## Free Statistics

of Irreproducible Research!

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, 24 Nov 2009 11:29:59 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/24/t12590874782x5cd6u1nixzcil.htm/, Retrieved Fri, 14 Jun 2024 19:19:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59215, Retrieved Fri, 14 Jun 2024 19:19:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact165
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2009-11-20 13:55:26] [5482608004c1d7bbf873930172393a2d]
-   PD        [Multiple Regression] [Workshop7/module4] [2009-11-24 18:29:59] [f94f05f163a3ee3ab544c4fef41db0eb] [Current]
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Dataseries X:
114.08	136.49
112.95	142.62
135.31	141.71
134.31	149.51
133.03	147.39
140.11	131.96
124.69	136.38
131.68	127.34
150.95	133.85
137.26	125.14
130.51	141.25
143.15	149.32
118.01	120.92
122.56	134.85
147.97	131.93
135.74	134.22
151.62	143.07
154.82	145.37
145.59	134.32
147.12	126.31
175.86	162.21
140.66	124.09
152.69	153.91
154.38	154.34
132.45	138.70
136.44	150.98
153.24	146.39
154.11	178.30
155.93	168.23
142.53	162.52
148.73	158.86
147.73	152.17
166.79	171.01
144.30	171.49
156.07	189.62
161.70	177.46
152.10	179.98
140.45	156.96
155.56	167.89
174.53	194.78
167.16	192.78
159.48	165.06
173.22	196.60
176.13	151.64
180.31	187.02
185.84	210.99
169.43	219.08
195.25	235.68
174.99	241.44
156.42	187.46
182.08	229.57
182.00	208.44
153.28	215.09
136.72	217.00
130.19	171.08
132.04	178.41
143.89	196.34
133.38	172.11
127.98	154.93
150.45	182.26
133.55	181.74


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59215&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59215&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59215&T=0

As an alternative you can also use a QR Code:

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

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135

 Multiple Linear Regression - Estimated Regression Equation InvoerEU[t] = + 75.9046167757232 + 0.469785279416574InvoerAM[t] -0.14179725109994t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
InvoerEU[t] =  +  75.9046167757232 +  0.469785279416574InvoerAM[t] -0.14179725109994t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59215&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]InvoerEU[t] =  +  75.9046167757232 +  0.469785279416574InvoerAM[t] -0.14179725109994t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59215&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59215&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 InvoerEU[t] = + 75.9046167757232 + 0.469785279416574InvoerAM[t] -0.14179725109994t + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 75.9046167757232 12.229983 6.2064 0 0 InvoerAM 0.469785279416574 0.093544 5.0221 5e-06 3e-06 t -0.14179725109994 0.159358 -0.8898 0.377249 0.188624

\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) & 75.9046167757232 & 12.229983 & 6.2064 & 0 & 0 \tabularnewline
InvoerAM & 0.469785279416574 & 0.093544 & 5.0221 & 5e-06 & 3e-06 \tabularnewline
t & -0.14179725109994 & 0.159358 & -0.8898 & 0.377249 & 0.188624 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59215&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]75.9046167757232[/C][C]12.229983[/C][C]6.2064[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]InvoerAM[/C][C]0.469785279416574[/C][C]0.093544[/C][C]5.0221[/C][C]5e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]t[/C][C]-0.14179725109994[/C][C]0.159358[/C][C]-0.8898[/C][C]0.377249[/C][C]0.188624[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59215&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59215&T=2

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 75.9046167757232 12.229983 6.2064 0 0 InvoerAM 0.469785279416574 0.093544 5.0221 5e-06 3e-06 t -0.14179725109994 0.159358 -0.8898 0.377249 0.188624

 Multiple Linear Regression - Regression Statistics Multiple R 0.6678574402613 R-squared 0.446033560512376 Adjusted R-squared 0.426931269495561 F-TEST (value) 23.3497416681464 F-TEST (DF numerator) 2 F-TEST (DF denominator) 58 p-value 3.63931959013186e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 14.0324313472979 Sum Squared Residuals 11420.7295119645

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.6678574402613 \tabularnewline
R-squared & 0.446033560512376 \tabularnewline
F-TEST (value) & 23.3497416681464 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 3.63931959013186e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 14.0324313472979 \tabularnewline
Sum Squared Residuals & 11420.7295119645 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59215&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.6678574402613[/C][/ROW]
[ROW][C]R-squared[/C][C]0.446033560512376[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]23.3497416681464[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]3.63931959013186e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]14.0324313472979[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]11420.7295119645[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59215&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59215&T=3

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Regression Statistics Multiple R 0.6678574402613 R-squared 0.446033560512376 Adjusted R-squared 0.426931269495561 F-TEST (value) 23.3497416681464 F-TEST (DF numerator) 2 F-TEST (DF denominator) 58 p-value 3.63931959013186e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 14.0324313472979 Sum Squared Residuals 11420.7295119645

