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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 19 Nov 2009 13:47:35 -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/19/t1258663721n00kwiceoelxh7s.htm/, Retrieved Sat, 27 Apr 2024 01:55:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57948, Retrieved Sat, 27 Apr 2024 01:55:37 +0000

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User-defined keywords

Estimated Impact

171

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
F R PD    [Multiple Regression] [Model 1] [2009-11-19 20:32:12] [c0117c881d5fcd069841276db0c34efe]
F    D      [Multiple Regression] [Model 2] [2009-11-19 20:38:54] [c0117c881d5fcd069841276db0c34efe]
-             [Multiple Regression] [Model 3] [2009-11-19 20:41:09] [c0117c881d5fcd069841276db0c34efe]
-    D            [Multiple Regression] [Model 4] [2009-11-19 20:47:35] [d5837f25ec8937f9733a894c487f865c] [Current]
-   PD              [Multiple Regression] [Model 5] [2009-11-20 15:42:18] [c0117c881d5fcd069841276db0c34efe] 

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Dataseries X:

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Boxplots

3.75	99.9	100.1	100.7	101.1	101.2
4.11	99.7	99.9	100.1	100.7	101.1
4.25	99.5	99.7	99.9	100.1	100.7
4.25	99.2	99.5	99.7	99.9	100.1
4.5	99	99.2	99.5	99.7	99.9
4.7	99	99	99.2	99.5	99.7
4.75	99.3	99	99	99.2	99.5
4.75	99.5	99.3	99	99	99.2
4.75	99.7	99.5	99.3	99	99
4.75	100	99.7	99.5	99.3	99
4.75	100.4	100	99.7	99.5	99.3
4.75	100.6	100.4	100	99.7	99.5
4.58	100.7	100.6	100.4	100	99.7
4.5	100.7	100.7	100.6	100.4	100
4.5	100.6	100.7	100.7	100.6	100.4
4.49	100.5	100.6	100.7	100.7	100.6
4.03	100.6	100.5	100.6	100.7	100.7
3.75	100.5	100.6	100.5	100.6	100.7
3.39	100.4	100.5	100.6	100.5	100.6
3.25	100.3	100.4	100.5	100.6	100.5
3.25	100.4	100.3	100.4	100.5	100.6
3.25	100.4	100.4	100.3	100.4	100.5
3.25	100.4	100.4	100.4	100.3	100.4
3.25	100.4	100.4	100.4	100.4	100.3
3.25	100.4	100.4	100.4	100.4	100.4
3.25	100.5	100.4	100.4	100.4	100.4
3.25	100.6	100.5	100.4	100.4	100.4
3.25	100.6	100.6	100.5	100.4	100.4
3.25	100.5	100.6	100.6	100.5	100.4
3.25	100.5	100.5	100.6	100.6	100.5
3.25	100.7	100.5	100.5	100.6	100.6
2.85	101.1	100.7	100.5	100.5	100.6
2.75	101.5	101.1	100.7	100.5	100.5
2.75	101.9	101.5	101.1	100.7	100.5
2.55	102.1	101.9	101.5	101.1	100.7
2.5	102.1	102.1	101.9	101.5	101.1
2.5	102.1	102.1	102.1	101.9	101.5
2.1	102.4	102.1	102.1	102.1	101.9
2	102.8	102.4	102.1	102.1	102.1
2	103.1	102.8	102.4	102.1	102.1
2	103.1	103.1	102.8	102.4	102.1
2	102.9	103.1	103.1	102.8	102.4
2	102.4	102.9	103.1	103.1	102.8
2	101.9	102.4	102.9	103.1	103.1
2	101.3	101.9	102.4	102.9	103.1
2	100.7	101.3	101.9	102.4	102.9
2	100.6	100.7	101.3	101.9	102.4
2	101	100.6	100.7	101.3	101.9
2	101.5	101	100.6	100.7	101.3
2	101.9	101.5	101	100.6	100.7
2	102.1	101.9	101.5	101	100.6
2	102.3	102.1	101.9	101.5	101
2	102.5	102.3	102.1	101.9	101.5
2	102.9	102.5	102.3	102.1	101.9
2	103.6	102.9	102.5	102.3	102.1
2	104.3	103.6	102.9	102.5	102.3

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57948&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]3 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=57948&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57948&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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Tprod[t] = + 9.03287437636819 + 0.0100535761591053Rente[t] + 2.06088898976321y1[t] -1.65997795678992y2[t] + 0.66397755103948y3[t] -0.156268465277844y4[t] + 0.00673278043645512t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Tprod[t] =  +  9.03287437636819 +  0.0100535761591053Rente[t] +  2.06088898976321y1[t] -1.65997795678992y2[t] +  0.66397755103948y3[t] -0.156268465277844y4[t] +  0.00673278043645512t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57948&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Tprod[t] =  +  9.03287437636819 +  0.0100535761591053Rente[t] +  2.06088898976321y1[t] -1.65997795678992y2[t] +  0.66397755103948y3[t] -0.156268465277844y4[t] +  0.00673278043645512t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57948&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57948&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
Tprod[t] = + 9.03287437636819 + 0.0100535761591053Rente[t] + 2.06088898976321y1[t] -1.65997795678992y2[t] + 0.66397755103948y3[t] -0.156268465277844y4[t] + 0.00673278043645512t + 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)	9.03287437636819	4.691774	1.9253	0.060012	0.030006
Rente	0.0100535761591053	0.071091	0.1414	0.88812	0.44406
y1	2.06088898976321	0.14162	14.5523	0	0
y2	-1.65997795678992	0.316673	-5.2419	3e-06	2e-06
y3	0.66397755103948	0.322999	2.0557	0.045164	0.022582
y4	-0.156268465277844	0.152604	-1.024	0.310858	0.155429
t	0.00673278043645512	0.003976	1.6935	0.096715	0.048358

