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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Sun, 12 Dec 2010 17:20:00 +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/12/t1292174325ispsdqleet1fddi.htm/, Retrieved Tue, 07 May 2024 05:49:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=108568, Retrieved Tue, 07 May 2024 05:49:07 +0000

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

-       [Multiple Regression] [onderzoek link 2] [2010-12-12 17:20:00] [b881b0959d750616b68c30017e4e0761] [Current]

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1000.00	6600.00	6.3	2.00	8.30	4.50	42.00	3.00	1.00	3.00
2547000.00	4603000.00	2.1	1.80	3.90	69.00	624.00	3.00	5.00	4.00
10550.00	179500.00	9.1	0.70	9.80	27.00	180.00	4.00	4.00	4.00
0.02	.300	15.8	3.90	19.70	19.00	35.00	1.00	1.00	1.00
160000.00	169000.00	5.2	1.00	6.20	30.40	392.00	4.00	5.00	4.00
3300.00	25600.00	10.9	3.60	14.50	28.00	63.00	1.00	2.00	1.00
52160.00	440000.00	8.3	1.40	9.70	50.00	230.00	1.00	1.00	1.00
0.43	6400.00	11.0	1.40	12.50	7.00	112.00	5.00	4.00	4.00
465000.00	423000.00	3.2	0.70	3.90	30.00	281.00	5.00	5.00	5.00
0.75	1200.00	6.3	2.10	8.40	3.50	42.00	1.00	1.00	1.00
0.79	3500.00	6.6	4.10	10.70	6.00	42.00	2.00	2.00	2.00
0.20	5000.00	9.5	1.20	10.70	10.40	120.00	2.00	2.00	2.00
27660.00	115000.00	3.3	0.50	3.80	20.00	148.00	5.00	5.00	5.00
0.12	1000.00	11.0	3.40	14.40	3.90	16.00	3.00	1.00	2.00
85000.00	325000.00	4.7	1.50	6.20	41.00	310.00	1.00	3.00	1.00
0.10	4000.00	10.4	3.40	13.80	9.00	28.00	5.00	1.00	3.00
1040.00	5500.00	7.4	0.80	8.20	7.60	68.00	5.00	3.00	4.00
521000.00	655000.00	2.1	0.80	2.90	46.00	336.00	5.00	5.00	5.00
0.10	0.25	17.9	2.00	19.90	24.00	50.00	1.00	1.00	1.00
62000.00	1320000.00	6.1	1.90	8.00	100.00	267.00	1.00	1.00	1.00
0.23	0.40	11.9	1.30	13.20	3.20	19.00	4.00	1.00	3.00
1700.00	6300.00	13.8	5.60	19.40	5.00	12.00	2.00	1.00	1.00
3500.00	10800.00	14.3	3.10	17.40	6.50	120.00	2.00	1.00	1.00
0.48	15500.00	15.2	1.80	17.00	12.00	140.00	2.00	2.00	2.00
10000.00	115000.00	10.0	0.90	10.90	20.20	170.00	4.00	4.00	4.00
1620.00	11400.00	11.9	1.80	13.70	13.00	17.00	2.00	1.00	2.00
192000.00	180000.00	6.5	1.90	8.40	27.00	115.00	4.00	4.00	4.00
2500.00	12100.00	7.5	0.90	8.40	18.00	31.00	5.00	5.00	5.00
0.28	1900.00	10.6	2.60	13.20	4.70	21.00	3.00	1.00	3.00
4235.00	50400.00	7.4	2.40	9.80	9.80	52.00	1.00	1.00	1.00
6800.00	179000.00	8.4	1.20	9.60	29.00	164.00	2.00	3.00	2.00
0.75	12300.00	5.7	0.90	6.60	7.00	225.00	2.00	2.00	2.00
3600.00	21000.00	4.9	0.50	5.40	6.00	225.00	3.00	2.00	3.00
55500.00	175000.00	3.2	0.60	3.80	20.00	151.00	5.00	5.00	5.00
0.90	2600.00	11.0	2.30	13.30	4.50	60.00	2.00	1.00	2.00
2000.00	12300.00	4.9	0.50	5.40	7.50	200.00	3.00	1.00	3.00
0.10	2500.00	13.2	2.60	15.80	2.30	46.00	3.00	2.00	2.00
4190.00	58000.00	9.7	0.60	10.30	24.00	210.00	4.00	3.00	4.00
3500.00	3900.00	12.8	6.60	19.40	3.00	14.00	2.00	1.00	1.00

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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108568&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108568&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108568&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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation
paradoxical[t] = + 0.0103628511468773 + 3.85439210376215e-08body[t] -2.50098441001066e-08brain[t] -1.00261472952337slowwave[t] + 1.00167966608105total_sleep[t] + 0.000330522132720004lifespan[t] -8.48554963988697e-06gestation[t] -0.00925530811622734predation[t] -0.00583287894048159sleepexp.[t] + 0.0109670647062305danger[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