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 114.08 139.883812312192 -25.8038123121919 2 112.95 142.621798823915 -29.6717988239151 3 135.31 142.052496968546 -6.74249696854607 4 134.31 145.575024896895 -11.2650248968954 5 133.03 144.437282853432 -11.4072828534323 6 140.11 137.046698740935 3.06330125906536 7 124.69 138.981352424856 -14.2913524248560 8 131.68 134.592696247830 -2.9126962478302 9 150.95 137.509201165732 13.4407988342678 10 137.26 133.275574130914 3.98442586908613 11 130.51 140.702017731215 -10.1920177312149 12 143.15 144.351387685007 -1.20138768500673 13 118.01 130.867688498476 -12.8576884984761 14 122.56 137.270000189649 -14.7100001896490 15 147.97 135.756429922653 12.2135700773473 16 135.74 136.690440961417 -0.9504409614167 17 151.62 140.706243433153 10.9137565668466 18 154.82 141.644952324712 13.1750476752884 19 145.59 136.312027736059 9.27797226394146 20 147.12 132.407250396832 14.7127496031682 21 175.86 149.130744676787 26.7292553232131 22 140.66 131.080732574327 9.57926742567281 23 152.69 144.947932355429 7.74206764457053 24 154.38 145.008142774479 9.37185722552134 25 132.45 137.518903753304 -5.06890375330351 26 136.44 143.146069733439 -6.70606973343908 27 153.24 140.847958049817 12.3920419501829 28 154.11 155.6970090649 -1.58700906490000 29 155.93 150.824474050075 5.10552594992484 30 142.53 148.000202853507 -5.47020285350659 31 148.73 146.138991479742 2.59100852025799 32 147.73 142.854330709345 4.87566929065482 33 166.79 151.563288122453 15.2267118775465 34 144.3 151.646987805474 -7.34698780547349 35 156.07 160.022397670196 -3.95239767019605 36 161.7 154.168011421391 7.53198857860942 37 152.1 155.210073074420 -3.11007307442039 38 140.45 144.253818691151 -3.80381869115093 39 155.56 149.246774544074 6.31322545592588 40 174.53 161.737503456486 12.7924965435141 41 167.16 160.656135646553 6.50386435344722 42 159.48 147.491890450025 11.9881095499746 43 173.22 162.167120911724 11.0528790882758 44 176.13 140.903777498055 35.2262225019449 45 180.31 157.382983432714 22.9270165672865 46 185.84 168.501939329229 17.3380606707711 47 169.43 172.160704988609 -2.73070498860903 48 195.25 179.817343375824 15.4326566241758 49 174.99 182.381509334164 -7.39150933416373 50 156.42 156.880702700157 -0.460702700157158 51 182.08 176.521563565289 5.55843643471088 52 182 166.453203360117 15.5467966398830 53 153.28 169.435478217137 -16.1554782171373 54 136.72 170.190970849723 -33.470970849723 55 130.19 148.476633567814 -18.2866335678140 56 132.04 151.778362414838 -19.7383624148375 57 143.89 160.059815223677 -16.1698152236768 58 133.38 148.535120652313 -15.1551206523132 59 127.98 140.322412300837 -12.3424123008365 60 150.45 153.019846736192 -2.56984673619157 61 133.55 152.633761139795 -19.083761139795

