Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 10:35:53 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t1321976246csrpub8xh9jtp5i.htm/, Retrieved Thu, 28 Mar 2024 13:10:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146292, Retrieved Thu, 28 Mar 2024 13:10:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact104
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [] [2011-11-21 14:40:07] [a1957df0bc37aec4aa3c994e6a08412c]
-    D    [Multiple Regression] [] [2011-11-21 16:00:17] [a1957df0bc37aec4aa3c994e6a08412c]
-    D        [Multiple Regression] [] [2011-11-22 15:35:53] [fdaf10f0fcbe7b8f79ecbd42ec74e6ad] [Current]
-    D          [Multiple Regression] [] [2011-11-22 17:36:48] [a1957df0bc37aec4aa3c994e6a08412c]
-    D          [Multiple Regression] [] [2011-11-22 17:46:03] [a1957df0bc37aec4aa3c994e6a08412c]
-    D            [Multiple Regression] [] [2011-11-22 18:09:11] [a1957df0bc37aec4aa3c994e6a08412c]
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Dataseries X:
-1	8,5	6	1,01
-2	8,6	6	1,02
-5	8,6	5	1,04
-4	8,6	5	1,06
-6	8,6	3	1,06
-2	8,4	5	1,06
-2	8	5	1,06
-2	7,9	5	1,06
-2	8	3	1,02
2	8	6	0,98
1	8	6	0,99
-8	8	4	0,99
-1	7,9	6	0,94
1	7,9	5	0,96
-1	7,9	4	0,98
2	8	5	1,01
2	7,9	5	1,01
1	7,5	4	1,02
-1	7,2	3	1,04
-2	7	2	1,03
-2	6,9	3	1,05
-1	7,1	2	1,08
-8	7,1	-1	1,17
-4	7,2	0	1,11
-6	7,1	-2	1,11
-3	6,9	1	1,11
-3	6,8	-2	1,2
-7	6,7	-2	1,21
-9	6,7	-2	1,31
-11	6,9	-6	1,37
-13	7,3	-4	1,37
-11	7,4	-2	1,26
-9	7,3	0	1,23
-17	7,1	-5	1,17
-22	7	-4	1,06
-25	7,1	-5	0,95
-20	7,5	-1	0,92
-24	7,7	-2	0,92
-24	7,8	-4	0,9
-22	7,7	-1	0,93
-19	7,7	1	0,93
-18	7,8	1	0,97
-17	8	-2	0,96
-11	8,1	1	0,99
-11	8,1	1	0,98
-12	8	3	0,96
-10	8,1	3	1
-15	8,2	1	0,99
-15	8,3	1	1,03
-15	8,4	0	1,02
-13	8,5	2	1,07
-8	8,5	2	1,13
-13	8,5	-1	1,15
-9	8,5	1	1,16
-7	8,5	0	1,14
-4	8,3	1	1,15
-4	8,2	1	1,15
-2	8,1	3	1,16
0	7,9	2	1,17
-2	7,6	0	1,22
-3	7,3	0	1,26
1	7,1	3	1,29
-2	7	-2	1,36
-1	7	0	1,38
1	7	1	1,37
-3	7	-1	1,37
-4	6,9	-2	1,37




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146292&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146292&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146292&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Consumentenvertrouwen[t] = -15.0408875261446 -3.78642684773228Werkloosheidsgraad[t] + 2.47940830834836`Financiële_situatie_gezinnen`[t] + 31.1240171018526Diesel[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Consumentenvertrouwen[t] =  -15.0408875261446 -3.78642684773228Werkloosheidsgraad[t] +  2.47940830834836`Financiële_situatie_gezinnen`[t] +  31.1240171018526Diesel[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146292&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Consumentenvertrouwen[t] =  -15.0408875261446 -3.78642684773228Werkloosheidsgraad[t] +  2.47940830834836`Financiële_situatie_gezinnen`[t] +  31.1240171018526Diesel[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146292&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146292&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
Consumentenvertrouwen[t] = -15.0408875261446 -3.78642684773228Werkloosheidsgraad[t] + 2.47940830834836`Financiële_situatie_gezinnen`[t] + 31.1240171018526Diesel[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-15.04088752614468.362891-1.79850.0768840.038442
Werkloosheidsgraad-3.786426847732280.829263-4.5662.4e-051.2e-05
`Financiële_situatie_gezinnen`2.479408308348360.15042816.482300
Diesel31.12401710185263.3048879.417600

\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) & -15.0408875261446 & 8.362891 & -1.7985 & 0.076884 & 0.038442 \tabularnewline
Werkloosheidsgraad & -3.78642684773228 & 0.829263 & -4.566 & 2.4e-05 & 1.2e-05 \tabularnewline
`Financiële_situatie_gezinnen` & 2.47940830834836 & 0.150428 & 16.4823 & 0 & 0 \tabularnewline
Diesel & 31.1240171018526 & 3.304887 & 9.4176 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146292&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]-15.0408875261446[/C][C]8.362891[/C][C]-1.7985[/C][C]0.076884[/C][C]0.038442[/C][/ROW]
[ROW][C]Werkloosheidsgraad[/C][C]-3.78642684773228[/C][C]0.829263[/C][C]-4.566[/C][C]2.4e-05[/C][C]1.2e-05[/C][/ROW]
[ROW][C]`Financiële_situatie_gezinnen`[/C][C]2.47940830834836[/C][C]0.150428[/C][C]16.4823[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Diesel[/C][C]31.1240171018526[/C][C]3.304887[/C][C]9.4176[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146292&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146292&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-15.04088752614468.362891-1.79850.0768840.038442
Werkloosheidsgraad-3.786426847732280.829263-4.5662.4e-051.2e-05
`Financiële_situatie_gezinnen`2.479408308348360.15042816.482300
Diesel31.12401710185263.3048879.417600







Multiple Linear Regression - Regression Statistics
