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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 15 Dec 2010 19:03:30 +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/t12924398151re7ki30sx0emcl.htm/, Retrieved Fri, 03 May 2024 10:09:30 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=110663, Retrieved Fri, 03 May 2024 10:09:30 +0000

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117

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-       [Multiple Regression] [Science Experimen...] [2010-12-15 19:03:30] [13dfa60174f50d862e8699db2153bfc5] [Current]

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6.3	0.301029996	0.653212514	0	0.819543936	1.62324929	3	1	3
2.1	0.255272505	1.838849091	3.406028945	3.663040975	2.79518459	3	5	4
9.1	-0.15490196	1.431363764	1.02325246	2.254064453	2.255272505	4	4	4
15.8	0.591064607	1.278753601	-1.638272164	-0.522878745	1.544068044	1	1	1
5.2	0	1.482873584	2.204119983	2.227886705	2.593286067	4	5	4
10.9	0.556302501	1.447158031	0.51851394	1.408239965	1.799340549	1	2	1
8.3	0.146128036	1.698970004	1.717337583	2.643452676	2.361727836	1	1	1
11	0.176091259	0.84509804	-0.37161107	0.806179974	2.049218023	5	4	4
3.2	-0.15490196	1.477121255	2.667452953	2.626340367	2.44870632	5	5	5
6.3	0.322219295	0.544068044	-1.124938737	0.079181246	1.62324929	1	1	1
6.6	0.612783857	0.77815125	-0.105130343	0.544068044	1.62324929	2	2	2
9.5	0.079181246	1.017033339	-0.698970004	0.698970004	2.079181246	2	2	2
3.3	-0.301029996	1.301029996	1.441852176	2.06069784	2.170261715	5	5	5
11	0.531478917	0.591064607	-0.920818754	0	1.204119983	3	1	2
4.7	0.176091259	1.612783857	1.929418926	2.511883361	2.491361694	1	3	1
10.4	0.531478917	0.954242509	-0.995678626	0.602059991	1.447158031	5	1	3
7.4	-0.096910013	0.880813592	0.017033339	0.740362689	1.832508913	5	3	4
2.1	-0.096910013	1.662757832	2.716837723	2.8162413	2.526339277	5	5	5
17.9	0.301029996	1.380211242	-2	-0.602059991	1.698970004	1	1	1
6.1	0.278753601	2	1.792391689	3.120573931	2.426511261	1	1	1
11.9	0.113943352	0.505149978	-1.638272164	-0.397940009	1.278753601	4	1	3
13.8	0.748188027	0.698970004	0.230448921	0.799340549	1.079181246	2	1	1
14.3	0.491361694	0.812913357	0.544068044	1.033423755	2.079181246	2	1	1
15.2	0.255272505	1.079181246	-0.318758763	1.190331698	2.146128036	2	2	2
10	-0.045757491	1.305351369	1	2.06069784	2.230448921	4	4	4
11.9	0.255272505	1.113943352	0.209515015	1.056904851	1.230448921	2	1	2
6.5	0.278753601	1.431363764	2.283301229	2.255272505	2.06069784	4	4	4
7.5	-0.045757491	1.255272505	0.397940009	1.08278537	1.491361694	5	5	5
10.6	0.414973348	0.672097858	-0.552841969	0.278753601	1.322219295	3	1	3
7.4	0.380211242	0.991226076	0.626853415	1.702430536	1.716003344	1	1	1
8.4	0.079181246	1.462397998	0.832508913	2.252853031	2.214843848	2	3	2
