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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, 23 Nov 2010 20:21:47 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/23/t129054363421iezygol543p7y.htm/, Retrieved Fri, 29 Mar 2024 14:30:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=99633, Retrieved Fri, 29 Mar 2024 14:30:13 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120
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]
-   PD    [Multiple Regression] [WS 7 Multiple reg...] [2010-11-23 20:21:47] [b47314d83d48c7bf812ec2bcd743b159] [Current]
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Dataseries X:
102,89	167,16	100,70	106,88	97,69
102,64	179,84	99,62	107,45	101,69
103,33	174,44	99,83	107,65	102,72
103,56	180,35	100,74	107,72	101,85
103,60	193,17	100,84	108,10	114,94
104,24	195,16	100,85	108,38	106,20
105,31	202,43	99,71	108,62	106,76
105,40	189,91	100,80	108,79	107,24
105,89	195,98	100,06	109,03	106,50
105,89	212,09	100,57	109,34	106,77
105,54	205,81	99,79	109,73	108,24
106,15	204,31	99,90	109,76	104,43
106,14	196,07	100,12	109,96	100,90
105,85	199,98	100,40	110,49	103,91
106,27	199,10	100,51	111,37	103,81
106,51	198,31	100,70	111,56	104,59
106,82	195,72	100,62	111,90	104,94
106,53	223,04	99,70	111,96	111,64
107,14	238,41	99,48	112,25	111,27
107,39	259,73	99,36	112,39	106,82
107,33	326,54	99,39	112,30	106,07
107,53	335,15	99,45	112,49	111,35
107,42	321,81	99,28	112,77	112,59
108,25	368,62	99,40	113,15	108,59
108,26	369,59	99,10	113,15	106,83
108,93	425,00	99,48	113,28	112,51
109,43	439,72	99,74	113,83	113,61
109,61	362,23	100,42	114,49	114,96
109,74	328,76	100,80	114,76	118,66
110,12	348,55	100,66	114,96	116,84
110,16	328,18	101,03	115,41	121,19
110,44	329,34	101,22	115,84	117,42
111,23	295,55	101,23	116,31	116,88
112,86	237,38	100,10	117,23	115,01
112,77	226,85	99,98	117,97	111,81
113,04	220,14	99,91	118,08	110,61
112,79	239,36	99,84	118,27	110,67
113,87	224,69	99,68	118,88	113,28
114,28	230,98	99,74	119,11	112,08
115,51	233,47	99,71	119,29	111,41
116,76	256,70	99,35	119,36	113,81
116,91	253,41	99,21	119,48	109,16
116,47	224,95	99,21	120,10	105,09
116,94	210,37	99,16	120,30	102,23
117,24	191,09	99,20	120,54	101,95
116,82	198,85	99,08	120,86	104,75
117,48	211,04	98,16	121,10	107,25
117,11	206,25	98,00	121,42	105,25
117,31	201,19	97,90	121,81	102,75
117,77	194,37	97,88	122,21	107,21
118,37	191,08	97,56	122,82	107,24
117,91	192,87	96,86	123,02	106,01
118,12	181,61	96,86	123,14	121,36
118,02	157,67	96,75	123,12	120,44
117,77	196,14	97,12	123,42	109,40
117,85	246,35	97,22	123,50	111,51
118,68	271,90	97,52	125,77	111,97
118,90	270,29	97,57	125,99	114,64




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99633&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]6 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=99633&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99633&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Bier[t] = -17.2101366875923 -0.0019115819018345Tarwe[t] + 0.187982201415514Suiker[t] + 1.00066611432293Mineraalwater[t] -0.0520181143946909Fruit[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Bier[t] =  -17.2101366875923 -0.0019115819018345Tarwe[t] +  0.187982201415514Suiker[t] +  1.00066611432293Mineraalwater[t] -0.0520181143946909Fruit[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99633&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Bier[t] =  -17.2101366875923 -0.0019115819018345Tarwe[t] +  0.187982201415514Suiker[t] +  1.00066611432293Mineraalwater[t] -0.0520181143946909Fruit[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99633&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99633&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
Bier[t] = -17.2101366875923 -0.0019115819018345Tarwe[t] + 0.187982201415514Suiker[t] + 1.00066611432293Mineraalwater[t] -0.0520181143946909Fruit[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-17.210136687592316.756988-1.0270.3090650.154533
Tarwe-0.00191158190183450.001873-1.02050.3121050.156053
Suiker0.1879822014155140.1433221.31160.1953060.097653
Mineraalwater1.000666114322930.03309930.232600
Fruit-0.05201811439469090.024704-2.10560.0399920.019996

\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) & -17.2101366875923 & 16.756988 & -1.027 & 0.309065 & 0.154533 \tabularnewline
Tarwe & -0.0019115819018345 & 0.001873 & -1.0205 & 0.312105 & 0.156053 \tabularnewline
