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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 11 Dec 2010 16:17:47 +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/11/t1292084207zs4b8khkuqaxrhv.htm/, Retrieved Mon, 06 May 2024 15:01:49 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108236, Retrieved Mon, 06 May 2024 15:01:49 +0000

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Estimated Impact

121

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

-       [Multiple Regression] [] [2010-12-11 16:17:47] [558c060a42ec367ec2c020fab85c25c7] [Current]
-    D    [Multiple Regression] [] [2010-12-14 10:07:08] [39e83c7b0ac936e906a817a1bb402750] 
-    D    [Multiple Regression] [] [2010-12-14 10:13:16] [39e83c7b0ac936e906a817a1bb402750] 

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1	7	6,3	4,5	42	3	1	3
2.547	4.603	2,1	69	624	3	5	4
11	180	9,1	27	180	4	4	4
0,023	0,3	15,8	19	35	1	1	1
160	169	5,2	30,4	392	4	5	4
3	26	10,9	28	63	1	2	1
52	440	8,3	50	230	1	1	1
0,425	6	11	7	112	5	4	4
465	423	3,2	30	281	5	5	5
0,075	1	6,3	3,5	42	1	1	1
3	25	8,6	50	28	2	2	2
0,785	4	6,6	6	42	2	2	2
0,2	5	9,5	10,4	120	2	2	2
28	115	3,3	20	148	5	5	5
0,12	1	11	3,9	16	3	1	2
85	325	4,7	41	310	1	3	1
0,101	4	10,4	9	28	5	1	3
1	6	7,4	7,6	68	5	3	4
521	655	2,1	46	336	5	5	5
0,005	0,14	7,7	2,6	21,5	5	2	4
0,01	0,25	17,9	24	50	1	1	1
62	1.320	6,1	100	267	1	1	1
0,023	0,4	11,9	3,2	19	4	1	3
0,048	0,33	10,8	2	30	4	1	3
2	6	13,8	5	12	2	1	1
4	11	14,3	6,5	120	2	1	1
0,48	16	15,2	12	140	2	2	2
10	115	10	20,2	170	4	4	4
2	11	11,9	13	17	2	1	2
192	180	6,5	27	115	4	4	4
3	12	7,5	18	31	5	5	5
0,28	2	10,6	4,7	21	3	1	3
4	50	7,4	9,8	52	1	1	1
7	179	8,4	29	164	2	3	2
0,75	12	5,7	7	225	2	2	2
4	21	4,9	6	225	3	2	3
56	175	3,2	20	151	5	5	5
0,9	3	11	4,5	60	2	1	2
2	12	4,9	7,5	200	3	1	3
0,104	3	13,2	2,3	46	3	2	2
4	58	9,7	24	210	4	3	4
4	4	12,8	3	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	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 5 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108236&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108236&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108236&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	5 seconds
R Server	'RServer@AstonUniversity' @ vre.aston.ac.uk

Multiple Linear Regression - Estimated Regression Equation
SWS[t] = + 12.7894647555942 -0.000920360007190503Wb[t] -0.00407543480665229Wbr[t] -0.00545130109419674L[t] -0.0102385857977621Tg[t] + 1.43725443584876P[t] + 0.436070831135465S[t] -2.79782099005727D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SWS[t] =  +  12.7894647555942 -0.000920360007190503Wb[t] -0.00407543480665229Wbr[t] -0.00545130109419674L[t] -0.0102385857977621Tg[t] +  1.43725443584876P[t] +  0.436070831135465S[t] -2.79782099005727D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108236&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SWS[t] =  +  12.7894647555942 -0.000920360007190503Wb[t] -0.00407543480665229Wbr[t] -0.00545130109419674L[t] -0.0102385857977621Tg[t] +  1.43725443584876P[t] +  0.436070831135465S[t] -2.79782099005727D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108236&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108236&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] = + 12.7894647555942 -0.000920360007190503Wb[t] -0.00407543480665229Wbr[t] -0.00545130109419674L[t] -0.0102385857977621Tg[t] + 1.43725443584876P[t] + 0.436070831135465S[t] -2.79782099005727D[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)	12.7894647555942	1.271119	10.0616	0	0
Wb	-0.000920360007190503	0.00749	-0.1229	0.902921	0.45146
Wbr	-0.00407543480665229	0.005843	-0.6974	0.490272	0.245136
L	-0.00545130109419674	0.030974	-0.176	0.86134	0.43067
Tg	-0.0102385857977621	0.005506	-1.8597	0.071604	0.035802
P	1.43725443584876	1.01571	1.415	0.166156	0.083078
S	0.436070831135465	0.613711	0.7105	0.48221	0.241105
D	-2.79782099005727	1.260299	-2.22	0.033193	0.016596

