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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 20 Nov 2009 17:24:09 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/21/t12587631224snohktrjg1wpgc.htm/, Retrieved Sat, 27 Apr 2024 21:12:17 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58503, Retrieved Sat, 27 Apr 2024 21:12:17 +0000

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181

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

-       [Multiple Regression] [shwws7vr1] [2009-11-21 00:24:09] [d447d4b3e35da686436a520338c962fc] [Current]

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

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=58503&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=58503&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58503&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
y(t)[t] = + 3.23484343028908 + 0.00980623397008176`x(t)`[t] + 1.05967389184518`y(t-1)`[t] -0.078236397966839`y(t-2)`[t] -0.259322390600045`y(t-3)`[t] + 0.235745370015547`y(t-4)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y(t)[t] =  +  3.23484343028908 +  0.00980623397008176`x(t)`[t] +  1.05967389184518`y(t-1)`[t] -0.078236397966839`y(t-2)`[t] -0.259322390600045`y(t-3)`[t] +  0.235745370015547`y(t-4)`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58503&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y(t)[t] =  +  3.23484343028908 +  0.00980623397008176`x(t)`[t] +  1.05967389184518`y(t-1)`[t] -0.078236397966839`y(t-2)`[t] -0.259322390600045`y(t-3)`[t] +  0.235745370015547`y(t-4)`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58503&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58503&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
y(t)[t] = + 3.23484343028908 + 0.00980623397008176`x(t)`[t] + 1.05967389184518`y(t-1)`[t] -0.078236397966839`y(t-2)`[t] -0.259322390600045`y(t-3)`[t] + 0.235745370015547`y(t-4)`[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)	3.23484343028908	5.350932	0.6045	0.549385	0.274693
`x(t)`	0.00980623397008176	0.00935	1.0488	0.301438	0.150719
`y(t-1)`	1.05967389184518	0.166392	6.3685	0	0
`y(t-2)`	-0.078236397966839	0.241364	-0.3241	0.747759	0.37388
`y(t-3)`	-0.259322390600045	0.242875	-1.0677	0.292957	0.146479
`y(t-4)`	0.235745370015547	0.167281	1.4093	0.167576	0.083788

\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) & 3.23484343028908 & 5.350932 & 0.6045 & 0.549385 & 0.274693 \tabularnewline
`x(t)` & 0.00980623397008176 & 0.00935 & 1.0488 & 0.301438 & 0.150719 \tabularnewline
`y(t-1)` & 1.05967389184518 & 0.166392 & 6.3685 & 0 & 0 \tabularnewline
`y(t-2)` & -0.078236397966839 & 0.241364 & -0.3241 & 0.747759 & 0.37388 \tabularnewline
`y(t-3)` & -0.259322390600045 & 0.242875 & -1.0677 & 0.292957 & 0.146479 \tabularnewline
`y(t-4)` & 0.235745370015547 & 0.167281 & 1.4093 & 0.167576 & 0.083788 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58503&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]3.23484343028908[/C][C]5.350932[/C][C]0.6045[/C][C]0.549385[/C][C]0.274693[/C][/ROW]
[ROW][C]`x(t)`[/C][C]0.00980623397008176[/C][C]0.00935[/C][C]1.0488[/C][C]0.301438[/C][C]0.150719[/C][/ROW]
[ROW][C]`y(t-1)`[/C][C]1.05967389184518[/C][C]0.166392[/C][C]6.3685[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`y(t-2)`[/C][C]-0.078236397966839[/C][C]0.241364[/C][C]-0.3241[/C][C]0.747759[/C][C]0.37388[/C][/ROW]
[ROW][C]`y(t-3)`[/C][C]-0.259322390600045[/C][C]0.242875[/C][C]-1.0677[/C][C]0.292957[/C][C]0.146479[/C][/ROW]
[ROW][C]`y(t-4)`[/C][C]0.235745370015547[/C][C]0.167281[/C][C]1.4093[/C][C]0.167576[/C][C]0.083788[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58503&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58503&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)	3.23484343028908	5.350932	0.6045	0.549385	0.274693
`x(t)`	0.00980623397008176	0.00935	1.0488	0.301438	0.150719
`y(t-1)`	1.05967389184518	0.166392	6.3685	0	0
`y(t-2)`	-0.078236397966839	0.241364	-0.3241	0.747759	0.37388
`y(t-3)`	-0.259322390600045	0.242875	-1.0677	0.292957	0.146479
`y(t-4)`	0.235745370015547	0.167281	1.4093	0.167576	0.083788