Suiker & 0.187982201415514 & 0.143322 & 1.3116 & 0.195306 & 0.097653 \tabularnewline
Mineraalwater & 1.00066611432293 & 0.033099 & 30.2326 & 0 & 0 \tabularnewline
Fruit & -0.0520181143946909 & 0.024704 & -2.1056 & 0.039992 & 0.019996 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99633&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]-17.2101366875923[/C][C]16.756988[/C][C]-1.027[/C][C]0.309065[/C][C]0.154533[/C][/ROW]
[ROW][C]Tarwe[/C][C]-0.0019115819018345[/C][C]0.001873[/C][C]-1.0205[/C][C]0.312105[/C][C]0.156053[/C][/ROW]
[ROW][C]Suiker[/C][C]0.187982201415514[/C][C]0.143322[/C][C]1.3116[/C][C]0.195306[/C][C]0.097653[/C][/ROW]
[ROW][C]Mineraalwater[/C][C]1.00066611432293[/C][C]0.033099[/C][C]30.2326[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Fruit[/C][C]-0.0520181143946909[/C][C]0.024704[/C][C]-2.1056[/C][C]0.039992[/C][C]0.019996[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99633&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99633&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)-17.210136687592316.756988-1.0270.3090650.154533
Tarwe-0.00191158190183450.001873-1.02050.3121050.156053
Suiker0.1879822014155140.1433221.31160.1953060.097653
Mineraalwater1.000666114322930.03309930.232600
Fruit-0.05201811439469090.024704-2.10560.0399920.019996







Multiple Linear Regression - Regression Statistics
Multiple R0.988890264126732
R-squared0.977903954484638
Adjusted R-squared0.976236328408007
F-TEST (value)586.404811119399
F-TEST (DF numerator)4
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.812567660931656
Sum Squared Residuals34.9941087903729

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.988890264126732 \tabularnewline
R-squared & 0.977903954484638 \tabularnewline
Adjusted R-squared & 0.976236328408007 \tabularnewline
F-TEST (value) & 586.404811119399 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.812567660931656 \tabularnewline
Sum Squared Residuals & 34.9941087903729 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99633&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.988890264126732[/C][/ROW]
[ROW][C]R-squared[/C][C]0.977903954484638[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.976236328408007[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]586.404811119399[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/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]0.812567660931656[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]34.9941087903729[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99633&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99633&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 R0.988890264126732
R-squared0.977903954484638
Adjusted R-squared0.976236328408007
F-TEST (value)586.404811119399
F-TEST (DF numerator)4
F-TEST (DF denominator)53
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.812567660931656
Sum Squared Residuals34.9941087903729







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1102.89103.269675667857-0.37967566785704
2102.64103.404723259398-0.764723259398334
3103.33103.601076629004-0.271076629003569
4103.56103.876145370778-0.316145370777806
5103.6103.5697731169540.0302268830459472
6104.24104.302673722804-0.0626737228035759
7105.31104.285506536141.02449346385997
8105.4104.6594846856190.740515314380644
9105.89104.7874278265171.10257217348268
10105.89105.1486647693540.741335230645782
11105.54105.3278365430190.212163456980617
12106.15105.5795909573010.570409042698698
13106.14106.0204556431620.119544356838327
14105.85106.439394890585-0.589394890584986
15106.27107.347543116858-1.07754311685796
16106.51107.534322317323-1.02432231732285
17106.82107.846254877167-1.02625487716703
18106.53107.332605434722-0.802605434721564
19107.14107.571308212059-0.431308212058648
20107.39107.879569286803-0.48956928680326
21107.33107.706449601491-0.376449601491115
22107.53107.616740731119-0.0867407311186347
23107.42107.825968309609-0.405968309609473
24108.25108.347370605976-0.097370605975953
25108.26108.380673592441-0.120673592441168
26108.93108.1808097808990.749190219101455
27109.43108.6946931047150.735306895284975
28109.61109.5608646642710.0491353357289608
29109.74109.773991374670-0.0339913746701849
30110.12110.0046498516980.115350148302389
31110.16110.337163143390-0.177163143390146
32110.44110.997057047080-0.557057047079817