paradoxical[t] =  +  0.0103628511468773 +  3.85439210376215e-08body[t] -2.50098441001066e-08brain[t] -1.00261472952337slowwave[t] +  1.00167966608105total_sleep[t] +  0.000330522132720004lifespan[t] -8.48554963988697e-06gestation[t] -0.00925530811622734predation[t] -0.00583287894048159sleepexp.[t] +  0.0109670647062305danger[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108568&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]paradoxical[t] =  +  0.0103628511468773 +  3.85439210376215e-08body[t] -2.50098441001066e-08brain[t] -1.00261472952337slowwave[t] +  1.00167966608105total_sleep[t] +  0.000330522132720004lifespan[t] -8.48554963988697e-06gestation[t] -0.00925530811622734predation[t] -0.00583287894048159sleepexp.[t] +  0.0109670647062305danger[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108568&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108568&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
paradoxical[t] = + 0.0103628511468773 + 3.85439210376215e-08body[t] -2.50098441001066e-08brain[t] -1.00261472952337slowwave[t] + 1.00167966608105total_sleep[t] + 0.000330522132720004lifespan[t] -8.48554963988697e-06gestation[t] -0.00925530811622734predation[t] -0.00583287894048159sleepexp.[t] + 0.0109670647062305danger[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)	0.0103628511468773	0.020374	0.5086	0.614864	0.307432
body	3.85439210376215e-08	0	1.0237	0.314459	0.157229
brain	-2.50098441001066e-08	0	-1.1623	0.254576	0.127288
slowwave	-1.00261472952337	0.003509	-285.7359	0	0
total_sleep	1.00167966608105	0.003411	293.6964	0	0
lifespan	0.000330522132720004	0.000302	1.0933	0.283262	0.141631
gestation	-8.48554963988697e-06	5e-05	-0.168	0.867711	0.433856
predation	-0.00925530811622734	0.006937	-1.3342	0.192525	0.096262
sleepexp.	-0.00583287894048159	0.003931	-1.484	0.148597	0.074299
danger	0.0109670647062305	0.009771	1.1224	0.27091	0.135455

\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) & 0.0103628511468773 & 0.020374 & 0.5086 & 0.614864 & 0.307432 \tabularnewline
body & 3.85439210376215e-08 & 0 & 1.0237 & 0.314459 & 0.157229 \tabularnewline
brain & -2.50098441001066e-08 & 0 & -1.1623 & 0.254576 & 0.127288 \tabularnewline
slowwave & -1.00261472952337 & 0.003509 & -285.7359 & 0 & 0 \tabularnewline
total_sleep & 1.00167966608105 & 0.003411 & 293.6964 & 0 & 0 \tabularnewline
lifespan & 0.000330522132720004 & 0.000302 & 1.0933 & 0.283262 & 0.141631 \tabularnewline
gestation & -8.48554963988697e-06 & 5e-05 & -0.168 & 0.867711 & 0.433856 \tabularnewline
predation & -0.00925530811622734 & 0.006937 & -1.3342 & 0.192525 & 0.096262 \tabularnewline
sleepexp. & -0.00583287894048159 & 0.003931 & -1.484 & 0.148597 & 0.074299 \tabularnewline
danger & 0.0109670647062305 & 0.009771 & 1.1224 & 0.27091 & 0.135455 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108568&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]0.0103628511468773[/C][C]0.020374[/C][C]0.5086[/C][C]0.614864[/C][C]0.307432[/C][/ROW]
[ROW][C]body[/C][C]3.85439210376215e-08[/C][C]0[/C][C]1.0237[/C][C]0.314459[/C][C]0.157229[/C][/ROW]
[ROW][C]brain[/C][C]-2.50098441001066e-08[/C][C]0[/C][C]-1.1623[/C][C]0.254576[/C][C]0.127288[/C][/ROW]
[ROW][C]slowwave[/C][C]-1.00261472952337[/C][C]0.003509[/C][C]-285.7359[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]total_sleep[/C][C]1.00167966608105[/C][C]0.003411[/C][C]293.6964[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]lifespan[/C][C]0.000330522132720004[/C][C]0.000302[/C][C]1.0933[/C][C]0.283262[/C][C]0.141631[/C][/ROW]