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 114.08 & 139.883812312192 & -25.8038123121919 \tabularnewline
2 & 112.95 & 142.621798823915 & -29.6717988239151 \tabularnewline
3 & 135.31 & 142.052496968546 & -6.74249696854607 \tabularnewline
4 & 134.31 & 145.575024896895 & -11.2650248968954 \tabularnewline
5 & 133.03 & 144.437282853432 & -11.4072828534323 \tabularnewline
6 & 140.11 & 137.046698740935 & 3.06330125906536 \tabularnewline
7 & 124.69 & 138.981352424856 & -14.2913524248560 \tabularnewline
8 & 131.68 & 134.592696247830 & -2.9126962478302 \tabularnewline
9 & 150.95 & 137.509201165732 & 13.4407988342678 \tabularnewline
10 & 137.26 & 133.275574130914 & 3.98442586908613 \tabularnewline
11 & 130.51 & 140.702017731215 & -10.1920177312149 \tabularnewline
12 & 143.15 & 144.351387685007 & -1.20138768500673 \tabularnewline
13 & 118.01 & 130.867688498476 & -12.8576884984761 \tabularnewline
14 & 122.56 & 137.270000189649 & -14.7100001896490 \tabularnewline
15 & 147.97 & 135.756429922653 & 12.2135700773473 \tabularnewline
16 & 135.74 & 136.690440961417 & -0.9504409614167 \tabularnewline
17 & 151.62 & 140.706243433153 & 10.9137565668466 \tabularnewline
18 & 154.82 & 141.644952324712 & 13.1750476752884 \tabularnewline
19 & 145.59 & 136.312027736059 & 9.27797226394146 \tabularnewline
20 & 147.12 & 132.407250396832 & 14.7127496031682 \tabularnewline
21 & 175.86 & 149.130744676787 & 26.7292553232131 \tabularnewline
22 & 140.66 & 131.080732574327 & 9.57926742567281 \tabularnewline
23 & 152.69 & 144.947932355429 & 7.74206764457053 \tabularnewline
24 & 154.38 & 145.008142774479 & 9.37185722552134 \tabularnewline
25 & 132.45 & 137.518903753304 & -5.06890375330351 \tabularnewline
26 & 136.44 & 143.146069733439 & -6.70606973343908 \tabularnewline
27 & 153.24 & 140.847958049817 & 12.3920419501829 \tabularnewline
28 & 154.11 & 155.6970090649 & -1.58700906490000 \tabularnewline
29 & 155.93 & 150.824474050075 & 5.10552594992484 \tabularnewline
30 & 142.53 & 148.000202853507 & -5.47020285350659 \tabularnewline
31 & 148.73 & 146.138991479742 & 2.59100852025799 \tabularnewline
32 & 147.73 & 142.854330709345 & 4.87566929065482 \tabularnewline
33 & 166.79 & 151.563288122453 & 15.2267118775465 \tabularnewline
34 & 144.3 & 151.646987805474 & -7.34698780547349 \tabularnewline
35 & 156.07 & 160.022397670196 & -3.95239767019605 \tabularnewline
36 & 161.7 & 154.168011421391 & 7.53198857860942 \tabularnewline
37 & 152.1 & 155.210073074420 & -3.11007307442039 \tabularnewline
38 & 140.45 & 144.253818691151 & -3.80381869115093 \tabularnewline
39 & 155.56 & 149.246774544074 & 6.31322545592588 \tabularnewline
40 & 174.53 & 161.737503456486 & 12.7924965435141 \tabularnewline
41 & 167.16 & 160.656135646553 & 6.50386435344722 \tabularnewline
42 & 159.48 & 147.491890450025 & 11.9881095499746 \tabularnewline
43 & 173.22 & 162.167120911724 & 11.0528790882758 \tabularnewline
44 & 176.13 & 140.903777498055 & 35.2262225019449 \tabularnewline
45 & 180.31 & 157.382983432714 & 22.9270165672865 \tabularnewline
46 & 185.84 & 168.501939329229 & 17.3380606707711 \tabularnewline
47 & 169.43 & 172.160704988609 & -2.73070498860903 \tabularnewline
48 & 195.25 & 179.817343375824 & 15.4326566241758 \tabularnewline
49 & 174.99 & 182.381509334164 & -7.39150933416373 \tabularnewline
50 & 156.42 & 156.880702700157 & -0.460702700157158 \tabularnewline
51 & 182.08 & 176.521563565289 & 5.55843643471088 \tabularnewline
52 & 182 & 166.453203360117 & 15.5467966398830 \tabularnewline
53 & 153.28 & 169.435478217137 & -16.1554782171373 \tabularnewline
54 & 136.72 & 170.190970849723 & -33.470970849723 \tabularnewline
55 & 130.19 & 148.476633567814 & -18.2866335678140 \tabularnewline
56 & 132.04 & 151.778362414838 & -19.7383624148375 \tabularnewline
57 & 143.89 & 160.059815223677 & -16.1698152236768 \tabularnewline
58 & 133.38 & 148.535120652313 & -15.1551206523132 \tabularnewline
59 & 127.98 & 140.322412300837 & -12.3424123008365 \tabularnewline
60 & 150.45 & 153.019846736192 & -2.56984673619157 \tabularnewline
61 & 133.55 & 152.633761139795 & -19.083761139795 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59215&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]114.08[/C][C]139.883812312192[/C][C]-25.8038123121919[/C][/ROW]
[ROW][C]2[/C][C]112.95[/C][C]142.621798823915[/C][C]-29.6717988239151[/C][/ROW]
[ROW][C]3[/C][C]135.31[/C][C]142.052496968546[/C][C]-6.74249696854607[/C][/ROW]
[ROW][C]4[/C][C]134.31[/C][C]145.575024896895[/C][C]-11.2650248968954[/C][/ROW]
[ROW][C]5[/C][C]133.03[/C][C]144.437282853432[/C][C]-11.4072828534323[/C][/ROW]