33111.23111.561932077062-0.331932077061866
34112.86112.4785956077870.381404392212776
35112.77113.383117591706-0.61311759170566
36113.04113.555280562017-0.515280562017024
37112.79113.692386678622-0.902386678622353
38113.87114.164991484063-0.294991484062631
39114.28114.456821509553-0.176821509552931
40115.51114.6613942417970.848605758202528
41116.76114.4945177551642.26548224483639
42116.91114.8364535170772.07354648292345
43116.47115.7229838544690.747016145530638
44116.94116.0903606385610.84963936143926
45117.24116.3894601651530.85053983484724
46116.82116.5266308617030.293369138297139
47117.48116.4404996344681.03950036553201
48117.11116.8438283449240.266171655075977
49117.31117.355007799778-0.0450077997784247
50117.77117.5325507998490.237449200150527
51118.37118.0875313861590.282468613841315
52117.91118.216637617134-0.306637617133607
53118.12117.5597639071090.560236092891495
54118.02117.6126924786390.407307521360612
55117.77118.483187154614-0.71318715461382
56117.85118.376299915237-0.526299915237298
57118.68120.631437404962-1.95143740496156
58118.9120.725172341612-1.82517234161151

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 102.89 & 103.269675667857 & -0.37967566785704 \tabularnewline
2 & 102.64 & 103.404723259398 & -0.764723259398334 \tabularnewline
3 & 103.33 & 103.601076629004 & -0.271076629003569 \tabularnewline
4 & 103.56 & 103.876145370778 & -0.316145370777806 \tabularnewline
5 & 103.6 & 103.569773116954 & 0.0302268830459472 \tabularnewline
6 & 104.24 & 104.302673722804 & -0.0626737228035759 \tabularnewline
7 & 105.31 & 104.28550653614 & 1.02449346385997 \tabularnewline
8 & 105.4 & 104.659484685619 & 0.740515314380644 \tabularnewline
9 & 105.89 & 104.787427826517 & 1.10257217348268 \tabularnewline
10 & 105.89 & 105.148664769354 & 0.741335230645782 \tabularnewline
11 & 105.54 & 105.327836543019 & 0.212163456980617 \tabularnewline
12 & 106.15 & 105.579590957301 & 0.570409042698698 \tabularnewline
13 & 106.14 & 106.020455643162 & 0.119544356838327 \tabularnewline
14 & 105.85 & 106.439394890585 & -0.589394890584986 \tabularnewline
15 & 106.27 & 107.347543116858 & -1.07754311685796 \tabularnewline
16 & 106.51 & 107.534322317323 & -1.02432231732285 \tabularnewline
17 & 106.82 & 107.846254877167 & -1.02625487716703 \tabularnewline
18 & 106.53 & 107.332605434722 & -0.802605434721564 \tabularnewline
19 & 107.14 & 107.571308212059 & -0.431308212058648 \tabularnewline
20 & 107.39 & 107.879569286803 & -0.48956928680326 \tabularnewline
21 & 107.33 & 107.706449601491 & -0.376449601491115 \tabularnewline
22 & 107.53 & 107.616740731119 & -0.0867407311186347 \tabularnewline
23 & 107.42 & 107.825968309609 & -0.405968309609473 \tabularnewline
24 & 108.25 & 108.347370605976 & -0.097370605975953 \tabularnewline
25 & 108.26 & 108.380673592441 & -0.120673592441168 \tabularnewline
26 & 108.93 & 108.180809780899 & 0.749190219101455 \tabularnewline
27 & 109.43 & 108.694693104715 & 0.735306895284975 \tabularnewline
28 & 109.61 & 109.560864664271 & 0.0491353357289608 \tabularnewline
29 & 109.74 & 109.773991374670 & -0.0339913746701849 \tabularnewline
30 & 110.12 & 110.004649851698 & 0.115350148302389 \tabularnewline
31 & 110.16 & 110.337163143390 & -0.177163143390146 \tabularnewline
32 & 110.44 & 110.997057047080 & -0.557057047079817 \tabularnewline
33 & 111.23 & 111.561932077062 & -0.331932077061866 \tabularnewline
34 & 112.86 & 112.478595607787 & 0.381404392212776 \tabularnewline
35 & 112.77 & 113.383117591706 & -0.61311759170566 \tabularnewline
36 & 113.04 & 113.555280562017 & -0.515280562017024 \tabularnewline
37 & 112.79 & 113.692386678622 & -0.902386678622353 \tabularnewline
38 & 113.87 & 114.164991484063 & -0.294991484062631 \tabularnewline
39 & 114.28 & 114.456821509553 & -0.176821509552931 \tabularnewline
40 & 115.51 & 114.661394241797 & 0.848605758202528 \tabularnewline
41 & 116.76 & 114.494517755164 & 2.26548224483639 \tabularnewline
42 & 116.91 & 114.836453517077 & 2.07354648292345 \tabularnewline
43 & 116.47 & 115.722983854469 & 0.747016145530638 \tabularnewline
44 & 116.94 & 116.090360638561 & 0.84963936143926 \tabularnewline
45 & 117.24 & 116.389460165153 & 0.85053983484724 \tabularnewline
46 & 116.82 & 116.526630861703 & 0.293369138297139 \tabularnewline
47 & 117.48 & 116.440499634468 & 1.03950036553201 \tabularnewline
48 & 117.11 & 116.843828344924 & 0.266171655075977 \tabularnewline
