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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 20 Nov 2009 13:52:37 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258750445yeagxt0zm51zbnb.htm/, Retrieved Fri, 19 Apr 2024 17:31:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58462, Retrieved Fri, 19 Apr 2024 17:31:07 +0000

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IsPrivate?

No (this computation is public)

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Estimated Impact

138

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [Shwws7_v1] [2009-11-20 20:52:37] [93b66894f6318f3da4fcda772f2ffa6f] [Current]

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Dataseries X:

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Histogram

Boxplots

100,35	102,1
100,35	102,86
100,36	102,99
100,39	103,73
100,34	105,02
100,34	104,43
100,35	104,63
100,43	104,93
100,47	105,87
100,67	105,66
100,75	106,76
100,78	106
100,79	107,22
100,67	107,33
100,64	107,11
100,64	108,86
100,76	107,72
100,79	107,88
100,79	108,38
100,9	107,72
100,98	108,41
101,11	109,9
101,18	111,45
101,22	112,18
101,23	113,34
101,09	113,46
101,26	114,06
101,28	115,54
101,43	116,39
101,53	115,94
101,54	116,97
101,54	115,94
101,79	115,91
102,18	116,43
102,37	116,26
102,46	116,35
102,46	117,9
102,03	117,7
102,26	117,53
102,33	117,86
102,44	117,65
102,5	116,51
102,52	115,93
102,66	115,31
102,72	115

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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=58462&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=58462&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58462&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
ktot[t] = + 86.1933134730192 + 0.135872441268998vmtot[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ktot[t] =  +  86.1933134730192 +  0.135872441268998vmtot[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58462&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ktot[t] =  +  86.1933134730192 +  0.135872441268998vmtot[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58462&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58462&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
ktot[t] = + 86.1933134730192 + 0.135872441268998vmtot[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	86.1933134730192	1.09882	78.4417	0	0
vmtot	0.135872441268998	0.009884	13.7461	0	0

\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) & 86.1933134730192 & 1.09882 & 78.4417 & 0 & 0 \tabularnewline
vmtot & 0.135872441268998 & 0.009884 & 13.7461 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58462&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]86.1933134730192[/C][C]1.09882[/C][C]78.4417[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]vmtot[/C][C]0.135872441268998[/C][C]0.009884[/C][C]13.7461[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58462&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58462&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	86.1933134730192	1.09882	78.4417	0	0
vmtot	0.135872441268998	0.009884	13.7461	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.902562117697146
R-squared	0.814618376301957
Adjusted R-squared	0.81030717575084
F-TEST (value)	188.953950678736
F-TEST (DF numerator)	1
F-TEST (DF denominator)	43
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.341291632139295
Sum Squared Residuals	5.00863906123707

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.902562117697146 \tabularnewline
R-squared & 0.814618376301957 \tabularnewline
Adjusted R-squared & 0.81030717575084 \tabularnewline
F-TEST (value) & 188.953950678736 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 43 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.341291632139295 \tabularnewline
Sum Squared Residuals & 5.00863906123707 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58462&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.902562117697146[/C][/ROW]
[ROW][C]R-squared[/C][C]0.814618376301957[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.81030717575084[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]188.953950678736[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]43[/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]0.341291632139295[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5.00863906123707[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58462&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58462&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 R	0.902562117697146
R-squared	0.814618376301957
Adjusted R-squared	0.81030717575084
F-TEST (value)	188.953950678736
F-TEST (DF numerator)	1
F-TEST (DF denominator)	43
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.341291632139295
Sum Squared Residuals	5.00863906123707