[ROW][C]6[/C][C]140.11[/C][C]137.046698740935[/C][C]3.06330125906536[/C][/ROW]
[ROW][C]7[/C][C]124.69[/C][C]138.981352424856[/C][C]-14.2913524248560[/C][/ROW]
[ROW][C]8[/C][C]131.68[/C][C]134.592696247830[/C][C]-2.9126962478302[/C][/ROW]
[ROW][C]9[/C][C]150.95[/C][C]137.509201165732[/C][C]13.4407988342678[/C][/ROW]
[ROW][C]10[/C][C]137.26[/C][C]133.275574130914[/C][C]3.98442586908613[/C][/ROW]
[ROW][C]11[/C][C]130.51[/C][C]140.702017731215[/C][C]-10.1920177312149[/C][/ROW]
[ROW][C]12[/C][C]143.15[/C][C]144.351387685007[/C][C]-1.20138768500673[/C][/ROW]
[ROW][C]13[/C][C]118.01[/C][C]130.867688498476[/C][C]-12.8576884984761[/C][/ROW]
[ROW][C]14[/C][C]122.56[/C][C]137.270000189649[/C][C]-14.7100001896490[/C][/ROW]
[ROW][C]15[/C][C]147.97[/C][C]135.756429922653[/C][C]12.2135700773473[/C][/ROW]
[ROW][C]16[/C][C]135.74[/C][C]136.690440961417[/C][C]-0.9504409614167[/C][/ROW]
[ROW][C]17[/C][C]151.62[/C][C]140.706243433153[/C][C]10.9137565668466[/C][/ROW]
[ROW][C]18[/C][C]154.82[/C][C]141.644952324712[/C][C]13.1750476752884[/C][/ROW]
[ROW][C]19[/C][C]145.59[/C][C]136.312027736059[/C][C]9.27797226394146[/C][/ROW]
[ROW][C]20[/C][C]147.12[/C][C]132.407250396832[/C][C]14.7127496031682[/C][/ROW]
[ROW][C]21[/C][C]175.86[/C][C]149.130744676787[/C][C]26.7292553232131[/C][/ROW]
[ROW][C]22[/C][C]140.66[/C][C]131.080732574327[/C][C]9.57926742567281[/C][/ROW]
[ROW][C]23[/C][C]152.69[/C][C]144.947932355429[/C][C]7.74206764457053[/C][/ROW]
[ROW][C]24[/C][C]154.38[/C][C]145.008142774479[/C][C]9.37185722552134[/C][/ROW]
[ROW][C]25[/C][C]132.45[/C][C]137.518903753304[/C][C]-5.06890375330351[/C][/ROW]
[ROW][C]26[/C][C]136.44[/C][C]143.146069733439[/C][C]-6.70606973343908[/C][/ROW]
[ROW][C]27[/C][C]153.24[/C][C]140.847958049817[/C][C]12.3920419501829[/C][/ROW]
[ROW][C]28[/C][C]154.11[/C][C]155.6970090649[/C][C]-1.58700906490000[/C][/ROW]
[ROW][C]29[/C][C]155.93[/C][C]150.824474050075[/C][C]5.10552594992484[/C][/ROW]
[ROW][C]30[/C][C]142.53[/C][C]148.000202853507[/C][C]-5.47020285350659[/C][/ROW]
[ROW][C]31[/C][C]148.73[/C][C]146.138991479742[/C][C]2.59100852025799[/C][/ROW]
[ROW][C]32[/C][C]147.73[/C][C]142.854330709345[/C][C]4.87566929065482[/C][/ROW]
[ROW][C]33[/C][C]166.79[/C][C]151.563288122453[/C][C]15.2267118775465[/C][/ROW]
[ROW][C]34[/C][C]144.3[/C][C]151.646987805474[/C][C]-7.34698780547349[/C][/ROW]
[ROW][C]35[/C][C]156.07[/C][C]160.022397670196[/C][C]-3.95239767019605[/C][/ROW]
[ROW][C]36[/C][C]161.7[/C][C]154.168011421391[/C][C]7.53198857860942[/C][/ROW]
[ROW][C]37[/C][C]152.1[/C][C]155.210073074420[/C][C]-3.11007307442039[/C][/ROW]
[ROW][C]38[/C][C]140.45[/C][C]144.253818691151[/C][C]-3.80381869115093[/C][/ROW]
[ROW][C]39[/C][C]155.56[/C][C]149.246774544074[/C][C]6.31322545592588[/C][/ROW]
[ROW][C]40[/C][C]174.53[/C][C]161.737503456486[/C][C]12.7924965435141[/C][/ROW]
[ROW][C]41[/C][C]167.16[/C][C]160.656135646553[/C][C]6.50386435344722[/C][/ROW]
[ROW][C]42[/C][C]159.48[/C][C]147.491890450025[/C][C]11.9881095499746[/C][/ROW]
[ROW][C]43[/C][C]173.22[/C][C]162.167120911724[/C][C]11.0528790882758[/C][/ROW]
[ROW][C]44[/C][C]176.13[/C][C]140.903777498055[/C][C]35.2262225019449[/C][/ROW]
[ROW][C]45[/C][C]180.31[/C][C]157.382983432714[/C][C]22.9270165672865[/C][/ROW]
[ROW][C]46[/C][C]185.84[/C][C]168.501939329229[/C][C]17.3380606707711[/C][/ROW]
[ROW][C]47[/C][C]169.43[/C][C]172.160704988609[/C][C]-2.73070498860903[/C][/ROW]
[ROW][C]48[/C][C]195.25[/C][C]179.817343375824[/C][C]15.4326566241758[/C][/ROW]
[ROW][C]49[/C][C]174.99[/C][C]182.381509334164[/C][C]-7.39150933416373[/C][/ROW]
[ROW][C]50[/C][C]156.42[/C][C]156.880702700157[/C][C]-0.460702700157158[/C][/ROW]
[ROW][C]51[/C][C]182.08[/C][C]176.521563565289[/C][C]5.55843643471088[/C][/ROW]
[ROW][C]52[/C][C]182[/C][C]166.453203360117[/C][C]15.5467966398830[/C][/ROW]
[ROW][C]53[/C][C]153.28[/C][C]169.435478217137[/C][C]-16.1554782171373[/C][/ROW]
[ROW][C]54[/C][C]136.72[/C][C]170.190970849723[/C][C]-33.470970849723[/C][/ROW]
[ROW][C]55[/C][C]130.19[/C][C]148.476633567814[/C][C]-18.2866335678140[/C][/ROW]
[ROW][C]56[/C][C]132.04[/C][C]151.778362414838[/C][C]-19.7383624148375[/C][/ROW]
[ROW][C]57[/C][C]143.89[/C][C]160.059815223677[/C][C]-16.1698152236768[/C][/ROW]
[ROW][C]58[/C][C]133.38[/C][C]148.535120652313[/C][C]-15.1551206523132[/C][/ROW]
[ROW][C]59[/C][C]127.98[/C][C]140.322412300837[/C][C]-12.3424123008365[/C][/ROW]
[ROW][C]60[/C][C]150.45[/C][C]153.019846736192[/C][C]-2.56984673619157[/C][/ROW]
[ROW][C]61[/C][C]133.55[/C][C]152.633761139795[/C][C]-19.083761139795[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59215&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59215&T=4

