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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 13 Dec 2010 20:52:56 +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/13/t12922735676xirz7qp66chbo9.htm/, Retrieved Mon, 06 May 2024 17:07:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109195, Retrieved Mon, 06 May 2024 17:07:34 +0000

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139

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

-       [Multiple Regression] [Bonus - Multiple ...] [2010-12-13 20:52:56] [708f372e2a7a3c78ea31b4de2d1213f8] [Current]

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6,30	4,5	1000	6600	42	3	1	3
2,10	69	2547000	4603000	624	3	5	4
9,10	27	10550	179500	180	4	4	4
15,80	19	23	300	35	1	1	1
5,20	30,4	160000	169000	392	4	5	4
10,90	28	3300	25600	63	1	2	1
8,30	50	52160	440000	230	1	1	1
11,00	7	425	6400	112	5	4	4
3,20	30	465000	423000	281	5	5	5
6,30	3,5	75	1200	42	1	1	1
8,60	50	3000	25000	28	2	2	2
6,60	6	785	3500	42	2	2	2
9,50	10,4	200	5000	120	2	2	2
3,30	20	27660	115000	148	5	5	5
11,00	3,9	120	1000	16	3	1	2
4,70	41	85000	325000	310	1	3	1
10,40	9	101	4000	28	5	1	3
7,40	7,6	1040	5500	68	5	3	4
2,10	46	521000	655000	336	5	5	5
7,70	2,6	5	140	21,5	5	2	4
17,90	24	10	250	50	1	1	1
6,10	100	62000	1320000	267	1	1	1
11,90	3,2	23	400	19	4	1	3
10,80	2	48	330	30	4	1	3
13,80	5	1700	6300	12	2	1	1
14,30	6,5	3500	10800	120	2	1	1
15,20	12	480	15500	140	2	2	2
10,00	20,2	10000	115000	170	4	4	4
11,90	13	1620	11400	17	2	1	2
6,50	27	192000	180000	115	4	4	4
7,50	18	2500	12100	31	5	5	5
10,60	4,7	280	1900	21	3	1	3
7,40	9,8	4235	50400	52	1	1	1
8,40	29	6800	179000	164	2	3	2
5,70	7	750	12300	225	2	2	2
4,90	6	3600	21000	225	3	2	3
3,20	20	55500	175000	151	5	5	5
11,00	4,5	900	2600	60	2	1	2
4,90	7,5	2000	12300	200	3	1	3
13,20	2,3	104	2500	46	3	2	2
9,70	24	4190	58000	210	4	3	4
12,80	3	3500	3900	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=109195&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=109195&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109195&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] = + 13.3314877028357 -0.00118099576118958L[t] + 3.33203879623901e-06Wb[t] -1.29400146501444e-06Wbr[t] -0.0138038212724133Tg[t] + 1.41477355759408P[t] + 0.224417796700898S[t] -2.79911484965773D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SWS[t] =  +  13.3314877028357 -0.00118099576118958L[t] +  3.33203879623901e-06Wb[t] -1.29400146501444e-06Wbr[t] -0.0138038212724133Tg[t] +  1.41477355759408P[t] +  0.224417796700898S[t] -2.79911484965773D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109195&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SWS[t] =  +  13.3314877028357 -0.00118099576118958L[t] +  3.33203879623901e-06Wb[t] -1.29400146501444e-06Wbr[t] -0.0138038212724133Tg[t] +  1.41477355759408P[t] +  0.224417796700898S[t] -2.79911484965773D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109195&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109195&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] = + 13.3314877028357 -0.00118099576118958L[t] + 3.33203879623901e-06Wb[t] -1.29400146501444e-06Wbr[t] -0.0138038212724133Tg[t] + 1.41477355759408P[t] + 0.224417796700898S[t] -2.79911484965773D[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)	13.3314877028357	1.256475	10.6102	0	0
L	-0.00118099576118958	0.043509	-0.0271	0.978504	0.489252
Wb	3.33203879623901e-06	6e-06	0.5985	0.5535	0.27675
Wbr	-1.29400146501444e-06	3e-06	-0.3872	0.700988	0.350494
Tg	-0.0138038212724133	0.006563	-2.1034	0.042903	0.021452
P	1.41477355759408	1.02735	1.3771	0.177478	0.088739
S	0.224417796700898	0.643644	0.3487	0.729489	0.364744
D	-2.79911484965773	1.27563	-2.1943	0.035145	0.017572

