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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 20 Nov 2009 05:15:31 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t12587194252shp7opct0rwr77.htm/, Retrieved Tue, 23 Apr 2024 13:12:39 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58074, Retrieved Tue, 23 Apr 2024 13:12:39 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

WS7,MR2

Estimated Impact

113

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

-       [Multiple Regression] [] [2009-11-20 12:15:31] [30f5b608e5a1bbbae86b1702c0071566] [Current]

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Dataseries X:

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1.3	2
1.2	2.1
1.1	2.1
1.4	2.5
1.2	2.2
1.5	2.3
1.1	2.3
1.3	2.2
1.5	2.2
1.1	1.6
1.4	1.8
1.3	1.7
1.5	1.9
1.6	1.8
1.7	1.9
1.1	1.5
1.6	1
1.3	0.8
1.7	1.1
1.6	1.5
1.7	1.7
1.9	2.3
1.8	2.4
1.9	3
1.6	3
1.5	3.2
1.6	3.2
1.6	3.2
1.7	3.5
2	4
2	4.3
1.9	4.1
1.7	4
1.8	4.1
1.9	4.2
1.7	4.5
2	5.6
2.1	6.5
2.4	7.6
2.5	8.5
2.5	8.7
2.6	8.3
2.2	8.3
2.5	8.5
2.8	8.7
2.8	8.7
2.9	8.5
3	7.9
3.1	7
2.9	5.8
2.7	4.5
2.2	3.7
2.5	3.1
2.3	2.7
2.6	2.3
2.3	1.8
2.2	1.5
1.8	1.2
1.8	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58074&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58074&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58074&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
inflatie[t] = + 1.26580173511677 + 0.165894330966839inflatie_levensmiddelen[t] -0.0127896258874350M1[t] -0.0494717392680991M2[t] -0.00615385264876214M3[t] -0.149471739268099M4[t] + 0.0203892403059319M5[t] + 0.073660786783279M6[t] + 0.0470250135446055M7[t] + 0.0536607867832790M8[t] + 0.113660786783279M9[t] + 0.0202965600219525M10[t] + 0.100296560021952M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
inflatie[t] =  +  1.26580173511677 +  0.165894330966839inflatie_levensmiddelen[t] -0.0127896258874350M1[t] -0.0494717392680991M2[t] -0.00615385264876214M3[t] -0.149471739268099M4[t] +  0.0203892403059319M5[t] +  0.073660786783279M6[t] +  0.0470250135446055M7[t] +  0.0536607867832790M8[t] +  0.113660786783279M9[t] +  0.0202965600219525M10[t] +  0.100296560021952M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58074&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]inflatie[t] =  +  1.26580173511677 +  0.165894330966839inflatie_levensmiddelen[t] -0.0127896258874350M1[t] -0.0494717392680991M2[t] -0.00615385264876214M3[t] -0.149471739268099M4[t] +  0.0203892403059319M5[t] +  0.073660786783279M6[t] +  0.0470250135446055M7[t] +  0.0536607867832790M8[t] +  0.113660786783279M9[t] +  0.0202965600219525M10[t] +  0.100296560021952M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58074&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58074&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] = + 1.26580173511677 + 0.165894330966839inflatie_levensmiddelen[t] -0.0127896258874350M1[t] -0.0494717392680991M2[t] -0.00615385264876214M3[t] -0.149471739268099M4[t] + 0.0203892403059319M5[t] + 0.073660786783279M6[t] + 0.0470250135446055M7[t] + 0.0536607867832790M8[t] + 0.113660786783279M9[t] + 0.0202965600219525M10[t] + 0.100296560021952M11[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)	1.26580173511677	0.21735	5.8238	1e-06	0
inflatie_levensmiddelen	0.165894330966839	0.021002	7.899	0	0
M1	-0.0127896258874350	0.26568	-0.0481	0.961814	0.480907
M2	-0.0494717392680991	0.265693	-0.1862	0.853107	0.426554
M3	-0.00615385264876214	0.265706	-0.0232	0.981623	0.490811
M4	-0.149471739268099	0.265693	-0.5626	0.576457	0.288229
M5	0.0203892403059319	0.265838	0.0767	0.939196	0.469598
M6	0.073660786783279	0.265919	0.277	0.783018	0.391509
M7	0.0470250135446055	0.265877	0.1769	0.860389	0.430195
M8	0.0536607867832790	0.265919	0.2018	0.840968	0.420484
M9	0.113660786783279	0.265919	0.4274	0.671064	0.335532
M10	0.0202965600219525	0.265964	0.0763	0.939501	0.46975
M11	0.100296560021952	0.265964	0.3771	0.707829	0.353915