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.35	100.065889726584	0.284110273416090
2	100.35	100.169152781948	0.180847218051646
3	100.36	100.186816199313	0.173183800686682
4	100.39	100.287361805852	0.102638194147623
5	100.34	100.462637255089	-0.122637255089381
6	100.34	100.382472514741	-0.0424725147406731
7	100.35	100.409647002994	-0.0596470029944803
8	100.43	100.450408735375	-0.0204087353751689
9	100.47	100.578128830168	-0.108128830168035
10	100.67	100.549595617502	0.120404382498459
11	100.75	100.699055302897	0.0509446971025573
12	100.78	100.595792247533	0.184207752466998
13	100.79	100.761556625881	0.0284433741188252
14	100.67	100.776502594421	-0.106502594420769
15	100.64	100.746610657342	-0.106610657341591
16	100.64	100.984387429562	-0.344387429562338
17	100.76	100.829492846516	-0.0694928465156753
18	100.79	100.851232437119	-0.0612324371187134
19	100.79	100.919168657753	-0.129168657753213
20	100.9	100.829492846516	0.0705071534843253
21	100.98	100.923244830991	0.056755169008715
22	101.11	101.125694768482	-0.0156947684820985
23	101.18	101.336297052449	-0.156297052449038
24	101.22	101.435483934575	-0.215483934575416
25	101.23	101.593095966447	-0.363095966447448
26	101.09	101.609400659400	-0.519400659399727
27	101.26	101.690924124161	-0.430924124161126
28	101.28	101.892015337239	-0.612015337239248
29	101.43	102.007506912318	-0.57750691231789
30	101.53	101.946364313747	-0.416364313746846
31	101.54	102.086312928254	-0.54631292825391
32	101.54	101.946364313747	-0.406364313746841
33	101.79	101.942288140509	-0.152288140508771
34	102.18	102.012941809969	0.167058190031349
35	102.37	101.989843494953	0.380156505047077
36	102.46	102.002072014667	0.457927985332857
37	102.46	102.212674298634	0.247325701365908
38	102.03	102.185499810380	-0.155499810380284
39	102.26	102.162401495365	0.0975985046354496
40	102.33	102.207239400983	0.122760599016673
41	102.44	102.178706188317	0.261293811683162
42	102.5	102.023811605270	0.476188394729822
43	102.52	101.945005589334	0.574994410665837
44	102.66	101.860764675747	0.799235324252617
45	102.72	101.818644218954	0.90135578104601