Multiple R0.912457485386655
R-squared0.832578662638138
Adjusted R-squared0.824606218001859
F-TEST (value)104.432040687926
F-TEST (DF numerator)3
F-TEST (DF denominator)63
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.08752694018768
Sum Squared Residuals600.567824202236

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.912457485386655 \tabularnewline
R-squared & 0.832578662638138 \tabularnewline
Adjusted R-squared & 0.824606218001859 \tabularnewline
F-TEST (value) & 104.432040687926 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 63 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.08752694018768 \tabularnewline
Sum Squared Residuals & 600.567824202236 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146292&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.912457485386655[/C][/ROW]
[ROW][C]R-squared[/C][C]0.832578662638138[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.824606218001859[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]104.432040687926[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]63[/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]3.08752694018768[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]600.567824202236[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146292&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146292&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.912457485386655
R-squared0.832578662638138
Adjusted R-squared0.824606218001859
F-TEST (value)104.432040687926
F-TEST (DF numerator)3
F-TEST (DF denominator)63
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.08752694018768
Sum Squared Residuals600.567824202236







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-1-0.91380860890775-0.0861913910922503
2-2-0.981211122662471-1.01878887733753
3-5-2.83813908897377-2.16186091102623
4-4-2.21565874693671-1.78434125306329
5-6-7.174475363633431.17447536363343
6-2-1.45837337739027-0.541626622609734
7-20.056197361702647-2.05619736170265
8-20.434840046475874-2.43484004647587
9-2-6.147579939068174.14757993906817
1020.0456843019027971.9543156980972
1110.3569244729213220.643075527078678
12-8-4.60189214377539-3.39810785622461
13-1-0.820633697398081-0.179366302601919
141-2.677561663709393.67756166370939
15-1-4.534489630020693.53448963002069
162-1.500003493389983.50000349338998
172-1.121360808616753.12136080861675
181-1.774958206853672.77495820685367
19-1-2.49595811884531.4959581188453
20-2-4.529321228665722.52932122866572
21-2-1.04878989350709-0.951210106492913
22-1-3.351763058346322.35176305834632
23-8-7.98882644422466-0.0111735557753398
24-4-7.755501846760683.75550184676068
25-6-12.33567577868426.33567577868417
26-3-4.140165484092651.14016548409265
27-3-8.398586185197765.39858618519776
28-7-7.7087033294060.708703329406003
29-9-4.59630161922074-4.40369838077926
30-11-13.40377919604952.40377919604947
31-13-9.95953331844566-3.04046668155433
32-11-8.80300126772597-2.19699873227403
33-9-4.3992624793116-4.6007375206884
34-17-17.90645967761810.906459677618085
35-22-18.4720505657003-3.52794943429972
36-25-24.7537434400257-0.246256559974348
37-20-17.2844014587807-2.71559854121929
38-24-20.5210951366755-3.47890486332447
39-24-26.48103478018252.48103478018252
40-22-17.7304466573086-4.26955334269136
41-19-12.7716300406119-6.22836995938807
42-18-11.9053120413111-6.09468795868894
43-17-20.41206250692113.41206250692111
44-11-12.41875975359371.41875975359369
45-11-12.72999992461221.72999992461221
46-12-8.01502096517933-3.98497903482067
47-10-7.14870296587845-2.85129703412155
48-15-12.7974024383669-2.20259756163309
49-15-11.931084439066-3.06891556093396
50-15-15.10037560320620.100375603206151
51-13-8.96400081619004-4.03599918380996
52-8-7.09655979007889-0.903440209921114
53-13-13.91230437308690.912304373086904
54-9-8.64224758537167-0.357752414628334
55-7-11.74413623575714.74413623575707
56-4-8.196202386843744.19620238684374
57-4-7.81755970207053.8175597020705
58-2-2.168860229582040.16886022958204
590-3.579742997365423.57974299736542
60-2-5.846430704649813.84643070464981
61-3-3.465541966256030.465541966256026
6215.66368884139108-4.66368884139108
63-2-4.17602881844782.1760288184478
64-11.40526814028596-2.40526814028596
6513.5734362776158-2.5734362776158
66-3-1.38538033908091-1.61461966091909
67-4-3.48614596265604-0.513854037343955

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -1 & -0.91380860890775 & -0.0861913910922503 \tabularnewline
2 & -2 & -0.981211122662471 & -1.01878887733753 \tabularnewline
3 & -5 & -2.83813908897377 & -2.16186091102623 \tabularnewline
4 & -4 & -2.21565874693671 & -1.78434125306329 \tabularnewline
5 & -6 & -7.17447536363343 & 1.17447536363343 \tabularnewline
6 & -2 & -1.45837337739027 & -0.541626622609734 \tabularnewline