[ROW][C]gestation[/C][C]-8.48554963988697e-06[/C][C]5e-05[/C][C]-0.168[/C][C]0.867711[/C][C]0.433856[/C][/ROW]
[ROW][C]predation[/C][C]-0.00925530811622734[/C][C]0.006937[/C][C]-1.3342[/C][C]0.192525[/C][C]0.096262[/C][/ROW]
[ROW][C]sleepexp.[/C][C]-0.00583287894048159[/C][C]0.003931[/C][C]-1.484[/C][C]0.148597[/C][C]0.074299[/C][/ROW]
[ROW][C]danger[/C][C]0.0109670647062305[/C][C]0.009771[/C][C]1.1224[/C][C]0.27091[/C][C]0.135455[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108568&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108568&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)	0.0103628511468773	0.020374	0.5086	0.614864	0.307432
body	3.85439210376215e-08	0	1.0237	0.314459	0.157229
brain	-2.50098441001066e-08	0	-1.1623	0.254576	0.127288
slowwave	-1.00261472952337	0.003509	-285.7359	0	0
total_sleep	1.00167966608105	0.003411	293.6964	0	0
lifespan	0.000330522132720004	0.000302	1.0933	0.283262	0.141631
gestation	-8.48554963988697e-06	5e-05	-0.168	0.867711	0.433856
predation	-0.00925530811622734	0.006937	-1.3342	0.192525	0.096262
sleepexp.	-0.00583287894048159	0.003931	-1.484	0.148597	0.074299
danger	0.0109670647062305	0.009771	1.1224	0.27091	0.135455

Multiple Linear Regression - Regression Statistics
Multiple R	0.999948080361548
R-squared	0.999896163418744
Adjusted R-squared	0.999863938272837
F-TEST (value)	31028.4448767099
F-TEST (DF numerator)	9
F-TEST (DF denominator)	29
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0164064833511368
Sum Squared Residuals	0.00780600818258273

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999948080361548 \tabularnewline
R-squared & 0.999896163418744 \tabularnewline
Adjusted R-squared & 0.999863938272837 \tabularnewline
F-TEST (value) & 31028.4448767099 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 29 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0164064833511368 \tabularnewline
Sum Squared Residuals & 0.00780600818258273 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108568&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999948080361548[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999896163418744[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999863938272837[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]31028.4448767099[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]29[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0164064833511368[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.00780600818258273[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108568&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108568&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	0.999948080361548
R-squared	0.999896163418744
Adjusted R-squared	0.999863938272837
F-TEST (value)	31028.4448767099
F-TEST (DF numerator)	9
F-TEST (DF denominator)	29
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0164064833511368
Sum Squared Residuals	0.00780600818258273

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2	2.00813810991424	-0.00813810991423942
2	1.8	1.79892265531016	0.00107734468984229
3	0.7	0.689859120675839	0.0101408793241612
4	3.9	3.90400134367608	-0.00400134367608236
5	1	0.993524720074432	0.00647527992556754
6	3.6	3.60447042924575	-0.00447042924574755
7	1.4	1.40641278447473	-0.00641278447472853
8	1.4	1.47806008182583	-0.078060081825828
