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of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 15 Dec 2012 06:57:43 -0500
Cite this page as followsStatistical 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 Thu, 31 Oct 2024 23:50:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=199852, Retrieved Thu, 31 Oct 2024 23:50:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact178
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:
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.358915696560230.4906858.883300
beurswaarde-1.24880250097788e-060-2.70340.009070.004535
werkloosheid1.08573074824427e-050.0014060.00770.9938640.496932
failliet-0.002772727105996150.000733-3.78080.0003820.000191

\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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.358915696560230.4906858.883300
beurswaarde-1.24880250097788e-060-2.70340.009070.004535
werkloosheid1.08573074824427e-050.0014060.00770.9938640.496932
failliet-0.002772727105996150.000733-3.78080.0003820.000191







Multiple Linear Regression - Regression Statistics
Multiple R0.61098302178297
R-squared0.373300252907049
Adjusted R-squared0.339727052169926
F-TEST (value)11.1189950529288
F-TEST (DF numerator)3
F-TEST (DF denominator)56
p-value7.89864764538795e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.503152512180858
Sum Squared Residuals14.1770972287789

\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 R0.61098302178297
R-squared0.373300252907049
Adjusted R-squared0.339727052169926
F-TEST (value)11.1189950529288
F-TEST (DF numerator)3
F-TEST (DF denominator)56
p-value7.89864764538795e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.503152512180858
Sum Squared Residuals14.1770972287789







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.51.51921019516524-0.0192101951652368
21.61.376577345733540.223422654266464
31.80.9757415394133580.824258460586642
41.51.470864211518320.0291357884816822
51.31.4057655483941-0.105765548394099
61.61.166624532379540.433375467620457
71.61.409398376729020.190601623270975
81.81.46578663876290.334213361237105
91.81.360059048287780.439940951712222
101.61.48796690127490.112033098725103
111.81.050715451467930.749284548532074
1221.545104798877470.454895201122534
131.31.268292412665750.0317075873342496
141.11.27556903087156-0.175569030871559
1511.03257509054417-0.0325750905441691
161.21.39130684033977-0.191306840339765
171.21.26608057851872-0.0660805785187164
181.31.59962905831145-0.29962905831145
191.31.55122614383232-0.251226143832316
201.41.49022863177324-0.0902286317732371
211.11.45888755786188-0.358887557861882
220.91.51145738075974-0.611457380759741
2311.52720363819079-0.527203638190792
241.11.61572151414189-0.515721514141895
251.41.233235906421370.166764093578627
261.51.62917885604129-0.129178856041293
271.81.487632975222250.312367024777754
281.81.578546608446630.221453391553366
291.81.334676321574620.465323678425378
301.71.621656763315980.0783432366840167
311.51.51828027752732-0.0182802775273243
321.11.64498147932327-0.544981479323274
331.31.68800818867469-0.38800818867469
341.61.384797097216110.215202902783892
351.91.784256994734230.115743005265774
361.92.19957665736489-0.29957665736489
3721.651030720988140.348969279011856
382.22.192102545267390.00789745473261408
392.22.36455520773171-0.164555207731708
4022.06345870743817-0.0634587074381653
412.32.55649460996868-0.256494609968685
422.62.256010768543910.343989231456092
433.22.205243457470480.994756542529524
443.22.383734581981970.81626541801803
453.12.258440524717650.841559475282353
462.82.410224054486250.38977594551375
472.32.09383778380530.206162216194701
481.92.14940748005772-0.24940748005772
491.91.796853085817210.103146914182791
5021.88777925919530.112220740804698
5121.151744514472680.848255485527317
521.81.342758292221770.457241707778227
531.61.88105017423335-0.281050174233347
541.41.14293612727970.257063872720297
550.21.36175984076092-1.16175984076092
560.31.78030504386925-1.48030504386925
570.41.37824343121475-0.978243431214752
580.71.60325211064546-0.903252110645461
5911.50664408229786-0.506644082297865
601.11.55531300385636-0.455313003856362

