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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 15 Dec 2010 16:03:07 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/15/t1292428900kj8dr8vakpwrpo6.htm/, Retrieved Fri, 03 May 2024 09:23:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110514, Retrieved Fri, 03 May 2024 09:23:43 +0000

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Estimated Impact

122

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

-       [Multiple Regression] [MLRM 1] [2010-12-15 16:03:07] [6a374a3321fe5d3cfaebff7ea97302d4] [Current]

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6.3	2	4.5	1	6.6	42	3	1	3
2.1	1.8	69	2547	4603	624	3	5	4
9.1	0.7	27	10.55	179.5	180	4	4	4
15.8	3.9	19	0.023	0.3	35	1	1	1
5.2	1	30.4	160	169	392	4	5	4
10.9	3.6	28	3.3	25.6	63	1	2	1
8.3	1.4	50	52.16	440	230	1	1	1
11	1.5	7	0.425	6.4	112	5	4	4
3.2	0.7	30	465	423	281	5	5	5
6.3	2.1	3.5	0.075	1.2	42	1	1	1
8.6	0	50	3	25	28	2	2	2
6.6	4.1	6	0.785	3.5	42	2	2	2
9.5	1.2	10.4	0.2	5	120	2	2	2
3.3	0.5	20	27.66	115	148	5	5	5
11	3.4	3.9	0.12	1	16	3	1	2
4.7	1.5	41	85	325	310	1	3	1
10.4	3.4	9	0.101	4	28	5	1	3
7.4	0.8	7.6	1.04	5.5	68	5	3	4
2.1	0.8	46	521	655	336	5	5	5
7.7	1.4	2.6	0.005	0.14	21.5	5	2	4
17.9	2	24	0.01	0.25	50	1	1	1
6.1	1.9	100	62	1320	267	1	1	1
11.9	1.3	3.2	0.023	0.4	19	4	1	3
10.8	2	2	0.048	0.33	30	4	1	3
13.8	5.6	5	1.7	6.3	12	2	1	1
14.3	3.1	6.5	3.5	10.8	120	2	1	1
10	0.9	20.2	10	115	170	4	4	4
11.9	1.8	13	1.62	11.4	17	2	1	2
6.5	1.9	27	192	180	115	4	4	4
7.5	0.9	18	2.5	12.1	31	5	5	5
10.6	2.6	4.7	0.28	1.9	21	3	1	3
7.4	2.4	9.8	4.235	50.4	52	1	1	1
8.4	1.2	29	6.8	179	164	2	3	2
5.7	0.9	7	0.75	12.3	225	2	2	2
4.9	0.5	6	3.6	21	225	3	2	3
3.2	0.6	20	55.5	175	151	5	5	5
11	2.3	4.5	0.9	2.6	60	2	1	2
4.9	0.5	7.5	2	12.3	200	3	1	3
13.2	2.6	2.3	0.104	2.5	46	3	2	2
9.7	0.6	24	4.19	58	210	4	3	4
12.8	6.6	3	3.5	3.9	14	2	1	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110514&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110514&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110514&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	7 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation
SWS[t] = + 12.3197087181946 + 0.142634167491983PS[t] + 0.0110188228044140LS[t] + 0.00358575159323686BW[t] -0.00147763369106797BRW[t] -0.0142418480170465GT[t] + 1.51015954701529PI[t] + 0.137314401734720SEI[t] -2.67140908301206ODI[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SWS[t] =  +  12.3197087181946 +  0.142634167491983PS[t] +  0.0110188228044140LS[t] +  0.00358575159323686BW[t] -0.00147763369106797BRW[t] -0.0142418480170465GT[t] +  1.51015954701529PI[t] +  0.137314401734720SEI[t] -2.67140908301206ODI[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110514&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SWS[t] =  +  12.3197087181946 +  0.142634167491983PS[t] +  0.0110188228044140LS[t] +  0.00358575159323686BW[t] -0.00147763369106797BRW[t] -0.0142418480170465GT[t] +  1.51015954701529PI[t] +  0.137314401734720SEI[t] -2.67140908301206ODI[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110514&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110514&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
SWS[t] = + 12.3197087181946 + 0.142634167491983PS[t] + 0.0110188228044140LS[t] + 0.00358575159323686BW[t] -0.00147763369106797BRW[t] -0.0142418480170465GT[t] + 1.51015954701529PI[t] + 0.137314401734720SEI[t] -2.67140908301206ODI[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)	12.3197087181946	2.239418	5.5013	5e-06	2e-06
PS	0.142634167491983	0.49663	0.2872	0.775808	0.387904
LS	0.0110188228044140	0.042204	0.2611	0.795699	0.39785
BW	0.00358575159323686	0.005347	0.6706	0.507257	0.253628
BRW	-0.00147763369106797	0.003185	-0.4639	0.645861	0.32293
GT	-0.0142418480170465	0.006737	-2.1141	0.042394	0.021197
PI	1.51015954701529	1.078434	1.4003	0.171038	0.085519
SEI	0.137314401734720	0.638624	0.215	0.831119	0.41556
ODI	-2.67140908301206	1.489998	-1.7929	0.082448	0.041224