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100.35 & 100.065889726584 & 0.284110273416090 \tabularnewline
2 & 100.35 & 100.169152781948 & 0.180847218051646 \tabularnewline
3 & 100.36 & 100.186816199313 & 0.173183800686682 \tabularnewline
4 & 100.39 & 100.287361805852 & 0.102638194147623 \tabularnewline
5 & 100.34 & 100.462637255089 & -0.122637255089381 \tabularnewline
6 & 100.34 & 100.382472514741 & -0.0424725147406731 \tabularnewline
7 & 100.35 & 100.409647002994 & -0.0596470029944803 \tabularnewline
8 & 100.43 & 100.450408735375 & -0.0204087353751689 \tabularnewline
9 & 100.47 & 100.578128830168 & -0.108128830168035 \tabularnewline
10 & 100.67 & 100.549595617502 & 0.120404382498459 \tabularnewline
11 & 100.75 & 100.699055302897 & 0.0509446971025573 \tabularnewline
12 & 100.78 & 100.595792247533 & 0.184207752466998 \tabularnewline
13 & 100.79 & 100.761556625881 & 0.0284433741188252 \tabularnewline
14 & 100.67 & 100.776502594421 & -0.106502594420769 \tabularnewline
15 & 100.64 & 100.746610657342 & -0.106610657341591 \tabularnewline
16 & 100.64 & 100.984387429562 & -0.344387429562338 \tabularnewline
17 & 100.76 & 100.829492846516 & -0.0694928465156753 \tabularnewline
18 & 100.79 & 100.851232437119 & -0.0612324371187134 \tabularnewline
19 & 100.79 & 100.919168657753 & -0.129168657753213 \tabularnewline
20 & 100.9 & 100.829492846516 & 0.0705071534843253 \tabularnewline
21 & 100.98 & 100.923244830991 & 0.056755169008715 \tabularnewline
22 & 101.11 & 101.125694768482 & -0.0156947684820985 \tabularnewline
23 & 101.18 & 101.336297052449 & -0.156297052449038 \tabularnewline
24 & 101.22 & 101.435483934575 & -0.215483934575416 \tabularnewline
25 & 101.23 & 101.593095966447 & -0.363095966447448 \tabularnewline
26 & 101.09 & 101.609400659400 & -0.519400659399727 \tabularnewline
27 & 101.26 & 101.690924124161 & -0.430924124161126 \tabularnewline
28 & 101.28 & 101.892015337239 & -0.612015337239248 \tabularnewline
29 & 101.43 & 102.007506912318 & -0.57750691231789 \tabularnewline
30 & 101.53 & 101.946364313747 & -0.416364313746846 \tabularnewline
31 & 101.54 & 102.086312928254 & -0.54631292825391 \tabularnewline
32 & 101.54 & 101.946364313747 & -0.406364313746841 \tabularnewline
33 & 101.79 & 101.942288140509 & -0.152288140508771 \tabularnewline
34 & 102.18 & 102.012941809969 & 0.167058190031349 \tabularnewline
35 & 102.37 & 101.989843494953 & 0.380156505047077 \tabularnewline
36 & 102.46 & 102.002072014667 & 0.457927985332857 \tabularnewline
37 & 102.46 & 102.212674298634 & 0.247325701365908 \tabularnewline
38 & 102.03 & 102.185499810380 & -0.155499810380284 \tabularnewline
39 & 102.26 & 102.162401495365 & 0.0975985046354496 \tabularnewline
40 & 102.33 & 102.207239400983 & 0.122760599016673 \tabularnewline
41 & 102.44 & 102.178706188317 & 0.261293811683162 \tabularnewline
42 & 102.5 & 102.023811605270 & 0.476188394729822 \tabularnewline
43 & 102.52 & 101.945005589334 & 0.574994410665837 \tabularnewline
44 & 102.66 & 101.860764675747 & 0.799235324252617 \tabularnewline
45 & 102.72 & 101.818644218954 & 0.90135578104601 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58462&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]100.35[/C][C]100.065889726584[/C][C]0.284110273416090[/C][/ROW]
[ROW][C]2[/C][C]100.35[/C][C]100.169152781948[/C][C]0.180847218051646[/C][/ROW]
[ROW][C]3[/C][C]100.36[/C][C]100.186816199313[/C][C]0.173183800686682[/C][/ROW]
[ROW][C]4[/C][C]100.39[/C][C]100.287361805852[/C][C]0.102638194147623[/C][/ROW]
[ROW][C]5[/C][C]100.34[/C][C]100.462637255089[/C][C]-0.122637255089381[/C][/ROW]
[ROW][C]6[/C][C]100.34[/C][C]100.382472514741[/C][C]-0.0424725147406731[/C][/ROW]
[ROW][C]7[/C][C]100.35[/C][C]100.409647002994[/C][C]-0.0596470029944803[/C][/ROW]
[ROW][C]8[/C][C]100.43[/C][C]100.450408735375[/C][C]-0.0204087353751689[/C][/ROW]
[ROW][C]9[/C][C]100.47[/C][C]100.578128830168[/C][C]-0.108128830168035[/C][/ROW]
[ROW][C]10[/C][C]100.67[/C][C]100.549595617502[/C][C]0.120404382498459[/C][/ROW]
[ROW][C]11[/C][C]100.75[/C][C]100.699055302897[/C][C]0.0509446971025573[/C][/ROW]
[ROW][C]12[/C][C]100.78[/C][C]100.595792247533[/C][C]0.184207752466998[/C][/ROW]
[ROW][C]13[/C][C]100.79[/C][C]100.761556625881[/C][C]0.0284433741188252[/C][/ROW]
[ROW][C]14[/C][C]100.67[/C][C]100.776502594421[/C][C]-0.106502594420769[/C][/ROW]
[ROW][C]15[/C][C]100.64[/C][C]100.746610657342[/C][C]-0.106610657341591[/C][/ROW]
[ROW][C]16[/C][C]100.64[/C][C]100.984387429562[/C][C]-0.344387429562338[/C][/ROW]
[ROW][C]17[/C][C]100.76[/C][C]100.829492846516[/C][C]-0.0694928465156753[/C][/ROW]