5.7	-0.045757491	0.84509804	-0.124938737	1.089905111	2.352182518	2	2	2
4.9	-0.301029996	0.77815125	0.556302501	1.322219295	2.352182518	3	2	3
3.2	-0.22184875	1.301029996	1.744292983	2.243038049	2.178976947	5	5	5
11	0.361727836	0.653212514	-0.045757491	0.414973348	1.77815125	2	1	2
4.9	-0.301029996	0.875061263	0.301029996	1.089905111	2.301029996	3	1	3
13.2	0.414973348	0.361727836	-0.982966661	0.397940009	1.662757832	3	2	2
9.7	-0.22184875	1.380211242	0.622214023	1.763427994	2.322219295	4	3	4
12.8	0.819543936	0.477121255	0.544068044	0.591064607 1.146128036	2	1	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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 & 8 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110663&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]8 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=110663&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110663&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	8 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation
SWS[t] = + 7.19519299579455 + 3.36679474202788PS[t] + 3.43730615240639L[t] -1.65097896744842Wb[t] -0.880440616381254Wbr[t] -0.315657209275309Tg[t] + 1.33733451027365P[t] + 0.314979429813487S[t] -1.8837396565904D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SWS[t] =  +  7.19519299579455 +  3.36679474202788PS[t] +  3.43730615240639L[t] -1.65097896744842Wb[t] -0.880440616381254Wbr[t] -0.315657209275309Tg[t] +  1.33733451027365P[t] +  0.314979429813487S[t] -1.8837396565904D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110663&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SWS[t] =  +  7.19519299579455 +  3.36679474202788PS[t] +  3.43730615240639L[t] -1.65097896744842Wb[t] -0.880440616381254Wbr[t] -0.315657209275309Tg[t] +  1.33733451027365P[t] +  0.314979429813487S[t] -1.8837396565904D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110663&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110663&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] = + 7.19519299579455 + 3.36679474202788PS[t] + 3.43730615240639L[t] -1.65097896744842Wb[t] -0.880440616381254Wbr[t] -0.315657209275309Tg[t] + 1.33733451027365P[t] + 0.314979429813487S[t] -1.8837396565904D[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)	7.19519299579455	4.516729	1.593	0.121641	0.060821
PS	3.36679474202788	2.745137	1.2265	0.229566	0.114783
L	3.43730615240639	1.782017	1.9289	0.063254	0.031627
Wb	-1.65097896744842	1.157343	-1.4265	0.164042	0.082021
Wbr	-0.880440616381254	1.620254	-0.5434	0.590872	0.295436
Tg	-0.315657209275309	1.911325	-0.1652	0.869933	0.434967
P	1.33733451027365	0.993734	1.3458	0.188461	0.09423
S	0.314979429813487	0.626066	0.5031	0.618562	0.309281
D	-1.8837396565904	1.344971	-1.4006	0.171599	0.085799