\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) & 12.7894647555942 & 1.271119 & 10.0616 & 0 & 0 \tabularnewline
Wb & -0.000920360007190503 & 0.00749 & -0.1229 & 0.902921 & 0.45146 \tabularnewline
Wbr & -0.00407543480665229 & 0.005843 & -0.6974 & 0.490272 & 0.245136 \tabularnewline
L & -0.00545130109419674 & 0.030974 & -0.176 & 0.86134 & 0.43067 \tabularnewline
Tg & -0.0102385857977621 & 0.005506 & -1.8597 & 0.071604 & 0.035802 \tabularnewline
P & 1.43725443584876 & 1.01571 & 1.415 & 0.166156 & 0.083078 \tabularnewline
S & 0.436070831135465 & 0.613711 & 0.7105 & 0.48221 & 0.241105 \tabularnewline
D & -2.79782099005727 & 1.260299 & -2.22 & 0.033193 & 0.016596 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108236&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]12.7894647555942[/C][C]1.271119[/C][C]10.0616[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Wb[/C][C]-0.000920360007190503[/C][C]0.00749[/C][C]-0.1229[/C][C]0.902921[/C][C]0.45146[/C][/ROW]
[ROW][C]Wbr[/C][C]-0.00407543480665229[/C][C]0.005843[/C][C]-0.6974[/C][C]0.490272[/C][C]0.245136[/C][/ROW]
[ROW][C]L[/C][C]-0.00545130109419674[/C][C]0.030974[/C][C]-0.176[/C][C]0.86134[/C][C]0.43067[/C][/ROW]
[ROW][C]Tg[/C][C]-0.0102385857977621[/C][C]0.005506[/C][C]-1.8597[/C][C]0.071604[/C][C]0.035802[/C][/ROW]
[ROW][C]P[/C][C]1.43725443584876[/C][C]1.01571[/C][C]1.415[/C][C]0.166156[/C][C]0.083078[/C][/ROW]
[ROW][C]S[/C][C]0.436070831135465[/C][C]0.613711[/C][C]0.7105[/C][C]0.48221[/C][C]0.241105[/C][/ROW]
[ROW][C]D[/C][C]-2.79782099005727[/C][C]1.260299[/C][C]-2.22[/C][C]0.033193[/C][C]0.016596[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108236&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108236&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)	12.7894647555942	1.271119	10.0616	0	0
Wb	-0.000920360007190503	0.00749	-0.1229	0.902921	0.45146
Wbr	-0.00407543480665229	0.005843	-0.6974	0.490272	0.245136
L	-0.00545130109419674	0.030974	-0.176	0.86134	0.43067
Tg	-0.0102385857977621	0.005506	-1.8597	0.071604	0.035802
P	1.43725443584876	1.01571	1.415	0.166156	0.083078
S	0.436070831135465	0.613711	0.7105	0.48221	0.241105
D	-2.79782099005727	1.260299	-2.22	0.033193	0.016596

Multiple Linear Regression - Regression Statistics
Multiple R	0.741625986682653
R-squared	0.550009104123018
Adjusted R-squared	0.457363919677757
F-TEST (value)	5.93672631142516
F-TEST (DF numerator)	7
F-TEST (DF denominator)	34
p-value	0.00014631561076639
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.82680337537443
Sum Squared Residuals	271.68778898296

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.741625986682653 \tabularnewline
R-squared & 0.550009104123018 \tabularnewline
Adjusted R-squared & 0.457363919677757 \tabularnewline
F-TEST (value) & 5.93672631142516 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 34 \tabularnewline
p-value & 0.00014631561076639 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.82680337537443 \tabularnewline
Sum Squared Residuals & 271.68778898296 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108236&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.741625986682653[/C][/ROW]
[ROW][C]R-squared[/C][C]0.550009104123018[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.457363919677757[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.93672631142516[/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]0.00014631561076639[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.82680337537443[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]271.68778898296[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108236&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108236&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	0.741625986682653
R-squared	0.550009104123018
Adjusted R-squared	0.457363919677757
F-TEST (value)	5.93672631142516
F-TEST (DF numerator)	7
F-TEST (DF denominator)	34
p-value	0.00014631561076639
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.82680337537443
Sum Squared Residuals	271.68778898296