\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) & 12.3197087181946 & 2.239418 & 5.5013 & 5e-06 & 2e-06 \tabularnewline
PS & 0.142634167491983 & 0.49663 & 0.2872 & 0.775808 & 0.387904 \tabularnewline
LS & 0.0110188228044140 & 0.042204 & 0.2611 & 0.795699 & 0.39785 \tabularnewline
BW & 0.00358575159323686 & 0.005347 & 0.6706 & 0.507257 & 0.253628 \tabularnewline
BRW & -0.00147763369106797 & 0.003185 & -0.4639 & 0.645861 & 0.32293 \tabularnewline
GT & -0.0142418480170465 & 0.006737 & -2.1141 & 0.042394 & 0.021197 \tabularnewline
PI & 1.51015954701529 & 1.078434 & 1.4003 & 0.171038 & 0.085519 \tabularnewline
SEI & 0.137314401734720 & 0.638624 & 0.215 & 0.831119 & 0.41556 \tabularnewline
ODI & -2.67140908301206 & 1.489998 & -1.7929 & 0.082448 & 0.041224 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110514&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]12.3197087181946[/C][C]2.239418[/C][C]5.5013[/C][C]5e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]PS[/C][C]0.142634167491983[/C][C]0.49663[/C][C]0.2872[/C][C]0.775808[/C][C]0.387904[/C][/ROW]
[ROW][C]LS[/C][C]0.0110188228044140[/C][C]0.042204[/C][C]0.2611[/C][C]0.795699[/C][C]0.39785[/C][/ROW]
[ROW][C]BW[/C][C]0.00358575159323686[/C][C]0.005347[/C][C]0.6706[/C][C]0.507257[/C][C]0.253628[/C][/ROW]
[ROW][C]BRW[/C][C]-0.00147763369106797[/C][C]0.003185[/C][C]-0.4639[/C][C]0.645861[/C][C]0.32293[/C][/ROW]
[ROW][C]GT[/C][C]-0.0142418480170465[/C][C]0.006737[/C][C]-2.1141[/C][C]0.042394[/C][C]0.021197[/C][/ROW]
[ROW][C]PI[/C][C]1.51015954701529[/C][C]1.078434[/C][C]1.4003[/C][C]0.171038[/C][C]0.085519[/C][/ROW]
[ROW][C]SEI[/C][C]0.137314401734720[/C][C]0.638624[/C][C]0.215[/C][C]0.831119[/C][C]0.41556[/C][/ROW]
[ROW][C]ODI[/C][C]-2.67140908301206[/C][C]1.489998[/C][C]-1.7929[/C][C]0.082448[/C][C]0.041224[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110514&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110514&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)	12.3197087181946	2.239418	5.5013	5e-06	2e-06
PS	0.142634167491983	0.49663	0.2872	0.775808	0.387904
LS	0.0110188228044140	0.042204	0.2611	0.795699	0.39785
BW	0.00358575159323686	0.005347	0.6706	0.507257	0.253628
BRW	-0.00147763369106797	0.003185	-0.4639	0.645861	0.32293
GT	-0.0142418480170465	0.006737	-2.1141	0.042394	0.021197
PI	1.51015954701529	1.078434	1.4003	0.171038	0.085519
SEI	0.137314401734720	0.638624	0.215	0.831119	0.41556
ODI	-2.67140908301206	1.489998	-1.7929	0.082448	0.041224