49 & 117.31 & 117.355007799778 & -0.0450077997784247 \tabularnewline
50 & 117.77 & 117.532550799849 & 0.237449200150527 \tabularnewline
51 & 118.37 & 118.087531386159 & 0.282468613841315 \tabularnewline
52 & 117.91 & 118.216637617134 & -0.306637617133607 \tabularnewline
53 & 118.12 & 117.559763907109 & 0.560236092891495 \tabularnewline
54 & 118.02 & 117.612692478639 & 0.407307521360612 \tabularnewline
55 & 117.77 & 118.483187154614 & -0.71318715461382 \tabularnewline
56 & 117.85 & 118.376299915237 & -0.526299915237298 \tabularnewline
57 & 118.68 & 120.631437404962 & -1.95143740496156 \tabularnewline
58 & 118.9 & 120.725172341612 & -1.82517234161151 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99633&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]102.89[/C][C]103.269675667857[/C][C]-0.37967566785704[/C][/ROW]
[ROW][C]2[/C][C]102.64[/C][C]103.404723259398[/C][C]-0.764723259398334[/C][/ROW]
[ROW][C]3[/C][C]103.33[/C][C]103.601076629004[/C][C]-0.271076629003569[/C][/ROW]
[ROW][C]4[/C][C]103.56[/C][C]103.876145370778[/C][C]-0.316145370777806[/C][/ROW]
[ROW][C]5[/C][C]103.6[/C][C]103.569773116954[/C][C]0.0302268830459472[/C][/ROW]
[ROW][C]6[/C][C]104.24[/C][C]104.302673722804[/C][C]-0.0626737228035759[/C][/ROW]
[ROW][C]7[/C][C]105.31[/C][C]104.28550653614[/C][C]1.02449346385997[/C][/ROW]
[ROW][C]8[/C][C]105.4[/C][C]104.659484685619[/C][C]0.740515314380644[/C][/ROW]
[ROW][C]9[/C][C]105.89[/C][C]104.787427826517[/C][C]1.10257217348268[/C][/ROW]
[ROW][C]10[/C][C]105.89[/C][C]105.148664769354[/C][C]0.741335230645782[/C][/ROW]
[ROW][C]11[/C][C]105.54[/C][C]105.327836543019[/C][C]0.212163456980617[/C][/ROW]
[ROW][C]12[/C][C]106.15[/C][C]105.579590957301[/C][C]0.570409042698698[/C][/ROW]
[ROW][C]13[/C][C]106.14[/C][C]106.020455643162[/C][C]0.119544356838327[/C][/ROW]
[ROW][C]14[/C][C]105.85[/C][C]106.439394890585[/C][C]-0.589394890584986[/C][/ROW]
[ROW][C]15[/C][C]106.27[/C][C]107.347543116858[/C][C]-1.07754311685796[/C][/ROW]
[ROW][C]16[/C][C]106.51[/C][C]107.534322317323[/C][C]-1.02432231732285[/C][/ROW]
[ROW][C]17[/C][C]106.82[/C][C]107.846254877167[/C][C]-1.02625487716703[/C][/ROW]
[ROW][C]18[/C][C]106.53[/C][C]107.332605434722[/C][C]-0.802605434721564[/C][/ROW]
[ROW][C]19[/C][C]107.14[/C][C]107.571308212059[/C][C]-0.431308212058648[/C][/ROW]
[ROW][C]20[/C][C]107.39[/C][C]107.879569286803[/C][C]-0.48956928680326[/C][/ROW]
[ROW][C]21[/C][C]107.33[/C][C]107.706449601491[/C][C]-0.376449601491115[/C][/ROW]
[ROW][C]22[/C][C]107.53[/C][C]107.616740731119[/C][C]-0.0867407311186347[/C][/ROW]
[ROW][C]23[/C][C]107.42[/C][C]107.825968309609[/C][C]-0.405968309609473[/C][/ROW]
[ROW][C]24[/C][C]108.25[/C][C]108.347370605976[/C][C]-0.097370605975953[/C][/ROW]
[ROW][C]25[/C][C]108.26[/C][C]108.380673592441[/C][C]-0.120673592441168[/C][/ROW]
[ROW][C]26[/C][C]108.93[/C][C]108.180809780899[/C][C]0.749190219101455[/C][/ROW]
[ROW][C]27[/C][C]109.43[/C][C]108.694693104715[/C][C]0.735306895284975[/C][/ROW]
[ROW][C]28[/C][C]109.61[/C][C]109.560864664271[/C][C]0.0491353357289608[/C][/ROW]
[ROW][C]29[/C][C]109.74[/C][C]109.773991374670[/C][C]-0.0339913746701849[/C][/ROW]
[ROW][C]30[/C][C]110.12[/C][C]110.004649851698[/C][C]0.115350148302389[/C][/ROW]
[ROW][C]31[/C][C]110.16[/C][C]110.337163143390[/C][C]-0.177163143390146[/C][/ROW]
[ROW][C]32[/C][C]110.44[/C][C]110.997057047080[/C][C]-0.557057047079817[/C][/ROW]
[ROW][C]33[/C][C]111.23[/C][C]111.561932077062[/C][C]-0.331932077061866[/C][/ROW]
[ROW][C]34[/C][C]112.86[/C][C]112.478595607787[/C][C]0.381404392212776[/C][/ROW]
[ROW][C]35[/C][C]112.77[/C][C]113.383117591706[/C][C]-0.61311759170566[/C][/ROW]
[ROW][C]36[/C][C]113.04[/C][C]113.555280562017[/C][C]-0.515280562017024[/C][/ROW]
[ROW][C]37[/C][C]112.79[/C][C]113.692386678622[/C][C]-0.902386678622353[/C][/ROW]
[ROW][C]38[/C][C]113.87[/C][C]114.164991484063[/C][C]-0.294991484062631[/C][/ROW]
[ROW][C]39[/C][C]114.28[/C][C]114.456821509553[/C][C]-0.176821509552931[/C][/ROW]
[ROW][C]40[/C][C]115.51[/C][C]114.661394241797[/C][C]0.848605758202528[/C][/ROW]
[ROW][C]41[/C][C]116.76[/C][C]114.494517755164[/C][C]2.26548224483639[/C][/ROW]
[ROW][C]42[/C][C]116.91[/C][C]114.836453517077[/C][C]2.07354648292345[/C][/ROW]
[ROW][C]43[/C][C]116.47[/C][C]115.722983854469[/C][C]0.747016145530638[/C][/ROW]
[ROW][C]44[/C][C]116.94[/C][C]116.090360638561[/C][C]0.84963936143926[/C][/ROW]