\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) & 13.3314877028357 & 1.256475 & 10.6102 & 0 & 0 \tabularnewline
L & -0.00118099576118958 & 0.043509 & -0.0271 & 0.978504 & 0.489252 \tabularnewline
Wb & 3.33203879623901e-06 & 6e-06 & 0.5985 & 0.5535 & 0.27675 \tabularnewline
Wbr & -1.29400146501444e-06 & 3e-06 & -0.3872 & 0.700988 & 0.350494 \tabularnewline
Tg & -0.0138038212724133 & 0.006563 & -2.1034 & 0.042903 & 0.021452 \tabularnewline
P & 1.41477355759408 & 1.02735 & 1.3771 & 0.177478 & 0.088739 \tabularnewline
S & 0.224417796700898 & 0.643644 & 0.3487 & 0.729489 & 0.364744 \tabularnewline
D & -2.79911484965773 & 1.27563 & -2.1943 & 0.035145 & 0.017572 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109195&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]13.3314877028357[/C][C]1.256475[/C][C]10.6102[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]L[/C][C]-0.00118099576118958[/C][C]0.043509[/C][C]-0.0271[/C][C]0.978504[/C][C]0.489252[/C][/ROW]
[ROW][C]Wb[/C][C]3.33203879623901e-06[/C][C]6e-06[/C][C]0.5985[/C][C]0.5535[/C][C]0.27675[/C][/ROW]
[ROW][C]Wbr[/C][C]-1.29400146501444e-06[/C][C]3e-06[/C][C]-0.3872[/C][C]0.700988[/C][C]0.350494[/C][/ROW]
[ROW][C]Tg[/C][C]-0.0138038212724133[/C][C]0.006563[/C][C]-2.1034[/C][C]0.042903[/C][C]0.021452[/C][/ROW]
[ROW][C]P[/C][C]1.41477355759408[/C][C]1.02735[/C][C]1.3771[/C][C]0.177478[/C][C]0.088739[/C][/ROW]
[ROW][C]S[/C][C]0.224417796700898[/C][C]0.643644[/C][C]0.3487[/C][C]0.729489[/C][C]0.364744[/C][/ROW]
[ROW][C]D[/C][C]-2.79911484965773[/C][C]1.27563[/C][C]-2.1943[/C][C]0.035145[/C][C]0.017572[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109195&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109195&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)	13.3314877028357	1.256475	10.6102	0	0
L	-0.00118099576118958	0.043509	-0.0271	0.978504	0.489252
Wb	3.33203879623901e-06	6e-06	0.5985	0.5535	0.27675
Wbr	-1.29400146501444e-06	3e-06	-0.3872	0.700988	0.350494
Tg	-0.0138038212724133	0.006563	-2.1034	0.042903	0.021452
P	1.41477355759408	1.02735	1.3771	0.177478	0.088739
S	0.224417796700898	0.643644	0.3487	0.729489	0.364744
D	-2.79911484965773	1.27563	-2.1943	0.035145	0.017572

Multiple Linear Regression - Regression Statistics
Multiple R	0.735121557008726
R-squared	0.540403703578934
Adjusted R-squared	0.445780936668714
F-TEST (value)	5.71113825166075
F-TEST (DF numerator)	7
F-TEST (DF denominator)	34
p-value	0.000201404658730864
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.85681421218203
Sum Squared Residuals	277.487173059458

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.735121557008726 \tabularnewline
R-squared & 0.540403703578934 \tabularnewline
Adjusted R-squared & 0.445780936668714 \tabularnewline
F-TEST (value) & 5.71113825166075 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 34 \tabularnewline
p-value & 0.000201404658730864 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.85681421218203 \tabularnewline
Sum Squared Residuals & 277.487173059458 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109195&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.735121557008726[/C][/ROW]
[ROW][C]R-squared[/C][C]0.540403703578934[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.445780936668714[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.71113825166075[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]34[/C][/ROW]
[ROW][C]p-value[/C][C]0.000201404658730864[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.85681421218203[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]277.487173059458[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109195&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109195&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	0.735121557008726
R-squared	0.540403703578934
Adjusted R-squared	0.445780936668714
F-TEST (value)	5.71113825166075
F-TEST (DF numerator)	7
F-TEST (DF denominator)	34
p-value	0.000201404658730864
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.85681421218203
Sum Squared Residuals	277.487173059458