\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) & 1.26580173511677 & 0.21735 & 5.8238 & 1e-06 & 0 \tabularnewline
inflatie_levensmiddelen & 0.165894330966839 & 0.021002 & 7.899 & 0 & 0 \tabularnewline
M1 & -0.0127896258874350 & 0.26568 & -0.0481 & 0.961814 & 0.480907 \tabularnewline
M2 & -0.0494717392680991 & 0.265693 & -0.1862 & 0.853107 & 0.426554 \tabularnewline
M3 & -0.00615385264876214 & 0.265706 & -0.0232 & 0.981623 & 0.490811 \tabularnewline
M4 & -0.149471739268099 & 0.265693 & -0.5626 & 0.576457 & 0.288229 \tabularnewline
M5 & 0.0203892403059319 & 0.265838 & 0.0767 & 0.939196 & 0.469598 \tabularnewline
M6 & 0.073660786783279 & 0.265919 & 0.277 & 0.783018 & 0.391509 \tabularnewline
M7 & 0.0470250135446055 & 0.265877 & 0.1769 & 0.860389 & 0.430195 \tabularnewline
M8 & 0.0536607867832790 & 0.265919 & 0.2018 & 0.840968 & 0.420484 \tabularnewline
M9 & 0.113660786783279 & 0.265919 & 0.4274 & 0.671064 & 0.335532 \tabularnewline
M10 & 0.0202965600219525 & 0.265964 & 0.0763 & 0.939501 & 0.46975 \tabularnewline
M11 & 0.100296560021952 & 0.265964 & 0.3771 & 0.707829 & 0.353915 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58074&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]1.26580173511677[/C][C]0.21735[/C][C]5.8238[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]inflatie_levensmiddelen[/C][C]0.165894330966839[/C][C]0.021002[/C][C]7.899[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-0.0127896258874350[/C][C]0.26568[/C][C]-0.0481[/C][C]0.961814[/C][C]0.480907[/C][/ROW]
[ROW][C]M2[/C][C]-0.0494717392680991[/C][C]0.265693[/C][C]-0.1862[/C][C]0.853107[/C][C]0.426554[/C][/ROW]
[ROW][C]M3[/C][C]-0.00615385264876214[/C][C]0.265706[/C][C]-0.0232[/C][C]0.981623[/C][C]0.490811[/C][/ROW]
[ROW][C]M4[/C][C]-0.149471739268099[/C][C]0.265693[/C][C]-0.5626[/C][C]0.576457[/C][C]0.288229[/C][/ROW]
[ROW][C]M5[/C][C]0.0203892403059319[/C][C]0.265838[/C][C]0.0767[/C][C]0.939196[/C][C]0.469598[/C][/ROW]
[ROW][C]M6[/C][C]0.073660786783279[/C][C]0.265919[/C][C]0.277[/C][C]0.783018[/C][C]0.391509[/C][/ROW]
[ROW][C]M7[/C][C]0.0470250135446055[/C][C]0.265877[/C][C]0.1769[/C][C]0.860389[/C][C]0.430195[/C][/ROW]
[ROW][C]M8[/C][C]0.0536607867832790[/C][C]0.265919[/C][C]0.2018[/C][C]0.840968[/C][C]0.420484[/C][/ROW]
[ROW][C]M9[/C][C]0.113660786783279[/C][C]0.265919[/C][C]0.4274[/C][C]0.671064[/C][C]0.335532[/C][/ROW]
[ROW][C]M10[/C][C]0.0202965600219525[/C][C]0.265964[/C][C]0.0763[/C][C]0.939501[/C][C]0.46975[/C][/ROW]
[ROW][C]M11[/C][C]0.100296560021952[/C][C]0.265964[/C][C]0.3771[/C][C]0.707829[/C][C]0.353915[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58074&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58074&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)	1.26580173511677	0.21735	5.8238	1e-06	0
inflatie_levensmiddelen	0.165894330966839	0.021002	7.899	0	0
M1	-0.0127896258874350	0.26568	-0.0481	0.961814	0.480907
M2	-0.0494717392680991	0.265693	-0.1862	0.853107	0.426554
M3	-0.00615385264876214	0.265706	-0.0232	0.981623	0.490811
M4	-0.149471739268099	0.265693	-0.5626	0.576457	0.288229
M5	0.0203892403059319	0.265838	0.0767	0.939196	0.469598
M6	0.073660786783279	0.265919	0.277	0.783018	0.391509
M7	0.0470250135446055	0.265877	0.1769	0.860389	0.430195
M8	0.0536607867832790	0.265919	0.2018	0.840968	0.420484
M9	0.113660786783279	0.265919	0.4274	0.671064	0.335532
M10	0.0202965600219525	0.265964	0.0763	0.939501	0.46975
M11	0.100296560021952	0.265964	0.3771	0.707829	0.353915

Multiple Linear Regression - Regression Statistics
Multiple R	0.76178189947505
R-squared	0.580311662367816
Adjusted R-squared	0.470827748202899
F-TEST (value)	5.30042853138848
F-TEST (DF numerator)	12
F-TEST (DF denominator)	46
p-value	1.6074668327537e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.395878426184873
Sum Squared Residuals	7.20910750265615

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.76178189947505 \tabularnewline
R-squared & 0.580311662367816 \tabularnewline
Adjusted R-squared & 0.470827748202899 \tabularnewline
F-TEST (value) & 5.30042853138848 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 1.6074668327537e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.395878426184873 \tabularnewline
Sum Squared Residuals & 7.20910750265615 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58074&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.76178189947505[/C][/ROW]
[ROW][C]R-squared[/C][C]0.580311662367816[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.470827748202899[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.30042853138848[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]1.6074668327537e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.395878426184873[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7.20910750265615[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58074&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58074&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	0.76178189947505
R-squared	0.580311662367816
Adjusted R-squared	0.470827748202899
F-TEST (value)	5.30042853138848
F-TEST (DF numerator)	12
F-TEST (DF denominator)	46
p-value	1.6074668327537e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.395878426184873
Sum Squared Residuals	7.20910750265615