[ROW][C]45[/C][C]117.24[/C][C]116.389460165153[/C][C]0.85053983484724[/C][/ROW]
[ROW][C]46[/C][C]116.82[/C][C]116.526630861703[/C][C]0.293369138297139[/C][/ROW]
[ROW][C]47[/C][C]117.48[/C][C]116.440499634468[/C][C]1.03950036553201[/C][/ROW]
[ROW][C]48[/C][C]117.11[/C][C]116.843828344924[/C][C]0.266171655075977[/C][/ROW]
[ROW][C]49[/C][C]117.31[/C][C]117.355007799778[/C][C]-0.0450077997784247[/C][/ROW]
[ROW][C]50[/C][C]117.77[/C][C]117.532550799849[/C][C]0.237449200150527[/C][/ROW]
[ROW][C]51[/C][C]118.37[/C][C]118.087531386159[/C][C]0.282468613841315[/C][/ROW]
[ROW][C]52[/C][C]117.91[/C][C]118.216637617134[/C][C]-0.306637617133607[/C][/ROW]
[ROW][C]53[/C][C]118.12[/C][C]117.559763907109[/C][C]0.560236092891495[/C][/ROW]
[ROW][C]54[/C][C]118.02[/C][C]117.612692478639[/C][C]0.407307521360612[/C][/ROW]
[ROW][C]55[/C][C]117.77[/C][C]118.483187154614[/C][C]-0.71318715461382[/C][/ROW]
[ROW][C]56[/C][C]117.85[/C][C]118.376299915237[/C][C]-0.526299915237298[/C][/ROW]
[ROW][C]57[/C][C]118.68[/C][C]120.631437404962[/C][C]-1.95143740496156[/C][/ROW]
[ROW][C]58[/C][C]118.9[/C][C]120.725172341612[/C][C]-1.82517234161151[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99633&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99633&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
1102.89103.269675667857-0.37967566785704
2102.64103.404723259398-0.764723259398334
3103.33103.601076629004-0.271076629003569
4103.56103.876145370778-0.316145370777806
5103.6103.5697731169540.0302268830459472
6104.24104.302673722804-0.0626737228035759
7105.31104.285506536141.02449346385997
8105.4104.6594846856190.740515314380644
9105.89104.7874278265171.10257217348268
10105.89105.1486647693540.741335230645782
11105.54105.3278365430190.212163456980617
12106.15105.5795909573010.570409042698698
13106.14106.0204556431620.119544356838327
14105.85106.439394890585-0.589394890584986
15106.27107.347543116858-1.07754311685796
16106.51107.534322317323-1.02432231732285
17106.82107.846254877167-1.02625487716703
18106.53107.332605434722-0.802605434721564
19107.14107.571308212059-0.431308212058648
20107.39107.879569286803-0.48956928680326
21107.33107.706449601491-0.376449601491115
22107.53107.616740731119-0.0867407311186347
23107.42107.825968309609-0.405968309609473
24108.25108.347370605976-0.097370605975953
25108.26108.380673592441-0.120673592441168
26108.93108.1808097808990.749190219101455
27109.43108.6946931047150.735306895284975
28109.61109.5608646642710.0491353357289608
29109.74109.773991374670-0.0339913746701849
30110.12110.0046498516980.115350148302389
31110.16110.337163143390-0.177163143390146
32110.44110.997057047080-0.557057047079817
33111.23111.561932077062-0.331932077061866
34112.86112.4785956077870.381404392212776
35112.77113.383117591706-0.61311759170566
36113.04113.555280562017-0.515280562017024
37112.79113.692386678622-0.902386678622353
38113.87114.164991484063-0.294991484062631
39114.28114.456821509553-0.176821509552931
40115.51114.6613942417970.848605758202528
41116.76114.4945177551642.26548224483639
42116.91114.8364535170772.07354648292345
43116.47115.7229838544690.747016145530638
44116.94116.0903606385610.84963936143926
45117.24116.3894601651530.85053983484724
46116.82116.5266308617030.293369138297139
47117.48116.4404996344681.03950036553201
48117.11116.8438283449240.266171655075977
49117.31117.355007799778-0.0450077997784247
50117.77117.5325507998490.237449200150527
51118.37118.0875313861590.282468613841315
52117.91118.216637617134-0.306637617133607
53118.12117.5597639071090.560236092891495
54118.02117.6126924786390.407307521360612
55117.77118.483187154614-0.71318715461382
56117.85118.376299915237-0.526299915237298
57118.68120.631437404962-1.95143740496156
58118.9120.725172341612-1.82517234161151







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.2120884624132190.4241769248264380.787911537586781
90.1089524442561020.2179048885122040.891047555743898
100.06402702421963850.1280540484392770.935972975780361
110.1284074321808500.2568148643616990.87159256781915
120.08001683738703070.1600336747740610.919983162612969
130.0556488065523630.1112976131047260.944351193447637
140.07768654955288410.1553730991057680.922313450447116
150.06948082451964480.1389616490392900.930519175480355
160.0455714084137270.0911428168274540.954428591586273
170.03209888189288300.06419776378576610.967901118107117
180.05999716344228050.1199943268845610.94000283655772