Multiple Linear Regression - Regression Statistics
Multiple R	0.987945997772003
R-squared	0.976037294513718
Adjusted R-squared	0.97261405087282
F-TEST (value)	285.120603994712
F-TEST (DF numerator)	5
F-TEST (DF denominator)	35
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.126325888335065
Sum Squared Residuals	0.558538052227512

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.987945997772003 \tabularnewline
R-squared & 0.976037294513718 \tabularnewline
Adjusted R-squared & 0.97261405087282 \tabularnewline
F-TEST (value) & 285.120603994712 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 35 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.126325888335065 \tabularnewline
Sum Squared Residuals & 0.558538052227512 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58503&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.987945997772003[/C][/ROW]
[ROW][C]R-squared[/C][C]0.976037294513718[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.97261405087282[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]285.120603994712[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]35[/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]0.126325888335065[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.558538052227512[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58503&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58503&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	0.987945997772003
R-squared	0.976037294513718
Adjusted R-squared	0.97261405087282
F-TEST (value)	285.120603994712
F-TEST (DF numerator)	5
F-TEST (DF denominator)	35
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.126325888335065
Sum Squared Residuals	0.558538052227512

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.34	100.427597208559	-0.0875972085586901
2	100.34	100.363887520079	-0.0238875200790140
3	100.35	100.364338368754	-0.0143383687535358
4	100.43	100.397915458493	0.0320845415065422
5	100.47	100.479337597293	-0.009337597292526
6	100.67	100.510813108089	0.159186891910742
7	100.75	100.712016950359	0.0379830496411366
8	100.78	100.782177578273	-0.00217757827309182
9	100.79	100.777237825315	0.0127621746847935
10	100.67	100.812969440786	-0.142969440786479
11	100.64	100.693948796195	-0.0539487961952083
12	100.64	100.693186993838	-0.0531869938379806
13	100.76	100.717831119623	0.0421688803767487
14	100.79	100.826051211396	-0.0360512113960248
15	100.79	100.84628381628	-0.0562838162799347
16	100.9	100.806345923049	0.0936540769513304
17	100.98	100.950186125275	0.0298138747251378
18	101.11	101.048037682562	0.0619623174379843
19	101.18	101.166210576352	0.0137894236478478
20	101.22	101.242561767298	-0.0225617672975086
21	101.23	101.275995125342	-0.0459951253421565
22	101.09	101.297133487178	-0.207133487178366
23	101.26	101.160009799000	0.099990201000491
24	101.28	101.372457273499	-0.0924572734988925
25	101.43	101.427348450940	0.00265154905984417
26	101.53	101.503232843267	0.026767156733113
27	101.54	101.642455458836	-0.102455458836195
28	101.54	101.600944685779	-0.060944685779087
29	101.79	101.609297701223	0.180702298777354
30	102.18	101.900296728944	0.279703271056065
31	102.37	102.294700841197	0.0752991588029078
32	102.46	102.401578648848	0.0584213511520858
33	102.46	102.455084656324	0.00491534367623404
34	102.03	102.488751573805	-0.458751573804783
35	102.26	102.052877345685	0.207122654314603
36	102.33	102.354697132447	-0.0246971324470634
37	102.44	102.520329252168	-0.0803292521681463
38	102.5	102.459223066743	0.0407769332571525
39	102.52	102.544578748536	-0.0245787485361407
40	102.66	102.542974890369	0.117025109631340
41	102.72	102.697097222003	0.022902777997371