[ROW][C]18[/C][C]100.79[/C][C]100.851232437119[/C][C]-0.0612324371187134[/C][/ROW]
[ROW][C]19[/C][C]100.79[/C][C]100.919168657753[/C][C]-0.129168657753213[/C][/ROW]
[ROW][C]20[/C][C]100.9[/C][C]100.829492846516[/C][C]0.0705071534843253[/C][/ROW]
[ROW][C]21[/C][C]100.98[/C][C]100.923244830991[/C][C]0.056755169008715[/C][/ROW]
[ROW][C]22[/C][C]101.11[/C][C]101.125694768482[/C][C]-0.0156947684820985[/C][/ROW]
[ROW][C]23[/C][C]101.18[/C][C]101.336297052449[/C][C]-0.156297052449038[/C][/ROW]
[ROW][C]24[/C][C]101.22[/C][C]101.435483934575[/C][C]-0.215483934575416[/C][/ROW]
[ROW][C]25[/C][C]101.23[/C][C]101.593095966447[/C][C]-0.363095966447448[/C][/ROW]
[ROW][C]26[/C][C]101.09[/C][C]101.609400659400[/C][C]-0.519400659399727[/C][/ROW]
[ROW][C]27[/C][C]101.26[/C][C]101.690924124161[/C][C]-0.430924124161126[/C][/ROW]
[ROW][C]28[/C][C]101.28[/C][C]101.892015337239[/C][C]-0.612015337239248[/C][/ROW]
[ROW][C]29[/C][C]101.43[/C][C]102.007506912318[/C][C]-0.57750691231789[/C][/ROW]
[ROW][C]30[/C][C]101.53[/C][C]101.946364313747[/C][C]-0.416364313746846[/C][/ROW]
[ROW][C]31[/C][C]101.54[/C][C]102.086312928254[/C][C]-0.54631292825391[/C][/ROW]
[ROW][C]32[/C][C]101.54[/C][C]101.946364313747[/C][C]-0.406364313746841[/C][/ROW]
[ROW][C]33[/C][C]101.79[/C][C]101.942288140509[/C][C]-0.152288140508771[/C][/ROW]
[ROW][C]34[/C][C]102.18[/C][C]102.012941809969[/C][C]0.167058190031349[/C][/ROW]
[ROW][C]35[/C][C]102.37[/C][C]101.989843494953[/C][C]0.380156505047077[/C][/ROW]
[ROW][C]36[/C][C]102.46[/C][C]102.002072014667[/C][C]0.457927985332857[/C][/ROW]
[ROW][C]37[/C][C]102.46[/C][C]102.212674298634[/C][C]0.247325701365908[/C][/ROW]
[ROW][C]38[/C][C]102.03[/C][C]102.185499810380[/C][C]-0.155499810380284[/C][/ROW]
[ROW][C]39[/C][C]102.26[/C][C]102.162401495365[/C][C]0.0975985046354496[/C][/ROW]
[ROW][C]40[/C][C]102.33[/C][C]102.207239400983[/C][C]0.122760599016673[/C][/ROW]
[ROW][C]41[/C][C]102.44[/C][C]102.178706188317[/C][C]0.261293811683162[/C][/ROW]
[ROW][C]42[/C][C]102.5[/C][C]102.023811605270[/C][C]0.476188394729822[/C][/ROW]
[ROW][C]43[/C][C]102.52[/C][C]101.945005589334[/C][C]0.574994410665837[/C][/ROW]
[ROW][C]44[/C][C]102.66[/C][C]101.860764675747[/C][C]0.799235324252617[/C][/ROW]
[ROW][C]45[/C][C]102.72[/C][C]101.818644218954[/C][C]0.90135578104601[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58462&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58462&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.35	100.065889726584	0.284110273416090
2	100.35	100.169152781948	0.180847218051646
3	100.36	100.186816199313	0.173183800686682
4	100.39	100.287361805852	0.102638194147623
5	100.34	100.462637255089	-0.122637255089381
6	100.34	100.382472514741	-0.0424725147406731
7	100.35	100.409647002994	-0.0596470029944803
8	100.43	100.450408735375	-0.0204087353751689
9	100.47	100.578128830168	-0.108128830168035
10	100.67	100.549595617502	0.120404382498459
11	100.75	100.699055302897	0.0509446971025573
12	100.78	100.595792247533	0.184207752466998
13	100.79	100.761556625881	0.0284433741188252
14	100.67	100.776502594421	-0.106502594420769
15	100.64	100.746610657342	-0.106610657341591
16	100.64	100.984387429562	-0.344387429562338
17	100.76	100.829492846516	-0.0694928465156753
18	100.79	100.851232437119	-0.0612324371187134
19	100.79	100.919168657753	-0.129168657753213
20	100.9	100.829492846516	0.0705071534843253
21	100.98	100.923244830991	0.056755169008715
22	101.11	101.125694768482	-0.0156947684820985
23	101.18	101.336297052449	-0.156297052449038
24	101.22	101.435483934575	-0.215483934575416
25	101.23	101.593095966447	-0.363095966447448
26	101.09	101.609400659400	-0.519400659399727
27	101.26	101.690924124161	-0.430924124161126
28	101.28	101.892015337239	-0.612015337239248
29	101.43	102.007506912318	-0.57750691231789
30	101.53	101.946364313747	-0.416364313746846
31	101.54	102.086312928254	-0.54631292825391
32	101.54	101.946364313747	-0.406364313746841
33	101.79	101.942288140509	-0.152288140508771
34	102.18	102.012941809969	0.167058190031349
35	102.37	101.989843494953	0.380156505047077
36	102.46	102.002072014667	0.457927985332857
37	102.46	102.212674298634	0.247325701365908
38	102.03	102.185499810380	-0.155499810380284
39	102.26	102.162401495365	0.0975985046354496
40	102.33	102.207239400983	0.122760599016673
41	102.44	102.178706188317	0.261293811683162
42	102.5	102.023811605270	0.476188394729822
43	102.52	101.945005589334	0.574994410665837
44	102.66	101.860764675747	0.799235324252617
45	102.72	101.818644218954	0.90135578104601

