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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 15 Dec 2012 06:57:43 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/15/t1355572682t8b0zevajmangj2.htm/, Retrieved Tue, 30 Apr 2024 10:40:17 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=199852, Retrieved Tue, 30 Apr 2024 10:40:17 +0000

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No (this computation is public)

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Estimated Impact

112

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

-     [Multiple Regression] [] [2010-12-24 13:15:50] [64a7ae6044525e7ca71ecb546c042c9e]
- R  D    [Multiple Regression] [Multiple regression] [2012-12-15 11:57:43] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- R  D      [Multiple Regression] [meervoudige regre...] [2012-12-21 20:07:27] [93b3e8d0ee7e4ccb504c2c04707a9358] 

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Dataseries X:

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Histogram

Boxplots

1.5	508643	493	797
1.6	527568	514	840
1.8	520008	522	988
1.5	498484	490	819
1.3	523917	484	831
1.6	553522	506	904
1.6	558901	501	814
1.8	548933	462	798
1.8	567013	465	828
1.6	551085	454	789
1.8	588245	464	930
2	605010	427	744
1.3	631572	460	832
1.1	639180	473	826
1	653847	465	907
1.2	657073	422	776
1.2	626291	415	835
1.3	625616	413	715
1.3	633352	420	729
1.4	672820	363	733
1.1	691369	376	736
0.9	702595	380	712
1	692241	384	711
1.1	718722	346	667
1.4	732297	389	799
1.5	721798	407	661
1.8	766192	393	692
1.8	788456	346	649
1.8	806132	348	729
1.7	813944	353	622
1.5	788025	364	671
1.1	765985	305	635
1.3	702684	307	648
1.6	730159	312	745
1.9	678942	312	624
1.9	672527	286	477
2	594783	324	710
2.2	594575	336	515
2.2	576299	327	461
2	530770	302	590
2.3	524491	299	415
2.6	456590	311	554
3.2	428448	315	585
3.2	444937	264	513
3.1	372206	278	591
2.8	317272	278	561
2.3	297604	287	684
1.9	288561	279	668
1.9	289287	324	795
2	258923	354	776
2	255493	354	1043
1.8	277992	360	964
1.6	295474	363	762
1.4	291680	385	1030
0.2	318736	412	939
0.3	338463	370	779
0.4	351963	389	918
0.7	347240	395	839
1	347081	417	874
1.1	383486	404	840

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	'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199852&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199852&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199852&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	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
inflatie[t] = + 4.35891569656023 -1.24880250097788e-06beurswaarde[t] + 1.08573074824427e-05werkloosheid[t] -0.00277272710599615failliet[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
inflatie[t] =  +  4.35891569656023 -1.24880250097788e-06beurswaarde[t] +  1.08573074824427e-05werkloosheid[t] -0.00277272710599615failliet[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199852&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]inflatie[t] =  +  4.35891569656023 -1.24880250097788e-06beurswaarde[t] +  1.08573074824427e-05werkloosheid[t] -0.00277272710599615failliet[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199852&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199852&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
inflatie[t] = + 4.35891569656023 -1.24880250097788e-06beurswaarde[t] + 1.08573074824427e-05werkloosheid[t] -0.00277272710599615failliet[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)	4.35891569656023	0.490685	8.8833	0	0
beurswaarde	-1.24880250097788e-06	0	-2.7034	0.00907	0.004535
werkloosheid	1.08573074824427e-05	0.001406	0.0077	0.993864	0.496932
failliet	-0.00277272710599615	0.000733	-3.7808	0.000382	0.000191

\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) & 4.35891569656023 & 0.490685 & 8.8833 & 0 & 0 \tabularnewline
beurswaarde & -1.24880250097788e-06 & 0 & -2.7034 & 0.00907 & 0.004535 \tabularnewline
werkloosheid & 1.08573074824427e-05 & 0.001406 & 0.0077 & 0.993864 & 0.496932 \tabularnewline
failliet & -0.00277272710599615 & 0.000733 & -3.7808 & 0.000382 & 0.000191 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199852&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]4.35891569656023[/C][C]0.490685[/C][C]8.8833[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]beurswaarde[/C][C]-1.24880250097788e-06[/C][C]0[/C][C]-2.7034[/C][C]0.00907[/C][C]0.004535[/C][/ROW]
[ROW][C]werkloosheid[/C][C]1.08573074824427e-05[/C][C]0.001406[/C][C]0.0077[/C][C]0.993864[/C][C]0.496932[/C][/ROW]
[ROW][C]failliet[/C][C]-0.00277272710599615[/C][C]0.000733[/C][C]-3.7808[/C][C]0.000382[/C][C]0.000191[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199852&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199852&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)	4.35891569656023	0.490685	8.8833	0	0
beurswaarde	-1.24880250097788e-06	0	-2.7034	0.00907	0.004535
werkloosheid	1.08573074824427e-05	0.001406	0.0077	0.993864	0.496932
failliet	-0.00277272710599615	0.000733	-3.7808	0.000382	0.000191

Multiple Linear Regression - Regression Statistics
Multiple R	0.61098302178297
R-squared	0.373300252907049
Adjusted R-squared	0.339727052169926
F-TEST (value)	11.1189950529288
F-TEST (DF numerator)	3
F-TEST (DF denominator)	56
p-value	7.89864764538795e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.503152512180858
Sum Squared Residuals	14.1770972287789