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6.3	8.6598360620205	-2.35983606202049
2	2.1	1.3041775619323	0.795822438067702
3	9.1	6.35764906488505	2.74235093511495
4	15.8	11.4018000100876	4.39819998991242
5	5.2	4.51230142497647	0.687698575023526
6	10.9	11.3946501427656	-0.494650142765568
7	8.3	7.39647920902513	0.903520790974872
8	11	9.3190118202991	1.6809881797009
9	3.2	2.97452817167446	0.225471828325545
10	6.3	11.4117244133783	-5.11172441337827
11	8.6	10.2765809022131	-1.67658090221306
12	6.6	10.4607206775447	-3.86072067754467
13	9.5	9.63458823630232	-0.134588236302321
14	3.3	6.04820433730993	-2.74820433730993
15	11	11.7523935891223	-0.752393589122338
16	4.7	7.93689883985059	-3.23689883985059
17	10.4	11.6662079880292	-1.26620798802923
18	7.4	9.32963877660452	-1.92963877660452
19	2.1	1.3271440997444	0.772855900255604
20	7.7	9.4217164967101	-1.72171649671011
21	17.9	11.2211804540706	6.6788195459294
22	6.1	8.52369462070842	-2.42369462070842
23	11.9	10.3674617240912	1.53253827590882
24	10.8	10.2616411130651	0.538358886934887
25	13.8	13.1258106044715	0.67418939552851
26	14.3	11.9896484926242	2.31035150737575
27	15.2	9.37600695492118	5.82399304507882
28	10	6.7629273927428	3.23707260725721
29	11.9	10.2128091026386	1.68719089736142
30	6.5	6.8565719804381	-0.356571980438101
31	7.5	7.69980026310144	-0.199800263101435
32	10.6	8.8947959367931	1.7052040632069
33	7.4	11.071686639953	-3.67168663995302
34	8.4	8.8033829875778	-0.403382987577802
35	5.7	8.54903690960705	-2.84903690960705
36	4.9	7.1542515732095	-2.25425157320951
37	3.2	5.74719241131617	-2.54719241131617
38	11	9.85250184709661	1.14749815290339
39	4.9	7.00448806865105	-2.10448806865105
40	13.2	11.8818927842225	1.31810721577753
41	9.7	6.13442012956122	3.56557987043878
42	12.8	13.1225461846633	-0.322546184663286