\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) & 7.19519299579455 & 4.516729 & 1.593 & 0.121641 & 0.060821 \tabularnewline
PS & 3.36679474202788 & 2.745137 & 1.2265 & 0.229566 & 0.114783 \tabularnewline
L & 3.43730615240639 & 1.782017 & 1.9289 & 0.063254 & 0.031627 \tabularnewline
Wb & -1.65097896744842 & 1.157343 & -1.4265 & 0.164042 & 0.082021 \tabularnewline
Wbr & -0.880440616381254 & 1.620254 & -0.5434 & 0.590872 & 0.295436 \tabularnewline
Tg & -0.315657209275309 & 1.911325 & -0.1652 & 0.869933 & 0.434967 \tabularnewline
P & 1.33733451027365 & 0.993734 & 1.3458 & 0.188461 & 0.09423 \tabularnewline
S & 0.314979429813487 & 0.626066 & 0.5031 & 0.618562 & 0.309281 \tabularnewline
D & -1.8837396565904 & 1.344971 & -1.4006 & 0.171599 & 0.085799 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110663&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]7.19519299579455[/C][C]4.516729[/C][C]1.593[/C][C]0.121641[/C][C]0.060821[/C][/ROW]
[ROW][C]PS[/C][C]3.36679474202788[/C][C]2.745137[/C][C]1.2265[/C][C]0.229566[/C][C]0.114783[/C][/ROW]
[ROW][C]L[/C][C]3.43730615240639[/C][C]1.782017[/C][C]1.9289[/C][C]0.063254[/C][C]0.031627[/C][/ROW]
[ROW][C]Wb[/C][C]-1.65097896744842[/C][C]1.157343[/C][C]-1.4265[/C][C]0.164042[/C][C]0.082021[/C][/ROW]
[ROW][C]Wbr[/C][C]-0.880440616381254[/C][C]1.620254[/C][C]-0.5434[/C][C]0.590872[/C][C]0.295436[/C][/ROW]
[ROW][C]Tg[/C][C]-0.315657209275309[/C][C]1.911325[/C][C]-0.1652[/C][C]0.869933[/C][C]0.434967[/C][/ROW]
[ROW][C]P[/C][C]1.33733451027365[/C][C]0.993734[/C][C]1.3458[/C][C]0.188461[/C][C]0.09423[/C][/ROW]
[ROW][C]S[/C][C]0.314979429813487[/C][C]0.626066[/C][C]0.5031[/C][C]0.618562[/C][C]0.309281[/C][/ROW]
[ROW][C]D[/C][C]-1.8837396565904[/C][C]1.344971[/C][C]-1.4006[/C][C]0.171599[/C][C]0.085799[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110663&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110663&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)	7.19519299579455	4.516729	1.593	0.121641	0.060821
PS	3.36679474202788	2.745137	1.2265	0.229566	0.114783
L	3.43730615240639	1.782017	1.9289	0.063254	0.031627
Wb	-1.65097896744842	1.157343	-1.4265	0.164042	0.082021
Wbr	-0.880440616381254	1.620254	-0.5434	0.590872	0.295436
Tg	-0.315657209275309	1.911325	-0.1652	0.869933	0.434967
P	1.33733451027365	0.993734	1.3458	0.188461	0.09423
S	0.314979429813487	0.626066	0.5031	0.618562	0.309281
D	-1.8837396565904	1.344971	-1.4006	0.171599	0.085799