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6.3	8.81259827810611	-2.51259827810611
2	2.1	1.33677884954285	0.763221150457153
3	9.1	5.97809875312846	3.12190124687154
4	15.8	11.6656799799287	4.13432002007128
5	5.2	3.78365126797027	1.41634873202973
6	10.9	11.4711426732218	-0.571142673221754
7	8.3	8.54207402576392	-0.242074025763916
8	11	8.5454068332525	2.45459316674749
9	3.2	4.6196019961882	-1.4196019961882
10	6.3	11.5863673300192	-5.28636733001916
11	8.6	10.5437300081865	-1.94373000818653
12	6.6	10.4228808894292	-3.82288088942923
13	9.5	9.33709620393844	0.162903796061559
14	3.3	5.40863878710834	-2.10863878710834
15	11	11.9756352923667	-0.975635292366696
16	4.7	8.1554672017685	-3.45546720176851
17	10.4	11.8304533111139	-1.43045331111393
18	7.4	8.93086238025928	-1.53086238025928
19	2.1	3.72788172673247	-1.62788172673247
20	7.7	9.35771443922992	-1.65771443922992
21	17.9	11.4527390656055	6.44726093439453
22	6.1	6.86634882316743	-0.766348823167428
23	11.9	10.5511624266344	1.34883757336558
24	10.8	10.4009114686238	0.399088531376247
25	13.8	13.4122991877162	0.387700812283833
26	14.3	11.9198896598944	2.38011034010558
27	15.2	9.04647614075257	6.15352385924743
28	10	6.20579821018418	3.79420178981582
29	11.9	10.5278512950316	1.37214870496842
30	6.5	7.47929857468038	-0.979298574680377
31	7.5	7.07536652213968	0.424633477860317
32	10.6	9.10592506462683	1.49407493537317
33	7.4	11.3910852533132	-3.99108525331317
34	8.4	8.7290145446377	-0.329014544637693
35	5.7	7.88409676656642	-2.18409676656642
36	4.9	6.49917496808762	-1.59917496808762
37	3.2	5.38235119547753	-2.18235119547753
38	11	9.95331358924676	1.04668641075324
39	4.9	6.62400776022691	-1.72400776022691
40	13.2	11.7858337292948	1.41416627070516
41	9.7	5.4791387167939	4.2208612832061
42	12.8	13.3961568100430	-0.596156810042984