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.61098302178297 \tabularnewline
R-squared & 0.373300252907049 \tabularnewline
Adjusted R-squared & 0.339727052169926 \tabularnewline
F-TEST (value) & 11.1189950529288 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 56 \tabularnewline
p-value & 7.89864764538795e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.503152512180858 \tabularnewline
Sum Squared Residuals & 14.1770972287789 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199852&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.61098302178297[/C][/ROW]
[ROW][C]R-squared[/C][C]0.373300252907049[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.339727052169926[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.1189950529288[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]56[/C][/ROW]
[ROW][C]p-value[/C][C]7.89864764538795e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.503152512180858[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]14.1770972287789[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199852&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199852&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	0.61098302178297
R-squared	0.373300252907049
Adjusted R-squared	0.339727052169926
F-TEST (value)	11.1189950529288
F-TEST (DF numerator)	3
F-TEST (DF denominator)	56
p-value	7.89864764538795e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.503152512180858
Sum Squared Residuals	14.1770972287789

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.5	1.51921019516524	-0.0192101951652368
2	1.6	1.37657734573354	0.223422654266464
3	1.8	0.975741539413358	0.824258460586642
4	1.5	1.47086421151832	0.0291357884816822
5	1.3	1.4057655483941	-0.105765548394099
6	1.6	1.16662453237954	0.433375467620457
7	1.6	1.40939837672902	0.190601623270975
8	1.8	1.4657866387629	0.334213361237105
9	1.8	1.36005904828778	0.439940951712222
10	1.6	1.4879669012749	0.112033098725103
11	1.8	1.05071545146793	0.749284548532074
12	2	1.54510479887747	0.454895201122534
13	1.3	1.26829241266575	0.0317075873342496
14	1.1	1.27556903087156	-0.175569030871559
15	1	1.03257509054417	-0.0325750905441691
16	1.2	1.39130684033977	-0.191306840339765
17	1.2	1.26608057851872	-0.0660805785187164
18	1.3	1.59962905831145	-0.29962905831145
19	1.3	1.55122614383232	-0.251226143832316
20	1.4	1.49022863177324	-0.0902286317732371
21	1.1	1.45888755786188	-0.358887557861882
22	0.9	1.51145738075974	-0.611457380759741
23	1	1.52720363819079	-0.527203638190792
24	1.1	1.61572151414189	-0.515721514141895
25	1.4	1.23323590642137	0.166764093578627
26	1.5	1.62917885604129	-0.129178856041293
27	1.8	1.48763297522225	0.312367024777754
28	1.8	1.57854660844663	0.221453391553366
29	1.8	1.33467632157462	0.465323678425378
30	1.7	1.62165676331598	0.0783432366840167
31	1.5	1.51828027752732	-0.0182802775273243
32	1.1	1.64498147932327	-0.544981479323274
33	1.3	1.68800818867469	-0.38800818867469
34	1.6	1.38479709721611	0.215202902783892
35	1.9	1.78425699473423	0.115743005265774
36	1.9	2.19957665736489	-0.29957665736489
37	2	1.65103072098814	0.348969279011856
38	2.2	2.19210254526739	0.00789745473261408
39	2.2	2.36455520773171	-0.164555207731708
40	2	2.06345870743817	-0.0634587074381653
41	2.3	2.55649460996868	-0.256494609968685
42	2.6	2.25601076854391	0.343989231456092
43	3.2	2.20524345747048	0.994756542529524
44	3.2	2.38373458198197	0.81626541801803
45	3.1	2.25844052471765	0.841559475282353
46	2.8	2.41022405448625	0.38977594551375
47	2.3	2.0938377838053	0.206162216194701
48	1.9	2.14940748005772	-0.24940748005772
49	1.9	1.79685308581721	0.103146914182791
50	2	1.8877792591953	0.112220740804698
51	2	1.15174451447268	0.848255485527317
52	1.8	1.34275829222177	0.457241707778227
53	1.6	1.88105017423335	-0.281050174233347
54	1.4	1.1429361272797	0.257063872720297
55	0.2	1.36175984076092	-1.16175984076092
56	0.3	1.78030504386925	-1.48030504386925
57	0.4	1.37824343121475	-0.978243431214752
58	0.7	1.60325211064546	-0.903252110645461
59	1	1.50664408229786	-0.506644082297865
60	1.1	1.55531300385636	-0.455313003856362