7 & -2 & 0.056197361702647 & -2.05619736170265 \tabularnewline
8 & -2 & 0.434840046475874 & -2.43484004647587 \tabularnewline
9 & -2 & -6.14757993906817 & 4.14757993906817 \tabularnewline
10 & 2 & 0.045684301902797 & 1.9543156980972 \tabularnewline
11 & 1 & 0.356924472921322 & 0.643075527078678 \tabularnewline
12 & -8 & -4.60189214377539 & -3.39810785622461 \tabularnewline
13 & -1 & -0.820633697398081 & -0.179366302601919 \tabularnewline
14 & 1 & -2.67756166370939 & 3.67756166370939 \tabularnewline
15 & -1 & -4.53448963002069 & 3.53448963002069 \tabularnewline
16 & 2 & -1.50000349338998 & 3.50000349338998 \tabularnewline
17 & 2 & -1.12136080861675 & 3.12136080861675 \tabularnewline
18 & 1 & -1.77495820685367 & 2.77495820685367 \tabularnewline
19 & -1 & -2.4959581188453 & 1.4959581188453 \tabularnewline
20 & -2 & -4.52932122866572 & 2.52932122866572 \tabularnewline
21 & -2 & -1.04878989350709 & -0.951210106492913 \tabularnewline
22 & -1 & -3.35176305834632 & 2.35176305834632 \tabularnewline
23 & -8 & -7.98882644422466 & -0.0111735557753398 \tabularnewline
24 & -4 & -7.75550184676068 & 3.75550184676068 \tabularnewline
25 & -6 & -12.3356757786842 & 6.33567577868417 \tabularnewline
26 & -3 & -4.14016548409265 & 1.14016548409265 \tabularnewline
27 & -3 & -8.39858618519776 & 5.39858618519776 \tabularnewline
28 & -7 & -7.708703329406 & 0.708703329406003 \tabularnewline
29 & -9 & -4.59630161922074 & -4.40369838077926 \tabularnewline
30 & -11 & -13.4037791960495 & 2.40377919604947 \tabularnewline
31 & -13 & -9.95953331844566 & -3.04046668155433 \tabularnewline
32 & -11 & -8.80300126772597 & -2.19699873227403 \tabularnewline
33 & -9 & -4.3992624793116 & -4.6007375206884 \tabularnewline
34 & -17 & -17.9064596776181 & 0.906459677618085 \tabularnewline
35 & -22 & -18.4720505657003 & -3.52794943429972 \tabularnewline
36 & -25 & -24.7537434400257 & -0.246256559974348 \tabularnewline
37 & -20 & -17.2844014587807 & -2.71559854121929 \tabularnewline
38 & -24 & -20.5210951366755 & -3.47890486332447 \tabularnewline
39 & -24 & -26.4810347801825 & 2.48103478018252 \tabularnewline
40 & -22 & -17.7304466573086 & -4.26955334269136 \tabularnewline
41 & -19 & -12.7716300406119 & -6.22836995938807 \tabularnewline
42 & -18 & -11.9053120413111 & -6.09468795868894 \tabularnewline
43 & -17 & -20.4120625069211 & 3.41206250692111 \tabularnewline
44 & -11 & -12.4187597535937 & 1.41875975359369 \tabularnewline
45 & -11 & -12.7299999246122 & 1.72999992461221 \tabularnewline
46 & -12 & -8.01502096517933 & -3.98497903482067 \tabularnewline
47 & -10 & -7.14870296587845 & -2.85129703412155 \tabularnewline
48 & -15 & -12.7974024383669 & -2.20259756163309 \tabularnewline
49 & -15 & -11.931084439066 & -3.06891556093396 \tabularnewline
50 & -15 & -15.1003756032062 & 0.100375603206151 \tabularnewline
51 & -13 & -8.96400081619004 & -4.03599918380996 \tabularnewline
52 & -8 & -7.09655979007889 & -0.903440209921114 \tabularnewline
53 & -13 & -13.9123043730869 & 0.912304373086904 \tabularnewline
54 & -9 & -8.64224758537167 & -0.357752414628334 \tabularnewline
55 & -7 & -11.7441362357571 & 4.74413623575707 \tabularnewline
56 & -4 & -8.19620238684374 & 4.19620238684374 \tabularnewline
57 & -4 & -7.8175597020705 & 3.8175597020705 \tabularnewline
58 & -2 & -2.16886022958204 & 0.16886022958204 \tabularnewline
59 & 0 & -3.57974299736542 & 3.57974299736542 \tabularnewline
60 & -2 & -5.84643070464981 & 3.84643070464981 \tabularnewline
61 & -3 & -3.46554196625603 & 0.465541966256026 \tabularnewline
62 & 1 & 5.66368884139108 & -4.66368884139108 \tabularnewline
63 & -2 & -4.1760288184478 & 2.1760288184478 \tabularnewline
64 & -1 & 1.40526814028596 & -2.40526814028596 \tabularnewline
65 & 1 & 3.5734362776158 & -2.5734362776158 \tabularnewline
66 & -3 & -1.38538033908091 & -1.61461966091909 \tabularnewline
67 & -4 & -3.48614596265604 & -0.513854037343955 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146292&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[/C][C]-0.91380860890775[/C][C]-0.0861913910922503[/C][/ROW]
[ROW][C]2[/C][C]-2[/C][C]-0.981211122662471[/C][C]-1.01878887733753[/C][/ROW]
[ROW][C]3[/C][C]-5[/C][C]-2.83813908897377[/C][C]-2.16186091102623[/C][/ROW]
[ROW][C]4[/C][C]-4[/C][C]-2.21565874693671[/C][C]-1.78434125306329[/C][/ROW]
[ROW][C]5[/C][C]-6[/C][C]-7.17447536363343[/C][C]1.17447536363343[/C][/ROW]
[ROW][C]6[/C][C]-2[/C][C]-1.45837337739027[/C][C]-0.541626622609734[/C][/ROW]
[ROW][C]7[/C][C]-2[/C][C]0.056197361702647[/C][C]-2.05619736170265[/C][/ROW]
[ROW][C]8[/C][C]-2[/C][C]0.434840046475874[/C][C]-2.43484004647587[/C][/ROW]
[ROW][C]9[/C][C]-2[/C][C]-6.14757993906817[/C][C]4.14757993906817[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]0.045684301902797[/C][C]1.9543156980972[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]0.356924472921322[/C][C]0.643075527078678[/C][/ROW]