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100.34 & 100.427597208559 & -0.0875972085586901 \tabularnewline
2 & 100.34 & 100.363887520079 & -0.0238875200790140 \tabularnewline
3 & 100.35 & 100.364338368754 & -0.0143383687535358 \tabularnewline
4 & 100.43 & 100.397915458493 & 0.0320845415065422 \tabularnewline
5 & 100.47 & 100.479337597293 & -0.009337597292526 \tabularnewline
6 & 100.67 & 100.510813108089 & 0.159186891910742 \tabularnewline
7 & 100.75 & 100.712016950359 & 0.0379830496411366 \tabularnewline
8 & 100.78 & 100.782177578273 & -0.00217757827309182 \tabularnewline
9 & 100.79 & 100.777237825315 & 0.0127621746847935 \tabularnewline
10 & 100.67 & 100.812969440786 & -0.142969440786479 \tabularnewline
11 & 100.64 & 100.693948796195 & -0.0539487961952083 \tabularnewline
12 & 100.64 & 100.693186993838 & -0.0531869938379806 \tabularnewline
13 & 100.76 & 100.717831119623 & 0.0421688803767487 \tabularnewline
14 & 100.79 & 100.826051211396 & -0.0360512113960248 \tabularnewline
15 & 100.79 & 100.84628381628 & -0.0562838162799347 \tabularnewline
16 & 100.9 & 100.806345923049 & 0.0936540769513304 \tabularnewline
17 & 100.98 & 100.950186125275 & 0.0298138747251378 \tabularnewline
18 & 101.11 & 101.048037682562 & 0.0619623174379843 \tabularnewline
19 & 101.18 & 101.166210576352 & 0.0137894236478478 \tabularnewline
20 & 101.22 & 101.242561767298 & -0.0225617672975086 \tabularnewline
21 & 101.23 & 101.275995125342 & -0.0459951253421565 \tabularnewline
22 & 101.09 & 101.297133487178 & -0.207133487178366 \tabularnewline
23 & 101.26 & 101.160009799000 & 0.099990201000491 \tabularnewline
24 & 101.28 & 101.372457273499 & -0.0924572734988925 \tabularnewline
25 & 101.43 & 101.427348450940 & 0.00265154905984417 \tabularnewline
26 & 101.53 & 101.503232843267 & 0.026767156733113 \tabularnewline
27 & 101.54 & 101.642455458836 & -0.102455458836195 \tabularnewline
28 & 101.54 & 101.600944685779 & -0.060944685779087 \tabularnewline
29 & 101.79 & 101.609297701223 & 0.180702298777354 \tabularnewline
30 & 102.18 & 101.900296728944 & 0.279703271056065 \tabularnewline
31 & 102.37 & 102.294700841197 & 0.0752991588029078 \tabularnewline
32 & 102.46 & 102.401578648848 & 0.0584213511520858 \tabularnewline
33 & 102.46 & 102.455084656324 & 0.00491534367623404 \tabularnewline
34 & 102.03 & 102.488751573805 & -0.458751573804783 \tabularnewline
35 & 102.26 & 102.052877345685 & 0.207122654314603 \tabularnewline
36 & 102.33 & 102.354697132447 & -0.0246971324470634 \tabularnewline
37 & 102.44 & 102.520329252168 & -0.0803292521681463 \tabularnewline
38 & 102.5 & 102.459223066743 & 0.0407769332571525 \tabularnewline
39 & 102.52 & 102.544578748536 & -0.0245787485361407 \tabularnewline
40 & 102.66 & 102.542974890369 & 0.117025109631340 \tabularnewline
41 & 102.72 & 102.697097222003 & 0.022902777997371 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58503&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]100.34[/C][C]100.427597208559[/C][C]-0.0875972085586901[/C][/ROW]
[ROW][C]2[/C][C]100.34[/C][C]100.363887520079[/C][C]-0.0238875200790140[/C][/ROW]
[ROW][C]3[/C][C]100.35[/C][C]100.364338368754[/C][C]-0.0143383687535358[/C][/ROW]
[ROW][C]4[/C][C]100.43[/C][C]100.397915458493[/C][C]0.0320845415065422[/C][/ROW]
[ROW][C]5[/C][C]100.47[/C][C]100.479337597293[/C][C]-0.009337597292526[/C][/ROW]
[ROW][C]6[/C][C]100.67[/C][C]100.510813108089[/C][C]0.159186891910742[/C][/ROW]
[ROW][C]7[/C][C]100.75[/C][C]100.712016950359[/C][C]0.0379830496411366[/C][/ROW]
[ROW][C]8[/C][C]100.78[/C][C]100.782177578273[/C][C]-0.00217757827309182[/C][/ROW]
[ROW][C]9[/C][C]100.79[/C][C]100.777237825315[/C][C]0.0127621746847935[/C][/ROW]
[ROW][C]10[/C][C]100.67[/C][C]100.812969440786[/C][C]-0.142969440786479[/C][/ROW]
[ROW][C]11[/C][C]100.64[/C][C]100.693948796195[/C][C]-0.0539487961952083[/C][/ROW]
[ROW][C]12[/C][C]100.64[/C][C]100.693186993838[/C][C]-0.0531869938379806[/C][/ROW]
[ROW][C]13[/C][C]100.76[/C][C]100.717831119623[/C][C]0.0421688803767487[/C][/ROW]
[ROW][C]14[/C][C]100.79[/C][C]100.826051211396[/C][C]-0.0360512113960248[/C][/ROW]
[ROW][C]15[/C][C]100.79[/C][C]100.84628381628[/C][C]-0.0562838162799347[/C][/ROW]