Multiple Linear Regression - Regression Statistics
Multiple R	0.760338768848172
R-squared	0.578115043413554
Adjusted R-squared	0.472643804266942
F-TEST (value)	5.48125771623805
F-TEST (DF numerator)	8
F-TEST (DF denominator)	32
p-value	0.000214898271983843
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.71971430821945
Sum Squared Residuals	236.699069386675

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.760338768848172 \tabularnewline
R-squared & 0.578115043413554 \tabularnewline
Adjusted R-squared & 0.472643804266942 \tabularnewline
F-TEST (value) & 5.48125771623805 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 32 \tabularnewline
p-value & 0.000214898271983843 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.71971430821945 \tabularnewline
Sum Squared Residuals & 236.699069386675 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110514&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.760338768848172[/C][/ROW]
[ROW][C]R-squared[/C][C]0.578115043413554[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.472643804266942[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.48125771623805[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]32[/C][/ROW]
[ROW][C]p-value[/C][C]0.000214898271983843[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.71971430821945[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]236.699069386675[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110514&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110514&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	0.760338768848172
R-squared	0.578115043413554
Adjusted R-squared	0.472643804266942
F-TEST (value)	5.48125771623805
F-TEST (DF numerator)	8
F-TEST (DF denominator)	32
p-value	0.000214898271983843
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.71971430821945
Sum Squared Residuals	236.699069386675

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6.3	8.70380330205908	-2.40380330205908
2	2.1	1.31261157620734	0.787388423792664
3	9.1	5.83038210280354	3.26961789719646
4	15.8	11.5625789720178	4.23742102798217
5	5.2	3.58008470207246	1.61991529792754
6	10.9	11.3318671598544	-0.431867159854397
7	8.3	8.30765149375464	-0.00765149375463509
8	11	8.42219084992565	2.57780915007436
9	3.2	4.67081779500448	-1.47081779500448
10	6.3	11.0342093697054	-4.73420936970541
11	8.6	10.3978242579169	-1.7978242579169
12	6.6	10.3222369535801	-3.72223695358011
13	9.5	8.8419024276445	0.6580975723555
14	3.3	5.31318709479185	-2.01318709479185
15	11	11.9436962615884	-0.943696261588399
16	4.7	7.64571042416454	-2.94571042416454
17	10.4	12.1733990623514	-1.77339906235140
18	7.4	8.82182024493103	-1.42182024493103
19	2.1	3.93607180858023	-1.83607180858023
20	7.7	9.38144702614722	-1.68144702614722
21	17.9	11.1330677144633	6.76693228553672
22	6.1	7.13792748862824	-1.03792748862825
23	11.9	10.4330150161543	1.46698498384567
24	10.8	10.3631690959941	0.43683090400588
25	13.8	13.4856630921752	0.314336907824777
26	14.3	11.6072913230689	2.69270867693112
27	10	6.01973463109999	3.98026536890001
28	11.9	10.2813627230918	1.6186372769082
29	6.5	7.57715903464924	-1.07715903464924
30	7.5	7.07633033089932	0.42366966910068
31	10.6	9.095029512674	1.50497048732599
32	7.4	10.9462168724777	-3.54621687247767
33	8.4	8.32408331906081	0.0759166809391907
34	5.7	7.2574395755035	-1.55743957550350
35	4.9	6.02548152863396	-1.12548152863396
36	3.2	5.29589427038155	-2.09589427038155
37	11	9.65704178360154	1.34295821639846
38	4.9	6.26785977209513	-1.36785977209513
39	13.2	11.5197439497690	1.68025605023102
40	9.7	5.37521948872675	4.32478051127325
41	12.8	13.5877765917507	-0.787776591750676