\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) & 9.03287437636819 & 4.691774 & 1.9253 & 0.060012 & 0.030006 \tabularnewline
Rente & 0.0100535761591053 & 0.071091 & 0.1414 & 0.88812 & 0.44406 \tabularnewline
y1 & 2.06088898976321 & 0.14162 & 14.5523 & 0 & 0 \tabularnewline
y2 & -1.65997795678992 & 0.316673 & -5.2419 & 3e-06 & 2e-06 \tabularnewline
y3 & 0.66397755103948 & 0.322999 & 2.0557 & 0.045164 & 0.022582 \tabularnewline
y4 & -0.156268465277844 & 0.152604 & -1.024 & 0.310858 & 0.155429 \tabularnewline
t & 0.00673278043645512 & 0.003976 & 1.6935 & 0.096715 & 0.048358 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57948&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]9.03287437636819[/C][C]4.691774[/C][C]1.9253[/C][C]0.060012[/C][C]0.030006[/C][/ROW]
[ROW][C]Rente[/C][C]0.0100535761591053[/C][C]0.071091[/C][C]0.1414[/C][C]0.88812[/C][C]0.44406[/C][/ROW]
[ROW][C]y1[/C][C]2.06088898976321[/C][C]0.14162[/C][C]14.5523[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]y2[/C][C]-1.65997795678992[/C][C]0.316673[/C][C]-5.2419[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]y3[/C][C]0.66397755103948[/C][C]0.322999[/C][C]2.0557[/C][C]0.045164[/C][C]0.022582[/C][/ROW]
[ROW][C]y4[/C][C]-0.156268465277844[/C][C]0.152604[/C][C]-1.024[/C][C]0.310858[/C][C]0.155429[/C][/ROW]
[ROW][C]t[/C][C]0.00673278043645512[/C][C]0.003976[/C][C]1.6935[/C][C]0.096715[/C][C]0.048358[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57948&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57948&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)	9.03287437636819	4.691774	1.9253	0.060012	0.030006
Rente	0.0100535761591053	0.071091	0.1414	0.88812	0.44406
y1	2.06088898976321	0.14162	14.5523	0	0
y2	-1.65997795678992	0.316673	-5.2419	3e-06	2e-06
y3	0.66397755103948	0.322999	2.0557	0.045164	0.022582
y4	-0.156268465277844	0.152604	-1.024	0.310858	0.155429
t	0.00673278043645512	0.003976	1.6935	0.096715	0.048358

Multiple Linear Regression - Regression Statistics
Multiple R	0.993522928795038
R-squared	0.98708781004147
Adjusted R-squared	0.985506725556751
F-TEST (value)	624.310604260666
F-TEST (DF numerator)	6
F-TEST (DF denominator)	49
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.147201856015383
Sum Squared Residuals	1.06175093430430

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.993522928795038 \tabularnewline
R-squared & 0.98708781004147 \tabularnewline
Adjusted R-squared & 0.985506725556751 \tabularnewline
F-TEST (value) & 624.310604260666 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 49 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.147201856015383 \tabularnewline
Sum Squared Residuals & 1.06175093430430 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57948&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.993522928795038[/C][/ROW]
[ROW][C]R-squared[/C][C]0.98708781004147[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.985506725556751[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]624.310604260666[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]49[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.147201856015383[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.06175093430430[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57948&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57948&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	0.993522928795038
R-squared	0.98708781004147
Adjusted R-squared	0.985506725556751
F-TEST (value)	624.310604260666
F-TEST (DF numerator)	6
F-TEST (DF denominator)	49
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.147201856015383
Sum Squared Residuals	1.06175093430430