[ROW][C]16[/C][C]100.9[/C][C]100.806345923049[/C][C]0.0936540769513304[/C][/ROW]
[ROW][C]17[/C][C]100.98[/C][C]100.950186125275[/C][C]0.0298138747251378[/C][/ROW]
[ROW][C]18[/C][C]101.11[/C][C]101.048037682562[/C][C]0.0619623174379843[/C][/ROW]
[ROW][C]19[/C][C]101.18[/C][C]101.166210576352[/C][C]0.0137894236478478[/C][/ROW]
[ROW][C]20[/C][C]101.22[/C][C]101.242561767298[/C][C]-0.0225617672975086[/C][/ROW]
[ROW][C]21[/C][C]101.23[/C][C]101.275995125342[/C][C]-0.0459951253421565[/C][/ROW]
[ROW][C]22[/C][C]101.09[/C][C]101.297133487178[/C][C]-0.207133487178366[/C][/ROW]
[ROW][C]23[/C][C]101.26[/C][C]101.160009799000[/C][C]0.099990201000491[/C][/ROW]
[ROW][C]24[/C][C]101.28[/C][C]101.372457273499[/C][C]-0.0924572734988925[/C][/ROW]
[ROW][C]25[/C][C]101.43[/C][C]101.427348450940[/C][C]0.00265154905984417[/C][/ROW]
[ROW][C]26[/C][C]101.53[/C][C]101.503232843267[/C][C]0.026767156733113[/C][/ROW]
[ROW][C]27[/C][C]101.54[/C][C]101.642455458836[/C][C]-0.102455458836195[/C][/ROW]
[ROW][C]28[/C][C]101.54[/C][C]101.600944685779[/C][C]-0.060944685779087[/C][/ROW]
[ROW][C]29[/C][C]101.79[/C][C]101.609297701223[/C][C]0.180702298777354[/C][/ROW]
[ROW][C]30[/C][C]102.18[/C][C]101.900296728944[/C][C]0.279703271056065[/C][/ROW]
[ROW][C]31[/C][C]102.37[/C][C]102.294700841197[/C][C]0.0752991588029078[/C][/ROW]
[ROW][C]32[/C][C]102.46[/C][C]102.401578648848[/C][C]0.0584213511520858[/C][/ROW]
[ROW][C]33[/C][C]102.46[/C][C]102.455084656324[/C][C]0.00491534367623404[/C][/ROW]
[ROW][C]34[/C][C]102.03[/C][C]102.488751573805[/C][C]-0.458751573804783[/C][/ROW]
[ROW][C]35[/C][C]102.26[/C][C]102.052877345685[/C][C]0.207122654314603[/C][/ROW]
[ROW][C]36[/C][C]102.33[/C][C]102.354697132447[/C][C]-0.0246971324470634[/C][/ROW]
[ROW][C]37[/C][C]102.44[/C][C]102.520329252168[/C][C]-0.0803292521681463[/C][/ROW]
[ROW][C]38[/C][C]102.5[/C][C]102.459223066743[/C][C]0.0407769332571525[/C][/ROW]
[ROW][C]39[/C][C]102.52[/C][C]102.544578748536[/C][C]-0.0245787485361407[/C][/ROW]
[ROW][C]40[/C][C]102.66[/C][C]102.542974890369[/C][C]0.117025109631340[/C][/ROW]
[ROW][C]41[/C][C]102.72[/C][C]102.697097222003[/C][C]0.022902777997371[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58503&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58503&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	100.34	100.427597208559	-0.0875972085586901
2	100.34	100.363887520079	-0.0238875200790140
3	100.35	100.364338368754	-0.0143383687535358
4	100.43	100.397915458493	0.0320845415065422
5	100.47	100.479337597293	-0.009337597292526
6	100.67	100.510813108089	0.159186891910742
7	100.75	100.712016950359	0.0379830496411366
8	100.78	100.782177578273	-0.00217757827309182
9	100.79	100.777237825315	0.0127621746847935
10	100.67	100.812969440786	-0.142969440786479
11	100.64	100.693948796195	-0.0539487961952083
12	100.64	100.693186993838	-0.0531869938379806
13	100.76	100.717831119623	0.0421688803767487
14	100.79	100.826051211396	-0.0360512113960248
15	100.79	100.84628381628	-0.0562838162799347
16	100.9	100.806345923049	0.0936540769513304
17	100.98	100.950186125275	0.0298138747251378
18	101.11	101.048037682562	0.0619623174379843
19	101.18	101.166210576352	0.0137894236478478
20	101.22	101.242561767298	-0.0225617672975086
21	101.23	101.275995125342	-0.0459951253421565
22	101.09	101.297133487178	-0.207133487178366
23	101.26	101.160009799000	0.099990201000491
24	101.28	101.372457273499	-0.0924572734988925
25	101.43	101.427348450940	0.00265154905984417
26	101.53	101.503232843267	0.026767156733113
27	101.54	101.642455458836	-0.102455458836195
28	101.54	101.600944685779	-0.060944685779087
29	101.79	101.609297701223	0.180702298777354
30	102.18	101.900296728944	0.279703271056065
31	102.37	102.294700841197	0.0752991588029078
32	102.46	102.401578648848	0.0584213511520858
33	102.46	102.455084656324	0.00491534367623404
34	102.03	102.488751573805	-0.458751573804783
35	102.26	102.052877345685	0.207122654314603
36	102.33	102.354697132447	-0.0246971324470634
37	102.44	102.520329252168	-0.0803292521681463
38	102.5	102.459223066743	0.0407769332571525
39	102.52	102.544578748536	-0.0245787485361407
40	102.66	102.542974890369	0.117025109631340
41	102.72	102.697097222003	0.022902777997371