9	0.7	0.702815786396743	-0.00281578639674306
10	2.1	2.10464857935467	-0.00464857935467079
11	4.1	4.10437505436573	-0.00437505436573254
12	1.2	1.19754722575294	0.00245277424705923
13	0.5	0.491055937167445	0.00894406283255459
14	3.4	3.39525160638479	0.00474839361520612
15	1.5	1.49876959286694	0.00123040713306228
16	3.4	3.38977789890198	0.0102221010980242
17	0.8	0.786717678329388	0.0132823216706118
18	0.8	0.805190147431608	-0.00519014743160751
19	2	2.00037167664621	-0.000371676646214936
20	1.9	1.9038925077626	-0.00389250776259970
21	1.3	1.29236268904466	0.00763731095533718
22	5.6	5.59494742194593	0.00505257805407105
23	3.1	3.08981690361952	0.0101830963804768
24	1.8	1.79332169562916	0.00667830437084161
25	0.9	0.88878273757575	0.0112172624242495
26	1.8	1.80377949167953	-0.00377949167952611
27	1.9	1.90183873519995	-0.00183873519995174
28	0.9	0.889736050089149	0.0102639499108511
29	2.6	2.60544845086842	-0.00544845086842071
30	2.4	2.4050540636001	-0.00505406360010157
31	1.2	1.1944276421643	0.00557235783569996
32	0.9	0.898399258383226	0.00160074161677424
33	0.5	0.499777820726067	0.00022217927393291
34	0.6	0.590864425586544	0.00913557441345591
35	2.3	2.30244424522104	-0.00244424522103632
36	0.5	0.506474536976637	-0.00647453697663672
37	2.6	2.58519693756779	0.0148030624322094
38	0.6	0.596510418775831	0.00348958122416917
39	6.6	6.59701353878829	0.00298646121170936

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 2.00813810991424 & -0.00813810991423942 \tabularnewline
2 & 1.8 & 1.79892265531016 & 0.00107734468984229 \tabularnewline
3 & 0.7 & 0.689859120675839 & 0.0101408793241612 \tabularnewline
4 & 3.9 & 3.90400134367608 & -0.00400134367608236 \tabularnewline
5 & 1 & 0.993524720074432 & 0.00647527992556754 \tabularnewline
6 & 3.6 & 3.60447042924575 & -0.00447042924574755 \tabularnewline
7 & 1.4 & 1.40641278447473 & -0.00641278447472853 \tabularnewline
8 & 1.4 & 1.47806008182583 & -0.078060081825828 \tabularnewline
9 & 0.7 & 0.702815786396743 & -0.00281578639674306 \tabularnewline
10 & 2.1 & 2.10464857935467 & -0.00464857935467079 \tabularnewline
11 & 4.1 & 4.10437505436573 & -0.00437505436573254 \tabularnewline
12 & 1.2 & 1.19754722575294 & 0.00245277424705923 \tabularnewline
13 & 0.5 & 0.491055937167445 & 0.00894406283255459 \tabularnewline
14 & 3.4 & 3.39525160638479 & 0.00474839361520612 \tabularnewline
15 & 1.5 & 1.49876959286694 & 0.00123040713306228 \tabularnewline
16 & 3.4 & 3.38977789890198 & 0.0102221010980242 \tabularnewline
17 & 0.8 & 0.786717678329388 & 0.0132823216706118 \tabularnewline
18 & 0.8 & 0.805190147431608 & -0.00519014743160751 \tabularnewline
19 & 2 & 2.00037167664621 & -0.000371676646214936 \tabularnewline
20 & 1.9 & 1.9038925077626 & -0.00389250776259970 \tabularnewline
21 & 1.3 & 1.29236268904466 & 0.00763731095533718 \tabularnewline
22 & 5.6 & 5.59494742194593 & 0.00505257805407105 \tabularnewline
23 & 3.1 & 3.08981690361952 & 0.0101830963804768 \tabularnewline
24 & 1.8 & 1.79332169562916 & 0.00667830437084161 \tabularnewline
25 & 0.9 & 0.88878273757575 & 0.0112172624242495 \tabularnewline
26 & 1.8 & 1.80377949167953 & -0.00377949167952611 \tabularnewline
27 & 1.9 & 1.90183873519995 & -0.00183873519995174 \tabularnewline
28 & 0.9 & 0.889736050089149 & 0.0102639499108511 \tabularnewline
29 & 2.6 & 2.60544845086842 & -0.00544845086842071 \tabularnewline
30 & 2.4 & 2.4050540636001 & -0.00505406360010157 \tabularnewline
31 & 1.2 & 1.1944276421643 & 0.00557235783569996 \tabularnewline
32 & 0.9 & 0.898399258383226 & 0.00160074161677424 \tabularnewline