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.3	1.58480077116300	-0.284800771163005
2	1.2	1.56470809087903	-0.364708090879029
3	1.1	1.60802597749836	-0.508025977498364
4	1.4	1.53106582326576	-0.131065823265763
5	1.2	1.65115850354974	-0.451158503549743
6	1.5	1.72101948312377	-0.221019483123773
7	1.1	1.6943837098851	-0.5943837098851
8	1.3	1.68443005002709	-0.384430050027089
9	1.5	1.74443005002709	-0.244430050027089
10	1.1	1.55152922468566	-0.451529224685660
11	1.4	1.66470809087903	-0.264708090879027
12	1.3	1.54782209776039	-0.247822097760391
13	1.5	1.56821133806632	-0.0682113380663236
14	1.6	1.51493979158898	0.0850602084110244
15	1.7	1.57484711130500	0.125152888695004
16	1.1	1.36517149229892	-0.265171492298924
17	1.6	1.45208530638954	0.147914693610464
18	1.3	1.47217798667352	-0.172177986673515
19	1.7	1.49531051272489	0.204689487275107
20	1.6	1.56830401835030	0.0316959816496978
21	1.7	1.66148288454367	0.0385171154563299
22	1.9	1.66765525636245	0.232344743637553
23	1.8	1.76424468945913	0.0357553105408696
24	1.9	1.76348472801728	0.136515271982719
25	1.6	1.75069510212985	-0.150695102129846
26	1.5	1.74719185494255	-0.247191854942550
27	1.6	1.79050974156189	-0.190509741561886
28	1.6	1.64719185494255	-0.0471918549425498
29	1.7	1.86682113380663	-0.166821133806632
30	2	2.0030398457674	-0.00303984576739860
31	2	2.02617237181878	-0.0261723718187767
32	1.9	1.99962927886408	-0.0996292788640825
33	1.7	2.0430398457674	-0.343039845767399
34	1.8	1.96626505210276	-0.166265052102756
35	1.9	2.06285448519944	-0.16285448519944
36	1.7	2.01232622446754	-0.312326224467539
37	2	2.18202036264363	-0.182020362643626
38	2.1	2.29464314713312	-0.194643147133117
39	2.4	2.52044479781598	-0.120444797815976
40	2.5	2.52643180906679	-0.0264318090667941
41	2.5	2.72947165483419	-0.229471654834193
42	2.6	2.71638546892480	-0.116385468924804
43	2.2	2.68974969568613	-0.489749695686131
44	2.5	2.72956433511817	-0.229564335118172
45	2.8	2.82274320131154	-0.02274320131154
46	2.8	2.72937897455021	0.0706210254497865
47	2.9	2.77620010835685	0.123799891643154
48	3	2.57636694975479	0.42363305024521
49	3.1	2.4142724259972	0.6857275740028
50	2.9	2.17851711545633	0.72148288454367
51	2.7	2.00617237181878	0.693827628181223
52	2.2	1.73013902042597	0.469860979574031
53	2.5	1.80046340141990	0.699536598580103
54	2.3	1.78737721551051	0.512622784489492
55	2.6	1.6943837098851	0.9056162901149
56	2.3	1.61807231764035	0.681927682359646
57	2.2	1.62830401835030	0.571695981649698
58	1.8	1.48517149229892	0.314828507701076
59	1.8	1.53199262610556	0.268007373894444