[ROW][C]12[/C][C]-8[/C][C]-4.60189214377539[/C][C]-3.39810785622461[/C][/ROW]
[ROW][C]13[/C][C]-1[/C][C]-0.820633697398081[/C][C]-0.179366302601919[/C][/ROW]
[ROW][C]14[/C][C]1[/C][C]-2.67756166370939[/C][C]3.67756166370939[/C][/ROW]
[ROW][C]15[/C][C]-1[/C][C]-4.53448963002069[/C][C]3.53448963002069[/C][/ROW]
[ROW][C]16[/C][C]2[/C][C]-1.50000349338998[/C][C]3.50000349338998[/C][/ROW]
[ROW][C]17[/C][C]2[/C][C]-1.12136080861675[/C][C]3.12136080861675[/C][/ROW]
[ROW][C]18[/C][C]1[/C][C]-1.77495820685367[/C][C]2.77495820685367[/C][/ROW]
[ROW][C]19[/C][C]-1[/C][C]-2.4959581188453[/C][C]1.4959581188453[/C][/ROW]
[ROW][C]20[/C][C]-2[/C][C]-4.52932122866572[/C][C]2.52932122866572[/C][/ROW]
[ROW][C]21[/C][C]-2[/C][C]-1.04878989350709[/C][C]-0.951210106492913[/C][/ROW]
[ROW][C]22[/C][C]-1[/C][C]-3.35176305834632[/C][C]2.35176305834632[/C][/ROW]
[ROW][C]23[/C][C]-8[/C][C]-7.98882644422466[/C][C]-0.0111735557753398[/C][/ROW]
[ROW][C]24[/C][C]-4[/C][C]-7.75550184676068[/C][C]3.75550184676068[/C][/ROW]
[ROW][C]25[/C][C]-6[/C][C]-12.3356757786842[/C][C]6.33567577868417[/C][/ROW]
[ROW][C]26[/C][C]-3[/C][C]-4.14016548409265[/C][C]1.14016548409265[/C][/ROW]
[ROW][C]27[/C][C]-3[/C][C]-8.39858618519776[/C][C]5.39858618519776[/C][/ROW]
[ROW][C]28[/C][C]-7[/C][C]-7.708703329406[/C][C]0.708703329406003[/C][/ROW]
[ROW][C]29[/C][C]-9[/C][C]-4.59630161922074[/C][C]-4.40369838077926[/C][/ROW]
[ROW][C]30[/C][C]-11[/C][C]-13.4037791960495[/C][C]2.40377919604947[/C][/ROW]
[ROW][C]31[/C][C]-13[/C][C]-9.95953331844566[/C][C]-3.04046668155433[/C][/ROW]
[ROW][C]32[/C][C]-11[/C][C]-8.80300126772597[/C][C]-2.19699873227403[/C][/ROW]
[ROW][C]33[/C][C]-9[/C][C]-4.3992624793116[/C][C]-4.6007375206884[/C][/ROW]
[ROW][C]34[/C][C]-17[/C][C]-17.9064596776181[/C][C]0.906459677618085[/C][/ROW]
[ROW][C]35[/C][C]-22[/C][C]-18.4720505657003[/C][C]-3.52794943429972[/C][/ROW]
[ROW][C]36[/C][C]-25[/C][C]-24.7537434400257[/C][C]-0.246256559974348[/C][/ROW]
[ROW][C]37[/C][C]-20[/C][C]-17.2844014587807[/C][C]-2.71559854121929[/C][/ROW]
[ROW][C]38[/C][C]-24[/C][C]-20.5210951366755[/C][C]-3.47890486332447[/C][/ROW]
[ROW][C]39[/C][C]-24[/C][C]-26.4810347801825[/C][C]2.48103478018252[/C][/ROW]
[ROW][C]40[/C][C]-22[/C][C]-17.7304466573086[/C][C]-4.26955334269136[/C][/ROW]
[ROW][C]41[/C][C]-19[/C][C]-12.7716300406119[/C][C]-6.22836995938807[/C][/ROW]
[ROW][C]42[/C][C]-18[/C][C]-11.9053120413111[/C][C]-6.09468795868894[/C][/ROW]
[ROW][C]43[/C][C]-17[/C][C]-20.4120625069211[/C][C]3.41206250692111[/C][/ROW]
[ROW][C]44[/C][C]-11[/C][C]-12.4187597535937[/C][C]1.41875975359369[/C][/ROW]
[ROW][C]45[/C][C]-11[/C][C]-12.7299999246122[/C][C]1.72999992461221[/C][/ROW]
[ROW][C]46[/C][C]-12[/C][C]-8.01502096517933[/C][C]-3.98497903482067[/C][/ROW]
[ROW][C]47[/C][C]-10[/C][C]-7.14870296587845[/C][C]-2.85129703412155[/C][/ROW]
[ROW][C]48[/C][C]-15[/C][C]-12.7974024383669[/C][C]-2.20259756163309[/C][/ROW]
[ROW][C]49[/C][C]-15[/C][C]-11.931084439066[/C][C]-3.06891556093396[/C][/ROW]
[ROW][C]50[/C][C]-15[/C][C]-15.1003756032062[/C][C]0.100375603206151[/C][/ROW]
[ROW][C]51[/C][C]-13[/C][C]-8.96400081619004[/C][C]-4.03599918380996[/C][/ROW]
[ROW][C]52[/C][C]-8[/C][C]-7.09655979007889[/C][C]-0.903440209921114[/C][/ROW]
[ROW][C]53[/C][C]-13[/C][C]-13.9123043730869[/C][C]0.912304373086904[/C][/ROW]
[ROW][C]54[/C][C]-9[/C][C]-8.64224758537167[/C][C]-0.357752414628334[/C][/ROW]
[ROW][C]55[/C][C]-7[/C][C]-11.7441362357571[/C][C]4.74413623575707[/C][/ROW]
[ROW][C]56[/C][C]-4[/C][C]-8.19620238684374[/C][C]4.19620238684374[/C][/ROW]
[ROW][C]57[/C][C]-4[/C][C]-7.8175597020705[/C][C]3.8175597020705[/C][/ROW]
[ROW][C]58[/C][C]-2[/C][C]-2.16886022958204[/C][C]0.16886022958204[/C][/ROW]
[ROW][C]59[/C][C]0[/C][C]-3.57974299736542[/C][C]3.57974299736542[/C][/ROW]
[ROW][C]60[/C][C]-2[/C][C]-5.84643070464981[/C][C]3.84643070464981[/C][/ROW]
[ROW][C]61[/C][C]-3[/C][C]-3.46554196625603[/C][C]0.465541966256026[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]5.66368884139108[/C][C]-4.66368884139108[/C][/ROW]
[ROW][C]63[/C][C]-2[/C][C]-4.1760288184478[/C][C]2.1760288184478[/C][/ROW]
[ROW][C]64[/C][C]-1[/C][C]1.40526814028596[/C][C]-2.40526814028596[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]3.5734362776158[/C][C]-2.5734362776158[/C][/ROW]
[ROW][C]66[/C][C]-3[/C][C]-1.38538033908091[/C][C]-1.61461966091909[/C][/ROW]
[ROW][C]67[/C][C]-4[/C][C]-3.48614596265604[/C][C]-0.513854037343955[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146292&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146292&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-1-0.91380860890775-0.0861913910922503
2-2-0.981211122662471-1.01878887733753
3-5-2.83813908897377-2.16186091102623
4-4-2.21565874693671-1.78434125306329