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.3 & 8.81259827810611 & -2.51259827810611 \tabularnewline
2 & 2.1 & 1.33677884954285 & 0.763221150457153 \tabularnewline
3 & 9.1 & 5.97809875312846 & 3.12190124687154 \tabularnewline
4 & 15.8 & 11.6656799799287 & 4.13432002007128 \tabularnewline
5 & 5.2 & 3.78365126797027 & 1.41634873202973 \tabularnewline
6 & 10.9 & 11.4711426732218 & -0.571142673221754 \tabularnewline
7 & 8.3 & 8.54207402576392 & -0.242074025763916 \tabularnewline
8 & 11 & 8.5454068332525 & 2.45459316674749 \tabularnewline
9 & 3.2 & 4.6196019961882 & -1.4196019961882 \tabularnewline
10 & 6.3 & 11.5863673300192 & -5.28636733001916 \tabularnewline
11 & 8.6 & 10.5437300081865 & -1.94373000818653 \tabularnewline
12 & 6.6 & 10.4228808894292 & -3.82288088942923 \tabularnewline
13 & 9.5 & 9.33709620393844 & 0.162903796061559 \tabularnewline
14 & 3.3 & 5.40863878710834 & -2.10863878710834 \tabularnewline
15 & 11 & 11.9756352923667 & -0.975635292366696 \tabularnewline
16 & 4.7 & 8.1554672017685 & -3.45546720176851 \tabularnewline
17 & 10.4 & 11.8304533111139 & -1.43045331111393 \tabularnewline
18 & 7.4 & 8.93086238025928 & -1.53086238025928 \tabularnewline
19 & 2.1 & 3.72788172673247 & -1.62788172673247 \tabularnewline
20 & 7.7 & 9.35771443922992 & -1.65771443922992 \tabularnewline
21 & 17.9 & 11.4527390656055 & 6.44726093439453 \tabularnewline
22 & 6.1 & 6.86634882316743 & -0.766348823167428 \tabularnewline
23 & 11.9 & 10.5511624266344 & 1.34883757336558 \tabularnewline
24 & 10.8 & 10.4009114686238 & 0.399088531376247 \tabularnewline
25 & 13.8 & 13.4122991877162 & 0.387700812283833 \tabularnewline
26 & 14.3 & 11.9198896598944 & 2.38011034010558 \tabularnewline
27 & 15.2 & 9.04647614075257 & 6.15352385924743 \tabularnewline
28 & 10 & 6.20579821018418 & 3.79420178981582 \tabularnewline
29 & 11.9 & 10.5278512950316 & 1.37214870496842 \tabularnewline
30 & 6.5 & 7.47929857468038 & -0.979298574680377 \tabularnewline
31 & 7.5 & 7.07536652213968 & 0.424633477860317 \tabularnewline
32 & 10.6 & 9.10592506462683 & 1.49407493537317 \tabularnewline
33 & 7.4 & 11.3910852533132 & -3.99108525331317 \tabularnewline
34 & 8.4 & 8.7290145446377 & -0.329014544637693 \tabularnewline
35 & 5.7 & 7.88409676656642 & -2.18409676656642 \tabularnewline
36 & 4.9 & 6.49917496808762 & -1.59917496808762 \tabularnewline
37 & 3.2 & 5.38235119547753 & -2.18235119547753 \tabularnewline
38 & 11 & 9.95331358924676 & 1.04668641075324 \tabularnewline
39 & 4.9 & 6.62400776022691 & -1.72400776022691 \tabularnewline
40 & 13.2 & 11.7858337292948 & 1.41416627070516 \tabularnewline
41 & 9.7 & 5.4791387167939 & 4.2208612832061 \tabularnewline
42 & 12.8 & 13.3961568100430 & -0.596156810042984 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109195&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]8.81259827810611[/C][C]-2.51259827810611[/C][/ROW]
[ROW][C]2[/C][C]2.1[/C][C]1.33677884954285[/C][C]0.763221150457153[/C][/ROW]
[ROW][C]3[/C][C]9.1[/C][C]5.97809875312846[/C][C]3.12190124687154[/C][/ROW]
[ROW][C]4[/C][C]15.8[/C][C]11.6656799799287[/C][C]4.13432002007128[/C][/ROW]
[ROW][C]5[/C][C]5.2[/C][C]3.78365126797027[/C][C]1.41634873202973[/C][/ROW]
[ROW][C]6[/C][C]10.9[/C][C]11.4711426732218[/C][C]-0.571142673221754[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]8.54207402576392[/C][C]-0.242074025763916[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]8.5454068332525[/C][C]2.45459316674749[/C][/ROW]
[ROW][C]9[/C][C]3.2[/C][C]4.6196019961882[/C][C]-1.4196019961882[/C][/ROW]
[ROW][C]10[/C][C]6.3[/C][C]11.5863673300192[/C][C]-5.28636733001916[/C][/ROW]
[ROW][C]11[/C][C]8.6[/C][C]10.5437300081865[/C][C]-1.94373000818653[/C][/ROW]
[ROW][C]12[/C][C]6.6[/C][C]10.4228808894292[/C][C]-3.82288088942923[/C][/ROW]
[ROW][C]13[/C][C]9.5[/C][C]9.33709620393844[/C][C]0.162903796061559[/C][/ROW]
[ROW][C]14[/C][C]3.3[/C][C]5.40863878710834[/C][C]-2.10863878710834[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.9756352923667[/C][C]-0.975635292366696[/C][/ROW]