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.000342369094410682	0.000684738188821363	0.99965763090559
6	2.19631162840807e-05	4.39262325681614e-05	0.999978036883716
7	1.04239163641696e-06	2.08478327283392e-06	0.999998957608364
8	2.46188157367723e-06	4.92376314735446e-06	0.999997538118426
9	1.51631937953053e-06	3.03263875906105e-06	0.99999848368062
10	0.000109555781856124	0.000219111563712248	0.999890444218144
11	0.000135767934125275	0.00027153586825055	0.999864232065875
12	0.000202420202925220	0.000404840405850441	0.999797579797075
13	7.13425922918265e-05	0.000142685184583653	0.999928657407708
14	2.13539224248931e-05	4.27078448497862e-05	0.999978646077575
15	6.10672489369811e-06	1.22134497873962e-05	0.999993893275106
16	4.91325394364517e-06	9.82650788729034e-06	0.999995086746056
17	1.41843882489512e-06	2.83687764979024e-06	0.999998581561175
18	4.12411497536744e-07	8.24822995073487e-07	0.999999587588502
19	9.85092500084599e-08	1.97018500016920e-07	0.99999990149075
20	8.03774541945972e-08	1.60754908389194e-07	0.999999919622546
21	6.6352821021149e-08	1.32705642042298e-07	0.999999933647179
22	3.70526485351663e-08	7.41052970703326e-08	0.999999962947351
23	9.65474930837686e-09	1.93094986167537e-08	0.99999999034525
24	2.20421855835674e-09	4.40843711671349e-09	0.999999997795781
25	6.97163422498064e-10	1.39432684499613e-09	0.999999999302837
26	1.13440742382762e-09	2.26881484765524e-09	0.999999998865593
27	7.55858612668382e-10	1.51171722533676e-09	0.999999999244141
28	3.49207994486241e-09	6.98415988972483e-09	0.99999999650792
29	9.73866646069665e-09	1.94773329213933e-08	0.999999990261333
30	4.97482163301949e-08	9.94964326603898e-08	0.999999950251784
31	4.57861652815101e-07	9.15723305630201e-07	0.999999542138347
32	0.000156276012723636	0.000312552025447273	0.999843723987276
33	0.1663009265904	0.3326018531808	0.8336990734096
34	0.737515761424077	0.524968477151847	0.262484238575923
35	0.90833899312973	0.183322013740541	0.0916610068702703
36	0.93225260393449	0.135494792131019	0.0677473960655093
37	0.95684529745877	0.0863094050824605	0.0431547025412303
38	0.99595164773361	0.00809670453277799	0.00404835226638899
39	0.997458906835098	0.00508218632980332	0.00254109316490166
40	0.98960478848302	0.0207904230339585	0.0103952115169793