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.5 & 1.51921019516524 & -0.0192101951652368 \tabularnewline
2 & 1.6 & 1.37657734573354 & 0.223422654266464 \tabularnewline
3 & 1.8 & 0.975741539413358 & 0.824258460586642 \tabularnewline
4 & 1.5 & 1.47086421151832 & 0.0291357884816822 \tabularnewline
5 & 1.3 & 1.4057655483941 & -0.105765548394099 \tabularnewline
6 & 1.6 & 1.16662453237954 & 0.433375467620457 \tabularnewline
7 & 1.6 & 1.40939837672902 & 0.190601623270975 \tabularnewline
8 & 1.8 & 1.4657866387629 & 0.334213361237105 \tabularnewline
9 & 1.8 & 1.36005904828778 & 0.439940951712222 \tabularnewline
10 & 1.6 & 1.4879669012749 & 0.112033098725103 \tabularnewline
11 & 1.8 & 1.05071545146793 & 0.749284548532074 \tabularnewline
12 & 2 & 1.54510479887747 & 0.454895201122534 \tabularnewline
13 & 1.3 & 1.26829241266575 & 0.0317075873342496 \tabularnewline
14 & 1.1 & 1.27556903087156 & -0.175569030871559 \tabularnewline
15 & 1 & 1.03257509054417 & -0.0325750905441691 \tabularnewline
16 & 1.2 & 1.39130684033977 & -0.191306840339765 \tabularnewline
17 & 1.2 & 1.26608057851872 & -0.0660805785187164 \tabularnewline
18 & 1.3 & 1.59962905831145 & -0.29962905831145 \tabularnewline
19 & 1.3 & 1.55122614383232 & -0.251226143832316 \tabularnewline
20 & 1.4 & 1.49022863177324 & -0.0902286317732371 \tabularnewline
21 & 1.1 & 1.45888755786188 & -0.358887557861882 \tabularnewline
22 & 0.9 & 1.51145738075974 & -0.611457380759741 \tabularnewline
23 & 1 & 1.52720363819079 & -0.527203638190792 \tabularnewline
24 & 1.1 & 1.61572151414189 & -0.515721514141895 \tabularnewline
25 & 1.4 & 1.23323590642137 & 0.166764093578627 \tabularnewline
26 & 1.5 & 1.62917885604129 & -0.129178856041293 \tabularnewline
27 & 1.8 & 1.48763297522225 & 0.312367024777754 \tabularnewline
28 & 1.8 & 1.57854660844663 & 0.221453391553366 \tabularnewline
29 & 1.8 & 1.33467632157462 & 0.465323678425378 \tabularnewline
30 & 1.7 & 1.62165676331598 & 0.0783432366840167 \tabularnewline
31 & 1.5 & 1.51828027752732 & -0.0182802775273243 \tabularnewline
32 & 1.1 & 1.64498147932327 & -0.544981479323274 \tabularnewline
33 & 1.3 & 1.68800818867469 & -0.38800818867469 \tabularnewline
34 & 1.6 & 1.38479709721611 & 0.215202902783892 \tabularnewline
35 & 1.9 & 1.78425699473423 & 0.115743005265774 \tabularnewline
36 & 1.9 & 2.19957665736489 & -0.29957665736489 \tabularnewline
37 & 2 & 1.65103072098814 & 0.348969279011856 \tabularnewline
38 & 2.2 & 2.19210254526739 & 0.00789745473261408 \tabularnewline
39 & 2.2 & 2.36455520773171 & -0.164555207731708 \tabularnewline
40 & 2 & 2.06345870743817 & -0.0634587074381653 \tabularnewline
41 & 2.3 & 2.55649460996868 & -0.256494609968685 \tabularnewline
42 & 2.6 & 2.25601076854391 & 0.343989231456092 \tabularnewline
43 & 3.2 & 2.20524345747048 & 0.994756542529524 \tabularnewline
44 & 3.2 & 2.38373458198197 & 0.81626541801803 \tabularnewline
45 & 3.1 & 2.25844052471765 & 0.841559475282353 \tabularnewline
46 & 2.8 & 2.41022405448625 & 0.38977594551375 \tabularnewline
47 & 2.3 & 2.0938377838053 & 0.206162216194701 \tabularnewline
48 & 1.9 & 2.14940748005772 & -0.24940748005772 \tabularnewline
49 & 1.9 & 1.79685308581721 & 0.103146914182791 \tabularnewline
50 & 2 & 1.8877792591953 & 0.112220740804698 \tabularnewline
51 & 2 & 1.15174451447268 & 0.848255485527317 \tabularnewline
52 & 1.8 & 1.34275829222177 & 0.457241707778227 \tabularnewline
53 & 1.6 & 1.88105017423335 & -0.281050174233347 \tabularnewline
54 & 1.4 & 1.1429361272797 & 0.257063872720297 \tabularnewline
55 & 0.2 & 1.36175984076092 & -1.16175984076092 \tabularnewline
56 & 0.3 & 1.78030504386925 & -1.48030504386925 \tabularnewline
57 & 0.4 & 1.37824343121475 & -0.978243431214752 \tabularnewline
58 & 0.7 & 1.60325211064546 & -0.903252110645461 \tabularnewline
59 & 1 & 1.50664408229786 & -0.506644082297865 \tabularnewline