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	99.9	99.5262774179268	0.373722582073162
2	99.7	99.870474288014	-0.170474288013908
3	99.5	99.4625532180054	0.0374467819945941
4	99.2	99.350069360806	-0.150069360806019
5	99	98.971502612559	0.0284973874410457
6	99	98.9645198801592	0.0354801198407734
7	99.3	99.1358113585054	0.164188641494644
8	99.5	99.6748958652462	-0.174895865246220
9	99.7	99.627066749654	0.0729332503460831
10	100	99.9131750019969	0.0868249980031247
11	100.4	100.292093858629	0.107906141371163
12	100.6	100.726730665086	-0.126730665085955
13	100.7	100.647880525068	0.052119474931733
14	100.7	100.746612807863	-0.0466128078628094
15	100.6	100.657635916717	-0.0576359167170165
16	100.5	100.493323324464	0.00667667553607671
17	100.6	100.439713510042	0.160286489957892
18	100.5	100.749320228705	-0.249320228705347
19	100.4	100.329576118493	0.0704238815069325
20	100.3	100.376834896602	-0.0768348966016582
21	100.4	100.261451972109	0.138548027890979
22	100.4	100.589500538625	-0.189500538624661
23	100.4	100.379464614806	0.0205353851940609
24	100.4	100.468221996874	-0.0682219968741338
25	100.4	100.459327930783	-0.0593279307828032
26	100.5	100.466060711219	0.0339392887807360
27	100.6	100.678882390632	-0.078882390632034
28	100.6	100.725706274366	-0.125706274365815
29	100.5	100.632839014227	-0.132839014227226
30	100.5	100.484253804264	0.0157461957364671
31	100.7	100.641357533851	0.0586424661488154
32	101.1	100.989848926673	0.110151073327290
33	101.5	101.503563200568	-0.00356320056830893
34	101.9	101.803455904402	0.0965440955980037
35	102.1	102.202879710156	-0.102879710156175
36	102.1	102.160380061326	-0.0603800613259744
37	102.1	102.038200884709	0.0617991152908789
38	102.4	102.111200358779	0.288799641221327
39	102.8	102.703940785473	0.0960592145273543
40	103.1	103.037035774777	0.0629642252226264
41	103.1	103.197237334739	-0.0972373347386817
42	102.9	102.924687208971	-0.0246872089705846
43	102.4	102.655928070655	-0.255928070655128
44	101.9	101.917331407985	-0.0173314079845912
45	101.3	101.590813161727	-0.290813161726524
46	100.7	100.890266444236	-0.190266444235819
47	100.6	100.402598062008	0.197401937992481
48	101	100.878976419557	0.121023580443197
49	101.5	101.571437140121	-0.0714371401205832
50	101.9	101.971986556785	-0.0719865567854098
51	102.1	102.254303821676	-0.154303821675791
52	102.3	102.278704606757	0.0212953932425151
53	102.5	102.553076381565	-0.0530763815654929
54	102.9	102.710279492693	0.189720507306649
55	103.6	103.310914094829	0.289085905170546
56	104.3	104.197819802536	0.102180197463521