33 & 0.5 & 0.499777820726067 & 0.00022217927393291 \tabularnewline
34 & 0.6 & 0.590864425586544 & 0.00913557441345591 \tabularnewline
35 & 2.3 & 2.30244424522104 & -0.00244424522103632 \tabularnewline
36 & 0.5 & 0.506474536976637 & -0.00647453697663672 \tabularnewline
37 & 2.6 & 2.58519693756779 & 0.0148030624322094 \tabularnewline
38 & 0.6 & 0.596510418775831 & 0.00348958122416917 \tabularnewline
39 & 6.6 & 6.59701353878829 & 0.00298646121170936 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108568&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]2[/C][C]2.00813810991424[/C][C]-0.00813810991423942[/C][/ROW]
[ROW][C]2[/C][C]1.8[/C][C]1.79892265531016[/C][C]0.00107734468984229[/C][/ROW]
[ROW][C]3[/C][C]0.7[/C][C]0.689859120675839[/C][C]0.0101408793241612[/C][/ROW]
[ROW][C]4[/C][C]3.9[/C][C]3.90400134367608[/C][C]-0.00400134367608236[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.993524720074432[/C][C]0.00647527992556754[/C][/ROW]
[ROW][C]6[/C][C]3.6[/C][C]3.60447042924575[/C][C]-0.00447042924574755[/C][/ROW]
[ROW][C]7[/C][C]1.4[/C][C]1.40641278447473[/C][C]-0.00641278447472853[/C][/ROW]
[ROW][C]8[/C][C]1.4[/C][C]1.47806008182583[/C][C]-0.078060081825828[/C][/ROW]
[ROW][C]9[/C][C]0.7[/C][C]0.702815786396743[/C][C]-0.00281578639674306[/C][/ROW]
[ROW][C]10[/C][C]2.1[/C][C]2.10464857935467[/C][C]-0.00464857935467079[/C][/ROW]
[ROW][C]11[/C][C]4.1[/C][C]4.10437505436573[/C][C]-0.00437505436573254[/C][/ROW]
[ROW][C]12[/C][C]1.2[/C][C]1.19754722575294[/C][C]0.00245277424705923[/C][/ROW]
[ROW][C]13[/C][C]0.5[/C][C]0.491055937167445[/C][C]0.00894406283255459[/C][/ROW]
[ROW][C]14[/C][C]3.4[/C][C]3.39525160638479[/C][C]0.00474839361520612[/C][/ROW]
[ROW][C]15[/C][C]1.5[/C][C]1.49876959286694[/C][C]0.00123040713306228[/C][/ROW]
[ROW][C]16[/C][C]3.4[/C][C]3.38977789890198[/C][C]0.0102221010980242[/C][/ROW]
[ROW][C]17[/C][C]0.8[/C][C]0.786717678329388[/C][C]0.0132823216706118[/C][/ROW]
[ROW][C]18[/C][C]0.8[/C][C]0.805190147431608[/C][C]-0.00519014743160751[/C][/ROW]
[ROW][C]19[/C][C]2[/C][C]2.00037167664621[/C][C]-0.000371676646214936[/C][/ROW]
[ROW][C]20[/C][C]1.9[/C][C]1.9038925077626[/C][C]-0.00389250776259970[/C][/ROW]
[ROW][C]21[/C][C]1.3[/C][C]1.29236268904466[/C][C]0.00763731095533718[/C][/ROW]
[ROW][C]22[/C][C]5.6[/C][C]5.59494742194593[/C][C]0.00505257805407105[/C][/ROW]
[ROW][C]23[/C][C]3.1[/C][C]3.08981690361952[/C][C]0.0101830963804768[/C][/ROW]
[ROW][C]24[/C][C]1.8[/C][C]1.79332169562916[/C][C]0.00667830437084161[/C][/ROW]
[ROW][C]25[/C][C]0.9[/C][C]0.88878273757575[/C][C]0.0112172624242495[/C][/ROW]
[ROW][C]26[/C][C]1.8[/C][C]1.80377949167953[/C][C]-0.00377949167952611[/C][/ROW]
[ROW][C]27[/C][C]1.9[/C][C]1.90183873519995[/C][C]-0.00183873519995174[/C][/ROW]
[ROW][C]28[/C][C]0.9[/C][C]0.889736050089149[/C][C]0.0102639499108511[/C][/ROW]
[ROW][C]29[/C][C]2.6[/C][C]2.60544845086842[/C][C]-0.00544845086842071[/C][/ROW]
[ROW][C]30[/C][C]2.4[/C][C]2.4050540636001[/C][C]-0.00505406360010157[/C][/ROW]
[ROW][C]31[/C][C]1.2[/C][C]1.1944276421643[/C][C]0.00557235783569996[/C][/ROW]
[ROW][C]32[/C][C]0.9[/C][C]0.898399258383226[/C][C]0.00160074161677424[/C][/ROW]
[ROW][C]33[/C][C]0.5[/C][C]0.499777820726067[/C][C]0.00022217927393291[/C][/ROW]
[ROW][C]34[/C][C]0.6[/C][C]0.590864425586544[/C][C]0.00913557441345591[/C][/ROW]
[ROW][C]35[/C][C]2.3[/C][C]2.30244424522104[/C][C]-0.00244424522103632[/C][/ROW]
[ROW][C]36[/C][C]0.5[/C][C]0.506474536976637[/C][C]-0.00647453697663672[/C][/ROW]