Multiple Linear Regression - Regression Statistics
Multiple R	0.827535230258193
R-squared	0.684814557318481
Adjusted R-squared	0.600765105936743
F-TEST (value)	8.14775761245805
F-TEST (DF numerator)	8
F-TEST (DF denominator)	30
p-value	8.60521098511313e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.50737645315805
Sum Squared Residuals	188.608100335543

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.827535230258193 \tabularnewline
R-squared & 0.684814557318481 \tabularnewline
Adjusted R-squared & 0.600765105936743 \tabularnewline
F-TEST (value) & 8.14775761245805 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 30 \tabularnewline
p-value & 8.60521098511313e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.50737645315805 \tabularnewline
Sum Squared Residuals & 188.608100335543 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110663&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.827535230258193[/C][/ROW]
[ROW][C]R-squared[/C][C]0.684814557318481[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.600765105936743[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.14775761245805[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]30[/C][/ROW]
[ROW][C]p-value[/C][C]8.60521098511313e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.50737645315805[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]188.608100335543[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110663&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110663&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	0.827535230258193
R-squared	0.684814557318481
Adjusted R-squared	0.600765105936743
F-TEST (value)	8.14775761245805
F-TEST (DF numerator)	8
F-TEST (DF denominator)	30
p-value	8.60521098511313e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.50737645315805
Sum Squared Residuals	188.608100335543

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6.3	7.89580447858142	-1.59580447858142
2	2.1	2.69658009911775	-0.596580099117745
3	9.1	6.28217128635656	2.81782871364344
4	15.8	16.0269484710272	-0.226948471027212
5	5.2	5.26249293291376	-0.0624929329137551
6	10.9	11.4621261596098	-0.562126159609779
7	8.3	7.38744267930867	0.912557320691332
8	11	10.3614264823333	0.638573517666651
9	3.2	3.10466202956652	0.0953379704334817
10	6.3	11.1938874110275	-4.89388741102751
11	6.6	10.6523611499443	-4.05236114994431
12	9.5	10.3770581634728	-0.877058163472793
13	3.3	4.616748165744	-1.31674816574400
14	11	12.7159103188329	-1.71591031883285
15	4.7	7.54681065752132	-2.84681065752132
16	10.4	14.2719906123763	-3.87199061237634
17	7.4	8.73478335079519	-1.33478335079519
18	2.1	3.66476364640126	-1.56476364640126
19	17.9	16.0172259551301	1.88277404486992
20	6.1	8.30425895410857	-2.20425895410858
21	11.9	11.9797381420859	-0.0797381420859159
22	13.8	11.7977816522966	2.00221834770342
23	14.3	10.2852261457175	4.01477385428248
24	15.2	10.1020748797700	5.09792512022995
25	10	6.43296801568258	3.56703198431742
26	11.9	9.44092968369552	2.45907031630448
27	6.5	5.72224181110631	0.777758188893687
28	7.5	8.11768636346933	-0.61768636346933
29	10.6	9.8282295932576	0.771770406742397
30	7.4	8.57552835820276	-1.17552835820276
31	8.4	8.28352818624708	0.116471813752919
32	5.7	7.98733730490201	-2.28733730490201
33	4.9	5.02211161876452	-0.122111618764523
34	3.2	4.22072100537454	-1.02072100537454
35	11	9.0294159061704	1.97058409382961
36	4.9	5.68237661528557	-0.782376615285571
37	13.2	11.4578087146267	1.74219128537329
38	9.7	6.63891893149884	3.06108106850116
39	12.8	10.9199240676769	1.88007593232306