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 99.9 & 99.5262774179268 & 0.373722582073162 \tabularnewline
2 & 99.7 & 99.870474288014 & -0.170474288013908 \tabularnewline
3 & 99.5 & 99.4625532180054 & 0.0374467819945941 \tabularnewline
4 & 99.2 & 99.350069360806 & -0.150069360806019 \tabularnewline
5 & 99 & 98.971502612559 & 0.0284973874410457 \tabularnewline
6 & 99 & 98.9645198801592 & 0.0354801198407734 \tabularnewline
7 & 99.3 & 99.1358113585054 & 0.164188641494644 \tabularnewline
8 & 99.5 & 99.6748958652462 & -0.174895865246220 \tabularnewline
9 & 99.7 & 99.627066749654 & 0.0729332503460831 \tabularnewline
10 & 100 & 99.9131750019969 & 0.0868249980031247 \tabularnewline
11 & 100.4 & 100.292093858629 & 0.107906141371163 \tabularnewline
12 & 100.6 & 100.726730665086 & -0.126730665085955 \tabularnewline
13 & 100.7 & 100.647880525068 & 0.052119474931733 \tabularnewline
14 & 100.7 & 100.746612807863 & -0.0466128078628094 \tabularnewline
15 & 100.6 & 100.657635916717 & -0.0576359167170165 \tabularnewline
16 & 100.5 & 100.493323324464 & 0.00667667553607671 \tabularnewline
17 & 100.6 & 100.439713510042 & 0.160286489957892 \tabularnewline
18 & 100.5 & 100.749320228705 & -0.249320228705347 \tabularnewline
19 & 100.4 & 100.329576118493 & 0.0704238815069325 \tabularnewline
20 & 100.3 & 100.376834896602 & -0.0768348966016582 \tabularnewline
21 & 100.4 & 100.261451972109 & 0.138548027890979 \tabularnewline
22 & 100.4 & 100.589500538625 & -0.189500538624661 \tabularnewline
23 & 100.4 & 100.379464614806 & 0.0205353851940609 \tabularnewline
24 & 100.4 & 100.468221996874 & -0.0682219968741338 \tabularnewline
25 & 100.4 & 100.459327930783 & -0.0593279307828032 \tabularnewline
26 & 100.5 & 100.466060711219 & 0.0339392887807360 \tabularnewline
27 & 100.6 & 100.678882390632 & -0.078882390632034 \tabularnewline
28 & 100.6 & 100.725706274366 & -0.125706274365815 \tabularnewline
29 & 100.5 & 100.632839014227 & -0.132839014227226 \tabularnewline
30 & 100.5 & 100.484253804264 & 0.0157461957364671 \tabularnewline
31 & 100.7 & 100.641357533851 & 0.0586424661488154 \tabularnewline
32 & 101.1 & 100.989848926673 & 0.110151073327290 \tabularnewline
33 & 101.5 & 101.503563200568 & -0.00356320056830893 \tabularnewline
34 & 101.9 & 101.803455904402 & 0.0965440955980037 \tabularnewline
35 & 102.1 & 102.202879710156 & -0.102879710156175 \tabularnewline
36 & 102.1 & 102.160380061326 & -0.0603800613259744 \tabularnewline
37 & 102.1 & 102.038200884709 & 0.0617991152908789 \tabularnewline
38 & 102.4 & 102.111200358779 & 0.288799641221327 \tabularnewline
39 & 102.8 & 102.703940785473 & 0.0960592145273543 \tabularnewline
40 & 103.1 & 103.037035774777 & 0.0629642252226264 \tabularnewline
41 & 103.1 & 103.197237334739 & -0.0972373347386817 \tabularnewline
42 & 102.9 & 102.924687208971 & -0.0246872089705846 \tabularnewline
43 & 102.4 & 102.655928070655 & -0.255928070655128 \tabularnewline
44 & 101.9 & 101.917331407985 & -0.0173314079845912 \tabularnewline
45 & 101.3 & 101.590813161727 & -0.290813161726524 \tabularnewline
46 & 100.7 & 100.890266444236 & -0.190266444235819 \tabularnewline
47 & 100.6 & 100.402598062008 & 0.197401937992481 \tabularnewline
48 & 101 & 100.878976419557 & 0.121023580443197 \tabularnewline
49 & 101.5 & 101.571437140121 & -0.0714371401205832 \tabularnewline
50 & 101.9 & 101.971986556785 & -0.0719865567854098 \tabularnewline
51 & 102.1 & 102.254303821676 & -0.154303821675791 \tabularnewline
52 & 102.3 & 102.278704606757 & 0.0212953932425151 \tabularnewline
53 & 102.5 & 102.553076381565 & -0.0530763815654929 \tabularnewline
54 & 102.9 & 102.710279492693 & 0.189720507306649 \tabularnewline
55 & 103.6 & 103.310914094829 & 0.289085905170546 \tabularnewline
56 & 104.3 & 104.197819802536 & 0.102180197463521 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57948&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]99.9[/C][C]99.5262774179268[/C][C]0.373722582073162[/C][/ROW]
[ROW][C]2[/C][C]99.7[/C][C]99.870474288014[/C][C]-0.170474288013908[/C][/ROW]
[ROW][C]3[/C][C]99.5[/C][C]99.4625532180054[/C][C]0.0374467819945941[/C][/ROW]
[ROW][C]4[/C][C]99.2[/C][C]99.350069360806[/C][C]-0.150069360806019[/C][/ROW]
[ROW][C]5[/C][C]99[/C][C]98.971502612559[/C][C]0.0284973874410457[/C][/ROW]
[ROW][C]6[/C][C]99[/C][C]98.9645198801592[/C][C]0.0354801198407734[/C][/ROW]
[ROW][C]7[/C][C]99.3[/C][C]99.1358113585054[/C][C]0.164188641494644[/C][/ROW]
[ROW][C]8[/C][C]99.5[/C][C]99.6748958652462[/C][C]-0.174895865246220[/C][/ROW]
[ROW][C]9[/C][C]99.7[/C][C]99.627066749654[/C][C]0.0729332503460831[/C][/ROW]
[ROW][C]10[/C][C]100[/C][C]99.9131750019969[/C][C]0.0868249980031247[/C][/ROW]
[ROW][C]11[/C][C]100.4[/C][C]100.292093858629[/C][C]0.107906141371163[/C][/ROW]
[ROW][C]12[/C][C]100.6[/C][C]100.726730665086[/C][C]-0.126730665085955[/C][/ROW]
[ROW][C]13[/C][C]100.7[/C][C]100.647880525068[/C][C]0.052119474931733[/C][/ROW]
[ROW][C]14[/C][C]100.7[/C][C]100.746612807863[/C][C]-0.0466128078628094[/C][/ROW]
[ROW][C]15[/C][C]100.6[/C][C]100.657635916717[/C][C]-0.0576359167170165[/C][/ROW]
[ROW][C]16[/C][C]100.5[/C][C]100.493323324464[/C][C]0.00667667553607671[/C][/ROW]
[ROW][C]17[/C][C]100.6[/C][C]100.439713510042[/C][C]0.160286489957892[/C][/ROW]
[ROW][C]18[/C][C]100.5[/C][C]100.749320228705[/C][C]-0.249320228705347[/C][/ROW]
[ROW][C]19[/C][C]100.4[/C][C]100.329576118493[/C][C]0.0704238815069325[/C][/ROW]
[ROW][C]20[/C][C]100.3[/C][C]100.376834896602[/C][C]-0.0768348966016582[/C][/ROW]
[ROW][C]21[/C][C]100.4[/C][C]100.261451972109[/C][C]0.138548027890979[/C][/ROW]