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 114.08 139.883812312192 -25.8038123121919 2 112.95 142.621798823915 -29.6717988239151 3 135.31 142.052496968546 -6.74249696854607 4 134.31 145.575024896895 -11.2650248968954 5 133.03 144.437282853432 -11.4072828534323 6 140.11 137.046698740935 3.06330125906536 7 124.69 138.981352424856 -14.2913524248560 8 131.68 134.592696247830 -2.9126962478302 9 150.95 137.509201165732 13.4407988342678 10 137.26 133.275574130914 3.98442586908613 11 130.51 140.702017731215 -10.1920177312149 12 143.15 144.351387685007 -1.20138768500673 13 118.01 130.867688498476 -12.8576884984761 14 122.56 137.270000189649 -14.7100001896490 15 147.97 135.756429922653 12.2135700773473 16 135.74 136.690440961417 -0.9504409614167 17 151.62 140.706243433153 10.9137565668466 18 154.82 141.644952324712 13.1750476752884 19 145.59 136.312027736059 9.27797226394146 20 147.12 132.407250396832 14.7127496031682 21 175.86 149.130744676787 26.7292553232131 22 140.66 131.080732574327 9.57926742567281 23 152.69 144.947932355429 7.74206764457053 24 154.38 145.008142774479 9.37185722552134 25 132.45 137.518903753304 -5.06890375330351 26 136.44 143.146069733439 -6.70606973343908 27 153.24 140.847958049817 12.3920419501829 28 154.11 155.6970090649 -1.58700906490000 29 155.93 150.824474050075 5.10552594992484 30 142.53 148.000202853507 -5.47020285350659 31 148.73 146.138991479742 2.59100852025799 32 147.73 142.854330709345 4.87566929065482 33 166.79 151.563288122453 15.2267118775465 34 144.3 151.646987805474 -7.34698780547349 35 156.07 160.022397670196 -3.95239767019605 36 161.7 154.168011421391 7.53198857860942 37 152.1 155.210073074420 -3.11007307442039 38 140.45 144.253818691151 -3.80381869115093 39 155.56 149.246774544074 6.31322545592588 40 174.53 161.737503456486 12.7924965435141 41 167.16 160.656135646553 6.50386435344722 42 159.48 147.491890450025 11.9881095499746 43 173.22 162.167120911724 11.0528790882758 44 176.13 140.903777498055 35.2262225019449 45 180.31 157.382983432714 22.9270165672865 46 185.84 168.501939329229 17.3380606707711 47 169.43 172.160704988609 -2.73070498860903 48 195.25 179.817343375824 15.4326566241758 49 174.99 182.381509334164 -7.39150933416373 50 156.42 156.880702700157 -0.460702700157158 51 182.08 176.521563565289 5.55843643471088 52 182 166.453203360117 15.5467966398830 53 153.28 169.435478217137 -16.1554782171373 54 136.72 170.190970849723 -33.470970849723 55 130.19 148.476633567814 -18.2866335678140 56 132.04 151.778362414838 -19.7383624148375 57 143.89 160.059815223677 -16.1698152236768 58 133.38 148.535120652313 -15.1551206523132 59 127.98 140.322412300837 -12.3424123008365 60 150.45 153.019846736192 -2.56984673619157 61 133.55 152.633761139795 -19.083761139795