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.3 & 7.89580447858142 & -1.59580447858142 \tabularnewline
2 & 2.1 & 2.69658009911775 & -0.596580099117745 \tabularnewline
3 & 9.1 & 6.28217128635656 & 2.81782871364344 \tabularnewline
4 & 15.8 & 16.0269484710272 & -0.226948471027212 \tabularnewline
5 & 5.2 & 5.26249293291376 & -0.0624929329137551 \tabularnewline
6 & 10.9 & 11.4621261596098 & -0.562126159609779 \tabularnewline
7 & 8.3 & 7.38744267930867 & 0.912557320691332 \tabularnewline
8 & 11 & 10.3614264823333 & 0.638573517666651 \tabularnewline
9 & 3.2 & 3.10466202956652 & 0.0953379704334817 \tabularnewline
10 & 6.3 & 11.1938874110275 & -4.89388741102751 \tabularnewline
11 & 6.6 & 10.6523611499443 & -4.05236114994431 \tabularnewline
12 & 9.5 & 10.3770581634728 & -0.877058163472793 \tabularnewline
13 & 3.3 & 4.616748165744 & -1.31674816574400 \tabularnewline
14 & 11 & 12.7159103188329 & -1.71591031883285 \tabularnewline
15 & 4.7 & 7.54681065752132 & -2.84681065752132 \tabularnewline
16 & 10.4 & 14.2719906123763 & -3.87199061237634 \tabularnewline
17 & 7.4 & 8.73478335079519 & -1.33478335079519 \tabularnewline
18 & 2.1 & 3.66476364640126 & -1.56476364640126 \tabularnewline
19 & 17.9 & 16.0172259551301 & 1.88277404486992 \tabularnewline
20 & 6.1 & 8.30425895410857 & -2.20425895410858 \tabularnewline
21 & 11.9 & 11.9797381420859 & -0.0797381420859159 \tabularnewline
22 & 13.8 & 11.7977816522966 & 2.00221834770342 \tabularnewline
23 & 14.3 & 10.2852261457175 & 4.01477385428248 \tabularnewline
24 & 15.2 & 10.1020748797700 & 5.09792512022995 \tabularnewline
25 & 10 & 6.43296801568258 & 3.56703198431742 \tabularnewline
26 & 11.9 & 9.44092968369552 & 2.45907031630448 \tabularnewline
27 & 6.5 & 5.72224181110631 & 0.777758188893687 \tabularnewline
28 & 7.5 & 8.11768636346933 & -0.61768636346933 \tabularnewline
29 & 10.6 & 9.8282295932576 & 0.771770406742397 \tabularnewline
30 & 7.4 & 8.57552835820276 & -1.17552835820276 \tabularnewline
31 & 8.4 & 8.28352818624708 & 0.116471813752919 \tabularnewline
32 & 5.7 & 7.98733730490201 & -2.28733730490201 \tabularnewline
33 & 4.9 & 5.02211161876452 & -0.122111618764523 \tabularnewline
34 & 3.2 & 4.22072100537454 & -1.02072100537454 \tabularnewline
35 & 11 & 9.0294159061704 & 1.97058409382961 \tabularnewline
36 & 4.9 & 5.68237661528557 & -0.782376615285571 \tabularnewline
37 & 13.2 & 11.4578087146267 & 1.74219128537329 \tabularnewline
38 & 9.7 & 6.63891893149884 & 3.06108106850116 \tabularnewline
39 & 12.8 & 10.9199240676769 & 1.88007593232306 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110663&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]7.89580447858142[/C][C]-1.59580447858142[/C][/ROW]
[ROW][C]2[/C][C]2.1[/C][C]2.69658009911775[/C][C]-0.596580099117745[/C][/ROW]
[ROW][C]3[/C][C]9.1[/C][C]6.28217128635656[/C][C]2.81782871364344[/C][/ROW]
[ROW][C]4[/C][C]15.8[/C][C]16.0269484710272[/C][C]-0.226948471027212[/C][/ROW]
[ROW][C]5[/C][C]5.2[/C][C]5.26249293291376[/C][C]-0.0624929329137551[/C][/ROW]
[ROW][C]6[/C][C]10.9[/C][C]11.4621261596098[/C][C]-0.562126159609779[/C][/ROW]
[ROW][C]7[/C][C]8.3[/C][C]7.38744267930867[/C][C]0.912557320691332[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]10.3614264823333[/C][C]0.638573517666651[/C][/ROW]
[ROW][C]9[/C][C]3.2[/C][C]3.10466202956652[/C][C]0.0953379704334817[/C][/ROW]
[ROW][C]10[/C][C]6.3[/C][C]11.1938874110275[/C][C]-4.89388741102751[/C][/ROW]
[ROW][C]11[/C][C]6.6[/C][C]10.6523611499443[/C][C]-4.05236114994431[/C][/ROW]
[ROW][C]12[/C][C]9.5[/C][C]10.3770581634728[/C][C]-0.877058163472793[/C][/ROW]
[ROW][C]13[/C][C]3.3[/C][C]4.616748165744[/C][C]-1.31674816574400[/C][/ROW]
[ROW][C]14[/C][C]11[/C][C]12.7159103188329[/C][C]-1.71591031883285[/C][/ROW]