[ROW][C]22[/C][C]100.4[/C][C]100.589500538625[/C][C]-0.189500538624661[/C][/ROW]
[ROW][C]23[/C][C]100.4[/C][C]100.379464614806[/C][C]0.0205353851940609[/C][/ROW]
[ROW][C]24[/C][C]100.4[/C][C]100.468221996874[/C][C]-0.0682219968741338[/C][/ROW]
[ROW][C]25[/C][C]100.4[/C][C]100.459327930783[/C][C]-0.0593279307828032[/C][/ROW]
[ROW][C]26[/C][C]100.5[/C][C]100.466060711219[/C][C]0.0339392887807360[/C][/ROW]
[ROW][C]27[/C][C]100.6[/C][C]100.678882390632[/C][C]-0.078882390632034[/C][/ROW]
[ROW][C]28[/C][C]100.6[/C][C]100.725706274366[/C][C]-0.125706274365815[/C][/ROW]
[ROW][C]29[/C][C]100.5[/C][C]100.632839014227[/C][C]-0.132839014227226[/C][/ROW]
[ROW][C]30[/C][C]100.5[/C][C]100.484253804264[/C][C]0.0157461957364671[/C][/ROW]
[ROW][C]31[/C][C]100.7[/C][C]100.641357533851[/C][C]0.0586424661488154[/C][/ROW]
[ROW][C]32[/C][C]101.1[/C][C]100.989848926673[/C][C]0.110151073327290[/C][/ROW]
[ROW][C]33[/C][C]101.5[/C][C]101.503563200568[/C][C]-0.00356320056830893[/C][/ROW]
[ROW][C]34[/C][C]101.9[/C][C]101.803455904402[/C][C]0.0965440955980037[/C][/ROW]
[ROW][C]35[/C][C]102.1[/C][C]102.202879710156[/C][C]-0.102879710156175[/C][/ROW]
[ROW][C]36[/C][C]102.1[/C][C]102.160380061326[/C][C]-0.0603800613259744[/C][/ROW]
[ROW][C]37[/C][C]102.1[/C][C]102.038200884709[/C][C]0.0617991152908789[/C][/ROW]
[ROW][C]38[/C][C]102.4[/C][C]102.111200358779[/C][C]0.288799641221327[/C][/ROW]
[ROW][C]39[/C][C]102.8[/C][C]102.703940785473[/C][C]0.0960592145273543[/C][/ROW]
[ROW][C]40[/C][C]103.1[/C][C]103.037035774777[/C][C]0.0629642252226264[/C][/ROW]
[ROW][C]41[/C][C]103.1[/C][C]103.197237334739[/C][C]-0.0972373347386817[/C][/ROW]
[ROW][C]42[/C][C]102.9[/C][C]102.924687208971[/C][C]-0.0246872089705846[/C][/ROW]
[ROW][C]43[/C][C]102.4[/C][C]102.655928070655[/C][C]-0.255928070655128[/C][/ROW]
[ROW][C]44[/C][C]101.9[/C][C]101.917331407985[/C][C]-0.0173314079845912[/C][/ROW]
[ROW][C]45[/C][C]101.3[/C][C]101.590813161727[/C][C]-0.290813161726524[/C][/ROW]
[ROW][C]46[/C][C]100.7[/C][C]100.890266444236[/C][C]-0.190266444235819[/C][/ROW]
[ROW][C]47[/C][C]100.6[/C][C]100.402598062008[/C][C]0.197401937992481[/C][/ROW]
[ROW][C]48[/C][C]101[/C][C]100.878976419557[/C][C]0.121023580443197[/C][/ROW]
[ROW][C]49[/C][C]101.5[/C][C]101.571437140121[/C][C]-0.0714371401205832[/C][/ROW]
[ROW][C]50[/C][C]101.9[/C][C]101.971986556785[/C][C]-0.0719865567854098[/C][/ROW]
[ROW][C]51[/C][C]102.1[/C][C]102.254303821676[/C][C]-0.154303821675791[/C][/ROW]
[ROW][C]52[/C][C]102.3[/C][C]102.278704606757[/C][C]0.0212953932425151[/C][/ROW]
[ROW][C]53[/C][C]102.5[/C][C]102.553076381565[/C][C]-0.0530763815654929[/C][/ROW]
[ROW][C]54[/C][C]102.9[/C][C]102.710279492693[/C][C]0.189720507306649[/C][/ROW]
[ROW][C]55[/C][C]103.6[/C][C]103.310914094829[/C][C]0.289085905170546[/C][/ROW]
[ROW][C]56[/C][C]104.3[/C][C]104.197819802536[/C][C]0.102180197463521[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57948&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57948&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	99.9	99.5262774179268	0.373722582073162
2	99.7	99.870474288014	-0.170474288013908
3	99.5	99.4625532180054	0.0374467819945941
4	99.2	99.350069360806	-0.150069360806019
5	99	98.971502612559	0.0284973874410457
6	99	98.9645198801592	0.0354801198407734
7	99.3	99.1358113585054	0.164188641494644
8	99.5	99.6748958652462	-0.174895865246220
9	99.7	99.627066749654	0.0729332503460831
10	100	99.9131750019969	0.0868249980031247
11	100.4	100.292093858629	0.107906141371163
12	100.6	100.726730665086	-0.126730665085955
13	100.7	100.647880525068	0.052119474931733
14	100.7	100.746612807863	-0.0466128078628094
15	100.6	100.657635916717	-0.0576359167170165
16	100.5	100.493323324464	0.00667667553607671
17	100.6	100.439713510042	0.160286489957892
18	100.5	100.749320228705	-0.249320228705347
19	100.4	100.329576118493	0.0704238815069325
20	100.3	100.376834896602	-0.0768348966016582
21	100.4	100.261451972109	0.138548027890979
22	100.4	100.589500538625	-0.189500538624661
23	100.4	100.379464614806	0.0205353851940609
24	100.4	100.468221996874	-0.0682219968741338
25	100.4	100.459327930783	-0.0593279307828032
26	100.5	100.466060711219	0.0339392887807360
27	100.6	100.678882390632	-0.078882390632034
28	100.6	100.725706274366	-0.125706274365815
29	100.5	100.632839014227	-0.132839014227226
30	100.5	100.484253804264	0.0157461957364671
31	100.7	100.641357533851	0.0586424661488154
32	101.1	100.989848926673	0.110151073327290
33	101.5	101.503563200568	-0.00356320056830893
34	101.9	101.803455904402	0.0965440955980037
35	102.1	102.202879710156	-0.102879710156175
36	102.1	102.160380061326	-0.0603800613259744
37	102.1	102.038200884709	0.0617991152908789
38	102.4	102.111200358779	0.288799641221327
39	102.8	102.703940785473	0.0960592145273543
40	103.1	103.037035774777	0.0629642252226264
41	103.1	103.197237334739	-0.0972373347386817
42	102.9	102.924687208971	-0.0246872089705846
43	102.4	102.655928070655	-0.255928070655128
44	101.9	101.917331407985	-0.0173314079845912
45	101.3	101.590813161727	-0.290813161726524
46	100.7	100.890266444236	-0.190266444235819
47	100.6	100.402598062008	0.197401937992481
48	101	100.878976419557	0.121023580443197
49	101.5	101.571437140121	-0.0714371401205832
50	101.9	101.971986556785	-0.0719865567854098
51	102.1	102.254303821676	-0.154303821675791
52	102.3	102.278704606757	0.0212953932425151
53	102.5	102.553076381565	-0.0530763815654929
54	102.9	102.710279492693	0.189720507306649
55	103.6	103.310914094829	0.289085905170546
56	104.3	104.197819802536	0.102180197463521