[ROW][C]16[/C][C]4.7[/C][C]8.1554672017685[/C][C]-3.45546720176851[/C][/ROW]
[ROW][C]17[/C][C]10.4[/C][C]11.8304533111139[/C][C]-1.43045331111393[/C][/ROW]
[ROW][C]18[/C][C]7.4[/C][C]8.93086238025928[/C][C]-1.53086238025928[/C][/ROW]
[ROW][C]19[/C][C]2.1[/C][C]3.72788172673247[/C][C]-1.62788172673247[/C][/ROW]
[ROW][C]20[/C][C]7.7[/C][C]9.35771443922992[/C][C]-1.65771443922992[/C][/ROW]
[ROW][C]21[/C][C]17.9[/C][C]11.4527390656055[/C][C]6.44726093439453[/C][/ROW]
[ROW][C]22[/C][C]6.1[/C][C]6.86634882316743[/C][C]-0.766348823167428[/C][/ROW]
[ROW][C]23[/C][C]11.9[/C][C]10.5511624266344[/C][C]1.34883757336558[/C][/ROW]
[ROW][C]24[/C][C]10.8[/C][C]10.4009114686238[/C][C]0.399088531376247[/C][/ROW]
[ROW][C]25[/C][C]13.8[/C][C]13.4122991877162[/C][C]0.387700812283833[/C][/ROW]
[ROW][C]26[/C][C]14.3[/C][C]11.9198896598944[/C][C]2.38011034010558[/C][/ROW]
[ROW][C]27[/C][C]15.2[/C][C]9.04647614075257[/C][C]6.15352385924743[/C][/ROW]
[ROW][C]28[/C][C]10[/C][C]6.20579821018418[/C][C]3.79420178981582[/C][/ROW]
[ROW][C]29[/C][C]11.9[/C][C]10.5278512950316[/C][C]1.37214870496842[/C][/ROW]
[ROW][C]30[/C][C]6.5[/C][C]7.47929857468038[/C][C]-0.979298574680377[/C][/ROW]
[ROW][C]31[/C][C]7.5[/C][C]7.07536652213968[/C][C]0.424633477860317[/C][/ROW]
[ROW][C]32[/C][C]10.6[/C][C]9.10592506462683[/C][C]1.49407493537317[/C][/ROW]
[ROW][C]33[/C][C]7.4[/C][C]11.3910852533132[/C][C]-3.99108525331317[/C][/ROW]
[ROW][C]34[/C][C]8.4[/C][C]8.7290145446377[/C][C]-0.329014544637693[/C][/ROW]
[ROW][C]35[/C][C]5.7[/C][C]7.88409676656642[/C][C]-2.18409676656642[/C][/ROW]
[ROW][C]36[/C][C]4.9[/C][C]6.49917496808762[/C][C]-1.59917496808762[/C][/ROW]
[ROW][C]37[/C][C]3.2[/C][C]5.38235119547753[/C][C]-2.18235119547753[/C][/ROW]
[ROW][C]38[/C][C]11[/C][C]9.95331358924676[/C][C]1.04668641075324[/C][/ROW]
[ROW][C]39[/C][C]4.9[/C][C]6.62400776022691[/C][C]-1.72400776022691[/C][/ROW]
[ROW][C]40[/C][C]13.2[/C][C]11.7858337292948[/C][C]1.41416627070516[/C][/ROW]
[ROW][C]41[/C][C]9.7[/C][C]5.4791387167939[/C][C]4.2208612832061[/C][/ROW]
[ROW][C]42[/C][C]12.8[/C][C]13.3961568100430[/C][C]-0.596156810042984[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109195&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109195&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	8.81259827810611	-2.51259827810611
2	2.1	1.33677884954285	0.763221150457153
3	9.1	5.97809875312846	3.12190124687154
4	15.8	11.6656799799287	4.13432002007128
5	5.2	3.78365126797027	1.41634873202973
6	10.9	11.4711426732218	-0.571142673221754
7	8.3	8.54207402576392	-0.242074025763916
8	11	8.5454068332525	2.45459316674749
9	3.2	4.6196019961882	-1.4196019961882
10	6.3	11.5863673300192	-5.28636733001916
11	8.6	10.5437300081865	-1.94373000818653
12	6.6	10.4228808894292	-3.82288088942923
13	9.5	9.33709620393844	0.162903796061559
14	3.3	5.40863878710834	-2.10863878710834
15	11	11.9756352923667	-0.975635292366696
16	4.7	8.1554672017685	-3.45546720176851
17	10.4	11.8304533111139	-1.43045331111393
18	7.4	8.93086238025928	-1.53086238025928
19	2.1	3.72788172673247	-1.62788172673247
20	7.7	9.35771443922992	-1.65771443922992
21	17.9	11.4527390656055	6.44726093439453
22	6.1	6.86634882316743	-0.766348823167428
23	11.9	10.5511624266344	1.34883757336558
24	10.8	10.4009114686238	0.399088531376247
25	13.8	13.4122991877162	0.387700812283833
26	14.3	11.9198896598944	2.38011034010558
27	15.2	9.04647614075257	6.15352385924743
28	10	6.20579821018418	3.79420178981582
29	11.9	10.5278512950316	1.37214870496842
30	6.5	7.47929857468038	-0.979298574680377
31	7.5	7.07536652213968	0.424633477860317
32	10.6	9.10592506462683	1.49407493537317
33	7.4	11.3910852533132	-3.99108525331317
34	8.4	8.7290145446377	-0.329014544637693
35	5.7	7.88409676656642	-2.18409676656642
36	4.9	6.49917496808762	-1.59917496808762
37	3.2	5.38235119547753	-2.18235119547753
38	11	9.95331358924676	1.04668641075324
39	4.9	6.62400776022691	-1.72400776022691
40	13.2	11.7858337292948	1.41416627070516
41	9.7	5.4791387167939	4.2208612832061
42	12.8	13.3961568100430	-0.596156810042984