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.200409520698652 0.400819041397305 0.799590479301348 7 0.361369166489672 0.722738332979344 0.638630833510328 8 0.236878552060553 0.473757104121106 0.763121447939447 9 0.198799583505457 0.397599167010913 0.801200416494543 10 0.130759053211962 0.261518106423923 0.869240946788038 11 0.224672158598207 0.449344317196414 0.775327841401793 12 0.155157528370919 0.310315056741839 0.84484247162908 13 0.325665027233271 0.651330054466543 0.674334972766729 14 0.436324713732386 0.872649427464772 0.563675286267614 15 0.406382460652449 0.812764921304898 0.593617539347551 16 0.347885897876889 0.695771795753779 0.65211410212311 17 0.289704788162917 0.579409576325833 0.710295211837083 18 0.230485765114461 0.460971530228922 0.769514234885539 19 0.169081429790242 0.338162859580483 0.830918570209758 20 0.123164616028218 0.246329232056436 0.876835383971782 21 0.123043022303712 0.246086044607425 0.876956977696288 22 0.0908949161294361 0.181789832258872 0.909105083870564 23 0.0848620471279466 0.169724094255893 0.915137952872053 24 0.0685090191586774 0.137018038317355 0.931490980841323 25 0.126807805210444 0.253615610420889 0.873192194789556 26 0.207552777550295 0.41510555510059 0.792447222449705 27 0.156646880058718 0.313293760117436 0.843353119941282 28 0.158321275218987 0.316642550437974 0.841678724781013 29 0.122696585741972 0.245393171483943 0.877303414258028 30 0.152985531175528 0.305971062351055 0.847014468824473 31 0.129541493258589 0.259082986517178 0.870458506741411 32 0.103672393565907 0.207344787131814 0.896327606434093 33 0.0782540633842072 0.156508126768414 0.921745936615793 34 0.115107882345866 0.230215764691731 0.884892117654134 35 0.126740718355195 0.253481436710390 0.873259281644805 36 0.0981275421568135 0.196255084313627 0.901872457843186 37 0.122038504534604 0.244077009069209 0.877961495465395 38 0.221243433870777 0.442486867741555 0.778756566129223 39 0.235052193055871 0.470104386111743 0.764947806944129 40 0.210575782932960 0.421151565865921 0.78942421706704 41 0.214943020057361 0.429886040114722 0.785056979942639 42 0.220407691147149 0.440815382294299 0.779592308852851 43 0.206798053481686 0.413596106963372 0.793201946518314 44 0.211589692262726 0.423179384525453 0.788410307737274 45 0.188857629391007 0.377715258782015 0.811142370608993 46 0.175938557430807 0.351877114861614 0.824061442569193 47 0.142815500941397 0.285631001882793 0.857184499058603 48 0.168956094992029 0.337912189984058 0.831043905007971 49 0.129181773477079 0.258363546954158 0.870818226522921 50 0.10136490550192 0.20272981100384 0.89863509449808 51 0.100032380172889 0.200064760345778 0.89996761982711 52 0.785977133083017 0.428045733833967 0.214022866916983 53 0.87401563680653 0.25196872638694 0.12598436319347 54 0.892151230689806 0.215697538620388 0.107848769310194 55 0.80708895897255 0.3858220820549 0.19291104102745