[ROW][C]15[/C][C]4.7[/C][C]7.54681065752132[/C][C]-2.84681065752132[/C][/ROW]
[ROW][C]16[/C][C]10.4[/C][C]14.2719906123763[/C][C]-3.87199061237634[/C][/ROW]
[ROW][C]17[/C][C]7.4[/C][C]8.73478335079519[/C][C]-1.33478335079519[/C][/ROW]
[ROW][C]18[/C][C]2.1[/C][C]3.66476364640126[/C][C]-1.56476364640126[/C][/ROW]
[ROW][C]19[/C][C]17.9[/C][C]16.0172259551301[/C][C]1.88277404486992[/C][/ROW]
[ROW][C]20[/C][C]6.1[/C][C]8.30425895410857[/C][C]-2.20425895410858[/C][/ROW]
[ROW][C]21[/C][C]11.9[/C][C]11.9797381420859[/C][C]-0.0797381420859159[/C][/ROW]
[ROW][C]22[/C][C]13.8[/C][C]11.7977816522966[/C][C]2.00221834770342[/C][/ROW]
[ROW][C]23[/C][C]14.3[/C][C]10.2852261457175[/C][C]4.01477385428248[/C][/ROW]
[ROW][C]24[/C][C]15.2[/C][C]10.1020748797700[/C][C]5.09792512022995[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]6.43296801568258[/C][C]3.56703198431742[/C][/ROW]
[ROW][C]26[/C][C]11.9[/C][C]9.44092968369552[/C][C]2.45907031630448[/C][/ROW]
[ROW][C]27[/C][C]6.5[/C][C]5.72224181110631[/C][C]0.777758188893687[/C][/ROW]
[ROW][C]28[/C][C]7.5[/C][C]8.11768636346933[/C][C]-0.61768636346933[/C][/ROW]
[ROW][C]29[/C][C]10.6[/C][C]9.8282295932576[/C][C]0.771770406742397[/C][/ROW]
[ROW][C]30[/C][C]7.4[/C][C]8.57552835820276[/C][C]-1.17552835820276[/C][/ROW]
[ROW][C]31[/C][C]8.4[/C][C]8.28352818624708[/C][C]0.116471813752919[/C][/ROW]
[ROW][C]32[/C][C]5.7[/C][C]7.98733730490201[/C][C]-2.28733730490201[/C][/ROW]
[ROW][C]33[/C][C]4.9[/C][C]5.02211161876452[/C][C]-0.122111618764523[/C][/ROW]
[ROW][C]34[/C][C]3.2[/C][C]4.22072100537454[/C][C]-1.02072100537454[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]9.0294159061704[/C][C]1.97058409382961[/C][/ROW]
[ROW][C]36[/C][C]4.9[/C][C]5.68237661528557[/C][C]-0.782376615285571[/C][/ROW]
[ROW][C]37[/C][C]13.2[/C][C]11.4578087146267[/C][C]1.74219128537329[/C][/ROW]
[ROW][C]38[/C][C]9.7[/C][C]6.63891893149884[/C][C]3.06108106850116[/C][/ROW]
[ROW][C]39[/C][C]12.8[/C][C]10.9199240676769[/C][C]1.88007593232306[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110663&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110663&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	7.89580447858142	-1.59580447858142
2	2.1	2.69658009911775	-0.596580099117745
3	9.1	6.28217128635656	2.81782871364344
4	15.8	16.0269484710272	-0.226948471027212
5	5.2	5.26249293291376	-0.0624929329137551
6	10.9	11.4621261596098	-0.562126159609779
7	8.3	7.38744267930867	0.912557320691332
8	11	10.3614264823333	0.638573517666651
9	3.2	3.10466202956652	0.0953379704334817
10	6.3	11.1938874110275	-4.89388741102751
11	6.6	10.6523611499443	-4.05236114994431
12	9.5	10.3770581634728	-0.877058163472793
13	3.3	4.616748165744	-1.31674816574400
14	11	12.7159103188329	-1.71591031883285
15	4.7	7.54681065752132	-2.84681065752132
16	10.4	14.2719906123763	-3.87199061237634
17	7.4	8.73478335079519	-1.33478335079519
18	2.1	3.66476364640126	-1.56476364640126
19	17.9	16.0172259551301	1.88277404486992
20	6.1	8.30425895410857	-2.20425895410858
21	11.9	11.9797381420859	-0.0797381420859159
22	13.8	11.7977816522966	2.00221834770342
23	14.3	10.2852261457175	4.01477385428248
24	15.2	10.1020748797700	5.09792512022995
25	10	6.43296801568258	3.56703198431742
26	11.9	9.44092968369552	2.45907031630448
27	6.5	5.72224181110631	0.777758188893687
28	7.5	8.11768636346933	-0.61768636346933
29	10.6	9.8282295932576	0.771770406742397
30	7.4	8.57552835820276	-1.17552835820276
31	8.4	8.28352818624708	0.116471813752919
32	5.7	7.98733730490201	-2.28733730490201
33	4.9	5.02211161876452	-0.122111618764523
34	3.2	4.22072100537454	-1.02072100537454
35	11	9.0294159061704	1.97058409382961
36	4.9	5.68237661528557	-0.782376615285571
37	13.2	11.4578087146267	1.74219128537329
38	9.7	6.63891893149884	3.06108106850116
39	12.8	10.9199240676769	1.88007593232306