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.3 & 1.58480077116300 & -0.284800771163005 \tabularnewline
2 & 1.2 & 1.56470809087903 & -0.364708090879029 \tabularnewline
3 & 1.1 & 1.60802597749836 & -0.508025977498364 \tabularnewline
4 & 1.4 & 1.53106582326576 & -0.131065823265763 \tabularnewline
5 & 1.2 & 1.65115850354974 & -0.451158503549743 \tabularnewline
6 & 1.5 & 1.72101948312377 & -0.221019483123773 \tabularnewline
7 & 1.1 & 1.6943837098851 & -0.5943837098851 \tabularnewline
8 & 1.3 & 1.68443005002709 & -0.384430050027089 \tabularnewline
9 & 1.5 & 1.74443005002709 & -0.244430050027089 \tabularnewline
10 & 1.1 & 1.55152922468566 & -0.451529224685660 \tabularnewline
11 & 1.4 & 1.66470809087903 & -0.264708090879027 \tabularnewline
12 & 1.3 & 1.54782209776039 & -0.247822097760391 \tabularnewline
13 & 1.5 & 1.56821133806632 & -0.0682113380663236 \tabularnewline
14 & 1.6 & 1.51493979158898 & 0.0850602084110244 \tabularnewline
15 & 1.7 & 1.57484711130500 & 0.125152888695004 \tabularnewline
16 & 1.1 & 1.36517149229892 & -0.265171492298924 \tabularnewline
17 & 1.6 & 1.45208530638954 & 0.147914693610464 \tabularnewline
18 & 1.3 & 1.47217798667352 & -0.172177986673515 \tabularnewline
19 & 1.7 & 1.49531051272489 & 0.204689487275107 \tabularnewline
20 & 1.6 & 1.56830401835030 & 0.0316959816496978 \tabularnewline
21 & 1.7 & 1.66148288454367 & 0.0385171154563299 \tabularnewline
22 & 1.9 & 1.66765525636245 & 0.232344743637553 \tabularnewline
23 & 1.8 & 1.76424468945913 & 0.0357553105408696 \tabularnewline
24 & 1.9 & 1.76348472801728 & 0.136515271982719 \tabularnewline
25 & 1.6 & 1.75069510212985 & -0.150695102129846 \tabularnewline
26 & 1.5 & 1.74719185494255 & -0.247191854942550 \tabularnewline
27 & 1.6 & 1.79050974156189 & -0.190509741561886 \tabularnewline
28 & 1.6 & 1.64719185494255 & -0.0471918549425498 \tabularnewline
29 & 1.7 & 1.86682113380663 & -0.166821133806632 \tabularnewline
30 & 2 & 2.0030398457674 & -0.00303984576739860 \tabularnewline
31 & 2 & 2.02617237181878 & -0.0261723718187767 \tabularnewline
32 & 1.9 & 1.99962927886408 & -0.0996292788640825 \tabularnewline
33 & 1.7 & 2.0430398457674 & -0.343039845767399 \tabularnewline
34 & 1.8 & 1.96626505210276 & -0.166265052102756 \tabularnewline
35 & 1.9 & 2.06285448519944 & -0.16285448519944 \tabularnewline
36 & 1.7 & 2.01232622446754 & -0.312326224467539 \tabularnewline
37 & 2 & 2.18202036264363 & -0.182020362643626 \tabularnewline
38 & 2.1 & 2.29464314713312 & -0.194643147133117 \tabularnewline
39 & 2.4 & 2.52044479781598 & -0.120444797815976 \tabularnewline
40 & 2.5 & 2.52643180906679 & -0.0264318090667941 \tabularnewline
41 & 2.5 & 2.72947165483419 & -0.229471654834193 \tabularnewline
42 & 2.6 & 2.71638546892480 & -0.116385468924804 \tabularnewline
43 & 2.2 & 2.68974969568613 & -0.489749695686131 \tabularnewline
44 & 2.5 & 2.72956433511817 & -0.229564335118172 \tabularnewline
45 & 2.8 & 2.82274320131154 & -0.02274320131154 \tabularnewline
46 & 2.8 & 2.72937897455021 & 0.0706210254497865 \tabularnewline
47 & 2.9 & 2.77620010835685 & 0.123799891643154 \tabularnewline
48 & 3 & 2.57636694975479 & 0.42363305024521 \tabularnewline
49 & 3.1 & 2.4142724259972 & 0.6857275740028 \tabularnewline
50 & 2.9 & 2.17851711545633 & 0.72148288454367 \tabularnewline
51 & 2.7 & 2.00617237181878 & 0.693827628181223 \tabularnewline
52 & 2.2 & 1.73013902042597 & 0.469860979574031 \tabularnewline
53 & 2.5 & 1.80046340141990 & 0.699536598580103 \tabularnewline
54 & 2.3 & 1.78737721551051 & 0.512622784489492 \tabularnewline
55 & 2.6 & 1.6943837098851 & 0.9056162901149 \tabularnewline
56 & 2.3 & 1.61807231764035 & 0.681927682359646 \tabularnewline
57 & 2.2 & 1.62830401835030 & 0.571695981649698 \tabularnewline