60 & 1.1 & 1.55531300385636 & -0.455313003856362 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199852&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]1.5[/C][C]1.51921019516524[/C][C]-0.0192101951652368[/C][/ROW]
[ROW][C]2[/C][C]1.6[/C][C]1.37657734573354[/C][C]0.223422654266464[/C][/ROW]
[ROW][C]3[/C][C]1.8[/C][C]0.975741539413358[/C][C]0.824258460586642[/C][/ROW]
[ROW][C]4[/C][C]1.5[/C][C]1.47086421151832[/C][C]0.0291357884816822[/C][/ROW]
[ROW][C]5[/C][C]1.3[/C][C]1.4057655483941[/C][C]-0.105765548394099[/C][/ROW]
[ROW][C]6[/C][C]1.6[/C][C]1.16662453237954[/C][C]0.433375467620457[/C][/ROW]
[ROW][C]7[/C][C]1.6[/C][C]1.40939837672902[/C][C]0.190601623270975[/C][/ROW]
[ROW][C]8[/C][C]1.8[/C][C]1.4657866387629[/C][C]0.334213361237105[/C][/ROW]
[ROW][C]9[/C][C]1.8[/C][C]1.36005904828778[/C][C]0.439940951712222[/C][/ROW]
[ROW][C]10[/C][C]1.6[/C][C]1.4879669012749[/C][C]0.112033098725103[/C][/ROW]
[ROW][C]11[/C][C]1.8[/C][C]1.05071545146793[/C][C]0.749284548532074[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]1.54510479887747[/C][C]0.454895201122534[/C][/ROW]
[ROW][C]13[/C][C]1.3[/C][C]1.26829241266575[/C][C]0.0317075873342496[/C][/ROW]
[ROW][C]14[/C][C]1.1[/C][C]1.27556903087156[/C][C]-0.175569030871559[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]1.03257509054417[/C][C]-0.0325750905441691[/C][/ROW]
[ROW][C]16[/C][C]1.2[/C][C]1.39130684033977[/C][C]-0.191306840339765[/C][/ROW]
[ROW][C]17[/C][C]1.2[/C][C]1.26608057851872[/C][C]-0.0660805785187164[/C][/ROW]
[ROW][C]18[/C][C]1.3[/C][C]1.59962905831145[/C][C]-0.29962905831145[/C][/ROW]
[ROW][C]19[/C][C]1.3[/C][C]1.55122614383232[/C][C]-0.251226143832316[/C][/ROW]
[ROW][C]20[/C][C]1.4[/C][C]1.49022863177324[/C][C]-0.0902286317732371[/C][/ROW]
[ROW][C]21[/C][C]1.1[/C][C]1.45888755786188[/C][C]-0.358887557861882[/C][/ROW]
[ROW][C]22[/C][C]0.9[/C][C]1.51145738075974[/C][C]-0.611457380759741[/C][/ROW]
[ROW][C]23[/C][C]1[/C][C]1.52720363819079[/C][C]-0.527203638190792[/C][/ROW]
[ROW][C]24[/C][C]1.1[/C][C]1.61572151414189[/C][C]-0.515721514141895[/C][/ROW]
[ROW][C]25[/C][C]1.4[/C][C]1.23323590642137[/C][C]0.166764093578627[/C][/ROW]
[ROW][C]26[/C][C]1.5[/C][C]1.62917885604129[/C][C]-0.129178856041293[/C][/ROW]
[ROW][C]27[/C][C]1.8[/C][C]1.48763297522225[/C][C]0.312367024777754[/C][/ROW]
[ROW][C]28[/C][C]1.8[/C][C]1.57854660844663[/C][C]0.221453391553366[/C][/ROW]
[ROW][C]29[/C][C]1.8[/C][C]1.33467632157462[/C][C]0.465323678425378[/C][/ROW]
[ROW][C]30[/C][C]1.7[/C][C]1.62165676331598[/C][C]0.0783432366840167[/C][/ROW]
[ROW][C]31[/C][C]1.5[/C][C]1.51828027752732[/C][C]-0.0182802775273243[/C][/ROW]
[ROW][C]32[/C][C]1.1[/C][C]1.64498147932327[/C][C]-0.544981479323274[/C][/ROW]
[ROW][C]33[/C][C]1.3[/C][C]1.68800818867469[/C][C]-0.38800818867469[/C][/ROW]
[ROW][C]34[/C][C]1.6[/C][C]1.38479709721611[/C][C]0.215202902783892[/C][/ROW]
[ROW][C]35[/C][C]1.9[/C][C]1.78425699473423[/C][C]0.115743005265774[/C][/ROW]
[ROW][C]36[/C][C]1.9[/C][C]2.19957665736489[/C][C]-0.29957665736489[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]1.65103072098814[/C][C]0.348969279011856[/C][/ROW]
[ROW][C]38[/C][C]2.2[/C][C]2.19210254526739[/C][C]0.00789745473261408[/C][/ROW]
[ROW][C]39[/C][C]2.2[/C][C]2.36455520773171[/C][C]-0.164555207731708[/C][/ROW]
[ROW][C]40[/C][C]2[/C][C]2.06345870743817[/C][C]-0.0634587074381653[/C][/ROW]
[ROW][C]41[/C][C]2.3[/C][C]2.55649460996868[/C][C]-0.256494609968685[/C][/ROW]
[ROW][C]42[/C][C]2.6[/C][C]2.25601076854391[/C][C]0.343989231456092[/C][/ROW]
[ROW][C]43[/C][C]3.2[/C][C]2.20524345747048[/C][C]0.994756542529524[/C][/ROW]
[ROW][C]44[/C][C]3.2[/C][C]2.38373458198197[/C][C]0.81626541801803[/C][/ROW]
[ROW][C]45[/C][C]3.1[/C][C]2.25844052471765[/C][C]0.841559475282353[/C][/ROW]
[ROW][C]46[/C][C]2.8[/C][C]2.41022405448625[/C][C]0.38977594551375[/C][/ROW]