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.3 & 8.6598360620205 & -2.35983606202049 \tabularnewline
2 & 2.1 & 1.3041775619323 & 0.795822438067702 \tabularnewline
3 & 9.1 & 6.35764906488505 & 2.74235093511495 \tabularnewline
4 & 15.8 & 11.4018000100876 & 4.39819998991242 \tabularnewline
5 & 5.2 & 4.51230142497647 & 0.687698575023526 \tabularnewline
6 & 10.9 & 11.3946501427656 & -0.494650142765568 \tabularnewline
7 & 8.3 & 7.39647920902513 & 0.903520790974872 \tabularnewline
8 & 11 & 9.3190118202991 & 1.6809881797009 \tabularnewline
9 & 3.2 & 2.97452817167446 & 0.225471828325545 \tabularnewline
10 & 6.3 & 11.4117244133783 & -5.11172441337827 \tabularnewline
11 & 8.6 & 10.2765809022131 & -1.67658090221306 \tabularnewline
12 & 6.6 & 10.4607206775447 & -3.86072067754467 \tabularnewline
13 & 9.5 & 9.63458823630232 & -0.134588236302321 \tabularnewline
14 & 3.3 & 6.04820433730993 & -2.74820433730993 \tabularnewline
15 & 11 & 11.7523935891223 & -0.752393589122338 \tabularnewline
16 & 4.7 & 7.93689883985059 & -3.23689883985059 \tabularnewline
17 & 10.4 & 11.6662079880292 & -1.26620798802923 \tabularnewline
18 & 7.4 & 9.32963877660452 & -1.92963877660452 \tabularnewline
19 & 2.1 & 1.3271440997444 & 0.772855900255604 \tabularnewline
20 & 7.7 & 9.4217164967101 & -1.72171649671011 \tabularnewline
21 & 17.9 & 11.2211804540706 & 6.6788195459294 \tabularnewline
22 & 6.1 & 8.52369462070842 & -2.42369462070842 \tabularnewline
23 & 11.9 & 10.3674617240912 & 1.53253827590882 \tabularnewline
24 & 10.8 & 10.2616411130651 & 0.538358886934887 \tabularnewline
25 & 13.8 & 13.1258106044715 & 0.67418939552851 \tabularnewline
26 & 14.3 & 11.9896484926242 & 2.31035150737575 \tabularnewline
27 & 15.2 & 9.37600695492118 & 5.82399304507882 \tabularnewline
28 & 10 & 6.7629273927428 & 3.23707260725721 \tabularnewline
29 & 11.9 & 10.2128091026386 & 1.68719089736142 \tabularnewline
30 & 6.5 & 6.8565719804381 & -0.356571980438101 \tabularnewline
31 & 7.5 & 7.69980026310144 & -0.199800263101435 \tabularnewline
32 & 10.6 & 8.8947959367931 & 1.7052040632069 \tabularnewline
33 & 7.4 & 11.071686639953 & -3.67168663995302 \tabularnewline
34 & 8.4 & 8.8033829875778 & -0.403382987577802 \tabularnewline
35 & 5.7 & 8.54903690960705 & -2.84903690960705 \tabularnewline
36 & 4.9 & 7.1542515732095 & -2.25425157320951 \tabularnewline
37 & 3.2 & 5.74719241131617 & -2.54719241131617 \tabularnewline
38 & 11 & 9.85250184709661 & 1.14749815290339 \tabularnewline
39 & 4.9 & 7.00448806865105 & -2.10448806865105 \tabularnewline
40 & 13.2 & 11.8818927842225 & 1.31810721577753 \tabularnewline
41 & 9.7 & 6.13442012956122 & 3.56557987043878 \tabularnewline
42 & 12.8 & 13.1225461846633 & -0.322546184663286 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108236&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]8.6598360620205[/C][C]-2.35983606202049[/C][/ROW]
[ROW][C]2[/C][C]2.1[/C][C]1.3041775619323[/C][C]0.795822438067702[/C][/ROW]
[ROW][C]3[/C][C]9.1[/C][C]6.35764906488505[/C][C]2.74235093511495[/C][/ROW]
[ROW][C]4[/C][C]15.8[/C][C]11.4018000100876[/C][C]4.39819998991242[/C][/ROW]
[ROW][C]5[/C][C]5.2[/C][C]4.51230142497647[/C][C]0.687698575023526[/C][/ROW]
[ROW][C]6[/C][C]10.9[/C][C]11.3946501427656[/C][C]-0.494650142765568[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]7.39647920902513[/C][C]0.903520790974872[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]9.3190118202991[/C][C]1.6809881797009[/C][/ROW]
[ROW][C]9[/C][C]3.2[/C][C]2.97452817167446[/C][C]0.225471828325545[/C][/ROW]
[ROW][C]10[/C][C]6.3[/C][C]11.4117244133783[/C][C]-5.11172441337827[/C][/ROW]
[ROW][C]11[/C][C]8.6[/C][C]10.2765809022131[/C][C]-1.67658090221306[/C][/ROW]
[ROW][C]12[/C][C]6.6[/C][C]10.4607206775447[/C][C]-3.86072067754467[/C][/ROW]
[ROW][C]13[/C][C]9.5[/C][C]9.63458823630232[/C][C]-0.134588236302321[/C][/ROW]
[ROW][C]14[/C][C]3.3[/C][C]6.04820433730993[/C][C]-2.74820433730993[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]11.7523935891223[/C][C]-0.752393589122338[/C][/ROW]