58 & 1.8 & 1.48517149229892 & 0.314828507701076 \tabularnewline
59 & 1.8 & 1.53199262610556 & 0.268007373894444 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58074&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]1.3[/C][C]1.58480077116300[/C][C]-0.284800771163005[/C][/ROW]
[ROW][C]2[/C][C]1.2[/C][C]1.56470809087903[/C][C]-0.364708090879029[/C][/ROW]
[ROW][C]3[/C][C]1.1[/C][C]1.60802597749836[/C][C]-0.508025977498364[/C][/ROW]
[ROW][C]4[/C][C]1.4[/C][C]1.53106582326576[/C][C]-0.131065823265763[/C][/ROW]
[ROW][C]5[/C][C]1.2[/C][C]1.65115850354974[/C][C]-0.451158503549743[/C][/ROW]
[ROW][C]6[/C][C]1.5[/C][C]1.72101948312377[/C][C]-0.221019483123773[/C][/ROW]
[ROW][C]7[/C][C]1.1[/C][C]1.6943837098851[/C][C]-0.5943837098851[/C][/ROW]
[ROW][C]8[/C][C]1.3[/C][C]1.68443005002709[/C][C]-0.384430050027089[/C][/ROW]
[ROW][C]9[/C][C]1.5[/C][C]1.74443005002709[/C][C]-0.244430050027089[/C][/ROW]
[ROW][C]10[/C][C]1.1[/C][C]1.55152922468566[/C][C]-0.451529224685660[/C][/ROW]
[ROW][C]11[/C][C]1.4[/C][C]1.66470809087903[/C][C]-0.264708090879027[/C][/ROW]
[ROW][C]12[/C][C]1.3[/C][C]1.54782209776039[/C][C]-0.247822097760391[/C][/ROW]
[ROW][C]13[/C][C]1.5[/C][C]1.56821133806632[/C][C]-0.0682113380663236[/C][/ROW]
[ROW][C]14[/C][C]1.6[/C][C]1.51493979158898[/C][C]0.0850602084110244[/C][/ROW]
[ROW][C]15[/C][C]1.7[/C][C]1.57484711130500[/C][C]0.125152888695004[/C][/ROW]
[ROW][C]16[/C][C]1.1[/C][C]1.36517149229892[/C][C]-0.265171492298924[/C][/ROW]
[ROW][C]17[/C][C]1.6[/C][C]1.45208530638954[/C][C]0.147914693610464[/C][/ROW]
[ROW][C]18[/C][C]1.3[/C][C]1.47217798667352[/C][C]-0.172177986673515[/C][/ROW]
[ROW][C]19[/C][C]1.7[/C][C]1.49531051272489[/C][C]0.204689487275107[/C][/ROW]
[ROW][C]20[/C][C]1.6[/C][C]1.56830401835030[/C][C]0.0316959816496978[/C][/ROW]
[ROW][C]21[/C][C]1.7[/C][C]1.66148288454367[/C][C]0.0385171154563299[/C][/ROW]
[ROW][C]22[/C][C]1.9[/C][C]1.66765525636245[/C][C]0.232344743637553[/C][/ROW]
[ROW][C]23[/C][C]1.8[/C][C]1.76424468945913[/C][C]0.0357553105408696[/C][/ROW]
[ROW][C]24[/C][C]1.9[/C][C]1.76348472801728[/C][C]0.136515271982719[/C][/ROW]
[ROW][C]25[/C][C]1.6[/C][C]1.75069510212985[/C][C]-0.150695102129846[/C][/ROW]
[ROW][C]26[/C][C]1.5[/C][C]1.74719185494255[/C][C]-0.247191854942550[/C][/ROW]
[ROW][C]27[/C][C]1.6[/C][C]1.79050974156189[/C][C]-0.190509741561886[/C][/ROW]
[ROW][C]28[/C][C]1.6[/C][C]1.64719185494255[/C][C]-0.0471918549425498[/C][/ROW]
[ROW][C]29[/C][C]1.7[/C][C]1.86682113380663[/C][C]-0.166821133806632[/C][/ROW]
[ROW][C]30[/C][C]2[/C][C]2.0030398457674[/C][C]-0.00303984576739860[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]2.02617237181878[/C][C]-0.0261723718187767[/C][/ROW]
[ROW][C]32[/C][C]1.9[/C][C]1.99962927886408[/C][C]-0.0996292788640825[/C][/ROW]
[ROW][C]33[/C][C]1.7[/C][C]2.0430398457674[/C][C]-0.343039845767399[/C][/ROW]
[ROW][C]34[/C][C]1.8[/C][C]1.96626505210276[/C][C]-0.166265052102756[/C][/ROW]
[ROW][C]35[/C][C]1.9[/C][C]2.06285448519944[/C][C]-0.16285448519944[/C][/ROW]
[ROW][C]36[/C][C]1.7[/C][C]2.01232622446754[/C][C]-0.312326224467539[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]2.18202036264363[/C][C]-0.182020362643626[/C][/ROW]
[ROW][C]38[/C][C]2.1[/C][C]2.29464314713312[/C][C]-0.194643147133117[/C][/ROW]
[ROW][C]39[/C][C]2.4[/C][C]2.52044479781598[/C][C]-0.120444797815976[/C][/ROW]
[ROW][C]40[/C][C]2.5[/C][C]2.52643180906679[/C][C]-0.0264318090667941[/C][/ROW]
[ROW][C]41[/C][C]2.5[/C][C]2.72947165483419[/C][C]-0.229471654834193[/C][/ROW]
[ROW][C]42[/C][C]2.6[/C][C]2.71638546892480[/C][C]-0.116385468924804[/C][/ROW]
[ROW][C]43[/C][C]2.2[/C][C]2.68974969568613[/C][C]-0.489749695686131[/C][/ROW]
[ROW][C]44[/C][C]2.5[/C][C]2.72956433511817[/C][C]-0.229564335118172[/C][/ROW]
[ROW][C]45[/C][C]2.8[/C][C]2.82274320131154[/C][C]-0.02274320131154[/C][/ROW]