[ROW][C]47[/C][C]2.3[/C][C]2.0938377838053[/C][C]0.206162216194701[/C][/ROW]
[ROW][C]48[/C][C]1.9[/C][C]2.14940748005772[/C][C]-0.24940748005772[/C][/ROW]
[ROW][C]49[/C][C]1.9[/C][C]1.79685308581721[/C][C]0.103146914182791[/C][/ROW]
[ROW][C]50[/C][C]2[/C][C]1.8877792591953[/C][C]0.112220740804698[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]1.15174451447268[/C][C]0.848255485527317[/C][/ROW]
[ROW][C]52[/C][C]1.8[/C][C]1.34275829222177[/C][C]0.457241707778227[/C][/ROW]
[ROW][C]53[/C][C]1.6[/C][C]1.88105017423335[/C][C]-0.281050174233347[/C][/ROW]
[ROW][C]54[/C][C]1.4[/C][C]1.1429361272797[/C][C]0.257063872720297[/C][/ROW]
[ROW][C]55[/C][C]0.2[/C][C]1.36175984076092[/C][C]-1.16175984076092[/C][/ROW]
[ROW][C]56[/C][C]0.3[/C][C]1.78030504386925[/C][C]-1.48030504386925[/C][/ROW]
[ROW][C]57[/C][C]0.4[/C][C]1.37824343121475[/C][C]-0.978243431214752[/C][/ROW]
[ROW][C]58[/C][C]0.7[/C][C]1.60325211064546[/C][C]-0.903252110645461[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]1.50664408229786[/C][C]-0.506644082297865[/C][/ROW]
[ROW][C]60[/C][C]1.1[/C][C]1.55531300385636[/C][C]-0.455313003856362[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199852&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199852&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	1.5	1.51921019516524	-0.0192101951652368
2	1.6	1.37657734573354	0.223422654266464
3	1.8	0.975741539413358	0.824258460586642
4	1.5	1.47086421151832	0.0291357884816822
5	1.3	1.4057655483941	-0.105765548394099
6	1.6	1.16662453237954	0.433375467620457
7	1.6	1.40939837672902	0.190601623270975
8	1.8	1.4657866387629	0.334213361237105
9	1.8	1.36005904828778	0.439940951712222
10	1.6	1.4879669012749	0.112033098725103
11	1.8	1.05071545146793	0.749284548532074
12	2	1.54510479887747	0.454895201122534
13	1.3	1.26829241266575	0.0317075873342496
14	1.1	1.27556903087156	-0.175569030871559
15	1	1.03257509054417	-0.0325750905441691
16	1.2	1.39130684033977	-0.191306840339765
17	1.2	1.26608057851872	-0.0660805785187164
18	1.3	1.59962905831145	-0.29962905831145
19	1.3	1.55122614383232	-0.251226143832316
20	1.4	1.49022863177324	-0.0902286317732371
21	1.1	1.45888755786188	-0.358887557861882
22	0.9	1.51145738075974	-0.611457380759741
23	1	1.52720363819079	-0.527203638190792
24	1.1	1.61572151414189	-0.515721514141895
25	1.4	1.23323590642137	0.166764093578627
26	1.5	1.62917885604129	-0.129178856041293
27	1.8	1.48763297522225	0.312367024777754
28	1.8	1.57854660844663	0.221453391553366
29	1.8	1.33467632157462	0.465323678425378
30	1.7	1.62165676331598	0.0783432366840167
31	1.5	1.51828027752732	-0.0182802775273243
32	1.1	1.64498147932327	-0.544981479323274
33	1.3	1.68800818867469	-0.38800818867469
34	1.6	1.38479709721611	0.215202902783892
35	1.9	1.78425699473423	0.115743005265774
36	1.9	2.19957665736489	-0.29957665736489
37	2	1.65103072098814	0.348969279011856
38	2.2	2.19210254526739	0.00789745473261408
39	2.2	2.36455520773171	-0.164555207731708
40	2	2.06345870743817	-0.0634587074381653
41	2.3	2.55649460996868	-0.256494609968685
42	2.6	2.25601076854391	0.343989231456092
43	3.2	2.20524345747048	0.994756542529524
44	3.2	2.38373458198197	0.81626541801803
45	3.1	2.25844052471765	0.841559475282353
46	2.8	2.41022405448625	0.38977594551375
47	2.3	2.0938377838053	0.206162216194701
48	1.9	2.14940748005772	-0.24940748005772
49	1.9	1.79685308581721	0.103146914182791
50	2	1.8877792591953	0.112220740804698
51	2	1.15174451447268	0.848255485527317
52	1.8	1.34275829222177	0.457241707778227
53	1.6	1.88105017423335	-0.281050174233347
54	1.4	1.1429361272797	0.257063872720297
55	0.2	1.36175984076092	-1.16175984076092
56	0.3	1.78030504386925	-1.48030504386925
57	0.4	1.37824343121475	-0.978243431214752
58	0.7	1.60325211064546	-0.903252110645461
59	1	1.50664408229786	-0.506644082297865
60	1.1	1.55531300385636	-0.455313003856362