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.924119303665596	0.151761392668808	0.075880696334404
12	0.915295342418858	0.169409315162283	0.0847046575811417
13	0.856081289276153	0.287837421447694	0.143918710723847
14	0.81811061380804	0.363778772383921	0.181889386191960
15	0.738423658065606	0.523152683868788	0.261576341934394
16	0.844454435801692	0.311091128396616	0.155545564198308
17	0.845984850004117	0.308030299991765	0.154015149995882
18	0.806056024412164	0.387887951175671	0.193943975587836
19	0.782421215062412	0.435157569875176	0.217578784937588
20	0.732550909058076	0.534898181883849	0.267449090941924
21	0.880217548748907	0.239564902502186	0.119782451251093
22	0.832582911879114	0.334834176241773	0.167417088120886
23	0.766617371328701	0.466765257342597	0.233382628671299
24	0.67549810878963	0.649003782420741	0.324501891210371
25	0.56751389497413	0.86497221005174	0.43248610502587
26	0.469355066815525	0.93871013363105	0.530644933184475
27	0.848150563054493	0.303698873891014	0.151849436945507
28	0.924880474243729	0.150239051512542	0.0751195257562708
29	0.852477923786558	0.295044152426885	0.147522076213442
30	0.750441243141576	0.499117513716849	0.249558756858424
31	0.916301819386176	0.167396361227647	0.0836981806138235