\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 IndexActualsInterpolationForecastResidualsPrediction Error
11.51.51921019516524-0.0192101951652368
21.61.376577345733540.223422654266464
31.80.9757415394133580.824258460586642
41.51.470864211518320.0291357884816822
51.31.4057655483941-0.105765548394099
61.61.166624532379540.433375467620457
71.61.409398376729020.190601623270975
81.81.46578663876290.334213361237105
91.81.360059048287780.439940951712222
101.61.48796690127490.112033098725103
111.81.050715451467930.749284548532074
1221.545104798877470.454895201122534
131.31.268292412665750.0317075873342496
141.11.27556903087156-0.175569030871559
1511.03257509054417-0.0325750905441691
161.21.39130684033977-0.191306840339765
171.21.26608057851872-0.0660805785187164
181.31.59962905831145-0.29962905831145
191.31.55122614383232-0.251226143832316
201.41.49022863177324-0.0902286317732371
211.11.45888755786188-0.358887557861882
220.91.51145738075974-0.611457380759741
2311.52720363819079-0.527203638190792
241.11.61572151414189-0.515721514141895
251.41.233235906421370.166764093578627
261.51.62917885604129-0.129178856041293
271.81.487632975222250.312367024777754
281.81.578546608446630.221453391553366
291.81.334676321574620.465323678425378
301.71.621656763315980.0783432366840167
311.51.51828027752732-0.0182802775273243
321.11.64498147932327-0.544981479323274
331.31.68800818867469-0.38800818867469
341.61.384797097216110.215202902783892
351.91.784256994734230.115743005265774
361.92.19957665736489-0.29957665736489
3721.651030720988140.348969279011856
382.22.192102545267390.00789745473261408
392.22.36455520773171-0.164555207731708
4022.06345870743817-0.0634587074381653
412.32.55649460996868-0.256494609968685
422.62.256010768543910.343989231456092
433.22.205243457470480.994756542529524
443.22.383734581981970.81626541801803
453.12.258440524717650.841559475282353
462.82.410224054486250.38977594551375
472.32.09383778380530.206162216194701
481.92.14940748005772-0.24940748005772
491.91.796853085817210.103146914182791
5021.88777925919530.112220740804698
5121.151744514472680.848255485527317
521.81.342758292221770.457241707778227
531.61.88105017423335-0.281050174233347
541.41.14293612727970.257063872720297
550.21.36175984076092-1.16175984076092
560.31.78030504386925-1.48030504386925
570.41.37824343121475-0.978243431214752
580.71.60325211064546-0.903252110645461
5911.50664408229786-0.506644082297865
601.11.55531300385636-0.455313003856362







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.00593480620108480.01186961240216960.994065193798915
80.0261998161376180.0523996322752360.973800183862382
90.008842169520048980.0176843390400980.991157830479951
100.002770450683529160.005540901367058320.997229549316471
110.001248419275226790.002496838550453580.998751580724773
120.0008408003055165710.001681600611033140.999159199694483
130.007228580269371270.01445716053874250.992771419730629
140.008490665626837180.01698133125367440.991509334373163
150.01374738267740680.02749476535481350.986252617322593
160.009303103441313380.01860620688262680.990696896558687
170.01078795747032320.02157591494064650.989212042529677
180.005895516718688050.01179103343737610.994104483281312
190.003090124569086130.006180249138172250.996909875430914
200.001427544529929830.002855089059859660.99857245547007
210.0007303104273903170.001460620854780630.99926968957261
220.0004688819333975220.0009377638667950430.999531118066602
230.0002277582918244090.0004555165836488170.999772241708176
240.0001206807141068240.0002413614282136490.999879319285893
250.0001502374948288780.0003004749896577560.999849762505171
260.0003550909182319780.0007101818364639560.999644909081768
270.00246347098795560.004926941975911190.997536529012044
280.004901709214905330.009803418429810660.995098290785095
290.008350298718562940.01670059743712590.991649701281437
300.007465193362948850.01493038672589770.992534806637051
310.006198130320284430.01239626064056890.993801869679716
320.00553311552761290.01106623105522580.994466884472387
330.004806655218413790.009613310436827570.995193344781586
340.004015280187595830.008030560375191670.995984719812404
350.004713314285174750.009426628570349510.995286685714825
360.007174030371665170.01434806074333030.992825969628335
370.006289739178078030.01257947835615610.993710260821922
380.005094300886261230.01018860177252250.994905699113739
390.003084816433685390.006169632867370780.996915183566315
400.002945134526155640.005890269052311280.997054865473844
410.002183319586461580.004366639172923160.997816680413538
420.001522967780690730.003045935561381460.998477032219309
430.01320285184663180.02640570369326350.986797148153368
440.01434722896550990.02869445793101990.98565277103449
450.0375817455756020.0751634911512040.962418254424398
460.06660112215652760.1332022443130550.933398877843472
470.07369466090684920.1473893218136980.926305339093151
480.08682018534725410.1736403706945080.913179814652746
490.06028625082453910.1205725016490780.939713749175461
500.04239793752360040.08479587504720080.9576020624764
510.02487689249711890.04975378499423790.975123107502881
520.0195417243328470.0390834486656940.980458275667153
530.07266055316327350.1453211063265470.927339446836727