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.0059348062010848	0.0118696124021696	0.994065193798915
8	0.026199816137618	0.052399632275236	0.973800183862382
9	0.00884216952004898	0.017684339040098	0.991157830479951
10	0.00277045068352916	0.00554090136705832	0.997229549316471
11	0.00124841927522679	0.00249683855045358	0.998751580724773
12	0.000840800305516571	0.00168160061103314	0.999159199694483
13	0.00722858026937127	0.0144571605387425	0.992771419730629
14	0.00849066562683718	0.0169813312536744	0.991509334373163
15	0.0137473826774068	0.0274947653548135	0.986252617322593
16	0.00930310344131338	0.0186062068826268	0.990696896558687
17	0.0107879574703232	0.0215759149406465	0.989212042529677
18	0.00589551671868805	0.0117910334373761	0.994104483281312
19	0.00309012456908613	0.00618024913817225	0.996909875430914
20	0.00142754452992983	0.00285508905985966	0.99857245547007
21	0.000730310427390317	0.00146062085478063	0.99926968957261
22	0.000468881933397522	0.000937763866795043	0.999531118066602
23	0.000227758291824409	0.000455516583648817	0.999772241708176
24	0.000120680714106824	0.000241361428213649	0.999879319285893
25	0.000150237494828878	0.000300474989657756	0.999849762505171
26	0.000355090918231978	0.000710181836463956	0.999644909081768
27	0.0024634709879556	0.00492694197591119	0.997536529012044
28	0.00490170921490533	0.00980341842981066	0.995098290785095
29	0.00835029871856294	0.0167005974371259	0.991649701281437
30	0.00746519336294885	0.0149303867258977	0.992534806637051
31	0.00619813032028443	0.0123962606405689	0.993801869679716
32	0.0055331155276129	0.0110662310552258	0.994466884472387
33	0.00480665521841379	0.00961331043682757	0.995193344781586
34	0.00401528018759583	0.00803056037519167	0.995984719812404
35	0.00471331428517475	0.00942662857034951	0.995286685714825
36	0.00717403037166517	0.0143480607433303	0.992825969628335
37	0.00628973917807803	0.0125794783561561	0.993710260821922
38	0.00509430088626123	0.0101886017725225	0.994905699113739
39	0.00308481643368539	0.00616963286737078	0.996915183566315
40	0.00294513452615564	0.00589026905231128	0.997054865473844
41	0.00218331958646158	0.00436663917292316	0.997816680413538
42	0.00152296778069073	0.00304593556138146	0.998477032219309
43	0.0132028518466318	0.0264057036932635	0.986797148153368
44	0.0143472289655099	0.0286944579310199	0.98565277103449
45	0.037581745575602	0.075163491151204	0.962418254424398
46	0.0666011221565276	0.133202244313055	0.933398877843472
47	0.0736946609068492	0.147389321813698	0.926305339093151
48	0.0868201853472541	0.173640370694508	0.913179814652746
49	0.0602862508245391	0.120572501649078	0.939713749175461
50	0.0423979375236004	0.0847958750472008	0.9576020624764
51	0.0248768924971189	0.0497537849942379	0.975123107502881
52	0.019541724332847	0.039083448665694	0.980458275667153
53	0.0726605531632735	0.145321106326547	0.927339446836727