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.271181533643645	0.542363067287290	0.728818466356355
11	0.617484323797822	0.765031352404357	0.382515676202178
12	0.748682317858286	0.502635364283427	0.251317682141714
13	0.714446407998396	0.571107184003208	0.285553592001604
14	0.63128678058339	0.73742643883322	0.36871321941661
15	0.518963628551646	0.962072742896709	0.481036371448354
16	0.429113147700908	0.858226295401816	0.570886852299092
17	0.433309302455932	0.866618604911864	0.566690697544068
18	0.52274649930562	0.95450700138876	0.47725350069438
19	0.456213793848702	0.912427587697403	0.543786206151298
20	0.382904594504487	0.765809189008973	0.617095405495513
21	0.421654931882504	0.843309863765008	0.578345068117496
22	0.403051616033867	0.806103232067733	0.596948383966133
23	0.340062673122351	0.680125346244703	0.659937326877649
24	0.272373771339463	0.544747542678925	0.727626228660537
25	0.204473000046142	0.408946000092284	0.795526999953858
26	0.159790512233035	0.31958102446607	0.840209487766965
27	0.118118936985378	0.236237873970756	0.881881063014622
28	0.0919210414102472	0.183842082820494	0.908078958589753
29	0.0886529171193103	0.177305834238621	0.91134708288069
30	0.0580341433475005	0.116068286695001	0.9419658566525
31	0.0533490258322642	0.106698051664528	0.946650974167736
32	0.090549438920489	0.181098877840978	0.909450561079511
33	0.0810178521635331	0.162035704327066	0.918982147836467
34	0.0727419075884224	0.145483815176845	0.927258092411578
35	0.0613366679856953	0.122673335971391	0.938663332014305
36	0.0448228584033816	0.089645716806763	0.955177141596618
37	0.0787022207466955	0.157404441493391	0.921297779253305
38	0.107111728595488	0.214223457190975	0.892888271404512
39	0.101966213059102	0.203932426118205	0.898033786940898
40	0.103420104704999	0.206840209409998	0.896579895295001
41	0.0897536164216421	0.179507232843284	0.910246383578358
42	0.185823457638471	0.371646915276942	0.814176542361529
43	0.188133462461684	0.376266924923368	0.811866537538316
44	0.653024817680585	0.693950364638830	0.346975182319415
45	0.673893861864632	0.652212276270736	0.326106138135368
46	0.620291329403054	0.759417341193892	0.379708670596946