5-6-7.174475363633431.17447536363343
6-2-1.45837337739027-0.541626622609734
7-20.056197361702647-2.05619736170265
8-20.434840046475874-2.43484004647587
9-2-6.147579939068174.14757993906817
1020.0456843019027971.9543156980972
1110.3569244729213220.643075527078678
12-8-4.60189214377539-3.39810785622461
13-1-0.820633697398081-0.179366302601919
141-2.677561663709393.67756166370939
15-1-4.534489630020693.53448963002069
162-1.500003493389983.50000349338998
172-1.121360808616753.12136080861675
181-1.774958206853672.77495820685367
19-1-2.49595811884531.4959581188453
20-2-4.529321228665722.52932122866572
21-2-1.04878989350709-0.951210106492913
22-1-3.351763058346322.35176305834632
23-8-7.98882644422466-0.0111735557753398
24-4-7.755501846760683.75550184676068
25-6-12.33567577868426.33567577868417
26-3-4.140165484092651.14016548409265
27-3-8.398586185197765.39858618519776
28-7-7.7087033294060.708703329406003
29-9-4.59630161922074-4.40369838077926
30-11-13.40377919604952.40377919604947
31-13-9.95953331844566-3.04046668155433
32-11-8.80300126772597-2.19699873227403
33-9-4.3992624793116-4.6007375206884
34-17-17.90645967761810.906459677618085
35-22-18.4720505657003-3.52794943429972
36-25-24.7537434400257-0.246256559974348
37-20-17.2844014587807-2.71559854121929
38-24-20.5210951366755-3.47890486332447
39-24-26.48103478018252.48103478018252
40-22-17.7304466573086-4.26955334269136
41-19-12.7716300406119-6.22836995938807
42-18-11.9053120413111-6.09468795868894
43-17-20.41206250692113.41206250692111
44-11-12.41875975359371.41875975359369
45-11-12.72999992461221.72999992461221
46-12-8.01502096517933-3.98497903482067
47-10-7.14870296587845-2.85129703412155
48-15-12.7974024383669-2.20259756163309
49-15-11.931084439066-3.06891556093396
50-15-15.10037560320620.100375603206151
51-13-8.96400081619004-4.03599918380996
52-8-7.09655979007889-0.903440209921114
53-13-13.91230437308690.912304373086904
54-9-8.64224758537167-0.357752414628334
55-7-11.74413623575714.74413623575707
56-4-8.196202386843744.19620238684374
57-4-7.81755970207053.8175597020705
58-2-2.168860229582040.16886022958204
590-3.579742997365423.57974299736542
60-2-5.846430704649813.84643070464981
61-3-3.465541966256030.465541966256026
6215.66368884139108-4.66368884139108
63-2-4.17602881844782.1760288184478
64-11.40526814028596-2.40526814028596
6513.5734362776158-2.5734362776158
66-3-1.38538033908091-1.61461966091909
67-4-3.48614596265604-0.513854037343955







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.05364621609196820.1072924321839360.946353783908032
80.01773842022167460.03547684044334920.982261579778325
90.006465206927718320.01293041385543660.993534793072282
100.001742558878624770.003485117757249540.998257441121375
110.000467115363930320.000934230727860640.99953288463607
120.1319424152690210.2638848305380430.868057584730978
130.08443218570211510.168864371404230.915567814297885
140.07902427930329290.1580485586065860.920975720696707
150.05855465121949720.1171093024389940.941445348780503
160.07317964268221170.1463592853644230.926820357317788
170.07054099379547290.1410819875909460.929459006204527
180.05046524905688770.1009304981137750.949534750943112
190.03579239295812030.07158478591624060.96420760704188
200.02709944673601890.05419889347203770.972900553263981
210.0232815564678940.0465631129357880.976718443532106
220.02026041539710650.0405208307942130.979739584602894
230.01227496683831360.02454993367662710.987725033161686
240.01338156909779360.02676313819558730.986618430902206
250.02244434609497770.04488869218995530.977555653905022
260.02092864092472060.04185728184944110.979071359075279
270.07562255697439350.1512451139487870.924377443025607
280.076154938992370.152309877984740.92384506100763
290.0701900009471050.140380001894210.929809999052895
300.05593887087831330.1118777417566270.944061129121687
310.08704278131183050.1740855626236610.912957218688169
320.09214662193521420.1842932438704280.907853378064786
330.1073149503656240.2146299007312480.892685049634376
340.2125415692387350.4250831384774710.787458430761265
350.6274095824693030.7451808350613940.372590417530697
360.6303279490436910.7393441019126170.369672050956309
370.6408841320120970.7182317359758050.359115867987903
380.6485378240856040.7029243518287920.351462175914396
390.6231620835633730.7536758328732530.376837916436627
400.6505509224245510.6988981551508980.349449077575449
410.7490611482888690.5018777034222620.250938851711131
420.851382308390030.2972353832199390.14861769160997
430.8480827792741880.3038344414516250.151917220725812
440.8261432770719810.3477134458560380.173856722928019
450.8313240372338980.3373519255322050.168675962766102
460.7987235461407740.4025529077184520.201276453859226
470.7449561752958320.5100876494083360.255043824704168
480.679669135662390.640661728675220.32033086433761