[ROW][C]37[/C][C]2.6[/C][C]2.58519693756779[/C][C]0.0148030624322094[/C][/ROW]
[ROW][C]38[/C][C]0.6[/C][C]0.596510418775831[/C][C]0.00348958122416917[/C][/ROW]
[ROW][C]39[/C][C]6.6[/C][C]6.59701353878829[/C][C]0.00298646121170936[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108568&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108568&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	2	2.00813810991424	-0.00813810991423942
2	1.8	1.79892265531016	0.00107734468984229
3	0.7	0.689859120675839	0.0101408793241612
4	3.9	3.90400134367608	-0.00400134367608236
5	1	0.993524720074432	0.00647527992556754
6	3.6	3.60447042924575	-0.00447042924574755
7	1.4	1.40641278447473	-0.00641278447472853
8	1.4	1.47806008182583	-0.078060081825828
9	0.7	0.702815786396743	-0.00281578639674306
10	2.1	2.10464857935467	-0.00464857935467079
11	4.1	4.10437505436573	-0.00437505436573254
12	1.2	1.19754722575294	0.00245277424705923
13	0.5	0.491055937167445	0.00894406283255459
14	3.4	3.39525160638479	0.00474839361520612
15	1.5	1.49876959286694	0.00123040713306228
16	3.4	3.38977789890198	0.0102221010980242
17	0.8	0.786717678329388	0.0132823216706118
18	0.8	0.805190147431608	-0.00519014743160751
19	2	2.00037167664621	-0.000371676646214936
20	1.9	1.9038925077626	-0.00389250776259970
21	1.3	1.29236268904466	0.00763731095533718
22	5.6	5.59494742194593	0.00505257805407105
23	3.1	3.08981690361952	0.0101830963804768
24	1.8	1.79332169562916	0.00667830437084161
25	0.9	0.88878273757575	0.0112172624242495
26	1.8	1.80377949167953	-0.00377949167952611
27	1.9	1.90183873519995	-0.00183873519995174
28	0.9	0.889736050089149	0.0102639499108511
29	2.6	2.60544845086842	-0.00544845086842071
30	2.4	2.4050540636001	-0.00505406360010157
31	1.2	1.1944276421643	0.00557235783569996
32	0.9	0.898399258383226	0.00160074161677424
33	0.5	0.499777820726067	0.00022217927393291
34	0.6	0.590864425586544	0.00913557441345591
35	2.3	2.30244424522104	-0.00244424522103632
36	0.5	0.506474536976637	-0.00647453697663672
37	2.6	2.58519693756779	0.0148030624322094
38	0.6	0.596510418775831	0.00348958122416917
39	6.6	6.59701353878829	0.00298646121170936

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
13	1	1.33823399697445e-188	6.69116998487225e-189
14	1	1.49178728325363e-195	7.45893641626815e-196
15	1	1.73212178198017e-182	8.66060890990086e-183
16	1	1.49221949467581e-170	7.46109747337905e-171
17	1	4.90040980982609e-158	2.45020490491305e-158
18	1	6.40657856975624e-145	3.20328928487812e-145
19	1	1.28207222514613e-131	6.41036112573065e-132
20	1	7.13482345630461e-118	3.56741172815231e-118
21	1	3.46682432340043e-104	1.73341216170022e-104
22	1	1.53132999949218e-91	7.65664999746092e-92
23	1	9.62301778837544e-79	4.81150889418772e-79
24	1	6.53081454031816e-66	3.26540727015908e-66
25	1	3.36146313227097e-52	1.68073156613548e-52
26	1	4.6541564608213e-40	2.32707823041065e-40

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 1 & 1.33823399697445e-188 & 6.69116998487225e-189 \tabularnewline
14 & 1 & 1.49178728325363e-195 & 7.45893641626815e-196 \tabularnewline
15 & 1 & 1.73212178198017e-182 & 8.66060890990086e-183 \tabularnewline
16 & 1 & 1.49221949467581e-170 & 7.46109747337905e-171 \tabularnewline
17 & 1 & 4.90040980982609e-158 & 2.45020490491305e-158 \tabularnewline
18 & 1 & 6.40657856975624e-145 & 3.20328928487812e-145 \tabularnewline
19 & 1 & 1.28207222514613e-131 & 6.41036112573065e-132 \tabularnewline
20 & 1 & 7.13482345630461e-118 & 3.56741172815231e-118 \tabularnewline