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.3 & 8.70380330205908 & -2.40380330205908 \tabularnewline
2 & 2.1 & 1.31261157620734 & 0.787388423792664 \tabularnewline
3 & 9.1 & 5.83038210280354 & 3.26961789719646 \tabularnewline
4 & 15.8 & 11.5625789720178 & 4.23742102798217 \tabularnewline
5 & 5.2 & 3.58008470207246 & 1.61991529792754 \tabularnewline
6 & 10.9 & 11.3318671598544 & -0.431867159854397 \tabularnewline
7 & 8.3 & 8.30765149375464 & -0.00765149375463509 \tabularnewline
8 & 11 & 8.42219084992565 & 2.57780915007436 \tabularnewline
9 & 3.2 & 4.67081779500448 & -1.47081779500448 \tabularnewline
10 & 6.3 & 11.0342093697054 & -4.73420936970541 \tabularnewline
11 & 8.6 & 10.3978242579169 & -1.7978242579169 \tabularnewline
12 & 6.6 & 10.3222369535801 & -3.72223695358011 \tabularnewline
13 & 9.5 & 8.8419024276445 & 0.6580975723555 \tabularnewline
14 & 3.3 & 5.31318709479185 & -2.01318709479185 \tabularnewline
15 & 11 & 11.9436962615884 & -0.943696261588399 \tabularnewline
16 & 4.7 & 7.64571042416454 & -2.94571042416454 \tabularnewline
17 & 10.4 & 12.1733990623514 & -1.77339906235140 \tabularnewline
18 & 7.4 & 8.82182024493103 & -1.42182024493103 \tabularnewline
19 & 2.1 & 3.93607180858023 & -1.83607180858023 \tabularnewline
20 & 7.7 & 9.38144702614722 & -1.68144702614722 \tabularnewline
21 & 17.9 & 11.1330677144633 & 6.76693228553672 \tabularnewline
22 & 6.1 & 7.13792748862824 & -1.03792748862825 \tabularnewline
23 & 11.9 & 10.4330150161543 & 1.46698498384567 \tabularnewline
24 & 10.8 & 10.3631690959941 & 0.43683090400588 \tabularnewline
25 & 13.8 & 13.4856630921752 & 0.314336907824777 \tabularnewline
26 & 14.3 & 11.6072913230689 & 2.69270867693112 \tabularnewline
27 & 10 & 6.01973463109999 & 3.98026536890001 \tabularnewline
28 & 11.9 & 10.2813627230918 & 1.6186372769082 \tabularnewline
29 & 6.5 & 7.57715903464924 & -1.07715903464924 \tabularnewline
30 & 7.5 & 7.07633033089932 & 0.42366966910068 \tabularnewline
31 & 10.6 & 9.095029512674 & 1.50497048732599 \tabularnewline
32 & 7.4 & 10.9462168724777 & -3.54621687247767 \tabularnewline
33 & 8.4 & 8.32408331906081 & 0.0759166809391907 \tabularnewline
34 & 5.7 & 7.2574395755035 & -1.55743957550350 \tabularnewline
35 & 4.9 & 6.02548152863396 & -1.12548152863396 \tabularnewline
36 & 3.2 & 5.29589427038155 & -2.09589427038155 \tabularnewline
37 & 11 & 9.65704178360154 & 1.34295821639846 \tabularnewline
38 & 4.9 & 6.26785977209513 & -1.36785977209513 \tabularnewline
39 & 13.2 & 11.5197439497690 & 1.68025605023102 \tabularnewline
40 & 9.7 & 5.37521948872675 & 4.32478051127325 \tabularnewline
41 & 12.8 & 13.5877765917507 & -0.787776591750676 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110514&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]6.3[/C][C]8.70380330205908[/C][C]-2.40380330205908[/C][/ROW]
[ROW][C]2[/C][C]2.1[/C][C]1.31261157620734[/C][C]0.787388423792664[/C][/ROW]
[ROW][C]3[/C][C]9.1[/C][C]5.83038210280354[/C][C]3.26961789719646[/C][/ROW]
[ROW][C]4[/C][C]15.8[/C][C]11.5625789720178[/C][C]4.23742102798217[/C][/ROW]
[ROW][C]5[/C][C]5.2[/C][C]3.58008470207246[/C][C]1.61991529792754[/C][/ROW]
[ROW][C]6[/C][C]10.9[/C][C]11.3318671598544[/C][C]-0.431867159854397[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]8.30765149375464[/C][C]-0.00765149375463509[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]8.42219084992565[/C][C]2.57780915007436[/C][/ROW]
[ROW][C]9[/C][C]3.2[/C][C]4.67081779500448[/C][C]-1.47081779500448[/C][/ROW]
[ROW][C]10[/C][C]6.3[/C][C]11.0342093697054[/C][C]-4.73420936970541[/C][/ROW]
[ROW][C]11[/C][C]8.6[/C][C]10.3978242579169[/C][C]-1.7978242579169[/C][/ROW]
[ROW][C]12[/C][C]6.6[/C][C]10.3222369535801[/C][C]-3.72223695358011[/C][/ROW]
[ROW][C]13[/C][C]9.5[/C][C]8.8419024276445[/C][C]0.6580975723555[/C][/ROW]
[ROW][C]14[/C][C]3.3[/C][C]5.31318709479185[/C][C]-2.01318709479185[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.9436962615884[/C][C]-0.943696261588399[/C][/ROW]