[ROW][C]46[/C][C]2.8[/C][C]2.72937897455021[/C][C]0.0706210254497865[/C][/ROW]
[ROW][C]47[/C][C]2.9[/C][C]2.77620010835685[/C][C]0.123799891643154[/C][/ROW]
[ROW][C]48[/C][C]3[/C][C]2.57636694975479[/C][C]0.42363305024521[/C][/ROW]
[ROW][C]49[/C][C]3.1[/C][C]2.4142724259972[/C][C]0.6857275740028[/C][/ROW]
[ROW][C]50[/C][C]2.9[/C][C]2.17851711545633[/C][C]0.72148288454367[/C][/ROW]
[ROW][C]51[/C][C]2.7[/C][C]2.00617237181878[/C][C]0.693827628181223[/C][/ROW]
[ROW][C]52[/C][C]2.2[/C][C]1.73013902042597[/C][C]0.469860979574031[/C][/ROW]
[ROW][C]53[/C][C]2.5[/C][C]1.80046340141990[/C][C]0.699536598580103[/C][/ROW]
[ROW][C]54[/C][C]2.3[/C][C]1.78737721551051[/C][C]0.512622784489492[/C][/ROW]
[ROW][C]55[/C][C]2.6[/C][C]1.6943837098851[/C][C]0.9056162901149[/C][/ROW]
[ROW][C]56[/C][C]2.3[/C][C]1.61807231764035[/C][C]0.681927682359646[/C][/ROW]
[ROW][C]57[/C][C]2.2[/C][C]1.62830401835030[/C][C]0.571695981649698[/C][/ROW]
[ROW][C]58[/C][C]1.8[/C][C]1.48517149229892[/C][C]0.314828507701076[/C][/ROW]
[ROW][C]59[/C][C]1.8[/C][C]1.53199262610556[/C][C]0.268007373894444[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58074&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58074&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	1.3	1.58480077116300	-0.284800771163005
2	1.2	1.56470809087903	-0.364708090879029
3	1.1	1.60802597749836	-0.508025977498364
4	1.4	1.53106582326576	-0.131065823265763
5	1.2	1.65115850354974	-0.451158503549743
6	1.5	1.72101948312377	-0.221019483123773
7	1.1	1.6943837098851	-0.5943837098851
8	1.3	1.68443005002709	-0.384430050027089
9	1.5	1.74443005002709	-0.244430050027089
10	1.1	1.55152922468566	-0.451529224685660
11	1.4	1.66470809087903	-0.264708090879027
12	1.3	1.54782209776039	-0.247822097760391
13	1.5	1.56821133806632	-0.0682113380663236
14	1.6	1.51493979158898	0.0850602084110244
15	1.7	1.57484711130500	0.125152888695004
16	1.1	1.36517149229892	-0.265171492298924
17	1.6	1.45208530638954	0.147914693610464
18	1.3	1.47217798667352	-0.172177986673515
19	1.7	1.49531051272489	0.204689487275107
20	1.6	1.56830401835030	0.0316959816496978
21	1.7	1.66148288454367	0.0385171154563299
22	1.9	1.66765525636245	0.232344743637553
23	1.8	1.76424468945913	0.0357553105408696
24	1.9	1.76348472801728	0.136515271982719
25	1.6	1.75069510212985	-0.150695102129846
26	1.5	1.74719185494255	-0.247191854942550
27	1.6	1.79050974156189	-0.190509741561886
28	1.6	1.64719185494255	-0.0471918549425498
29	1.7	1.86682113380663	-0.166821133806632
30	2	2.0030398457674	-0.00303984576739860
31	2	2.02617237181878	-0.0261723718187767
32	1.9	1.99962927886408	-0.0996292788640825
33	1.7	2.0430398457674	-0.343039845767399
34	1.8	1.96626505210276	-0.166265052102756
35	1.9	2.06285448519944	-0.16285448519944
36	1.7	2.01232622446754	-0.312326224467539
37	2	2.18202036264363	-0.182020362643626
38	2.1	2.29464314713312	-0.194643147133117
39	2.4	2.52044479781598	-0.120444797815976
40	2.5	2.52643180906679	-0.0264318090667941
41	2.5	2.72947165483419	-0.229471654834193
42	2.6	2.71638546892480	-0.116385468924804
43	2.2	2.68974969568613	-0.489749695686131
44	2.5	2.72956433511817	-0.229564335118172
45	2.8	2.82274320131154	-0.02274320131154
46	2.8	2.72937897455021	0.0706210254497865
47	2.9	2.77620010835685	0.123799891643154
48	3	2.57636694975479	0.42363305024521
49	3.1	2.4142724259972	0.6857275740028
50	2.9	2.17851711545633	0.72148288454367
51	2.7	2.00617237181878	0.693827628181223
52	2.2	1.73013902042597	0.469860979574031
53	2.5	1.80046340141990	0.699536598580103
54	2.3	1.78737721551051	0.512622784489492
55	2.6	1.6943837098851	0.9056162901149
56	2.3	1.61807231764035	0.681927682359646
57	2.2	1.62830401835030	0.571695981649698
58	1.8	1.48517149229892	0.314828507701076
59	1.8	1.53199262610556	0.268007373894444