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.200409520698652 & 0.400819041397305 & 0.799590479301348 \tabularnewline
7 & 0.361369166489672 & 0.722738332979344 & 0.638630833510328 \tabularnewline
8 & 0.236878552060553 & 0.473757104121106 & 0.763121447939447 \tabularnewline
9 & 0.198799583505457 & 0.397599167010913 & 0.801200416494543 \tabularnewline
10 & 0.130759053211962 & 0.261518106423923 & 0.869240946788038 \tabularnewline
11 & 0.224672158598207 & 0.449344317196414 & 0.775327841401793 \tabularnewline
12 & 0.155157528370919 & 0.310315056741839 & 0.84484247162908 \tabularnewline
13 & 0.325665027233271 & 0.651330054466543 & 0.674334972766729 \tabularnewline
14 & 0.436324713732386 & 0.872649427464772 & 0.563675286267614 \tabularnewline
15 & 0.406382460652449 & 0.812764921304898 & 0.593617539347551 \tabularnewline
16 & 0.347885897876889 & 0.695771795753779 & 0.65211410212311 \tabularnewline
17 & 0.289704788162917 & 0.579409576325833 & 0.710295211837083 \tabularnewline
18 & 0.230485765114461 & 0.460971530228922 & 0.769514234885539 \tabularnewline
19 & 0.169081429790242 & 0.338162859580483 & 0.830918570209758 \tabularnewline
20 & 0.123164616028218 & 0.246329232056436 & 0.876835383971782 \tabularnewline
21 & 0.123043022303712 & 0.246086044607425 & 0.876956977696288 \tabularnewline
22 & 0.0908949161294361 & 0.181789832258872 & 0.909105083870564 \tabularnewline
23 & 0.0848620471279466 & 0.169724094255893 & 0.915137952872053 \tabularnewline
24 & 0.0685090191586774 & 0.137018038317355 & 0.931490980841323 \tabularnewline
25 & 0.126807805210444 & 0.253615610420889 & 0.873192194789556 \tabularnewline
26 & 0.207552777550295 & 0.41510555510059 & 0.792447222449705 \tabularnewline
27 & 0.156646880058718 & 0.313293760117436 & 0.843353119941282 \tabularnewline
28 & 0.158321275218987 & 0.316642550437974 & 0.841678724781013 \tabularnewline
29 & 0.122696585741972 & 0.245393171483943 & 0.877303414258028 \tabularnewline
30 & 0.152985531175528 & 0.305971062351055 & 0.847014468824473 \tabularnewline
31 & 0.129541493258589 & 0.259082986517178 & 0.870458506741411 \tabularnewline
32 & 0.103672393565907 & 0.207344787131814 & 0.896327606434093 \tabularnewline
33 & 0.0782540633842072 & 0.156508126768414 & 0.921745936615793 \tabularnewline
34 & 0.115107882345866 & 0.230215764691731 & 0.884892117654134 \tabularnewline
35 & 0.126740718355195 & 0.253481436710390 & 0.873259281644805 \tabularnewline
36 & 0.0981275421568135 & 0.196255084313627 & 0.901872457843186 \tabularnewline
37 & 0.122038504534604 & 0.244077009069209 & 0.877961495465395 \tabularnewline
38 & 0.221243433870777 & 0.442486867741555 & 0.778756566129223 \tabularnewline
39 & 0.235052193055871 & 0.470104386111743 & 0.764947806944129 \tabularnewline
40 & 0.210575782932960 & 0.421151565865921 & 0.78942421706704 \tabularnewline
41 & 0.214943020057361 & 0.429886040114722 & 0.785056979942639 \tabularnewline
42 & 0.220407691147149 & 0.440815382294299 & 0.779592308852851 \tabularnewline
43 & 0.206798053481686 & 0.413596106963372 & 0.793201946518314 \tabularnewline
44 & 0.211589692262726 & 0.423179384525453 & 0.788410307737274 \tabularnewline
45 & 0.188857629391007 & 0.377715258782015 & 0.811142370608993 \tabularnewline
46 & 0.175938557430807 & 0.351877114861614 & 0.824061442569193 \tabularnewline
47 & 0.142815500941397 & 0.285631001882793 & 0.857184499058603 \tabularnewline
48 & 0.168956094992029 & 0.337912189984058 & 0.831043905007971 \tabularnewline
49 & 0.129181773477079 & 0.258363546954158 & 0.870818226522921 \tabularnewline
50 & 0.10136490550192 & 0.20272981100384 & 0.89863509449808 \tabularnewline
51 & 0.100032380172889 & 0.200064760345778 & 0.89996761982711 \tabularnewline
52 & 0.785977133083017 & 0.428045733833967 & 0.214022866916983 \tabularnewline
53 & 0.87401563680653 & 0.25196872638694 & 0.12598436319347 \tabularnewline
54 & 0.892151230689806 & 0.215697538620388 & 0.107848769310194 \tabularnewline
55 & 0.80708895897255 & 0.3858220820549 & 0.19291104102745 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59215&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]6[/C][C]0.200409520698652[/C][C]0.400819041397305[/C][C]0.799590479301348[/C][/ROW]
[ROW][C]7[/C][C]0.361369166489672[/C][C]0.722738332979344[/C][C]0.638630833510328[/C][/ROW]
[ROW][C]8[/C][C]0.236878552060553[/C][C]0.473757104121106[/C][C]0.763121447939447[/C][/ROW]
[ROW][C]9[/C][C]0.198799583505457[/C][C]0.397599167010913[/C][C]0.801200416494543[/C][/ROW]
[ROW][C]10[/C][C]0.130759053211962[/C][C]0.261518106423923[/C][C]0.869240946788038[/C][/ROW]
[ROW][C]11[/C][C]0.224672158598207[/C][C]0.449344317196414[/C][C]0.775327841401793[/C][/ROW]
[ROW][C]12[/C][C]0.155157528370919[/C][C]0.310315056741839[/C][C]0.84484247162908[/C][/ROW]
[ROW][C]13[/C][C]0.325665027233271[/C][C]0.651330054466543[/C][C]0.674334972766729[/C][/ROW]
[ROW][C]14[/C][C]0.436324713732386[/C][C]0.872649427464772[/C][C]0.563675286267614[/C][/ROW]
[ROW][C]15[/C][C]0.406382460652449[/C][C]0.812764921304898[/C][C]0.593617539347551[/C][/ROW]
[ROW][C]16[/C][C]0.347885897876889[/C][C]0.695771795753779[/C][C]0.65211410212311[/C][/ROW]
[ROW][C]17[/C][C]0.289704788162917[/C][C]0.579409576325833[/C][C]0.710295211837083[/C][/ROW]
[ROW][C]18[/C][C]0.230485765114461[/C][C]0.460971530228922[/C][C]0.769514234885539[/C][/ROW]
[ROW][C]19[/C][C]0.169081429790242[/C][C]0.338162859580483[/C][C]0.830918570209758[/C][/ROW]
[ROW][C]20[/C][C]0.123164616028218[/C][C]0.246329232056436[/C][C]0.876835383971782[/C][/ROW]
[ROW][C]21[/C][C]0.123043022303712[/C][C]0.246086044607425[/C][C]0.876956977696288[/C][/ROW]
[ROW][C]22[/C][C]0.0908949161294361[/C][C]0.181789832258872[/C][C]0.909105083870564[/C][/ROW]
[ROW][C]23[/C][C]0.0848620471279466[/C][C]0.169724094255893[/C][C]0.915137952872053[/C][/ROW]
[ROW][C]24[/C][C]0.0685090191586774[/C][C]0.137018038317355[/C][C]0.931490980841323[/C][/ROW]
[ROW][C]25[/C][C]0.126807805210444[/C][C]0.253615610420889[/C][C]0.873192194789556[/C][/ROW]
[ROW][C]26[/C][C]0.207552777550295[/C][C]0.41510555510059[/C][C]0.792447222449705[/C][/ROW]
[ROW][C]27[/C][C]0.156646880058718[/C][C]0.313293760117436[/C][C]0.843353119941282[/C][/ROW]
[ROW][C]28[/C][C]0.158321275218987[/C][C]0.316642550437974[/C][C]0.841678724781013[/C][/ROW]
[ROW][C]29[/C][C]0.122696585741972[/C][C]0.245393171483943[/C][C]0.877303414258028[/C][/ROW]
[ROW][C]30[/C][C]0.152985531175528[/C][C]0.305971062351055[/C][C]0.847014468824473[/C][/ROW]
[ROW][C]31[/C][C]0.129541493258589[/C][C]0.259082986517178[/C][C]0.870458506741411[/C][/ROW]
[ROW][C]32[/C][C]0.103672393565907[/C][C]0.207344787131814[/C][C]0.896327606434093[/C][/ROW]
[ROW][C]33[/C][C]0.0782540633842072[/C][C]0.156508126768414[/C][C]0.921745936615793[/C][/ROW]
[ROW][C]34[/C][C]0.115107882345866[/C][C]0.230215764691731[/C][C]0.884892117654134[/C][/ROW]
[ROW][C]35[/C][C]0.126740718355195[/C][C]0.253481436710390[/C][C]0.873259281644805[/C][/ROW]
[ROW][C]36[/C][C]0.0981275421568135[/C][C]0.196255084313627[/C][C]0.901872457843186[/C][/ROW]
[ROW][C]37[/C][C]0.122038504534604[/C][C]0.244077009069209[/C][C]0.877961495465395[/C][/ROW]
[ROW][C]38[/C][C]0.221243433870777[/C][C]0.442486867741555[/C][C]0.778756566129223[/C][/ROW]
[ROW][C]39[/C][C]0.235052193055871[/C][C]0.470104386111743[/C][C]0.764947806944129[/C][/ROW]
[ROW][C]40[/C][C]0.210575782932960[/C][C]0.421151565865921[/C][C]0.78942421706704[/C][/ROW]
[ROW][C]41[/C][C]0.214943020057361[/C][C]0.429886040114722[/C][C]0.785056979942639[/C][/ROW]
[ROW][C]42[/C][C]0.220407691147149[/C][C]0.440815382294299[/C][C]0.779592308852851[/C][/ROW]
[ROW][C]43[/C][C]0.206798053481686[/C][C]0.413596106963372[/C][C]0.793201946518314[/C][/ROW]
[ROW][C]44[/C][C]0.211589692262726[/C][C]0.423179384525453[/C][C]0.788410307737274[/C][/ROW]
[ROW][C]45[/C][C]0.188857629391007[/C][C]0.377715258782015[/C][C]0.811142370608993[/C][/ROW]
[ROW][C]46[/C][C]0.175938557430807[/C][C]0.351877114861614[/C][C]0.824061442569193[/C][/ROW]
[ROW][C]47[/C][C]0.142815500941397[/C][C]0.285631001882793[/C][C]0.857184499058603[/C][/ROW]
[ROW][C]48[/C][C]0.168956094992029[/C][C]0.337912189984058[/C][C]0.831043905007971[/C][/ROW]
[ROW][C]49[/C][C]0.129181773477079[/C][C]0.258363546954158[/C][C]0.870818226522921[/C][/ROW]
[ROW][C]50[/C][C]0.10136490550192[/C][C]0.20272981100384[/C][C]0.89863509449808[/C][/ROW]
[ROW][C]51[/C][C]0.100032380172889[/C][C]0.200064760345778[/C][C]0.89996761982711[/C][/ROW]
[ROW][C]52[/C][C]0.785977133083017[/C][C]0.428045733833967[/C][C]0.214022866916983[/C][/ROW]
[ROW][C]53[/C][C]0.87401563680653[/C][C]0.25196872638694[/C][C]0.12598436319347[/C][/ROW]
[ROW][C]54[/C][C]0.892151230689806[/C][C]0.215697538620388[/C][C]0.107848769310194[/C][/ROW]
[ROW][C]55[/C][C]0.80708895897255[/C][C]0.3858220820549[/C][C]0.19291104102745[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59215&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59215&T=5