\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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.00593480620108480.01186961240216960.994065193798915
80.0261998161376180.0523996322752360.973800183862382
90.008842169520048980.0176843390400980.991157830479951
100.002770450683529160.005540901367058320.997229549316471
110.001248419275226790.002496838550453580.998751580724773
120.0008408003055165710.001681600611033140.999159199694483
130.007228580269371270.01445716053874250.992771419730629
140.008490665626837180.01698133125367440.991509334373163
150.01374738267740680.02749476535481350.986252617322593
160.009303103441313380.01860620688262680.990696896558687
170.01078795747032320.02157591494064650.989212042529677
180.005895516718688050.01179103343737610.994104483281312
190.003090124569086130.006180249138172250.996909875430914
200.001427544529929830.002855089059859660.99857245547007
210.0007303104273903170.001460620854780630.99926968957261
220.0004688819333975220.0009377638667950430.999531118066602
230.0002277582918244090.0004555165836488170.999772241708176
240.0001206807141068240.0002413614282136490.999879319285893
250.0001502374948288780.0003004749896577560.999849762505171
260.0003550909182319780.0007101818364639560.999644909081768
270.00246347098795560.004926941975911190.997536529012044
280.004901709214905330.009803418429810660.995098290785095
290.008350298718562940.01670059743712590.991649701281437
300.007465193362948850.01493038672589770.992534806637051
310.006198130320284430.01239626064056890.993801869679716
320.00553311552761290.01106623105522580.994466884472387
330.004806655218413790.009613310436827570.995193344781586
340.004015280187595830.008030560375191670.995984719812404
350.004713314285174750.009426628570349510.995286685714825
360.007174030371665170.01434806074333030.992825969628335
370.006289739178078030.01257947835615610.993710260821922
380.005094300886261230.01018860177252250.994905699113739
390.003084816433685390.006169632867370780.996915183566315
400.002945134526155640.005890269052311280.997054865473844
410.002183319586461580.004366639172923160.997816680413538
420.001522967780690730.003045935561381460.998477032219309
430.01320285184663180.02640570369326350.986797148153368
440.01434722896550990.02869445793101990.98565277103449
450.0375817455756020.0751634911512040.962418254424398
460.06660112215652760.1332022443130550.933398877843472
470.07369466090684920.1473893218136980.926305339093151
480.08682018534725410.1736403706945080.913179814652746
490.06028625082453910.1205725016490780.939713749175461
500.04239793752360040.08479587504720080.9576020624764
510.02487689249711890.04975378499423790.975123107502881
520.0195417243328470.0390834486656940.980458275667153
530.07266055316327350.1453211063265470.927339446836727







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level200.425531914893617NOK
5% type I error level390.829787234042553NOK
10% type I error level420.893617021276596NOK

\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 testsOK/NOK
1% type I error level200.425531914893617NOK
5% type I error level390.829787234042553NOK
10% type I error level420.893617021276596NOK



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')
}