490.6967803224136350.606439355172730.303219677586365
500.7218450819680340.5563098360639310.278154918031966
510.9752481483229450.04950370335411010.024751851677055
520.9707008786450590.05859824270988110.0292991213549405
530.9922933872992420.01541322540151640.00770661270075818
540.9979323142784220.004135371443155140.00206768572157757
550.9972727293734150.00545454125316980.0027272706265849
560.9933541579916450.013291684016710.00664584200835498
570.9863213784588280.02735724308234360.0136786215411718
580.9941141050434320.01177178991313580.00588589495656792
590.9794496844089670.04110063118206590.020550315591033
600.9373838056550720.1252323886898560.0626161943449279

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.0536462160919682 & 0.107292432183936 & 0.946353783908032 \tabularnewline
8 & 0.0177384202216746 & 0.0354768404433492 & 0.982261579778325 \tabularnewline
9 & 0.00646520692771832 & 0.0129304138554366 & 0.993534793072282 \tabularnewline
10 & 0.00174255887862477 & 0.00348511775724954 & 0.998257441121375 \tabularnewline
11 & 0.00046711536393032 & 0.00093423072786064 & 0.99953288463607 \tabularnewline
12 & 0.131942415269021 & 0.263884830538043 & 0.868057584730978 \tabularnewline
13 & 0.0844321857021151 & 0.16886437140423 & 0.915567814297885 \tabularnewline
14 & 0.0790242793032929 & 0.158048558606586 & 0.920975720696707 \tabularnewline
15 & 0.0585546512194972 & 0.117109302438994 & 0.941445348780503 \tabularnewline
16 & 0.0731796426822117 & 0.146359285364423 & 0.926820357317788 \tabularnewline
17 & 0.0705409937954729 & 0.141081987590946 & 0.929459006204527 \tabularnewline
18 & 0.0504652490568877 & 0.100930498113775 & 0.949534750943112 \tabularnewline
19 & 0.0357923929581203 & 0.0715847859162406 & 0.96420760704188 \tabularnewline
20 & 0.0270994467360189 & 0.0541988934720377 & 0.972900553263981 \tabularnewline
21 & 0.023281556467894 & 0.046563112935788 & 0.976718443532106 \tabularnewline
22 & 0.0202604153971065 & 0.040520830794213 & 0.979739584602894 \tabularnewline
23 & 0.0122749668383136 & 0.0245499336766271 & 0.987725033161686 \tabularnewline
24 & 0.0133815690977936 & 0.0267631381955873 & 0.986618430902206 \tabularnewline
25 & 0.0224443460949777 & 0.0448886921899553 & 0.977555653905022 \tabularnewline
26 & 0.0209286409247206 & 0.0418572818494411 & 0.979071359075279 \tabularnewline
27 & 0.0756225569743935 & 0.151245113948787 & 0.924377443025607 \tabularnewline
28 & 0.07615493899237 & 0.15230987798474 & 0.92384506100763 \tabularnewline
29 & 0.070190000947105 & 0.14038000189421 & 0.929809999052895 \tabularnewline
30 & 0.0559388708783133 & 0.111877741756627 & 0.944061129121687 \tabularnewline
31 & 0.0870427813118305 & 0.174085562623661 & 0.912957218688169 \tabularnewline
32 & 0.0921466219352142 & 0.184293243870428 & 0.907853378064786 \tabularnewline
33 & 0.107314950365624 & 0.214629900731248 & 0.892685049634376 \tabularnewline
34 & 0.212541569238735 & 0.425083138477471 & 0.787458430761265 \tabularnewline
35 & 0.627409582469303 & 0.745180835061394 & 0.372590417530697 \tabularnewline
36 & 0.630327949043691 & 0.739344101912617 & 0.369672050956309 \tabularnewline
37 & 0.640884132012097 & 0.718231735975805 & 0.359115867987903 \tabularnewline
38 & 0.648537824085604 & 0.702924351828792 & 0.351462175914396 \tabularnewline
39 & 0.623162083563373 & 0.753675832873253 & 0.376837916436627 \tabularnewline
40 & 0.650550922424551 & 0.698898155150898 & 0.349449077575449 \tabularnewline
41 & 0.749061148288869 & 0.501877703422262 & 0.250938851711131 \tabularnewline
42 & 0.85138230839003 & 0.297235383219939 & 0.14861769160997 \tabularnewline
43 & 0.848082779274188 & 0.303834441451625 & 0.151917220725812 \tabularnewline
44 & 0.826143277071981 & 0.347713445856038 & 0.173856722928019 \tabularnewline
45 & 0.831324037233898 & 0.337351925532205 & 0.168675962766102 \tabularnewline
46 & 0.798723546140774 & 0.402552907718452 & 0.201276453859226 \tabularnewline
47 & 0.744956175295832 & 0.510087649408336 & 0.255043824704168 \tabularnewline
48 & 0.67966913566239 & 0.64066172867522 & 0.32033086433761 \tabularnewline
49 & 0.696780322413635 & 0.60643935517273 & 0.303219677586365 \tabularnewline
50 & 0.721845081968034 & 0.556309836063931 & 0.278154918031966 \tabularnewline
51 & 0.975248148322945 & 0.0495037033541101 & 0.024751851677055 \tabularnewline
52 & 0.970700878645059 & 0.0585982427098811 & 0.0292991213549405 \tabularnewline
53 & 0.992293387299242 & 0.0154132254015164 & 0.00770661270075818 \tabularnewline
54 & 0.997932314278422 & 0.00413537144315514 & 0.00206768572157757 \tabularnewline
55 & 0.997272729373415 & 0.0054545412531698 & 0.0027272706265849 \tabularnewline
56 & 0.993354157991645 & 0.01329168401671 & 0.00664584200835498 \tabularnewline
57 & 0.986321378458828 & 0.0273572430823436 & 0.0136786215411718 \tabularnewline