[ROW][C]16[/C][C]4.7[/C][C]7.64571042416454[/C][C]-2.94571042416454[/C][/ROW]
[ROW][C]17[/C][C]10.4[/C][C]12.1733990623514[/C][C]-1.77339906235140[/C][/ROW]
[ROW][C]18[/C][C]7.4[/C][C]8.82182024493103[/C][C]-1.42182024493103[/C][/ROW]
[ROW][C]19[/C][C]2.1[/C][C]3.93607180858023[/C][C]-1.83607180858023[/C][/ROW]
[ROW][C]20[/C][C]7.7[/C][C]9.38144702614722[/C][C]-1.68144702614722[/C][/ROW]
[ROW][C]21[/C][C]17.9[/C][C]11.1330677144633[/C][C]6.76693228553672[/C][/ROW]
[ROW][C]22[/C][C]6.1[/C][C]7.13792748862824[/C][C]-1.03792748862825[/C][/ROW]
[ROW][C]23[/C][C]11.9[/C][C]10.4330150161543[/C][C]1.46698498384567[/C][/ROW]
[ROW][C]24[/C][C]10.8[/C][C]10.3631690959941[/C][C]0.43683090400588[/C][/ROW]
[ROW][C]25[/C][C]13.8[/C][C]13.4856630921752[/C][C]0.314336907824777[/C][/ROW]
[ROW][C]26[/C][C]14.3[/C][C]11.6072913230689[/C][C]2.69270867693112[/C][/ROW]
[ROW][C]27[/C][C]10[/C][C]6.01973463109999[/C][C]3.98026536890001[/C][/ROW]
[ROW][C]28[/C][C]11.9[/C][C]10.2813627230918[/C][C]1.6186372769082[/C][/ROW]
[ROW][C]29[/C][C]6.5[/C][C]7.57715903464924[/C][C]-1.07715903464924[/C][/ROW]
[ROW][C]30[/C][C]7.5[/C][C]7.07633033089932[/C][C]0.42366966910068[/C][/ROW]
[ROW][C]31[/C][C]10.6[/C][C]9.095029512674[/C][C]1.50497048732599[/C][/ROW]
[ROW][C]32[/C][C]7.4[/C][C]10.9462168724777[/C][C]-3.54621687247767[/C][/ROW]
[ROW][C]33[/C][C]8.4[/C][C]8.32408331906081[/C][C]0.0759166809391907[/C][/ROW]
[ROW][C]34[/C][C]5.7[/C][C]7.2574395755035[/C][C]-1.55743957550350[/C][/ROW]
[ROW][C]35[/C][C]4.9[/C][C]6.02548152863396[/C][C]-1.12548152863396[/C][/ROW]
[ROW][C]36[/C][C]3.2[/C][C]5.29589427038155[/C][C]-2.09589427038155[/C][/ROW]
[ROW][C]37[/C][C]11[/C][C]9.65704178360154[/C][C]1.34295821639846[/C][/ROW]
[ROW][C]38[/C][C]4.9[/C][C]6.26785977209513[/C][C]-1.36785977209513[/C][/ROW]
[ROW][C]39[/C][C]13.2[/C][C]11.5197439497690[/C][C]1.68025605023102[/C][/ROW]
[ROW][C]40[/C][C]9.7[/C][C]5.37521948872675[/C][C]4.32478051127325[/C][/ROW]
[ROW][C]41[/C][C]12.8[/C][C]13.5877765917507[/C][C]-0.787776591750676[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110514&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110514&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	6.3	8.70380330205908	-2.40380330205908
2	2.1	1.31261157620734	0.787388423792664
3	9.1	5.83038210280354	3.26961789719646
4	15.8	11.5625789720178	4.23742102798217
5	5.2	3.58008470207246	1.61991529792754
6	10.9	11.3318671598544	-0.431867159854397
7	8.3	8.30765149375464	-0.00765149375463509
8	11	8.42219084992565	2.57780915007436
9	3.2	4.67081779500448	-1.47081779500448
10	6.3	11.0342093697054	-4.73420936970541
11	8.6	10.3978242579169	-1.7978242579169
12	6.6	10.3222369535801	-3.72223695358011
13	9.5	8.8419024276445	0.6580975723555
14	3.3	5.31318709479185	-2.01318709479185
15	11	11.9436962615884	-0.943696261588399
16	4.7	7.64571042416454	-2.94571042416454
17	10.4	12.1733990623514	-1.77339906235140
18	7.4	8.82182024493103	-1.42182024493103
19	2.1	3.93607180858023	-1.83607180858023
20	7.7	9.38144702614722	-1.68144702614722
21	17.9	11.1330677144633	6.76693228553672
22	6.1	7.13792748862824	-1.03792748862825
23	11.9	10.4330150161543	1.46698498384567
24	10.8	10.3631690959941	0.43683090400588
25	13.8	13.4856630921752	0.314336907824777
26	14.3	11.6072913230689	2.69270867693112
27	10	6.01973463109999	3.98026536890001
28	11.9	10.2813627230918	1.6186372769082
29	6.5	7.57715903464924	-1.07715903464924
30	7.5	7.07633033089932	0.42366966910068
31	10.6	9.095029512674	1.50497048732599
32	7.4	10.9462168724777	-3.54621687247767
33	8.4	8.32408331906081	0.0759166809391907
34	5.7	7.2574395755035	-1.55743957550350
35	4.9	6.02548152863396	-1.12548152863396
36	3.2	5.29589427038155	-2.09589427038155
37	11	9.65704178360154	1.34295821639846
38	4.9	6.26785977209513	-1.36785977209513
39	13.2	11.5197439497690	1.68025605023102
40	9.7	5.37521948872675	4.32478051127325
41	12.8	13.5877765917507	-0.787776591750676