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.239593239200557	0.479186478401115	0.760406760799442
10	0.122281369387027	0.244562738774054	0.877718630612973
11	0.0634053601452349	0.126810720290470	0.936594639854765
12	0.0278108972213377	0.0556217944426755	0.972189102778662
13	0.0220840982907650	0.0441681965815301	0.977915901709235
14	0.00898967159887451	0.0179793431977490	0.991010328401125
15	0.0116383329203802	0.0232766658407604	0.98836166707962
16	0.0175189356894709	0.0350378713789418	0.98248106431053
17	0.0100057060460361	0.0200114120920721	0.989994293953964
18	0.00471663820397071	0.00943327640794142	0.99528336179603
19	0.00203906894972595	0.00407813789945191	0.997960931050274
20	0.000915443151555947	0.00183088630311189	0.999084556848444
21	0.000385134081331131	0.000770268162662261	0.999614865918669
22	0.00195855422007866	0.00391710844015732	0.998041445779921
23	0.00320002758039618	0.00640005516079237	0.996799972419604
24	0.0026284648980084	0.0052569297960168	0.997371535101992
25	0.00125122328609191	0.00250244657218381	0.998748776713908
26	0.000674833794499011	0.00134966758899802	0.9993251662055
27	0.000661878575713988	0.00132375715142798	0.999338121424286
28	0.00247250374208703	0.00494500748417406	0.997527496257913
29	0.0293638156456113	0.0587276312912227	0.970636184354389
30	0.0386633551914963	0.0773267103829925	0.961336644808504
31	0.0388013029380512	0.0776026058761023	0.961198697061949
32	0.106479598587776	0.212959197175553	0.893520401412224