[ROW][C]16[/C][C]4.7[/C][C]7.93689883985059[/C][C]-3.23689883985059[/C][/ROW]
[ROW][C]17[/C][C]10.4[/C][C]11.6662079880292[/C][C]-1.26620798802923[/C][/ROW]
[ROW][C]18[/C][C]7.4[/C][C]9.32963877660452[/C][C]-1.92963877660452[/C][/ROW]
[ROW][C]19[/C][C]2.1[/C][C]1.3271440997444[/C][C]0.772855900255604[/C][/ROW]
[ROW][C]20[/C][C]7.7[/C][C]9.4217164967101[/C][C]-1.72171649671011[/C][/ROW]
[ROW][C]21[/C][C]17.9[/C][C]11.2211804540706[/C][C]6.6788195459294[/C][/ROW]
[ROW][C]22[/C][C]6.1[/C][C]8.52369462070842[/C][C]-2.42369462070842[/C][/ROW]
[ROW][C]23[/C][C]11.9[/C][C]10.3674617240912[/C][C]1.53253827590882[/C][/ROW]
[ROW][C]24[/C][C]10.8[/C][C]10.2616411130651[/C][C]0.538358886934887[/C][/ROW]
[ROW][C]25[/C][C]13.8[/C][C]13.1258106044715[/C][C]0.67418939552851[/C][/ROW]
[ROW][C]26[/C][C]14.3[/C][C]11.9896484926242[/C][C]2.31035150737575[/C][/ROW]
[ROW][C]27[/C][C]15.2[/C][C]9.37600695492118[/C][C]5.82399304507882[/C][/ROW]
[ROW][C]28[/C][C]10[/C][C]6.7629273927428[/C][C]3.23707260725721[/C][/ROW]
[ROW][C]29[/C][C]11.9[/C][C]10.2128091026386[/C][C]1.68719089736142[/C][/ROW]
[ROW][C]30[/C][C]6.5[/C][C]6.8565719804381[/C][C]-0.356571980438101[/C][/ROW]
[ROW][C]31[/C][C]7.5[/C][C]7.69980026310144[/C][C]-0.199800263101435[/C][/ROW]
[ROW][C]32[/C][C]10.6[/C][C]8.8947959367931[/C][C]1.7052040632069[/C][/ROW]
[ROW][C]33[/C][C]7.4[/C][C]11.071686639953[/C][C]-3.67168663995302[/C][/ROW]
[ROW][C]34[/C][C]8.4[/C][C]8.8033829875778[/C][C]-0.403382987577802[/C][/ROW]
[ROW][C]35[/C][C]5.7[/C][C]8.54903690960705[/C][C]-2.84903690960705[/C][/ROW]
[ROW][C]36[/C][C]4.9[/C][C]7.1542515732095[/C][C]-2.25425157320951[/C][/ROW]
[ROW][C]37[/C][C]3.2[/C][C]5.74719241131617[/C][C]-2.54719241131617[/C][/ROW]
[ROW][C]38[/C][C]11[/C][C]9.85250184709661[/C][C]1.14749815290339[/C][/ROW]
[ROW][C]39[/C][C]4.9[/C][C]7.00448806865105[/C][C]-2.10448806865105[/C][/ROW]
[ROW][C]40[/C][C]13.2[/C][C]11.8818927842225[/C][C]1.31810721577753[/C][/ROW]
[ROW][C]41[/C][C]9.7[/C][C]6.13442012956122[/C][C]3.56557987043878[/C][/ROW]
[ROW][C]42[/C][C]12.8[/C][C]13.1225461846633[/C][C]-0.322546184663286[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108236&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108236&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	8.6598360620205	-2.35983606202049
2	2.1	1.3041775619323	0.795822438067702
3	9.1	6.35764906488505	2.74235093511495
4	15.8	11.4018000100876	4.39819998991242
5	5.2	4.51230142497647	0.687698575023526
6	10.9	11.3946501427656	-0.494650142765568
7	8.3	7.39647920902513	0.903520790974872
8	11	9.3190118202991	1.6809881797009
9	3.2	2.97452817167446	0.225471828325545
10	6.3	11.4117244133783	-5.11172441337827
11	8.6	10.2765809022131	-1.67658090221306
12	6.6	10.4607206775447	-3.86072067754467
13	9.5	9.63458823630232	-0.134588236302321
14	3.3	6.04820433730993	-2.74820433730993
15	11	11.7523935891223	-0.752393589122338
16	4.7	7.93689883985059	-3.23689883985059
17	10.4	11.6662079880292	-1.26620798802923
18	7.4	9.32963877660452	-1.92963877660452
19	2.1	1.3271440997444	0.772855900255604
20	7.7	9.4217164967101	-1.72171649671011
21	17.9	11.2211804540706	6.6788195459294
22	6.1	8.52369462070842	-2.42369462070842
23	11.9	10.3674617240912	1.53253827590882
24	10.8	10.2616411130651	0.538358886934887
25	13.8	13.1258106044715	0.67418939552851
26	14.3	11.9896484926242	2.31035150737575
27	15.2	9.37600695492118	5.82399304507882
28	10	6.7629273927428	3.23707260725721
29	11.9	10.2128091026386	1.68719089736142
30	6.5	6.8565719804381	-0.356571980438101
31	7.5	7.69980026310144	-0.199800263101435
32	10.6	8.8947959367931	1.7052040632069
33	7.4	11.071686639953	-3.67168663995302
34	8.4	8.8033829875778	-0.403382987577802
35	5.7	8.54903690960705	-2.84903690960705
36	4.9	7.1542515732095	-2.25425157320951
37	3.2	5.74719241131617	-2.54719241131617
38	11	9.85250184709661	1.14749815290339
39	4.9	7.00448806865105	-2.10448806865105
40	13.2	11.8818927842225	1.31810721577753
41	9.7	6.13442012956122	3.56557987043878
42	12.8	13.1225461846633	-0.322546184663286