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.924119303665596 & 0.151761392668808 & 0.075880696334404 \tabularnewline
12 & 0.915295342418858 & 0.169409315162283 & 0.0847046575811417 \tabularnewline
13 & 0.856081289276153 & 0.287837421447694 & 0.143918710723847 \tabularnewline
14 & 0.81811061380804 & 0.363778772383921 & 0.181889386191960 \tabularnewline
15 & 0.738423658065606 & 0.523152683868788 & 0.261576341934394 \tabularnewline
16 & 0.844454435801692 & 0.311091128396616 & 0.155545564198308 \tabularnewline
17 & 0.845984850004117 & 0.308030299991765 & 0.154015149995882 \tabularnewline
18 & 0.806056024412164 & 0.387887951175671 & 0.193943975587836 \tabularnewline
19 & 0.782421215062412 & 0.435157569875176 & 0.217578784937588 \tabularnewline
20 & 0.732550909058076 & 0.534898181883849 & 0.267449090941924 \tabularnewline
21 & 0.880217548748907 & 0.239564902502186 & 0.119782451251093 \tabularnewline
22 & 0.832582911879114 & 0.334834176241773 & 0.167417088120886 \tabularnewline
23 & 0.766617371328701 & 0.466765257342597 & 0.233382628671299 \tabularnewline
24 & 0.67549810878963 & 0.649003782420741 & 0.324501891210371 \tabularnewline
25 & 0.56751389497413 & 0.86497221005174 & 0.43248610502587 \tabularnewline
26 & 0.469355066815525 & 0.93871013363105 & 0.530644933184475 \tabularnewline
27 & 0.848150563054493 & 0.303698873891014 & 0.151849436945507 \tabularnewline
28 & 0.924880474243729 & 0.150239051512542 & 0.0751195257562708 \tabularnewline
29 & 0.852477923786558 & 0.295044152426885 & 0.147522076213442 \tabularnewline
30 & 0.750441243141576 & 0.499117513716849 & 0.249558756858424 \tabularnewline
31 & 0.916301819386176 & 0.167396361227647 & 0.0836981806138235 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109195&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]11[/C][C]0.924119303665596[/C][C]0.151761392668808[/C][C]0.075880696334404[/C][/ROW]
[ROW][C]12[/C][C]0.915295342418858[/C][C]0.169409315162283[/C][C]0.0847046575811417[/C][/ROW]
[ROW][C]13[/C][C]0.856081289276153[/C][C]0.287837421447694[/C][C]0.143918710723847[/C][/ROW]
[ROW][C]14[/C][C]0.81811061380804[/C][C]0.363778772383921[/C][C]0.181889386191960[/C][/ROW]
[ROW][C]15[/C][C]0.738423658065606[/C][C]0.523152683868788[/C][C]0.261576341934394[/C][/ROW]
[ROW][C]16[/C][C]0.844454435801692[/C][C]0.311091128396616[/C][C]0.155545564198308[/C][/ROW]
[ROW][C]17[/C][C]0.845984850004117[/C][C]0.308030299991765[/C][C]0.154015149995882[/C][/ROW]
[ROW][C]18[/C][C]0.806056024412164[/C][C]0.387887951175671[/C][C]0.193943975587836[/C][/ROW]
[ROW][C]19[/C][C]0.782421215062412[/C][C]0.435157569875176[/C][C]0.217578784937588[/C][/ROW]
[ROW][C]20[/C][C]0.732550909058076[/C][C]0.534898181883849[/C][C]0.267449090941924[/C][/ROW]
[ROW][C]21[/C][C]0.880217548748907[/C][C]0.239564902502186[/C][C]0.119782451251093[/C][/ROW]
[ROW][C]22[/C][C]0.832582911879114[/C][C]0.334834176241773[/C][C]0.167417088120886[/C][/ROW]
[ROW][C]23[/C][C]0.766617371328701[/C][C]0.466765257342597[/C][C]0.233382628671299[/C][/ROW]
[ROW][C]24[/C][C]0.67549810878963[/C][C]0.649003782420741[/C][C]0.324501891210371[/C][/ROW]
[ROW][C]25[/C][C]0.56751389497413[/C][C]0.86497221005174[/C][C]0.43248610502587[/C][/ROW]
[ROW][C]26[/C][C]0.469355066815525[/C][C]0.93871013363105[/C][C]0.530644933184475[/C][/ROW]
[ROW][C]27[/C][C]0.848150563054493[/C][C]0.303698873891014[/C][C]0.151849436945507[/C][/ROW]
[ROW][C]28[/C][C]0.924880474243729[/C][C]0.150239051512542[/C][C]0.0751195257562708[/C][/ROW]
[ROW][C]29[/C][C]0.852477923786558[/C][C]0.295044152426885[/C][C]0.147522076213442[/C][/ROW]
[ROW][C]30[/C][C]0.750441243141576[/C][C]0.499117513716849[/C][C]0.249558756858424[/C][/ROW]
[ROW][C]31[/C][C]0.916301819386176[/C][C]0.167396361227647[/C][C]0.0836981806138235[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109195&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109195&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
11	0.924119303665596	0.151761392668808	0.075880696334404
12	0.915295342418858	0.169409315162283	0.0847046575811417
13	0.856081289276153	0.287837421447694	0.143918710723847
14	0.81811061380804	0.363778772383921	0.181889386191960
15	0.738423658065606	0.523152683868788	0.261576341934394
16	0.844454435801692	0.311091128396616	0.155545564198308
17	0.845984850004117	0.308030299991765	0.154015149995882
18	0.806056024412164	0.387887951175671	0.193943975587836
19	0.782421215062412	0.435157569875176	0.217578784937588
20	0.732550909058076	0.534898181883849	0.267449090941924
21	0.880217548748907	0.239564902502186	0.119782451251093
22	0.832582911879114	0.334834176241773	0.167417088120886
23	0.766617371328701	0.466765257342597	0.233382628671299
24	0.67549810878963	0.649003782420741	0.324501891210371
25	0.56751389497413	0.86497221005174	0.43248610502587
26	0.469355066815525	0.93871013363105	0.530644933184475
27	0.848150563054493	0.303698873891014	0.151849436945507
28	0.924880474243729	0.150239051512542	0.0751195257562708
29	0.852477923786558	0.295044152426885	0.147522076213442
30	0.750441243141576	0.499117513716849	0.249558756858424
31	0.916301819386176	0.167396361227647	0.0836981806138235

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	0	0	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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109195&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109195&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109195&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	0	0	OK

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):

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