190.07003379645469710.1400675929093940.929966203545303
200.0965667653704720.1931335307409440.903433234629528
210.1010167259619560.2020334519239120.898983274038044
220.07380002673770630.1476000534754130.926199973262294
230.07073990669532630.1414798133906530.929260093304674
240.05471110606896070.1094222121379210.94528889393104
250.05729929030864420.1145985806172880.942700709691356
260.04195161118828690.08390322237657370.958048388811713
270.02863349367388510.05726698734777030.971366506326115
280.01789893803642870.03579787607285740.982101061963571
290.01085737589041010.02171475178082020.98914262410959
300.006515310573357130.01303062114671430.993484689426643
310.003614973592489060.007229947184978130.99638502640751
320.002284027037070150.004568054074140310.99771597296293
330.001639749700185820.003279499400371640.998360250299814
340.005835618026142320.01167123605228460.994164381973858
350.007552895144211450.01510579028842290.992447104855789
360.01343488539126240.02686977078252470.986565114608738
370.1016459345346600.2032918690693190.89835406546534
380.3268705213851510.6537410427703010.67312947861485
390.9200836047128390.1598327905743220.079916395287161
400.9971703572420210.005659285515957820.00282964275797891
410.999031927332680.001936145334641150.000968072667320576
420.9996698761428960.0006602477142085140.000330123857104257
430.9993651736786390.001269652642722740.000634826321361371
440.9982268103717520.003546379256495170.00177318962824759
450.9961030174668030.007793965066393740.00389698253319687
460.9962300993607240.00753980127855190.00376990063927595
470.9931505367298350.01369892654032920.00684946327016459
480.9837745432081960.03245091358360740.0162254567918037
490.9733503105010780.05329937899784480.0266496894989224
500.9566914528836530.0866170942326950.0433085471163475

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.212088462413219 & 0.424176924826438 & 0.787911537586781 \tabularnewline
9 & 0.108952444256102 & 0.217904888512204 & 0.891047555743898 \tabularnewline
10 & 0.0640270242196385 & 0.128054048439277 & 0.935972975780361 \tabularnewline
11 & 0.128407432180850 & 0.256814864361699 & 0.87159256781915 \tabularnewline
12 & 0.0800168373870307 & 0.160033674774061 & 0.919983162612969 \tabularnewline
13 & 0.055648806552363 & 0.111297613104726 & 0.944351193447637 \tabularnewline
14 & 0.0776865495528841 & 0.155373099105768 & 0.922313450447116 \tabularnewline
15 & 0.0694808245196448 & 0.138961649039290 & 0.930519175480355 \tabularnewline
16 & 0.045571408413727 & 0.091142816827454 & 0.954428591586273 \tabularnewline
17 & 0.0320988818928830 & 0.0641977637857661 & 0.967901118107117 \tabularnewline
18 & 0.0599971634422805 & 0.119994326884561 & 0.94000283655772 \tabularnewline
19 & 0.0700337964546971 & 0.140067592909394 & 0.929966203545303 \tabularnewline
20 & 0.096566765370472 & 0.193133530740944 & 0.903433234629528 \tabularnewline
21 & 0.101016725961956 & 0.202033451923912 & 0.898983274038044 \tabularnewline
22 & 0.0738000267377063 & 0.147600053475413 & 0.926199973262294 \tabularnewline
23 & 0.0707399066953263 & 0.141479813390653 & 0.929260093304674 \tabularnewline
24 & 0.0547111060689607 & 0.109422212137921 & 0.94528889393104 \tabularnewline
25 & 0.0572992903086442 & 0.114598580617288 & 0.942700709691356 \tabularnewline
26 & 0.0419516111882869 & 0.0839032223765737 & 0.958048388811713 \tabularnewline
27 & 0.0286334936738851 & 0.0572669873477703 & 0.971366506326115 \tabularnewline
28 & 0.0178989380364287 & 0.0357978760728574 & 0.982101061963571 \tabularnewline
29 & 0.0108573758904101 & 0.0217147517808202 & 0.98914262410959 \tabularnewline
30 & 0.00651531057335713 & 0.0130306211467143 & 0.993484689426643 \tabularnewline
31 & 0.00361497359248906 & 0.00722994718497813 & 0.99638502640751 \tabularnewline
32 & 0.00228402703707015 & 0.00456805407414031 & 0.99771597296293 \tabularnewline
33 & 0.00163974970018582 & 0.00327949940037164 & 0.998360250299814 \tabularnewline
34 & 0.00583561802614232 & 0.0116712360522846 & 0.994164381973858 \tabularnewline
35 & 0.00755289514421145 & 0.0151057902884229 & 0.992447104855789 \tabularnewline
36 & 0.0134348853912624 & 0.0268697707825247 & 0.986565114608738 \tabularnewline
37 & 0.101645934534660 & 0.203291869069319 & 0.89835406546534 \tabularnewline
38 & 0.326870521385151 & 0.653741042770301 & 0.67312947861485 \tabularnewline