21 & 1 & 3.46682432340043e-104 & 1.73341216170022e-104 \tabularnewline
22 & 1 & 1.53132999949218e-91 & 7.65664999746092e-92 \tabularnewline
23 & 1 & 9.62301778837544e-79 & 4.81150889418772e-79 \tabularnewline
24 & 1 & 6.53081454031816e-66 & 3.26540727015908e-66 \tabularnewline
25 & 1 & 3.36146313227097e-52 & 1.68073156613548e-52 \tabularnewline
26 & 1 & 4.6541564608213e-40 & 2.32707823041065e-40 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108568&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]13[/C][C]1[/C][C]1.33823399697445e-188[/C][C]6.69116998487225e-189[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]1.49178728325363e-195[/C][C]7.45893641626815e-196[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]1.73212178198017e-182[/C][C]8.66060890990086e-183[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]1.49221949467581e-170[/C][C]7.46109747337905e-171[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]4.90040980982609e-158[/C][C]2.45020490491305e-158[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]6.40657856975624e-145[/C][C]3.20328928487812e-145[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.28207222514613e-131[/C][C]6.41036112573065e-132[/C][/ROW]
[ROW][C]20[/C][C]1[/C][C]7.13482345630461e-118[/C][C]3.56741172815231e-118[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]3.46682432340043e-104[/C][C]1.73341216170022e-104[/C][/ROW]
[ROW][C]22[/C][C]1[/C][C]1.53132999949218e-91[/C][C]7.65664999746092e-92[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]9.62301778837544e-79[/C][C]4.81150889418772e-79[/C][/ROW]
[ROW][C]24[/C][C]1[/C][C]6.53081454031816e-66[/C][C]3.26540727015908e-66[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]3.36146313227097e-52[/C][C]1.68073156613548e-52[/C][/ROW]
[ROW][C]26[/C][C]1[/C][C]4.6541564608213e-40[/C][C]2.32707823041065e-40[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108568&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108568&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
13	1	1.33823399697445e-188	6.69116998487225e-189
14	1	1.49178728325363e-195	7.45893641626815e-196
15	1	1.73212178198017e-182	8.66060890990086e-183
16	1	1.49221949467581e-170	7.46109747337905e-171
17	1	4.90040980982609e-158	2.45020490491305e-158
18	1	6.40657856975624e-145	3.20328928487812e-145
19	1	1.28207222514613e-131	6.41036112573065e-132
20	1	7.13482345630461e-118	3.56741172815231e-118
21	1	3.46682432340043e-104	1.73341216170022e-104
22	1	1.53132999949218e-91	7.65664999746092e-92
23	1	9.62301778837544e-79	4.81150889418772e-79
24	1	6.53081454031816e-66	3.26540727015908e-66
25	1	3.36146313227097e-52	1.68073156613548e-52
26	1	4.6541564608213e-40	2.32707823041065e-40

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	14	1	NOK
5% type I error level	14	1	NOK
10% type I error level	14	1	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 & 14 & 1 & NOK \tabularnewline
5% type I error level & 14 & 1 & NOK \tabularnewline
10% type I error level & 14 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=108568&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]14[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]14[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=108568&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=108568&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	14	1	NOK
5% type I error level	14	1	NOK
10% type I error level	14	1	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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