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.239593239200557 & 0.479186478401115 & 0.760406760799442 \tabularnewline
10 & 0.122281369387027 & 0.244562738774054 & 0.877718630612973 \tabularnewline
11 & 0.0634053601452349 & 0.126810720290470 & 0.936594639854765 \tabularnewline
12 & 0.0278108972213377 & 0.0556217944426755 & 0.972189102778662 \tabularnewline
13 & 0.0220840982907650 & 0.0441681965815301 & 0.977915901709235 \tabularnewline
14 & 0.00898967159887451 & 0.0179793431977490 & 0.991010328401125 \tabularnewline
15 & 0.0116383329203802 & 0.0232766658407604 & 0.98836166707962 \tabularnewline
16 & 0.0175189356894709 & 0.0350378713789418 & 0.98248106431053 \tabularnewline
17 & 0.0100057060460361 & 0.0200114120920721 & 0.989994293953964 \tabularnewline
18 & 0.00471663820397071 & 0.00943327640794142 & 0.99528336179603 \tabularnewline
19 & 0.00203906894972595 & 0.00407813789945191 & 0.997960931050274 \tabularnewline
20 & 0.000915443151555947 & 0.00183088630311189 & 0.999084556848444 \tabularnewline
21 & 0.000385134081331131 & 0.000770268162662261 & 0.999614865918669 \tabularnewline
22 & 0.00195855422007866 & 0.00391710844015732 & 0.998041445779921 \tabularnewline
23 & 0.00320002758039618 & 0.00640005516079237 & 0.996799972419604 \tabularnewline
24 & 0.0026284648980084 & 0.0052569297960168 & 0.997371535101992 \tabularnewline
25 & 0.00125122328609191 & 0.00250244657218381 & 0.998748776713908 \tabularnewline
26 & 0.000674833794499011 & 0.00134966758899802 & 0.9993251662055 \tabularnewline
27 & 0.000661878575713988 & 0.00132375715142798 & 0.999338121424286 \tabularnewline
28 & 0.00247250374208703 & 0.00494500748417406 & 0.997527496257913 \tabularnewline
29 & 0.0293638156456113 & 0.0587276312912227 & 0.970636184354389 \tabularnewline
30 & 0.0386633551914963 & 0.0773267103829925 & 0.961336644808504 \tabularnewline
31 & 0.0388013029380512 & 0.0776026058761023 & 0.961198697061949 \tabularnewline
32 & 0.106479598587776 & 0.212959197175553 & 0.893520401412224 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58503&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]9[/C][C]0.239593239200557[/C][C]0.479186478401115[/C][C]0.760406760799442[/C][/ROW]
[ROW][C]10[/C][C]0.122281369387027[/C][C]0.244562738774054[/C][C]0.877718630612973[/C][/ROW]
[ROW][C]11[/C][C]0.0634053601452349[/C][C]0.126810720290470[/C][C]0.936594639854765[/C][/ROW]
[ROW][C]12[/C][C]0.0278108972213377[/C][C]0.0556217944426755[/C][C]0.972189102778662[/C][/ROW]
[ROW][C]13[/C][C]0.0220840982907650[/C][C]0.0441681965815301[/C][C]0.977915901709235[/C][/ROW]
[ROW][C]14[/C][C]0.00898967159887451[/C][C]0.0179793431977490[/C][C]0.991010328401125[/C][/ROW]
[ROW][C]15[/C][C]0.0116383329203802[/C][C]0.0232766658407604[/C][C]0.98836166707962[/C][/ROW]
[ROW][C]16[/C][C]0.0175189356894709[/C][C]0.0350378713789418[/C][C]0.98248106431053[/C][/ROW]
[ROW][C]17[/C][C]0.0100057060460361[/C][C]0.0200114120920721[/C][C]0.989994293953964[/C][/ROW]
[ROW][C]18[/C][C]0.00471663820397071[/C][C]0.00943327640794142[/C][C]0.99528336179603[/C][/ROW]
[ROW][C]19[/C][C]0.00203906894972595[/C][C]0.00407813789945191[/C][C]0.997960931050274[/C][/ROW]
[ROW][C]20[/C][C]0.000915443151555947[/C][C]0.00183088630311189[/C][C]0.999084556848444[/C][/ROW]
[ROW][C]21[/C][C]0.000385134081331131[/C][C]0.000770268162662261[/C][C]0.999614865918669[/C][/ROW]
[ROW][C]22[/C][C]0.00195855422007866[/C][C]0.00391710844015732[/C][C]0.998041445779921[/C][/ROW]
[ROW][C]23[/C][C]0.00320002758039618[/C][C]0.00640005516079237[/C][C]0.996799972419604[/C][/ROW]