39 & 0.920083604712839 & 0.159832790574322 & 0.079916395287161 \tabularnewline
40 & 0.997170357242021 & 0.00565928551595782 & 0.00282964275797891 \tabularnewline
41 & 0.99903192733268 & 0.00193614533464115 & 0.000968072667320576 \tabularnewline
42 & 0.999669876142896 & 0.000660247714208514 & 0.000330123857104257 \tabularnewline
43 & 0.999365173678639 & 0.00126965264272274 & 0.000634826321361371 \tabularnewline
44 & 0.998226810371752 & 0.00354637925649517 & 0.00177318962824759 \tabularnewline
45 & 0.996103017466803 & 0.00779396506639374 & 0.00389698253319687 \tabularnewline
46 & 0.996230099360724 & 0.0075398012785519 & 0.00376990063927595 \tabularnewline
47 & 0.993150536729835 & 0.0136989265403292 & 0.00684946327016459 \tabularnewline
48 & 0.983774543208196 & 0.0324509135836074 & 0.0162254567918037 \tabularnewline
49 & 0.973350310501078 & 0.0532993789978448 & 0.0266496894989224 \tabularnewline
50 & 0.956691452883653 & 0.086617094232695 & 0.0433085471163475 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99633&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]8[/C][C]0.212088462413219[/C][C]0.424176924826438[/C][C]0.787911537586781[/C][/ROW]
[ROW][C]9[/C][C]0.108952444256102[/C][C]0.217904888512204[/C][C]0.891047555743898[/C][/ROW]
[ROW][C]10[/C][C]0.0640270242196385[/C][C]0.128054048439277[/C][C]0.935972975780361[/C][/ROW]
[ROW][C]11[/C][C]0.128407432180850[/C][C]0.256814864361699[/C][C]0.87159256781915[/C][/ROW]
[ROW][C]12[/C][C]0.0800168373870307[/C][C]0.160033674774061[/C][C]0.919983162612969[/C][/ROW]
[ROW][C]13[/C][C]0.055648806552363[/C][C]0.111297613104726[/C][C]0.944351193447637[/C][/ROW]
[ROW][C]14[/C][C]0.0776865495528841[/C][C]0.155373099105768[/C][C]0.922313450447116[/C][/ROW]
[ROW][C]15[/C][C]0.0694808245196448[/C][C]0.138961649039290[/C][C]0.930519175480355[/C][/ROW]
[ROW][C]16[/C][C]0.045571408413727[/C][C]0.091142816827454[/C][C]0.954428591586273[/C][/ROW]
[ROW][C]17[/C][C]0.0320988818928830[/C][C]0.0641977637857661[/C][C]0.967901118107117[/C][/ROW]
[ROW][C]18[/C][C]0.0599971634422805[/C][C]0.119994326884561[/C][C]0.94000283655772[/C][/ROW]
[ROW][C]19[/C][C]0.0700337964546971[/C][C]0.140067592909394[/C][C]0.929966203545303[/C][/ROW]
[ROW][C]20[/C][C]0.096566765370472[/C][C]0.193133530740944[/C][C]0.903433234629528[/C][/ROW]
[ROW][C]21[/C][C]0.101016725961956[/C][C]0.202033451923912[/C][C]0.898983274038044[/C][/ROW]
[ROW][C]22[/C][C]0.0738000267377063[/C][C]0.147600053475413[/C][C]0.926199973262294[/C][/ROW]
[ROW][C]23[/C][C]0.0707399066953263[/C][C]0.141479813390653[/C][C]0.929260093304674[/C][/ROW]
[ROW][C]24[/C][C]0.0547111060689607[/C][C]0.109422212137921[/C][C]0.94528889393104[/C][/ROW]
[ROW][C]25[/C][C]0.0572992903086442[/C][C]0.114598580617288[/C][C]0.942700709691356[/C][/ROW]
[ROW][C]26[/C][C]0.0419516111882869[/C][C]0.0839032223765737[/C][C]0.958048388811713[/C][/ROW]
[ROW][C]27[/C][C]0.0286334936738851[/C][C]0.0572669873477703[/C][C]0.971366506326115[/C][/ROW]
[ROW][C]28[/C][C]0.0178989380364287[/C][C]0.0357978760728574[/C][C]0.982101061963571[/C][/ROW]
[ROW][C]29[/C][C]0.0108573758904101[/C][C]0.0217147517808202[/C][C]0.98914262410959[/C][/ROW]
[ROW][C]30[/C][C]0.00651531057335713[/C][C]0.0130306211467143[/C][C]0.993484689426643[/C][/ROW]
[ROW][C]31[/C][C]0.00361497359248906[/C][C]0.00722994718497813[/C][C]0.99638502640751[/C][/ROW]
[ROW][C]32[/C][C]0.00228402703707015[/C][C]0.00456805407414031[/C][C]0.99771597296293[/C][/ROW]
[ROW][C]33[/C][C]0.00163974970018582[/C][C]0.00327949940037164[/C][C]0.998360250299814[/C][/ROW]
[ROW][C]34[/C][C]0.00583561802614232[/C][C]0.0116712360522846[/C][C]0.994164381973858[/C][/ROW]
[ROW][C]35[/C][C]0.00755289514421145[/C][C]0.0151057902884229[/C][C]0.992447104855789[/C][/ROW]
[ROW][C]36[/C][C]0.0134348853912624[/C][C]0.0268697707825247[/C][C]0.986565114608738[/C][/ROW]
[ROW][C]37[/C][C]0.101645934534660[/C][C]0.203291869069319[/C][C]0.89835406546534[/C][/ROW]
[ROW][C]38[/C][C]0.326870521385151[/C][C]0.653741042770301[/C][C]0.67312947861485[/C][/ROW]
[ROW][C]39[/C][C]0.920083604712839[/C][C]0.159832790574322[/C][C]0.079916395287161[/C][/ROW]
[ROW][C]40[/C][C]0.997170357242021[/C][C]0.00565928551595782[/C][C]0.00282964275797891[/C][/ROW]
[ROW][C]41[/C][C]0.99903192733268[/C][C]0.00193614533464115[/C][C]0.000968072667320576[/C][/ROW]
[ROW][C]42[/C][C]0.999669876142896[/C][C]0.000660247714208514[/C][C]0.000330123857104257[/C][/ROW]