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.856868583750217	0.286262832499565	0.143131416249782
12	0.845675728554153	0.308648542891695	0.154324271445847
13	0.754751096787312	0.490497806425375	0.245248903212688
14	0.69444830114245	0.6111033977151	0.30555169885755
15	0.600845054765997	0.798309890468006	0.399154945234003
16	0.615677393970328	0.768645212059343	0.384322606029672
17	0.552557319840022	0.894885360319956	0.447442680159978
18	0.461189679929969	0.922379359859939	0.538810320070031
19	0.386077796341983	0.772155592683966	0.613922203658017
20	0.309751836622774	0.619503673245549	0.690248163377226
21	0.696441628799634	0.607116742400732	0.303558371200366
22	0.833507766906959	0.332984466186082	0.166492233093041
23	0.768032243482033	0.463935513035934	0.231967756517967
24	0.676908420262236	0.646183159475529	0.323091579737764
25	0.572910953347738	0.854178093304525	0.427089046652262
26	0.472124245771711	0.944248491543422	0.527875754228289
27	0.839199431853468	0.321601136293064	0.160800568146532
28	0.916626373141186	0.166747253717629	0.0833736268588143
29	0.840248852597185	0.31950229480563	0.159751147402815
30	0.738157753068605	0.52368449386279	0.261842246931395
31	0.905481060936367	0.189037878127266	0.0945189390636328