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.938246797034884	0.123506405930233	0.0617532029651163
13	0.926200872666933	0.147598254666134	0.073799127333067
14	0.896848168459703	0.206303663080595	0.103151831540297
15	0.845601803851158	0.308796392297684	0.154398196148842
16	0.89591461264315	0.208170774713701	0.104085387356850
17	0.929918303588323	0.140163392823355	0.0700816964116775
18	0.904654847333977	0.190690305332047	0.0953451526660234
19	0.902742669696241	0.194514660607519	0.0972573303037594
20	0.889560178716122	0.220879642567756	0.110439821283878
21	0.971668670283401	0.0566626594331978	0.0283313297165989
22	0.946262751586848	0.107474496826304	0.0537372484131521
23	0.910475834290852	0.179048331418296	0.0895241657091479
24	0.887708149214981	0.224583701570038	0.112291850785019
25	0.813391378192087	0.373217243615827	0.186608621807913
26	0.730130563078931	0.539738873842138	0.269869436921069
27	0.849447750928149	0.301104498143703	0.150552249071851
28	0.731914639227026	0.536170721545948	0.268085360772974
29	0.5723244808622	0.8553510382756	0.4276755191378

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.938246797034884 & 0.123506405930233 & 0.0617532029651163 \tabularnewline
13 & 0.926200872666933 & 0.147598254666134 & 0.073799127333067 \tabularnewline
14 & 0.896848168459703 & 0.206303663080595 & 0.103151831540297 \tabularnewline
15 & 0.845601803851158 & 0.308796392297684 & 0.154398196148842 \tabularnewline
16 & 0.89591461264315 & 0.208170774713701 & 0.104085387356850 \tabularnewline
17 & 0.929918303588323 & 0.140163392823355 & 0.0700816964116775 \tabularnewline
18 & 0.904654847333977 & 0.190690305332047 & 0.0953451526660234 \tabularnewline
19 & 0.902742669696241 & 0.194514660607519 & 0.0972573303037594 \tabularnewline
20 & 0.889560178716122 & 0.220879642567756 & 0.110439821283878 \tabularnewline
21 & 0.971668670283401 & 0.0566626594331978 & 0.0283313297165989 \tabularnewline
22 & 0.946262751586848 & 0.107474496826304 & 0.0537372484131521 \tabularnewline
23 & 0.910475834290852 & 0.179048331418296 & 0.0895241657091479 \tabularnewline
24 & 0.887708149214981 & 0.224583701570038 & 0.112291850785019 \tabularnewline
25 & 0.813391378192087 & 0.373217243615827 & 0.186608621807913 \tabularnewline
26 & 0.730130563078931 & 0.539738873842138 & 0.269869436921069 \tabularnewline
27 & 0.849447750928149 & 0.301104498143703 & 0.150552249071851 \tabularnewline
28 & 0.731914639227026 & 0.536170721545948 & 0.268085360772974 \tabularnewline
29 & 0.5723244808622 & 0.8553510382756 & 0.4276755191378 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110514&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]12[/C][C]0.938246797034884[/C][C]0.123506405930233[/C][C]0.0617532029651163[/C][/ROW]
[ROW][C]13[/C][C]0.926200872666933[/C][C]0.147598254666134[/C][C]0.073799127333067[/C][/ROW]
[ROW][C]14[/C][C]0.896848168459703[/C][C]0.206303663080595[/C][C]0.103151831540297[/C][/ROW]
[ROW][C]15[/C][C]0.845601803851158[/C][C]0.308796392297684[/C][C]0.154398196148842[/C][/ROW]
[ROW][C]16[/C][C]0.89591461264315[/C][C]0.208170774713701[/C][C]0.104085387356850[/C][/ROW]