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.0297158298932441	0.0594316597864883	0.970284170106756
13	0.0605689334568977	0.121137866913795	0.939431066543102
14	0.0247739769265243	0.0495479538530486	0.975226023073476
15	0.0151794925406772	0.0303589850813544	0.984820507459323
16	0.450376711488359	0.900753422976717	0.549623288511641
17	0.342066127106946	0.684132254213892	0.657933872893054
18	0.329419603682306	0.658839207364613	0.670580396317694
19	0.229275160535754	0.458550321071507	0.770724839464246
20	0.463631916408468	0.927263832816936	0.536368083591532
21	0.489587473093327	0.979174946186653	0.510412526906673
22	0.622229258918395	0.75554148216321	0.377770741081605
23	0.727030459285787	0.545939081428426	0.272969540714213
24	0.844470163623206	0.311059672753588	0.155529836376794
25	0.916201265418629	0.167597469162742	0.0837987345813711
26	0.884110697014314	0.231778605971371	0.115889302985686
27	0.8062656576367	0.3874686847266	0.1937343423633

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.0297158298932441 & 0.0594316597864883 & 0.970284170106756 \tabularnewline
13 & 0.0605689334568977 & 0.121137866913795 & 0.939431066543102 \tabularnewline
14 & 0.0247739769265243 & 0.0495479538530486 & 0.975226023073476 \tabularnewline
15 & 0.0151794925406772 & 0.0303589850813544 & 0.984820507459323 \tabularnewline
16 & 0.450376711488359 & 0.900753422976717 & 0.549623288511641 \tabularnewline
17 & 0.342066127106946 & 0.684132254213892 & 0.657933872893054 \tabularnewline
18 & 0.329419603682306 & 0.658839207364613 & 0.670580396317694 \tabularnewline
19 & 0.229275160535754 & 0.458550321071507 & 0.770724839464246 \tabularnewline
20 & 0.463631916408468 & 0.927263832816936 & 0.536368083591532 \tabularnewline
21 & 0.489587473093327 & 0.979174946186653 & 0.510412526906673 \tabularnewline
22 & 0.622229258918395 & 0.75554148216321 & 0.377770741081605 \tabularnewline
23 & 0.727030459285787 & 0.545939081428426 & 0.272969540714213 \tabularnewline
24 & 0.844470163623206 & 0.311059672753588 & 0.155529836376794 \tabularnewline
25 & 0.916201265418629 & 0.167597469162742 & 0.0837987345813711 \tabularnewline
26 & 0.884110697014314 & 0.231778605971371 & 0.115889302985686 \tabularnewline
27 & 0.8062656576367 & 0.3874686847266 & 0.1937343423633 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110663&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]0.0297158298932441[/C][C]0.0594316597864883[/C][C]0.970284170106756[/C][/ROW]
[ROW][C]13[/C][C]0.0605689334568977[/C][C]0.121137866913795[/C][C]0.939431066543102[/C][/ROW]
[ROW][C]14[/C][C]0.0247739769265243[/C][C]0.0495479538530486[/C][C]0.975226023073476[/C][/ROW]
[ROW][C]15[/C][C]0.0151794925406772[/C][C]0.0303589850813544[/C][C]0.984820507459323[/C][/ROW]
[ROW][C]16[/C][C]0.450376711488359[/C][C]0.900753422976717[/C][C]0.549623288511641[/C][/ROW]
[ROW][C]17[/C][C]0.342066127106946[/C][C]0.684132254213892[/C][C]0.657933872893054[/C][/ROW]
[ROW][C]18[/C][C]0.329419603682306[/C][C]0.658839207364613[/C][C]0.670580396317694[/C][/ROW]
[ROW][C]19[/C][C]0.229275160535754[/C][C]0.458550321071507[/C][C]0.770724839464246[/C][/ROW]
[ROW][C]20[/C][C]0.463631916408468[/C][C]0.927263832816936[/C][C]0.536368083591532[/C][/ROW]
[ROW][C]21[/C][C]0.489587473093327[/C][C]0.979174946186653[/C][C]0.510412526906673[/C][/ROW]
[ROW][C]22[/C][C]0.622229258918395[/C][C]0.75554148216321[/C][C]0.377770741081605[/C][/ROW]
[ROW][C]23[/C][C]0.727030459285787[/C][C]0.545939081428426[/C][C]0.272969540714213[/C][/ROW]
[ROW][C]24[/C][C]0.844470163623206[/C][C]0.311059672753588[/C][C]0.155529836376794[/C][/ROW]
[ROW][C]25[/C][C]0.916201265418629[/C][C]0.167597469162742[/C][C]0.0837987345813711[/C][/ROW]
[ROW][C]26[/C][C]0.884110697014314[/C][C]0.231778605971371[/C][C]0.115889302985686[/C][/ROW]
[ROW][C]27[/C][C]0.8062656576367[/C][C]0.3874686847266[/C][C]0.1937343423633[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110663&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110663&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	0.0297158298932441	0.0594316597864883	0.970284170106756
13	0.0605689334568977	0.121137866913795	0.939431066543102
14	0.0247739769265243	0.0495479538530486	0.975226023073476
15	0.0151794925406772	0.0303589850813544	0.984820507459323
16	0.450376711488359	0.900753422976717	0.549623288511641
17	0.342066127106946	0.684132254213892	0.657933872893054
18	0.329419603682306	0.658839207364613	0.670580396317694
19	0.229275160535754	0.458550321071507	0.770724839464246
20	0.463631916408468	0.927263832816936	0.536368083591532
21	0.489587473093327	0.979174946186653	0.510412526906673
22	0.622229258918395	0.75554148216321	0.377770741081605
23	0.727030459285787	0.545939081428426	0.272969540714213
24	0.844470163623206	0.311059672753588	0.155529836376794
25	0.916201265418629	0.167597469162742	0.0837987345813711
26	0.884110697014314	0.231778605971371	0.115889302985686
27	0.8062656576367	0.3874686847266	0.1937343423633

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	2	0.125	NOK
10% type I error level	3	0.1875	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 2 & 0.125 & NOK \tabularnewline
10% type I error level & 3 & 0.1875 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=110663&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]2[/C][C]0.125[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.1875[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=110663&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=110663&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	2	0.125	NOK
10% type I error level	3	0.1875	NOK

Figure 1

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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