[ROW][C]43[/C][C]0.999365173678639[/C][C]0.00126965264272274[/C][C]0.000634826321361371[/C][/ROW]
[ROW][C]44[/C][C]0.998226810371752[/C][C]0.00354637925649517[/C][C]0.00177318962824759[/C][/ROW]
[ROW][C]45[/C][C]0.996103017466803[/C][C]0.00779396506639374[/C][C]0.00389698253319687[/C][/ROW]
[ROW][C]46[/C][C]0.996230099360724[/C][C]0.0075398012785519[/C][C]0.00376990063927595[/C][/ROW]
[ROW][C]47[/C][C]0.993150536729835[/C][C]0.0136989265403292[/C][C]0.00684946327016459[/C][/ROW]
[ROW][C]48[/C][C]0.983774543208196[/C][C]0.0324509135836074[/C][C]0.0162254567918037[/C][/ROW]
[ROW][C]49[/C][C]0.973350310501078[/C][C]0.0532993789978448[/C][C]0.0266496894989224[/C][/ROW]
[ROW][C]50[/C][C]0.956691452883653[/C][C]0.086617094232695[/C][C]0.0433085471163475[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99633&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99633&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
80.2120884624132190.4241769248264380.787911537586781
90.1089524442561020.2179048885122040.891047555743898
100.06402702421963850.1280540484392770.935972975780361
110.1284074321808500.2568148643616990.87159256781915
120.08001683738703070.1600336747740610.919983162612969
130.0556488065523630.1112976131047260.944351193447637
140.07768654955288410.1553730991057680.922313450447116
150.06948082451964480.1389616490392900.930519175480355
160.0455714084137270.0911428168274540.954428591586273
170.03209888189288300.06419776378576610.967901118107117
180.05999716344228050.1199943268845610.94000283655772
190.07003379645469710.1400675929093940.929966203545303
200.0965667653704720.1931335307409440.903433234629528
210.1010167259619560.2020334519239120.898983274038044
220.07380002673770630.1476000534754130.926199973262294
230.07073990669532630.1414798133906530.929260093304674
240.05471110606896070.1094222121379210.94528889393104
250.05729929030864420.1145985806172880.942700709691356
260.04195161118828690.08390322237657370.958048388811713
270.02863349367388510.05726698734777030.971366506326115
280.01789893803642870.03579787607285740.982101061963571
290.01085737589041010.02171475178082020.98914262410959
300.006515310573357130.01303062114671430.993484689426643
310.003614973592489060.007229947184978130.99638502640751
320.002284027037070150.004568054074140310.99771597296293
330.001639749700185820.003279499400371640.998360250299814
340.005835618026142320.01167123605228460.994164381973858
350.007552895144211450.01510579028842290.992447104855789
360.01343488539126240.02686977078252470.986565114608738
370.1016459345346600.2032918690693190.89835406546534
380.3268705213851510.6537410427703010.67312947861485
390.9200836047128390.1598327905743220.079916395287161
400.9971703572420210.005659285515957820.00282964275797891
410.999031927332680.001936145334641150.000968072667320576
420.9996698761428960.0006602477142085140.000330123857104257
430.9993651736786390.001269652642722740.000634826321361371
440.9982268103717520.003546379256495170.00177318962824759
450.9961030174668030.007793965066393740.00389698253319687
460.9962300993607240.00753980127855190.00376990063927595
470.9931505367298350.01369892654032920.00684946327016459
480.9837745432081960.03245091358360740.0162254567918037
490.9733503105010780.05329937899784480.0266496894989224
500.9566914528836530.0866170942326950.0433085471163475







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.232558139534884NOK
5% type I error level180.418604651162791NOK
10% type I error level240.558139534883721NOK

\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 & 10 & 0.232558139534884 & NOK \tabularnewline
5% type I error level & 18 & 0.418604651162791 & NOK \tabularnewline
10% type I error level & 24 & 0.558139534883721 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=99633&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]10[/C][C]0.232558139534884[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]18[/C][C]0.418604651162791[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]24[/C][C]0.558139534883721[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=99633&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=99633&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 level100.232558139534884NOK
5% type I error level180.418604651162791NOK
10% type I error level240.558139534883721NOK



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')
}