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.000342369094410682 & 0.000684738188821363 & 0.99965763090559 \tabularnewline
6 & 2.19631162840807e-05 & 4.39262325681614e-05 & 0.999978036883716 \tabularnewline
7 & 1.04239163641696e-06 & 2.08478327283392e-06 & 0.999998957608364 \tabularnewline
8 & 2.46188157367723e-06 & 4.92376314735446e-06 & 0.999997538118426 \tabularnewline
9 & 1.51631937953053e-06 & 3.03263875906105e-06 & 0.99999848368062 \tabularnewline
10 & 0.000109555781856124 & 0.000219111563712248 & 0.999890444218144 \tabularnewline
11 & 0.000135767934125275 & 0.00027153586825055 & 0.999864232065875 \tabularnewline
12 & 0.000202420202925220 & 0.000404840405850441 & 0.999797579797075 \tabularnewline
13 & 7.13425922918265e-05 & 0.000142685184583653 & 0.999928657407708 \tabularnewline
14 & 2.13539224248931e-05 & 4.27078448497862e-05 & 0.999978646077575 \tabularnewline
15 & 6.10672489369811e-06 & 1.22134497873962e-05 & 0.999993893275106 \tabularnewline
16 & 4.91325394364517e-06 & 9.82650788729034e-06 & 0.999995086746056 \tabularnewline
17 & 1.41843882489512e-06 & 2.83687764979024e-06 & 0.999998581561175 \tabularnewline
18 & 4.12411497536744e-07 & 8.24822995073487e-07 & 0.999999587588502 \tabularnewline
19 & 9.85092500084599e-08 & 1.97018500016920e-07 & 0.99999990149075 \tabularnewline
20 & 8.03774541945972e-08 & 1.60754908389194e-07 & 0.999999919622546 \tabularnewline
21 & 6.6352821021149e-08 & 1.32705642042298e-07 & 0.999999933647179 \tabularnewline
22 & 3.70526485351663e-08 & 7.41052970703326e-08 & 0.999999962947351 \tabularnewline
23 & 9.65474930837686e-09 & 1.93094986167537e-08 & 0.99999999034525 \tabularnewline
24 & 2.20421855835674e-09 & 4.40843711671349e-09 & 0.999999997795781 \tabularnewline
25 & 6.97163422498064e-10 & 1.39432684499613e-09 & 0.999999999302837 \tabularnewline
26 & 1.13440742382762e-09 & 2.26881484765524e-09 & 0.999999998865593 \tabularnewline
27 & 7.55858612668382e-10 & 1.51171722533676e-09 & 0.999999999244141 \tabularnewline
28 & 3.49207994486241e-09 & 6.98415988972483e-09 & 0.99999999650792 \tabularnewline
29 & 9.73866646069665e-09 & 1.94773329213933e-08 & 0.999999990261333 \tabularnewline
30 & 4.97482163301949e-08 & 9.94964326603898e-08 & 0.999999950251784 \tabularnewline
31 & 4.57861652815101e-07 & 9.15723305630201e-07 & 0.999999542138347 \tabularnewline
32 & 0.000156276012723636 & 0.000312552025447273 & 0.999843723987276 \tabularnewline
33 & 0.1663009265904 & 0.3326018531808 & 0.8336990734096 \tabularnewline
34 & 0.737515761424077 & 0.524968477151847 & 0.262484238575923 \tabularnewline
35 & 0.90833899312973 & 0.183322013740541 & 0.0916610068702703 \tabularnewline
36 & 0.93225260393449 & 0.135494792131019 & 0.0677473960655093 \tabularnewline
37 & 0.95684529745877 & 0.0863094050824605 & 0.0431547025412303 \tabularnewline
38 & 0.99595164773361 & 0.00809670453277799 & 0.00404835226638899 \tabularnewline
39 & 0.997458906835098 & 0.00508218632980332 & 0.00254109316490166 \tabularnewline
40 & 0.98960478848302 & 0.0207904230339585 & 0.0103952115169793 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58462&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]0.000342369094410682[/C][C]0.000684738188821363[/C][C]0.99965763090559[/C][/ROW]
[ROW][C]6[/C][C]2.19631162840807e-05[/C][C]4.39262325681614e-05[/C][C]0.999978036883716[/C][/ROW]