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.856868583750217 & 0.286262832499565 & 0.143131416249782 \tabularnewline
12 & 0.845675728554153 & 0.308648542891695 & 0.154324271445847 \tabularnewline
13 & 0.754751096787312 & 0.490497806425375 & 0.245248903212688 \tabularnewline
14 & 0.69444830114245 & 0.6111033977151 & 0.30555169885755 \tabularnewline
15 & 0.600845054765997 & 0.798309890468006 & 0.399154945234003 \tabularnewline
16 & 0.615677393970328 & 0.768645212059343 & 0.384322606029672 \tabularnewline
17 & 0.552557319840022 & 0.894885360319956 & 0.447442680159978 \tabularnewline
18 & 0.461189679929969 & 0.922379359859939 & 0.538810320070031 \tabularnewline
19 & 0.386077796341983 & 0.772155592683966 & 0.613922203658017 \tabularnewline
20 & 0.309751836622774 & 0.619503673245549 & 0.690248163377226 \tabularnewline
21 & 0.696441628799634 & 0.607116742400732 & 0.303558371200366 \tabularnewline
22 & 0.833507766906959 & 0.332984466186082 & 0.166492233093041 \tabularnewline
23 & 0.768032243482033 & 0.463935513035934 & 0.231967756517967 \tabularnewline
24 & 0.676908420262236 & 0.646183159475529 & 0.323091579737764 \tabularnewline
25 & 0.572910953347738 & 0.854178093304525 & 0.427089046652262 \tabularnewline
26 & 0.472124245771711 & 0.944248491543422 & 0.527875754228289 \tabularnewline
27 & 0.839199431853468 & 0.321601136293064 & 0.160800568146532 \tabularnewline
28 & 0.916626373141186 & 0.166747253717629 & 0.0833736268588143 \tabularnewline
29 & 0.840248852597185 & 0.31950229480563 & 0.159751147402815 \tabularnewline
30 & 0.738157753068605 & 0.52368449386279 & 0.261842246931395 \tabularnewline
31 & 0.905481060936367 & 0.189037878127266 & 0.0945189390636328 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108236&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]0.856868583750217[/C][C]0.286262832499565[/C][C]0.143131416249782[/C][/ROW]
[ROW][C]12[/C][C]0.845675728554153[/C][C]0.308648542891695[/C][C]0.154324271445847[/C][/ROW]
[ROW][C]13[/C][C]0.754751096787312[/C][C]0.490497806425375[/C][C]0.245248903212688[/C][/ROW]
[ROW][C]14[/C][C]0.69444830114245[/C][C]0.6111033977151[/C][C]0.30555169885755[/C][/ROW]
[ROW][C]15[/C][C]0.600845054765997[/C][C]0.798309890468006[/C][C]0.399154945234003[/C][/ROW]
[ROW][C]16[/C][C]0.615677393970328[/C][C]0.768645212059343[/C][C]0.384322606029672[/C][/ROW]
[ROW][C]17[/C][C]0.552557319840022[/C][C]0.894885360319956[/C][C]0.447442680159978[/C][/ROW]
[ROW][C]18[/C][C]0.461189679929969[/C][C]0.922379359859939[/C][C]0.538810320070031[/C][/ROW]
[ROW][C]19[/C][C]0.386077796341983[/C][C]0.772155592683966[/C][C]0.613922203658017[/C][/ROW]
[ROW][C]20[/C][C]0.309751836622774[/C][C]0.619503673245549[/C][C]0.690248163377226[/C][/ROW]
[ROW][C]21[/C][C]0.696441628799634[/C][C]0.607116742400732[/C][C]0.303558371200366[/C][/ROW]
[ROW][C]22[/C][C]0.833507766906959[/C][C]0.332984466186082[/C][C]0.166492233093041[/C][/ROW]
[ROW][C]23[/C][C]0.768032243482033[/C][C]0.463935513035934[/C][C]0.231967756517967[/C][/ROW]
[ROW][C]24[/C][C]0.676908420262236[/C][C]0.646183159475529[/C][C]0.323091579737764[/C][/ROW]
[ROW][C]25[/C][C]0.572910953347738[/C][C]0.854178093304525[/C][C]0.427089046652262[/C][/ROW]
[ROW][C]26[/C][C]0.472124245771711[/C][C]0.944248491543422[/C][C]0.527875754228289[/C][/ROW]
[ROW][C]27[/C][C]0.839199431853468[/C][C]0.321601136293064[/C][C]0.160800568146532[/C][/ROW]
[ROW][C]28[/C][C]0.916626373141186[/C][C]0.166747253717629[/C][C]0.0833736268588143[/C][/ROW]
[ROW][C]29[/C][C]0.840248852597185[/C][C]0.31950229480563[/C][C]0.159751147402815[/C][/ROW]
[ROW][C]30[/C][C]0.738157753068605[/C][C]0.52368449386279[/C][C]0.261842246931395[/C][/ROW]
[ROW][C]31[/C][C]0.905481060936367[/C][C]0.189037878127266[/C][C]0.0945189390636328[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108236&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108236&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	0.856868583750217	0.286262832499565	0.143131416249782
12	0.845675728554153	0.308648542891695	0.154324271445847
13	0.754751096787312	0.490497806425375	0.245248903212688
14	0.69444830114245	0.6111033977151	0.30555169885755
15	0.600845054765997	0.798309890468006	0.399154945234003
16	0.615677393970328	0.768645212059343	0.384322606029672
17	0.552557319840022	0.894885360319956	0.447442680159978
18	0.461189679929969	0.922379359859939	0.538810320070031
19	0.386077796341983	0.772155592683966	0.613922203658017
20	0.309751836622774	0.619503673245549	0.690248163377226
21	0.696441628799634	0.607116742400732	0.303558371200366
22	0.833507766906959	0.332984466186082	0.166492233093041
23	0.768032243482033	0.463935513035934	0.231967756517967
24	0.676908420262236	0.646183159475529	0.323091579737764
25	0.572910953347738	0.854178093304525	0.427089046652262
26	0.472124245771711	0.944248491543422	0.527875754228289
27	0.839199431853468	0.321601136293064	0.160800568146532
28	0.916626373141186	0.166747253717629	0.0833736268588143
29	0.840248852597185	0.31950229480563	0.159751147402815
30	0.738157753068605	0.52368449386279	0.261842246931395
31	0.905481060936367	0.189037878127266	0.0945189390636328

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=108236&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=108236&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108236&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 = 3 ; 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