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.271181533643645 & 0.542363067287290 & 0.728818466356355 \tabularnewline
11 & 0.617484323797822 & 0.765031352404357 & 0.382515676202178 \tabularnewline
12 & 0.748682317858286 & 0.502635364283427 & 0.251317682141714 \tabularnewline
13 & 0.714446407998396 & 0.571107184003208 & 0.285553592001604 \tabularnewline
14 & 0.63128678058339 & 0.73742643883322 & 0.36871321941661 \tabularnewline
15 & 0.518963628551646 & 0.962072742896709 & 0.481036371448354 \tabularnewline
16 & 0.429113147700908 & 0.858226295401816 & 0.570886852299092 \tabularnewline
17 & 0.433309302455932 & 0.866618604911864 & 0.566690697544068 \tabularnewline
18 & 0.52274649930562 & 0.95450700138876 & 0.47725350069438 \tabularnewline
19 & 0.456213793848702 & 0.912427587697403 & 0.543786206151298 \tabularnewline
20 & 0.382904594504487 & 0.765809189008973 & 0.617095405495513 \tabularnewline
21 & 0.421654931882504 & 0.843309863765008 & 0.578345068117496 \tabularnewline
22 & 0.403051616033867 & 0.806103232067733 & 0.596948383966133 \tabularnewline
23 & 0.340062673122351 & 0.680125346244703 & 0.659937326877649 \tabularnewline
24 & 0.272373771339463 & 0.544747542678925 & 0.727626228660537 \tabularnewline
25 & 0.204473000046142 & 0.408946000092284 & 0.795526999953858 \tabularnewline
26 & 0.159790512233035 & 0.31958102446607 & 0.840209487766965 \tabularnewline
27 & 0.118118936985378 & 0.236237873970756 & 0.881881063014622 \tabularnewline
28 & 0.0919210414102472 & 0.183842082820494 & 0.908078958589753 \tabularnewline
29 & 0.0886529171193103 & 0.177305834238621 & 0.91134708288069 \tabularnewline
30 & 0.0580341433475005 & 0.116068286695001 & 0.9419658566525 \tabularnewline
31 & 0.0533490258322642 & 0.106698051664528 & 0.946650974167736 \tabularnewline
32 & 0.090549438920489 & 0.181098877840978 & 0.909450561079511 \tabularnewline
33 & 0.0810178521635331 & 0.162035704327066 & 0.918982147836467 \tabularnewline
34 & 0.0727419075884224 & 0.145483815176845 & 0.927258092411578 \tabularnewline
35 & 0.0613366679856953 & 0.122673335971391 & 0.938663332014305 \tabularnewline
36 & 0.0448228584033816 & 0.089645716806763 & 0.955177141596618 \tabularnewline
37 & 0.0787022207466955 & 0.157404441493391 & 0.921297779253305 \tabularnewline
38 & 0.107111728595488 & 0.214223457190975 & 0.892888271404512 \tabularnewline
39 & 0.101966213059102 & 0.203932426118205 & 0.898033786940898 \tabularnewline
40 & 0.103420104704999 & 0.206840209409998 & 0.896579895295001 \tabularnewline
41 & 0.0897536164216421 & 0.179507232843284 & 0.910246383578358 \tabularnewline
42 & 0.185823457638471 & 0.371646915276942 & 0.814176542361529 \tabularnewline
43 & 0.188133462461684 & 0.376266924923368 & 0.811866537538316 \tabularnewline
44 & 0.653024817680585 & 0.693950364638830 & 0.346975182319415 \tabularnewline
45 & 0.673893861864632 & 0.652212276270736 & 0.326106138135368 \tabularnewline
46 & 0.620291329403054 & 0.759417341193892 & 0.379708670596946 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57948&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]10[/C][C]0.271181533643645[/C][C]0.542363067287290[/C][C]0.728818466356355[/C][/ROW]
[ROW][C]11[/C][C]0.617484323797822[/C][C]0.765031352404357[/C][C]0.382515676202178[/C][/ROW]
[ROW][C]12[/C][C]0.748682317858286[/C][C]0.502635364283427[/C][C]0.251317682141714[/C][/ROW]
[ROW][C]13[/C][C]0.714446407998396[/C][C]0.571107184003208[/C][C]0.285553592001604[/C][/ROW]
[ROW][C]14[/C][C]0.63128678058339[/C][C]0.73742643883322[/C][C]0.36871321941661[/C][/ROW]
[ROW][C]15[/C][C]0.518963628551646[/C][C]0.962072742896709[/C][C]0.481036371448354[/C][/ROW]
[ROW][C]16[/C][C]0.429113147700908[/C][C]0.858226295401816[/C][C]0.570886852299092[/C][/ROW]
[ROW][C]17[/C][C]0.433309302455932[/C][C]0.866618604911864[/C][C]0.566690697544068[/C][/ROW]
[ROW][C]18[/C][C]0.52274649930562[/C][C]0.95450700138876[/C][C]0.47725350069438[/C][/ROW]
[ROW][C]19[/C][C]0.456213793848702[/C][C]0.912427587697403[/C][C]0.543786206151298[/C][/ROW]
[ROW][C]20[/C][C]0.382904594504487[/C][C]0.765809189008973[/C][C]0.617095405495513[/C][/ROW]
[ROW][C]21[/C][C]0.421654931882504[/C][C]0.843309863765008[/C][C]0.578345068117496[/C][/ROW]
[ROW][C]22[/C][C]0.403051616033867[/C][C]0.806103232067733[/C][C]0.596948383966133[/C][/ROW]
[ROW][C]23[/C][C]0.340062673122351[/C][C]0.680125346244703[/C][C]0.659937326877649[/C][/ROW]
[ROW][C]24[/C][C]0.272373771339463[/C][C]0.544747542678925[/C][C]0.727626228660537[/C][/ROW]
[ROW][C]25[/C][C]0.204473000046142[/C][C]0.408946000092284[/C][C]0.795526999953858[/C][/ROW]
[ROW][C]26[/C][C]0.159790512233035[/C][C]0.31958102446607[/C][C]0.840209487766965[/C][/ROW]
[ROW][C]27[/C][C]0.118118936985378[/C][C]0.236237873970756[/C][C]0.881881063014622[/C][/ROW]