[ROW][C]24[/C][C]0.0026284648980084[/C][C]0.0052569297960168[/C][C]0.997371535101992[/C][/ROW]
[ROW][C]25[/C][C]0.00125122328609191[/C][C]0.00250244657218381[/C][C]0.998748776713908[/C][/ROW]
[ROW][C]26[/C][C]0.000674833794499011[/C][C]0.00134966758899802[/C][C]0.9993251662055[/C][/ROW]
[ROW][C]27[/C][C]0.000661878575713988[/C][C]0.00132375715142798[/C][C]0.999338121424286[/C][/ROW]
[ROW][C]28[/C][C]0.00247250374208703[/C][C]0.00494500748417406[/C][C]0.997527496257913[/C][/ROW]
[ROW][C]29[/C][C]0.0293638156456113[/C][C]0.0587276312912227[/C][C]0.970636184354389[/C][/ROW]
[ROW][C]30[/C][C]0.0386633551914963[/C][C]0.0773267103829925[/C][C]0.961336644808504[/C][/ROW]
[ROW][C]31[/C][C]0.0388013029380512[/C][C]0.0776026058761023[/C][C]0.961198697061949[/C][/ROW]
[ROW][C]32[/C][C]0.106479598587776[/C][C]0.212959197175553[/C][C]0.893520401412224[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58503&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58503&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
9	0.239593239200557	0.479186478401115	0.760406760799442
10	0.122281369387027	0.244562738774054	0.877718630612973
11	0.0634053601452349	0.126810720290470	0.936594639854765
12	0.0278108972213377	0.0556217944426755	0.972189102778662
13	0.0220840982907650	0.0441681965815301	0.977915901709235
14	0.00898967159887451	0.0179793431977490	0.991010328401125
15	0.0116383329203802	0.0232766658407604	0.98836166707962
16	0.0175189356894709	0.0350378713789418	0.98248106431053
17	0.0100057060460361	0.0200114120920721	0.989994293953964
18	0.00471663820397071	0.00943327640794142	0.99528336179603
19	0.00203906894972595	0.00407813789945191	0.997960931050274
20	0.000915443151555947	0.00183088630311189	0.999084556848444
21	0.000385134081331131	0.000770268162662261	0.999614865918669
22	0.00195855422007866	0.00391710844015732	0.998041445779921
23	0.00320002758039618	0.00640005516079237	0.996799972419604
24	0.0026284648980084	0.0052569297960168	0.997371535101992
25	0.00125122328609191	0.00250244657218381	0.998748776713908
26	0.000674833794499011	0.00134966758899802	0.9993251662055
27	0.000661878575713988	0.00132375715142798	0.999338121424286
28	0.00247250374208703	0.00494500748417406	0.997527496257913
29	0.0293638156456113	0.0587276312912227	0.970636184354389
30	0.0386633551914963	0.0773267103829925	0.961336644808504
31	0.0388013029380512	0.0776026058761023	0.961198697061949
32	0.106479598587776	0.212959197175553	0.893520401412224

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	11	0.458333333333333	NOK
5% type I error level	16	0.666666666666667	NOK
10% type I error level	20	0.833333333333333	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 & 11 & 0.458333333333333 & NOK \tabularnewline
5% type I error level & 16 & 0.666666666666667 & NOK \tabularnewline
10% type I error level & 20 & 0.833333333333333 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58503&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]11[/C][C]0.458333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.666666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.833333333333333[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58503&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58503&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	11	0.458333333333333	NOK
5% type I error level	16	0.666666666666667	NOK
10% type I error level	20	0.833333333333333	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

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