58 & 0.994114105043432 & 0.0117717899131358 & 0.00588589495656792 \tabularnewline
59 & 0.979449684408967 & 0.0411006311820659 & 0.020550315591033 \tabularnewline
60 & 0.937383805655072 & 0.125232388689856 & 0.0626161943449279 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146292&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.0536462160919682[/C][C]0.107292432183936[/C][C]0.946353783908032[/C][/ROW]
[ROW][C]8[/C][C]0.0177384202216746[/C][C]0.0354768404433492[/C][C]0.982261579778325[/C][/ROW]
[ROW][C]9[/C][C]0.00646520692771832[/C][C]0.0129304138554366[/C][C]0.993534793072282[/C][/ROW]
[ROW][C]10[/C][C]0.00174255887862477[/C][C]0.00348511775724954[/C][C]0.998257441121375[/C][/ROW]
[ROW][C]11[/C][C]0.00046711536393032[/C][C]0.00093423072786064[/C][C]0.99953288463607[/C][/ROW]
[ROW][C]12[/C][C]0.131942415269021[/C][C]0.263884830538043[/C][C]0.868057584730978[/C][/ROW]
[ROW][C]13[/C][C]0.0844321857021151[/C][C]0.16886437140423[/C][C]0.915567814297885[/C][/ROW]
[ROW][C]14[/C][C]0.0790242793032929[/C][C]0.158048558606586[/C][C]0.920975720696707[/C][/ROW]
[ROW][C]15[/C][C]0.0585546512194972[/C][C]0.117109302438994[/C][C]0.941445348780503[/C][/ROW]
[ROW][C]16[/C][C]0.0731796426822117[/C][C]0.146359285364423[/C][C]0.926820357317788[/C][/ROW]
[ROW][C]17[/C][C]0.0705409937954729[/C][C]0.141081987590946[/C][C]0.929459006204527[/C][/ROW]
[ROW][C]18[/C][C]0.0504652490568877[/C][C]0.100930498113775[/C][C]0.949534750943112[/C][/ROW]
[ROW][C]19[/C][C]0.0357923929581203[/C][C]0.0715847859162406[/C][C]0.96420760704188[/C][/ROW]
[ROW][C]20[/C][C]0.0270994467360189[/C][C]0.0541988934720377[/C][C]0.972900553263981[/C][/ROW]
[ROW][C]21[/C][C]0.023281556467894[/C][C]0.046563112935788[/C][C]0.976718443532106[/C][/ROW]
[ROW][C]22[/C][C]0.0202604153971065[/C][C]0.040520830794213[/C][C]0.979739584602894[/C][/ROW]
[ROW][C]23[/C][C]0.0122749668383136[/C][C]0.0245499336766271[/C][C]0.987725033161686[/C][/ROW]
[ROW][C]24[/C][C]0.0133815690977936[/C][C]0.0267631381955873[/C][C]0.986618430902206[/C][/ROW]
[ROW][C]25[/C][C]0.0224443460949777[/C][C]0.0448886921899553[/C][C]0.977555653905022[/C][/ROW]
[ROW][C]26[/C][C]0.0209286409247206[/C][C]0.0418572818494411[/C][C]0.979071359075279[/C][/ROW]
[ROW][C]27[/C][C]0.0756225569743935[/C][C]0.151245113948787[/C][C]0.924377443025607[/C][/ROW]
[ROW][C]28[/C][C]0.07615493899237[/C][C]0.15230987798474[/C][C]0.92384506100763[/C][/ROW]
[ROW][C]29[/C][C]0.070190000947105[/C][C]0.14038000189421[/C][C]0.929809999052895[/C][/ROW]
[ROW][C]30[/C][C]0.0559388708783133[/C][C]0.111877741756627[/C][C]0.944061129121687[/C][/ROW]
[ROW][C]31[/C][C]0.0870427813118305[/C][C]0.174085562623661[/C][C]0.912957218688169[/C][/ROW]
[ROW][C]32[/C][C]0.0921466219352142[/C][C]0.184293243870428[/C][C]0.907853378064786[/C][/ROW]
[ROW][C]33[/C][C]0.107314950365624[/C][C]0.214629900731248[/C][C]0.892685049634376[/C][/ROW]
[ROW][C]34[/C][C]0.212541569238735[/C][C]0.425083138477471[/C][C]0.787458430761265[/C][/ROW]
[ROW][C]35[/C][C]0.627409582469303[/C][C]0.745180835061394[/C][C]0.372590417530697[/C][/ROW]
[ROW][C]36[/C][C]0.630327949043691[/C][C]0.739344101912617[/C][C]0.369672050956309[/C][/ROW]
[ROW][C]37[/C][C]0.640884132012097[/C][C]0.718231735975805[/C][C]0.359115867987903[/C][/ROW]
[ROW][C]38[/C][C]0.648537824085604[/C][C]0.702924351828792[/C][C]0.351462175914396[/C][/ROW]
[ROW][C]39[/C][C]0.623162083563373[/C][C]0.753675832873253[/C][C]0.376837916436627[/C][/ROW]
[ROW][C]40[/C][C]0.650550922424551[/C][C]0.698898155150898[/C][C]0.349449077575449[/C][/ROW]
[ROW][C]41[/C][C]0.749061148288869[/C][C]0.501877703422262[/C][C]0.250938851711131[/C][/ROW]
[ROW][C]42[/C][C]0.85138230839003[/C][C]0.297235383219939[/C][C]0.14861769160997[/C][/ROW]
[ROW][C]43[/C][C]0.848082779274188[/C][C]0.303834441451625[/C][C]0.151917220725812[/C][/ROW]
[ROW][C]44[/C][C]0.826143277071981[/C][C]0.347713445856038[/C][C]0.173856722928019[/C][/ROW]
[ROW][C]45[/C][C]0.831324037233898[/C][C]0.337351925532205[/C][C]0.168675962766102[/C][/ROW]
[ROW][C]46[/C][C]0.798723546140774[/C][C]0.402552907718452[/C][C]0.201276453859226[/C][/ROW]
[ROW][C]47[/C][C]0.744956175295832[/C][C]0.510087649408336[/C][C]0.255043824704168[/C][/ROW]
[ROW][C]48[/C][C]0.67966913566239[/C][C]0.64066172867522[/C][C]0.32033086433761[/C][/ROW]
[ROW][C]49[/C][C]0.696780322413635[/C][C]0.60643935517273[/C][C]0.303219677586365[/C][/ROW]
[ROW][C]50[/C][C]0.721845081968034[/C][C]0.556309836063931[/C][C]0.278154918031966[/C][/ROW]
[ROW][C]51[/C][C]0.975248148322945[/C][C]0.0495037033541101[/C][C]0.024751851677055[/C][/ROW]
[ROW][C]52[/C][C]0.970700878645059[/C][C]0.0585982427098811[/C][C]0.0292991213549405[/C][/ROW]
[ROW][C]53[/C][C]0.992293387299242[/C][C]0.0154132254015164[/C][C]0.00770661270075818[/C][/ROW]