[ROW][C]7[/C][C]1.04239163641696e-06[/C][C]2.08478327283392e-06[/C][C]0.999998957608364[/C][/ROW]
[ROW][C]8[/C][C]2.46188157367723e-06[/C][C]4.92376314735446e-06[/C][C]0.999997538118426[/C][/ROW]
[ROW][C]9[/C][C]1.51631937953053e-06[/C][C]3.03263875906105e-06[/C][C]0.99999848368062[/C][/ROW]
[ROW][C]10[/C][C]0.000109555781856124[/C][C]0.000219111563712248[/C][C]0.999890444218144[/C][/ROW]
[ROW][C]11[/C][C]0.000135767934125275[/C][C]0.00027153586825055[/C][C]0.999864232065875[/C][/ROW]
[ROW][C]12[/C][C]0.000202420202925220[/C][C]0.000404840405850441[/C][C]0.999797579797075[/C][/ROW]
[ROW][C]13[/C][C]7.13425922918265e-05[/C][C]0.000142685184583653[/C][C]0.999928657407708[/C][/ROW]
[ROW][C]14[/C][C]2.13539224248931e-05[/C][C]4.27078448497862e-05[/C][C]0.999978646077575[/C][/ROW]
[ROW][C]15[/C][C]6.10672489369811e-06[/C][C]1.22134497873962e-05[/C][C]0.999993893275106[/C][/ROW]
[ROW][C]16[/C][C]4.91325394364517e-06[/C][C]9.82650788729034e-06[/C][C]0.999995086746056[/C][/ROW]
[ROW][C]17[/C][C]1.41843882489512e-06[/C][C]2.83687764979024e-06[/C][C]0.999998581561175[/C][/ROW]
[ROW][C]18[/C][C]4.12411497536744e-07[/C][C]8.24822995073487e-07[/C][C]0.999999587588502[/C][/ROW]
[ROW][C]19[/C][C]9.85092500084599e-08[/C][C]1.97018500016920e-07[/C][C]0.99999990149075[/C][/ROW]
[ROW][C]20[/C][C]8.03774541945972e-08[/C][C]1.60754908389194e-07[/C][C]0.999999919622546[/C][/ROW]
[ROW][C]21[/C][C]6.6352821021149e-08[/C][C]1.32705642042298e-07[/C][C]0.999999933647179[/C][/ROW]
[ROW][C]22[/C][C]3.70526485351663e-08[/C][C]7.41052970703326e-08[/C][C]0.999999962947351[/C][/ROW]
[ROW][C]23[/C][C]9.65474930837686e-09[/C][C]1.93094986167537e-08[/C][C]0.99999999034525[/C][/ROW]
[ROW][C]24[/C][C]2.20421855835674e-09[/C][C]4.40843711671349e-09[/C][C]0.999999997795781[/C][/ROW]
[ROW][C]25[/C][C]6.97163422498064e-10[/C][C]1.39432684499613e-09[/C][C]0.999999999302837[/C][/ROW]
[ROW][C]26[/C][C]1.13440742382762e-09[/C][C]2.26881484765524e-09[/C][C]0.999999998865593[/C][/ROW]
[ROW][C]27[/C][C]7.55858612668382e-10[/C][C]1.51171722533676e-09[/C][C]0.999999999244141[/C][/ROW]
[ROW][C]28[/C][C]3.49207994486241e-09[/C][C]6.98415988972483e-09[/C][C]0.99999999650792[/C][/ROW]
[ROW][C]29[/C][C]9.73866646069665e-09[/C][C]1.94773329213933e-08[/C][C]0.999999990261333[/C][/ROW]
[ROW][C]30[/C][C]4.97482163301949e-08[/C][C]9.94964326603898e-08[/C][C]0.999999950251784[/C][/ROW]
[ROW][C]31[/C][C]4.57861652815101e-07[/C][C]9.15723305630201e-07[/C][C]0.999999542138347[/C][/ROW]
[ROW][C]32[/C][C]0.000156276012723636[/C][C]0.000312552025447273[/C][C]0.999843723987276[/C][/ROW]
[ROW][C]33[/C][C]0.1663009265904[/C][C]0.3326018531808[/C][C]0.8336990734096[/C][/ROW]
[ROW][C]34[/C][C]0.737515761424077[/C][C]0.524968477151847[/C][C]0.262484238575923[/C][/ROW]
[ROW][C]35[/C][C]0.90833899312973[/C][C]0.183322013740541[/C][C]0.0916610068702703[/C][/ROW]
[ROW][C]36[/C][C]0.93225260393449[/C][C]0.135494792131019[/C][C]0.0677473960655093[/C][/ROW]
[ROW][C]37[/C][C]0.95684529745877[/C][C]0.0863094050824605[/C][C]0.0431547025412303[/C][/ROW]
[ROW][C]38[/C][C]0.99595164773361[/C][C]0.00809670453277799[/C][C]0.00404835226638899[/C][/ROW]
[ROW][C]39[/C][C]0.997458906835098[/C][C]0.00508218632980332[/C][C]0.00254109316490166[/C][/ROW]
[ROW][C]40[/C][C]0.98960478848302[/C][C]0.0207904230339585[/C][C]0.0103952115169793[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58462&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58462&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.000342369094410682	0.000684738188821363	0.99965763090559