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.440928768546686	0.881857537093371	0.559071231453314
17	0.318219880421505	0.636439760843009	0.681780119578495
18	0.25012387443636	0.50024774887272	0.74987612556364
19	0.236813924404655	0.47362784880931	0.763186075595345
20	0.157940324320193	0.315880648640385	0.842059675679807
21	0.0953023013185387	0.190604602637077	0.904697698681461
22	0.206486668889432	0.412973337778864	0.793513331110568
23	0.171323013836142	0.342646027672284	0.828676986163858
24	0.167924507425948	0.335849014851897	0.832075492574052
25	0.130445543862962	0.260891087725924	0.869554456137038
26	0.105777965521626	0.211555931043252	0.894222034478374
27	0.0883131803212159	0.176626360642432	0.911686819678784
28	0.0705901300797084	0.141180260159417	0.929409869920292
29	0.0564828563773212	0.112965712754642	0.943517143622679
30	0.0421569950515462	0.0843139901030924	0.957843004948454
31	0.0283329921709857	0.0566659843419715	0.971667007829014
32	0.0187653215609916	0.0375306431219833	0.981234678439008
33	0.0210149615479833	0.0420299230959666	0.978985038452017
34	0.0144054760887002	0.0288109521774003	0.9855945239113
35	0.00941824995907587	0.0188364999181517	0.990581750040924
36	0.0225949267378478	0.0451898534756955	0.977405073262152
37	0.0573789028743482	0.114757805748696	0.942621097125652
38	0.116336835418457	0.232673670836913	0.883663164581543
39	0.118766031277349	0.237532062554698	0.88123396872265
40	0.0712938348058931	0.142587669611786	0.928706165194107
41	0.0567607807066243	0.113521561413249	0.943239219293376
42	0.0286700793237157	0.0573401586474313	0.971329920676284
43	0.280612585874457	0.561225171748914	0.719387414125543