[ROW][C]17[/C][C]0.929918303588323[/C][C]0.140163392823355[/C][C]0.0700816964116775[/C][/ROW]
[ROW][C]18[/C][C]0.904654847333977[/C][C]0.190690305332047[/C][C]0.0953451526660234[/C][/ROW]
[ROW][C]19[/C][C]0.902742669696241[/C][C]0.194514660607519[/C][C]0.0972573303037594[/C][/ROW]
[ROW][C]20[/C][C]0.889560178716122[/C][C]0.220879642567756[/C][C]0.110439821283878[/C][/ROW]
[ROW][C]21[/C][C]0.971668670283401[/C][C]0.0566626594331978[/C][C]0.0283313297165989[/C][/ROW]
[ROW][C]22[/C][C]0.946262751586848[/C][C]0.107474496826304[/C][C]0.0537372484131521[/C][/ROW]
[ROW][C]23[/C][C]0.910475834290852[/C][C]0.179048331418296[/C][C]0.0895241657091479[/C][/ROW]
[ROW][C]24[/C][C]0.887708149214981[/C][C]0.224583701570038[/C][C]0.112291850785019[/C][/ROW]
[ROW][C]25[/C][C]0.813391378192087[/C][C]0.373217243615827[/C][C]0.186608621807913[/C][/ROW]
[ROW][C]26[/C][C]0.730130563078931[/C][C]0.539738873842138[/C][C]0.269869436921069[/C][/ROW]
[ROW][C]27[/C][C]0.849447750928149[/C][C]0.301104498143703[/C][C]0.150552249071851[/C][/ROW]
[ROW][C]28[/C][C]0.731914639227026[/C][C]0.536170721545948[/C][C]0.268085360772974[/C][/ROW]
[ROW][C]29[/C][C]0.5723244808622[/C][C]0.8553510382756[/C][C]0.4276755191378[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110514&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110514&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
12	0.938246797034884	0.123506405930233	0.0617532029651163
13	0.926200872666933	0.147598254666134	0.073799127333067
14	0.896848168459703	0.206303663080595	0.103151831540297
15	0.845601803851158	0.308796392297684	0.154398196148842
16	0.89591461264315	0.208170774713701	0.104085387356850
17	0.929918303588323	0.140163392823355	0.0700816964116775
18	0.904654847333977	0.190690305332047	0.0953451526660234
19	0.902742669696241	0.194514660607519	0.0972573303037594
20	0.889560178716122	0.220879642567756	0.110439821283878
21	0.971668670283401	0.0566626594331978	0.0283313297165989
22	0.946262751586848	0.107474496826304	0.0537372484131521
23	0.910475834290852	0.179048331418296	0.0895241657091479
24	0.887708149214981	0.224583701570038	0.112291850785019
25	0.813391378192087	0.373217243615827	0.186608621807913
26	0.730130563078931	0.539738873842138	0.269869436921069
27	0.849447750928149	0.301104498143703	0.150552249071851
28	0.731914639227026	0.536170721545948	0.268085360772974
29	0.5723244808622	0.8553510382756	0.4276755191378

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.0555555555555556	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.0555555555555556 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110514&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0555555555555556[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110514&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110514&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	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.0555555555555556	OK

Figure 1

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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):

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