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.0059348062010848 & 0.0118696124021696 & 0.994065193798915 \tabularnewline
8 & 0.026199816137618 & 0.052399632275236 & 0.973800183862382 \tabularnewline
9 & 0.00884216952004898 & 0.017684339040098 & 0.991157830479951 \tabularnewline
10 & 0.00277045068352916 & 0.00554090136705832 & 0.997229549316471 \tabularnewline
11 & 0.00124841927522679 & 0.00249683855045358 & 0.998751580724773 \tabularnewline
12 & 0.000840800305516571 & 0.00168160061103314 & 0.999159199694483 \tabularnewline
13 & 0.00722858026937127 & 0.0144571605387425 & 0.992771419730629 \tabularnewline
14 & 0.00849066562683718 & 0.0169813312536744 & 0.991509334373163 \tabularnewline
15 & 0.0137473826774068 & 0.0274947653548135 & 0.986252617322593 \tabularnewline
16 & 0.00930310344131338 & 0.0186062068826268 & 0.990696896558687 \tabularnewline
17 & 0.0107879574703232 & 0.0215759149406465 & 0.989212042529677 \tabularnewline
18 & 0.00589551671868805 & 0.0117910334373761 & 0.994104483281312 \tabularnewline
19 & 0.00309012456908613 & 0.00618024913817225 & 0.996909875430914 \tabularnewline
20 & 0.00142754452992983 & 0.00285508905985966 & 0.99857245547007 \tabularnewline
21 & 0.000730310427390317 & 0.00146062085478063 & 0.99926968957261 \tabularnewline
22 & 0.000468881933397522 & 0.000937763866795043 & 0.999531118066602 \tabularnewline
23 & 0.000227758291824409 & 0.000455516583648817 & 0.999772241708176 \tabularnewline
24 & 0.000120680714106824 & 0.000241361428213649 & 0.999879319285893 \tabularnewline
25 & 0.000150237494828878 & 0.000300474989657756 & 0.999849762505171 \tabularnewline
26 & 0.000355090918231978 & 0.000710181836463956 & 0.999644909081768 \tabularnewline
27 & 0.0024634709879556 & 0.00492694197591119 & 0.997536529012044 \tabularnewline
28 & 0.00490170921490533 & 0.00980341842981066 & 0.995098290785095 \tabularnewline
29 & 0.00835029871856294 & 0.0167005974371259 & 0.991649701281437 \tabularnewline
30 & 0.00746519336294885 & 0.0149303867258977 & 0.992534806637051 \tabularnewline
31 & 0.00619813032028443 & 0.0123962606405689 & 0.993801869679716 \tabularnewline
32 & 0.0055331155276129 & 0.0110662310552258 & 0.994466884472387 \tabularnewline
33 & 0.00480665521841379 & 0.00961331043682757 & 0.995193344781586 \tabularnewline
34 & 0.00401528018759583 & 0.00803056037519167 & 0.995984719812404 \tabularnewline
35 & 0.00471331428517475 & 0.00942662857034951 & 0.995286685714825 \tabularnewline
36 & 0.00717403037166517 & 0.0143480607433303 & 0.992825969628335 \tabularnewline
37 & 0.00628973917807803 & 0.0125794783561561 & 0.993710260821922 \tabularnewline
38 & 0.00509430088626123 & 0.0101886017725225 & 0.994905699113739 \tabularnewline
39 & 0.00308481643368539 & 0.00616963286737078 & 0.996915183566315 \tabularnewline
40 & 0.00294513452615564 & 0.00589026905231128 & 0.997054865473844 \tabularnewline
41 & 0.00218331958646158 & 0.00436663917292316 & 0.997816680413538 \tabularnewline
42 & 0.00152296778069073 & 0.00304593556138146 & 0.998477032219309 \tabularnewline
43 & 0.0132028518466318 & 0.0264057036932635 & 0.986797148153368 \tabularnewline
44 & 0.0143472289655099 & 0.0286944579310199 & 0.98565277103449 \tabularnewline
45 & 0.037581745575602 & 0.075163491151204 & 0.962418254424398 \tabularnewline
46 & 0.0666011221565276 & 0.133202244313055 & 0.933398877843472 \tabularnewline
47 & 0.0736946609068492 & 0.147389321813698 & 0.926305339093151 \tabularnewline
48 & 0.0868201853472541 & 0.173640370694508 & 0.913179814652746 \tabularnewline
49 & 0.0602862508245391 & 0.120572501649078 & 0.939713749175461 \tabularnewline
50 & 0.0423979375236004 & 0.0847958750472008 & 0.9576020624764 \tabularnewline
51 & 0.0248768924971189 & 0.0497537849942379 & 0.975123107502881 \tabularnewline
52 & 0.019541724332847 & 0.039083448665694 & 0.980458275667153 \tabularnewline
53 & 0.0726605531632735 & 0.145321106326547 & 0.927339446836727 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199852&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]7[/C][C]0.0059348062010848[/C][C]0.0118696124021696[/C][C]0.994065193798915[/C][/ROW]
[ROW][C]8[/C][C]0.026199816137618[/C][C]0.052399632275236[/C][C]0.973800183862382[/C][/ROW]
[ROW][C]9[/C][C]0.00884216952004898[/C][C]0.017684339040098[/C][C]0.991157830479951[/C][/ROW]
[ROW][C]10[/C][C]0.00277045068352916[/C][C]0.00554090136705832[/C][C]0.997229549316471[/C][/ROW]
[ROW][C]11[/C][C]0.00124841927522679[/C][C]0.00249683855045358[/C][C]0.998751580724773[/C][/ROW]
[ROW][C]12[/C][C]0.000840800305516571[/C][C]0.00168160061103314[/C][C]0.999159199694483[/C][/ROW]
[ROW][C]13[/C][C]0.00722858026937127[/C][C]0.0144571605387425[/C][C]0.992771419730629[/C][/ROW]
[ROW][C]14[/C][C]0.00849066562683718[/C][C]0.0169813312536744[/C][C]0.991509334373163[/C][/ROW]
[ROW][C]15[/C][C]0.0137473826774068[/C][C]0.0274947653548135[/C][C]0.986252617322593[/C][/ROW]
[ROW][C]16[/C][C]0.00930310344131338[/C][C]0.0186062068826268[/C][C]0.990696896558687[/C][/ROW]
[ROW][C]17[/C][C]0.0107879574703232[/C][C]0.0215759149406465[/C][C]0.989212042529677[/C][/ROW]
[ROW][C]18[/C][C]0.00589551671868805[/C][C]0.0117910334373761[/C][C]0.994104483281312[/C][/ROW]
[ROW][C]19[/C][C]0.00309012456908613[/C][C]0.00618024913817225[/C][C]0.996909875430914[/C][/ROW]
[ROW][C]20[/C][C]0.00142754452992983[/C][C]0.00285508905985966[/C][C]0.99857245547007[/C][/ROW]
[ROW][C]21[/C][C]0.000730310427390317[/C][C]0.00146062085478063[/C][C]0.99926968957261[/C][/ROW]
[ROW][C]22[/C][C]0.000468881933397522[/C][C]0.000937763866795043[/C][C]0.999531118066602[/C][/ROW]
[ROW][C]23[/C][C]0.000227758291824409[/C][C]0.000455516583648817[/C][C]0.999772241708176[/C][/ROW]
[ROW][C]24[/C][C]0.000120680714106824[/C][C]0.000241361428213649[/C][C]0.999879319285893[/C][/ROW]
[ROW][C]25[/C][C]0.000150237494828878[/C][C]0.000300474989657756[/C][C]0.999849762505171[/C][/ROW]
[ROW][C]26[/C][C]0.000355090918231978[/C][C]0.000710181836463956[/C][C]0.999644909081768[/C][/ROW]
[ROW][C]27[/C][C]0.0024634709879556[/C][C]0.00492694197591119[/C][C]0.997536529012044[/C][/ROW]
[ROW][C]28[/C][C]0.00490170921490533[/C][C]0.00980341842981066[/C][C]0.995098290785095[/C][/ROW]
[ROW][C]29[/C][C]0.00835029871856294[/C][C]0.0167005974371259[/C][C]0.991649701281437[/C][/ROW]
[ROW][C]30[/C][C]0.00746519336294885[/C][C]0.0149303867258977[/C][C]0.992534806637051[/C][/ROW]