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.440928768546686 & 0.881857537093371 & 0.559071231453314 \tabularnewline
17 & 0.318219880421505 & 0.636439760843009 & 0.681780119578495 \tabularnewline
18 & 0.25012387443636 & 0.50024774887272 & 0.74987612556364 \tabularnewline
19 & 0.236813924404655 & 0.47362784880931 & 0.763186075595345 \tabularnewline
20 & 0.157940324320193 & 0.315880648640385 & 0.842059675679807 \tabularnewline
21 & 0.0953023013185387 & 0.190604602637077 & 0.904697698681461 \tabularnewline
22 & 0.206486668889432 & 0.412973337778864 & 0.793513331110568 \tabularnewline
23 & 0.171323013836142 & 0.342646027672284 & 0.828676986163858 \tabularnewline
24 & 0.167924507425948 & 0.335849014851897 & 0.832075492574052 \tabularnewline
25 & 0.130445543862962 & 0.260891087725924 & 0.869554456137038 \tabularnewline
26 & 0.105777965521626 & 0.211555931043252 & 0.894222034478374 \tabularnewline
27 & 0.0883131803212159 & 0.176626360642432 & 0.911686819678784 \tabularnewline
28 & 0.0705901300797084 & 0.141180260159417 & 0.929409869920292 \tabularnewline
29 & 0.0564828563773212 & 0.112965712754642 & 0.943517143622679 \tabularnewline
30 & 0.0421569950515462 & 0.0843139901030924 & 0.957843004948454 \tabularnewline
31 & 0.0283329921709857 & 0.0566659843419715 & 0.971667007829014 \tabularnewline
32 & 0.0187653215609916 & 0.0375306431219833 & 0.981234678439008 \tabularnewline
33 & 0.0210149615479833 & 0.0420299230959666 & 0.978985038452017 \tabularnewline
34 & 0.0144054760887002 & 0.0288109521774003 & 0.9855945239113 \tabularnewline
35 & 0.00941824995907587 & 0.0188364999181517 & 0.990581750040924 \tabularnewline
36 & 0.0225949267378478 & 0.0451898534756955 & 0.977405073262152 \tabularnewline
37 & 0.0573789028743482 & 0.114757805748696 & 0.942621097125652 \tabularnewline
38 & 0.116336835418457 & 0.232673670836913 & 0.883663164581543 \tabularnewline
39 & 0.118766031277349 & 0.237532062554698 & 0.88123396872265 \tabularnewline
40 & 0.0712938348058931 & 0.142587669611786 & 0.928706165194107 \tabularnewline
41 & 0.0567607807066243 & 0.113521561413249 & 0.943239219293376 \tabularnewline
42 & 0.0286700793237157 & 0.0573401586474313 & 0.971329920676284 \tabularnewline
43 & 0.280612585874457 & 0.561225171748914 & 0.719387414125543 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58074&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]16[/C][C]0.440928768546686[/C][C]0.881857537093371[/C][C]0.559071231453314[/C][/ROW]
[ROW][C]17[/C][C]0.318219880421505[/C][C]0.636439760843009[/C][C]0.681780119578495[/C][/ROW]
[ROW][C]18[/C][C]0.25012387443636[/C][C]0.50024774887272[/C][C]0.74987612556364[/C][/ROW]
[ROW][C]19[/C][C]0.236813924404655[/C][C]0.47362784880931[/C][C]0.763186075595345[/C][/ROW]
[ROW][C]20[/C][C]0.157940324320193[/C][C]0.315880648640385[/C][C]0.842059675679807[/C][/ROW]
[ROW][C]21[/C][C]0.0953023013185387[/C][C]0.190604602637077[/C][C]0.904697698681461[/C][/ROW]
[ROW][C]22[/C][C]0.206486668889432[/C][C]0.412973337778864[/C][C]0.793513331110568[/C][/ROW]
[ROW][C]23[/C][C]0.171323013836142[/C][C]0.342646027672284[/C][C]0.828676986163858[/C][/ROW]
[ROW][C]24[/C][C]0.167924507425948[/C][C]0.335849014851897[/C][C]0.832075492574052[/C][/ROW]
[ROW][C]25[/C][C]0.130445543862962[/C][C]0.260891087725924[/C][C]0.869554456137038[/C][/ROW]
[ROW][C]26[/C][C]0.105777965521626[/C][C]0.211555931043252[/C][C]0.894222034478374[/C][/ROW]
[ROW][C]27[/C][C]0.0883131803212159[/C][C]0.176626360642432[/C][C]0.911686819678784[/C][/ROW]
[ROW][C]28[/C][C]0.0705901300797084[/C][C]0.141180260159417[/C][C]0.929409869920292[/C][/ROW]
[ROW][C]29[/C][C]0.0564828563773212[/C][C]0.112965712754642[/C][C]0.943517143622679[/C][/ROW]
[ROW][C]30[/C][C]0.0421569950515462[/C][C]0.0843139901030924[/C][C]0.957843004948454[/C][/ROW]
[ROW][C]31[/C][C]0.0283329921709857[/C][C]0.0566659843419715[/C][C]0.971667007829014[/C][/ROW]
[ROW][C]32[/C][C]0.0187653215609916[/C][C]0.0375306431219833[/C][C]0.981234678439008[/C][/ROW]
[ROW][C]33[/C][C]0.0210149615479833[/C][C]0.0420299230959666[/C][C]0.978985038452017[/C][/ROW]
[ROW][C]34[/C][C]0.0144054760887002[/C][C]0.0288109521774003[/C][C]0.9855945239113[/C][/ROW]
[ROW][C]35[/C][C]0.00941824995907587[/C][C]0.0188364999181517[/C][C]0.990581750040924[/C][/ROW]
[ROW][C]36[/C][C]0.0225949267378478[/C][C]0.0451898534756955[/C][C]0.977405073262152[/C][/ROW]
[ROW][C]37[/C][C]0.0573789028743482[/C][C]0.114757805748696[/C][C]0.942621097125652[/C][/ROW]
[ROW][C]38[/C][C]0.116336835418457[/C][C]0.232673670836913[/C][C]0.883663164581543[/C][/ROW]
[ROW][C]39[/C][C]0.118766031277349[/C][C]0.237532062554698[/C][C]0.88123396872265[/C][/ROW]
[ROW][C]40[/C][C]0.0712938348058931[/C][C]0.142587669611786[/C][C]0.928706165194107[/C][/ROW]
[ROW][C]41[/C][C]0.0567607807066243[/C][C]0.113521561413249[/C][C]0.943239219293376[/C][/ROW]
[ROW][C]42[/C][C]0.0286700793237157[/C][C]0.0573401586474313[/C][C]0.971329920676284[/C][/ROW]
[ROW][C]43[/C][C]0.280612585874457[/C][C]0.561225171748914[/C][C]0.719387414125543[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58074&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58074&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
16	0.440928768546686	0.881857537093371	0.559071231453314
17	0.318219880421505	0.636439760843009	0.681780119578495
18	0.25012387443636	0.50024774887272	0.74987612556364
19	0.236813924404655	0.47362784880931	0.763186075595345
20	0.157940324320193	0.315880648640385	0.842059675679807
21	0.0953023013185387	0.190604602637077	0.904697698681461
22	0.206486668889432	0.412973337778864	0.793513331110568
23	0.171323013836142	0.342646027672284	0.828676986163858
24	0.167924507425948	0.335849014851897	0.832075492574052
25	0.130445543862962	0.260891087725924	0.869554456137038
26	0.105777965521626	0.211555931043252	0.894222034478374
27	0.0883131803212159	0.176626360642432	0.911686819678784
28	0.0705901300797084	0.141180260159417	0.929409869920292
29	0.0564828563773212	0.112965712754642	0.943517143622679
30	0.0421569950515462	0.0843139901030924	0.957843004948454
31	0.0283329921709857	0.0566659843419715	0.971667007829014
32	0.0187653215609916	0.0375306431219833	0.981234678439008
33	0.0210149615479833	0.0420299230959666	0.978985038452017
34	0.0144054760887002	0.0288109521774003	0.9855945239113
35	0.00941824995907587	0.0188364999181517	0.990581750040924
36	0.0225949267378478	0.0451898534756955	0.977405073262152
37	0.0573789028743482	0.114757805748696	0.942621097125652
38	0.116336835418457	0.232673670836913	0.883663164581543
39	0.118766031277349	0.237532062554698	0.88123396872265
40	0.0712938348058931	0.142587669611786	0.928706165194107
41	0.0567607807066243	0.113521561413249	0.943239219293376
42	0.0286700793237157	0.0573401586474313	0.971329920676284
43	0.280612585874457	0.561225171748914	0.719387414125543

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	5	0.178571428571429	NOK
10% type I error level	8	0.285714285714286	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 5 & 0.178571428571429 & NOK \tabularnewline
10% type I error level & 8 & 0.285714285714286 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58074&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]5[/C][C]0.178571428571429[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.285714285714286[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58074&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58074&T=6

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	5	0.178571428571429	NOK
10% type I error level	8	0.285714285714286	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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