[ROW][C]28[/C][C]0.0919210414102472[/C][C]0.183842082820494[/C][C]0.908078958589753[/C][/ROW]
[ROW][C]29[/C][C]0.0886529171193103[/C][C]0.177305834238621[/C][C]0.91134708288069[/C][/ROW]
[ROW][C]30[/C][C]0.0580341433475005[/C][C]0.116068286695001[/C][C]0.9419658566525[/C][/ROW]
[ROW][C]31[/C][C]0.0533490258322642[/C][C]0.106698051664528[/C][C]0.946650974167736[/C][/ROW]
[ROW][C]32[/C][C]0.090549438920489[/C][C]0.181098877840978[/C][C]0.909450561079511[/C][/ROW]
[ROW][C]33[/C][C]0.0810178521635331[/C][C]0.162035704327066[/C][C]0.918982147836467[/C][/ROW]
[ROW][C]34[/C][C]0.0727419075884224[/C][C]0.145483815176845[/C][C]0.927258092411578[/C][/ROW]
[ROW][C]35[/C][C]0.0613366679856953[/C][C]0.122673335971391[/C][C]0.938663332014305[/C][/ROW]
[ROW][C]36[/C][C]0.0448228584033816[/C][C]0.089645716806763[/C][C]0.955177141596618[/C][/ROW]
[ROW][C]37[/C][C]0.0787022207466955[/C][C]0.157404441493391[/C][C]0.921297779253305[/C][/ROW]
[ROW][C]38[/C][C]0.107111728595488[/C][C]0.214223457190975[/C][C]0.892888271404512[/C][/ROW]
[ROW][C]39[/C][C]0.101966213059102[/C][C]0.203932426118205[/C][C]0.898033786940898[/C][/ROW]
[ROW][C]40[/C][C]0.103420104704999[/C][C]0.206840209409998[/C][C]0.896579895295001[/C][/ROW]
[ROW][C]41[/C][C]0.0897536164216421[/C][C]0.179507232843284[/C][C]0.910246383578358[/C][/ROW]
[ROW][C]42[/C][C]0.185823457638471[/C][C]0.371646915276942[/C][C]0.814176542361529[/C][/ROW]
[ROW][C]43[/C][C]0.188133462461684[/C][C]0.376266924923368[/C][C]0.811866537538316[/C][/ROW]
[ROW][C]44[/C][C]0.653024817680585[/C][C]0.693950364638830[/C][C]0.346975182319415[/C][/ROW]
[ROW][C]45[/C][C]0.673893861864632[/C][C]0.652212276270736[/C][C]0.326106138135368[/C][/ROW]
[ROW][C]46[/C][C]0.620291329403054[/C][C]0.759417341193892[/C][C]0.379708670596946[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57948&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57948&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
10	0.271181533643645	0.542363067287290	0.728818466356355
11	0.617484323797822	0.765031352404357	0.382515676202178
12	0.748682317858286	0.502635364283427	0.251317682141714
13	0.714446407998396	0.571107184003208	0.285553592001604
14	0.63128678058339	0.73742643883322	0.36871321941661
15	0.518963628551646	0.962072742896709	0.481036371448354
16	0.429113147700908	0.858226295401816	0.570886852299092
17	0.433309302455932	0.866618604911864	0.566690697544068
18	0.52274649930562	0.95450700138876	0.47725350069438
19	0.456213793848702	0.912427587697403	0.543786206151298
20	0.382904594504487	0.765809189008973	0.617095405495513
21	0.421654931882504	0.843309863765008	0.578345068117496
22	0.403051616033867	0.806103232067733	0.596948383966133
23	0.340062673122351	0.680125346244703	0.659937326877649
24	0.272373771339463	0.544747542678925	0.727626228660537
25	0.204473000046142	0.408946000092284	0.795526999953858
26	0.159790512233035	0.31958102446607	0.840209487766965
27	0.118118936985378	0.236237873970756	0.881881063014622
28	0.0919210414102472	0.183842082820494	0.908078958589753
29	0.0886529171193103	0.177305834238621	0.91134708288069
30	0.0580341433475005	0.116068286695001	0.9419658566525
31	0.0533490258322642	0.106698051664528	0.946650974167736
32	0.090549438920489	0.181098877840978	0.909450561079511
33	0.0810178521635331	0.162035704327066	0.918982147836467
34	0.0727419075884224	0.145483815176845	0.927258092411578
35	0.0613366679856953	0.122673335971391	0.938663332014305
36	0.0448228584033816	0.089645716806763	0.955177141596618
37	0.0787022207466955	0.157404441493391	0.921297779253305
38	0.107111728595488	0.214223457190975	0.892888271404512
39	0.101966213059102	0.203932426118205	0.898033786940898
40	0.103420104704999	0.206840209409998	0.896579895295001
41	0.0897536164216421	0.179507232843284	0.910246383578358
42	0.185823457638471	0.371646915276942	0.814176542361529
43	0.188133462461684	0.376266924923368	0.811866537538316
44	0.653024817680585	0.693950364638830	0.346975182319415
45	0.673893861864632	0.652212276270736	0.326106138135368
46	0.620291329403054	0.759417341193892	0.379708670596946

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	0	0	OK
10% type I error level	1	0.0270270270270270	OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.0270270270270270 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57948&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0270270270270270[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57948&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57948&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	0	0	OK
10% type I error level	1	0.0270270270270270	OK

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 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = 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