6	2.19631162840807e-05	4.39262325681614e-05	0.999978036883716
7	1.04239163641696e-06	2.08478327283392e-06	0.999998957608364
8	2.46188157367723e-06	4.92376314735446e-06	0.999997538118426
9	1.51631937953053e-06	3.03263875906105e-06	0.99999848368062
10	0.000109555781856124	0.000219111563712248	0.999890444218144
11	0.000135767934125275	0.00027153586825055	0.999864232065875
12	0.000202420202925220	0.000404840405850441	0.999797579797075
13	7.13425922918265e-05	0.000142685184583653	0.999928657407708
14	2.13539224248931e-05	4.27078448497862e-05	0.999978646077575
15	6.10672489369811e-06	1.22134497873962e-05	0.999993893275106
16	4.91325394364517e-06	9.82650788729034e-06	0.999995086746056
17	1.41843882489512e-06	2.83687764979024e-06	0.999998581561175
18	4.12411497536744e-07	8.24822995073487e-07	0.999999587588502
19	9.85092500084599e-08	1.97018500016920e-07	0.99999990149075
20	8.03774541945972e-08	1.60754908389194e-07	0.999999919622546
21	6.6352821021149e-08	1.32705642042298e-07	0.999999933647179
22	3.70526485351663e-08	7.41052970703326e-08	0.999999962947351
23	9.65474930837686e-09	1.93094986167537e-08	0.99999999034525
24	2.20421855835674e-09	4.40843711671349e-09	0.999999997795781
25	6.97163422498064e-10	1.39432684499613e-09	0.999999999302837
26	1.13440742382762e-09	2.26881484765524e-09	0.999999998865593
27	7.55858612668382e-10	1.51171722533676e-09	0.999999999244141
28	3.49207994486241e-09	6.98415988972483e-09	0.99999999650792
29	9.73866646069665e-09	1.94773329213933e-08	0.999999990261333
30	4.97482163301949e-08	9.94964326603898e-08	0.999999950251784
31	4.57861652815101e-07	9.15723305630201e-07	0.999999542138347
32	0.000156276012723636	0.000312552025447273	0.999843723987276
33	0.1663009265904	0.3326018531808	0.8336990734096
34	0.737515761424077	0.524968477151847	0.262484238575923
35	0.90833899312973	0.183322013740541	0.0916610068702703
36	0.93225260393449	0.135494792131019	0.0677473960655093
37	0.95684529745877	0.0863094050824605	0.0431547025412303
38	0.99595164773361	0.00809670453277799	0.00404835226638899
39	0.997458906835098	0.00508218632980332	0.00254109316490166
40	0.98960478848302	0.0207904230339585	0.0103952115169793

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	30	0.833333333333333	NOK
5% type I error level	31	0.861111111111111	NOK
10% type I error level	32	0.888888888888889	NOK

\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 & 30 & 0.833333333333333 & NOK \tabularnewline
5% type I error level & 31 & 0.861111111111111 & NOK \tabularnewline
10% type I error level & 32 & 0.888888888888889 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58462&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]30[/C][C]0.833333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.861111111111111[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]32[/C][C]0.888888888888889[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58462&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58462&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 tests	OK/NOK
1% type I error level	30	0.833333333333333	NOK
5% type I error level	31	0.861111111111111	NOK
10% type I error level	32	0.888888888888889	NOK

Figure 1

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code