[ROW][C]54[/C][C]0.997932314278422[/C][C]0.00413537144315514[/C][C]0.00206768572157757[/C][/ROW]
[ROW][C]55[/C][C]0.997272729373415[/C][C]0.0054545412531698[/C][C]0.0027272706265849[/C][/ROW]
[ROW][C]56[/C][C]0.993354157991645[/C][C]0.01329168401671[/C][C]0.00664584200835498[/C][/ROW]
[ROW][C]57[/C][C]0.986321378458828[/C][C]0.0273572430823436[/C][C]0.0136786215411718[/C][/ROW]
[ROW][C]58[/C][C]0.994114105043432[/C][C]0.0117717899131358[/C][C]0.00588589495656792[/C][/ROW]
[ROW][C]59[/C][C]0.979449684408967[/C][C]0.0411006311820659[/C][C]0.020550315591033[/C][/ROW]
[ROW][C]60[/C][C]0.937383805655072[/C][C]0.125232388689856[/C][C]0.0626161943449279[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146292&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146292&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.05364621609196820.1072924321839360.946353783908032
80.01773842022167460.03547684044334920.982261579778325
90.006465206927718320.01293041385543660.993534793072282
100.001742558878624770.003485117757249540.998257441121375
110.000467115363930320.000934230727860640.99953288463607
120.1319424152690210.2638848305380430.868057584730978
130.08443218570211510.168864371404230.915567814297885
140.07902427930329290.1580485586065860.920975720696707
150.05855465121949720.1171093024389940.941445348780503
160.07317964268221170.1463592853644230.926820357317788
170.07054099379547290.1410819875909460.929459006204527
180.05046524905688770.1009304981137750.949534750943112
190.03579239295812030.07158478591624060.96420760704188
200.02709944673601890.05419889347203770.972900553263981
210.0232815564678940.0465631129357880.976718443532106
220.02026041539710650.0405208307942130.979739584602894
230.01227496683831360.02454993367662710.987725033161686
240.01338156909779360.02676313819558730.986618430902206
250.02244434609497770.04488869218995530.977555653905022
260.02092864092472060.04185728184944110.979071359075279
270.07562255697439350.1512451139487870.924377443025607
280.076154938992370.152309877984740.92384506100763
290.0701900009471050.140380001894210.929809999052895
300.05593887087831330.1118777417566270.944061129121687
310.08704278131183050.1740855626236610.912957218688169
320.09214662193521420.1842932438704280.907853378064786
330.1073149503656240.2146299007312480.892685049634376
340.2125415692387350.4250831384774710.787458430761265
350.6274095824693030.7451808350613940.372590417530697
360.6303279490436910.7393441019126170.369672050956309
370.6408841320120970.7182317359758050.359115867987903
380.6485378240856040.7029243518287920.351462175914396
390.6231620835633730.7536758328732530.376837916436627
400.6505509224245510.6988981551508980.349449077575449
410.7490611482888690.5018777034222620.250938851711131
420.851382308390030.2972353832199390.14861769160997
430.8480827792741880.3038344414516250.151917220725812
440.8261432770719810.3477134458560380.173856722928019
450.8313240372338980.3373519255322050.168675962766102
460.7987235461407740.4025529077184520.201276453859226
470.7449561752958320.5100876494083360.255043824704168
480.679669135662390.640661728675220.32033086433761
490.6967803224136350.606439355172730.303219677586365
500.7218450819680340.5563098360639310.278154918031966
510.9752481483229450.04950370335411010.024751851677055
520.9707008786450590.05859824270988110.0292991213549405
530.9922933872992420.01541322540151640.00770661270075818
540.9979323142784220.004135371443155140.00206768572157757
550.9972727293734150.00545454125316980.0027272706265849
560.9933541579916450.013291684016710.00664584200835498
570.9863213784588280.02735724308234360.0136786215411718
580.9941141050434320.01177178991313580.00588589495656792
590.9794496844089670.04110063118206590.020550315591033
600.9373838056550720.1252323886898560.0626161943449279







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0740740740740741NOK
5% type I error level180.333333333333333NOK
10% type I error level210.388888888888889NOK

\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 & 4 & 0.0740740740740741 & NOK \tabularnewline
5% type I error level & 18 & 0.333333333333333 & NOK \tabularnewline
10% type I error level & 21 & 0.388888888888889 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146292&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]4[/C][C]0.0740740740740741[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]18[/C][C]0.333333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]21[/C][C]0.388888888888889[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146292&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146292&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0740740740740741NOK
5% type I error level180.333333333333333NOK
10% type I error level210.388888888888889NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}