757805837451[/C][C]0.00713515611674902[/C][C]0.996432421941626[/C][/ROW]
[ROW][C]24[/C][C]0.00142544316878395[/C][C]0.00285088633756789[/C][C]0.998574556831216[/C][/ROW]
[ROW][C]25[/C][C]0.000575130477490016[/C][C]0.00115026095498003[/C][C]0.99942486952251[/C][/ROW]
[ROW][C]26[/C][C]0.000229836935056387[/C][C]0.000459673870112774[/C][C]0.999770163064944[/C][/ROW]
[ROW][C]27[/C][C]0.000200396096535288[/C][C]0.000400792193070577[/C][C]0.999799603903465[/C][/ROW]
[ROW][C]28[/C][C]8.88327004843554e-05[/C][C]0.000177665400968711[/C][C]0.999911167299516[/C][/ROW]
[ROW][C]29[/C][C]3.86730824985778e-05[/C][C]7.73461649971555e-05[/C][C]0.999961326917501[/C][/ROW]
[ROW][C]30[/C][C]1.95028617443509e-05[/C][C]3.90057234887018e-05[/C][C]0.999980497138256[/C][/ROW]
[ROW][C]31[/C][C]1.01399014324411e-05[/C][C]2.02798028648822e-05[/C][C]0.999989860098567[/C][/ROW]
[ROW][C]32[/C][C]5.7716199531706e-06[/C][C]1.15432399063412e-05[/C][C]0.999994228380047[/C][/ROW]
[ROW][C]33[/C][C]3.46090948963644e-06[/C][C]6.92181897927289e-06[/C][C]0.99999653909051[/C][/ROW]
[ROW][C]34[/C][C]1.79815219201476e-06[/C][C]3.59630438402952e-06[/C][C]0.999998201847808[/C][/ROW]
[ROW][C]35[/C][C]5.24234058123672e-07[/C][C]1.04846811624734e-06[/C][C]0.999999475765942[/C][/ROW]
[ROW][C]36[/C][C]1.52038077527634e-07[/C][C]3.04076155055268e-07[/C][C]0.999999847961923[/C][/ROW]
[ROW][C]37[/C][C]3.93388972215717e-08[/C][C]7.86777944431433e-08[/C][C]0.999999960661103[/C][/ROW]
[ROW][C]38[/C][C]9.23729786479997e-09[/C][C]1.84745957295999e-08[/C][C]0.999999990762702[/C][/ROW]
[ROW][C]39[/C][C]2.75422225580247e-09[/C][C]5.50844451160495e-09[/C][C]0.999999997245778[/C][/ROW]
[ROW][C]40[/C][C]8.18741184863495e-10[/C][C]1.63748236972699e-09[/C][C]0.999999999181259[/C][/ROW]
[ROW][C]41[/C][C]1.62840713312882e-09[/C][C]3.25681426625765e-09[/C][C]0.999999998371593[/C][/ROW]
[ROW][C]42[/C][C]1.10011936209979e-08[/C][C]2.20023872419958e-08[/C][C]0.999999988998806[/C][/ROW]
[ROW][C]43[/C][C]1.98048392775034e-07[/C][C]3.96096785550067e-07[/C][C]0.999999801951607[/C][/ROW]
[ROW][C]44[/C][C]7.71802307836185e-06[/C][C]1.54360461567237e-05[/C][C]0.999992281976922[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58084&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58084&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.2819873552833300.5639747105666610.71801264471667
170.143329447930890.286658895861780.85667055206911
180.07836366467583060.1567273293516610.92163633532417
190.03963603114802160.07927206229604320.960363968851978
200.02386642541687930.04773285083375860.97613357458312
210.01867206372291320.03734412744582640.981327936277087
220.008753010450838620.01750602090167720.991246989549161
230.003567578058374510.007135156116749020.996432421941626
240.001425443168783950.002850886337567890.998574556831216
250.0005751304774900160.001150260954980030.99942486952251
260.0002298369350563870.0004596738701127740.999770163064944
270.0002003960965352880.0004007921930705770.999799603903465
288.88327004843554e-050.0001776654009687110.999911167299516
293.86730824985778e-057.73461649971555e-050.999961326917501
301.95028617443509e-053.90057234887018e-050.999980497138256
311.01399014324411e-052.02798028648822e-050.999989860098567
325.7716199531706e-061.15432399063412e-050.999994228380047
333.46090948963644e-066.92181897927289e-060.99999653909051
341.79815219201476e-063.59630438402952e-060.999998201847808
355.24234058123672e-071.04846811624734e-060.999999475765942
361.52038077527634e-073.04076155055268e-070.999999847961923
373.93388972215717e-087.86777944431433e-080.999999960661103
389.23729786479997e-091.84745957295999e-080.999999990762702
392.75422225580247e-095.50844451160495e-090.999999997245778
408.18741184863495e-101.63748236972699e-090.999999999181259
411.62840713312882e-093.25681426625765e-090.999999998371593
421.10011936209979e-082.20023872419958e-080.999999988998806
431.98048392775034e-073.96096785550067e-070.999999801951607
447.71802307836185e-061.54360461567237e-050.999992281976922







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level220.758620689655172NOK
5% type I error level250.862068965517241NOK
10% type I error level260.896551724137931NOK

\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 & 22 & 0.758620689655172 & NOK \tabularnewline
5% type I error level & 25 & 0.862068965517241 & NOK \tabularnewline
10% type I error level & 26 & 0.896551724137931 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58084&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]22[/C][C]0.758620689655172[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]25[/C][C]0.862068965517241[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.896551724137931[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58084&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58084&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 level220.758620689655172NOK
5% type I error level250.862068965517241NOK
10% type I error level260.896551724137931NOK



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