[ROW][C]31[/C][C]0.00619813032028443[/C][C]0.0123962606405689[/C][C]0.993801869679716[/C][/ROW]
[ROW][C]32[/C][C]0.0055331155276129[/C][C]0.0110662310552258[/C][C]0.994466884472387[/C][/ROW]
[ROW][C]33[/C][C]0.00480665521841379[/C][C]0.00961331043682757[/C][C]0.995193344781586[/C][/ROW]
[ROW][C]34[/C][C]0.00401528018759583[/C][C]0.00803056037519167[/C][C]0.995984719812404[/C][/ROW]
[ROW][C]35[/C][C]0.00471331428517475[/C][C]0.00942662857034951[/C][C]0.995286685714825[/C][/ROW]
[ROW][C]36[/C][C]0.00717403037166517[/C][C]0.0143480607433303[/C][C]0.992825969628335[/C][/ROW]
[ROW][C]37[/C][C]0.00628973917807803[/C][C]0.0125794783561561[/C][C]0.993710260821922[/C][/ROW]
[ROW][C]38[/C][C]0.00509430088626123[/C][C]0.0101886017725225[/C][C]0.994905699113739[/C][/ROW]
[ROW][C]39[/C][C]0.00308481643368539[/C][C]0.00616963286737078[/C][C]0.996915183566315[/C][/ROW]
[ROW][C]40[/C][C]0.00294513452615564[/C][C]0.00589026905231128[/C][C]0.997054865473844[/C][/ROW]
[ROW][C]41[/C][C]0.00218331958646158[/C][C]0.00436663917292316[/C][C]0.997816680413538[/C][/ROW]
[ROW][C]42[/C][C]0.00152296778069073[/C][C]0.00304593556138146[/C][C]0.998477032219309[/C][/ROW]
[ROW][C]43[/C][C]0.0132028518466318[/C][C]0.0264057036932635[/C][C]0.986797148153368[/C][/ROW]
[ROW][C]44[/C][C]0.0143472289655099[/C][C]0.0286944579310199[/C][C]0.98565277103449[/C][/ROW]
[ROW][C]45[/C][C]0.037581745575602[/C][C]0.075163491151204[/C][C]0.962418254424398[/C][/ROW]
[ROW][C]46[/C][C]0.0666011221565276[/C][C]0.133202244313055[/C][C]0.933398877843472[/C][/ROW]
[ROW][C]47[/C][C]0.0736946609068492[/C][C]0.147389321813698[/C][C]0.926305339093151[/C][/ROW]
[ROW][C]48[/C][C]0.0868201853472541[/C][C]0.173640370694508[/C][C]0.913179814652746[/C][/ROW]
[ROW][C]49[/C][C]0.0602862508245391[/C][C]0.120572501649078[/C][C]0.939713749175461[/C][/ROW]
[ROW][C]50[/C][C]0.0423979375236004[/C][C]0.0847958750472008[/C][C]0.9576020624764[/C][/ROW]
[ROW][C]51[/C][C]0.0248768924971189[/C][C]0.0497537849942379[/C][C]0.975123107502881[/C][/ROW]
[ROW][C]52[/C][C]0.019541724332847[/C][C]0.039083448665694[/C][C]0.980458275667153[/C][/ROW]
[ROW][C]53[/C][C]0.0726605531632735[/C][C]0.145321106326547[/C][C]0.927339446836727[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199852&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199852&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
7	0.0059348062010848	0.0118696124021696	0.994065193798915
8	0.026199816137618	0.052399632275236	0.973800183862382
9	0.00884216952004898	0.017684339040098	0.991157830479951
10	0.00277045068352916	0.00554090136705832	0.997229549316471
11	0.00124841927522679	0.00249683855045358	0.998751580724773
12	0.000840800305516571	0.00168160061103314	0.999159199694483
13	0.00722858026937127	0.0144571605387425	0.992771419730629
14	0.00849066562683718	0.0169813312536744	0.991509334373163
15	0.0137473826774068	0.0274947653548135	0.986252617322593
16	0.00930310344131338	0.0186062068826268	0.990696896558687
17	0.0107879574703232	0.0215759149406465	0.989212042529677
18	0.00589551671868805	0.0117910334373761	0.994104483281312
19	0.00309012456908613	0.00618024913817225	0.996909875430914
20	0.00142754452992983	0.00285508905985966	0.99857245547007
21	0.000730310427390317	0.00146062085478063	0.99926968957261
22	0.000468881933397522	0.000937763866795043	0.999531118066602
23	0.000227758291824409	0.000455516583648817	0.999772241708176
24	0.000120680714106824	0.000241361428213649	0.999879319285893
25	0.000150237494828878	0.000300474989657756	0.999849762505171
26	0.000355090918231978	0.000710181836463956	0.999644909081768
27	0.0024634709879556	0.00492694197591119	0.997536529012044
28	0.00490170921490533	0.00980341842981066	0.995098290785095
29	0.00835029871856294	0.0167005974371259	0.991649701281437
30	0.00746519336294885	0.0149303867258977	0.992534806637051
31	0.00619813032028443	0.0123962606405689	0.993801869679716
32	0.0055331155276129	0.0110662310552258	0.994466884472387
33	0.00480665521841379	0.00961331043682757	0.995193344781586
34	0.00401528018759583	0.00803056037519167	0.995984719812404
35	0.00471331428517475	0.00942662857034951	0.995286685714825
36	0.00717403037166517	0.0143480607433303	0.992825969628335
37	0.00628973917807803	0.0125794783561561	0.993710260821922
38	0.00509430088626123	0.0101886017725225	0.994905699113739
39	0.00308481643368539	0.00616963286737078	0.996915183566315
40	0.00294513452615564	0.00589026905231128	0.997054865473844
41	0.00218331958646158	0.00436663917292316	0.997816680413538
42	0.00152296778069073	0.00304593556138146	0.998477032219309
43	0.0132028518466318	0.0264057036932635	0.986797148153368
44	0.0143472289655099	0.0286944579310199	0.98565277103449
45	0.037581745575602	0.075163491151204	0.962418254424398
46	0.0666011221565276	0.133202244313055	0.933398877843472
47	0.0736946609068492	0.147389321813698	0.926305339093151
48	0.0868201853472541	0.173640370694508	0.913179814652746
49	0.0602862508245391	0.120572501649078	0.939713749175461
50	0.0423979375236004	0.0847958750472008	0.9576020624764
51	0.0248768924971189	0.0497537849942379	0.975123107502881
52	0.019541724332847	0.039083448665694	0.980458275667153
53	0.0726605531632735	0.145321106326547	0.927339446836727

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	20	0.425531914893617	NOK
5% type I error level	39	0.829787234042553	NOK
10% type I error level	42	0.893617021276596	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 & 20 & 0.425531914893617 & NOK \tabularnewline
5% type I error level & 39 & 0.829787234042553 & NOK \tabularnewline
10% type I error level & 42 & 0.893617021276596 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199852&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]20[/C][C]0.425531914893617[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]39[/C][C]0.829787234042553[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]42[/C][C]0.893617021276596[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199852&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199852&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	20	0.425531914893617	NOK
5% type I error level	39	0.829787234042553	NOK
10% type I error level	42	0.893617021276596	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 = 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