As an alternative you can also use a QR Code:

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

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.200409520698652 0.400819041397305 0.799590479301348 7 0.361369166489672 0.722738332979344 0.638630833510328 8 0.236878552060553 0.473757104121106 0.763121447939447 9 0.198799583505457 0.397599167010913 0.801200416494543 10 0.130759053211962 0.261518106423923 0.869240946788038 11 0.224672158598207 0.449344317196414 0.775327841401793 12 0.155157528370919 0.310315056741839 0.84484247162908 13 0.325665027233271 0.651330054466543 0.674334972766729 14 0.436324713732386 0.872649427464772 0.563675286267614 15 0.406382460652449 0.812764921304898 0.593617539347551 16 0.347885897876889 0.695771795753779 0.65211410212311 17 0.289704788162917 0.579409576325833 0.710295211837083 18 0.230485765114461 0.460971530228922 0.769514234885539 19 0.169081429790242 0.338162859580483 0.830918570209758 20 0.123164616028218 0.246329232056436 0.876835383971782 21 0.123043022303712 0.246086044607425 0.876956977696288 22 0.0908949161294361 0.181789832258872 0.909105083870564 23 0.0848620471279466 0.169724094255893 0.915137952872053 24 0.0685090191586774 0.137018038317355 0.931490980841323 25 0.126807805210444 0.253615610420889 0.873192194789556 26 0.207552777550295 0.41510555510059 0.792447222449705 27 0.156646880058718 0.313293760117436 0.843353119941282 28 0.158321275218987 0.316642550437974 0.841678724781013 29 0.122696585741972 0.245393171483943 0.877303414258028 30 0.152985531175528 0.305971062351055 0.847014468824473 31 0.129541493258589 0.259082986517178 0.870458506741411 32 0.103672393565907 0.207344787131814 0.896327606434093 33 0.0782540633842072 0.156508126768414 0.921745936615793 34 0.115107882345866 0.230215764691731 0.884892117654134 35 0.126740718355195 0.253481436710390 0.873259281644805 36 0.0981275421568135 0.196255084313627 0.901872457843186 37 0.122038504534604 0.244077009069209 0.877961495465395 38 0.221243433870777 0.442486867741555 0.778756566129223 39 0.235052193055871 0.470104386111743 0.764947806944129 40 0.210575782932960 0.421151565865921 0.78942421706704 41 0.214943020057361 0.429886040114722 0.785056979942639 42 0.220407691147149 0.440815382294299 0.779592308852851 43 0.206798053481686 0.413596106963372 0.793201946518314 44 0.211589692262726 0.423179384525453 0.788410307737274 45 0.188857629391007 0.377715258782015 0.811142370608993 46 0.175938557430807 0.351877114861614 0.824061442569193 47 0.142815500941397 0.285631001882793 0.857184499058603 48 0.168956094992029 0.337912189984058 0.831043905007971 49 0.129181773477079 0.258363546954158 0.870818226522921 50 0.10136490550192 0.20272981100384 0.89863509449808 51 0.100032380172889 0.200064760345778 0.89996761982711 52 0.785977133083017 0.428045733833967 0.214022866916983 53 0.87401563680653 0.25196872638694 0.12598436319347 54 0.892151230689806 0.215697538620388 0.107848769310194 55 0.80708895897255 0.3858220820549 0.19291104102745

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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59215&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59215&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59215&T=6

As an alternative you can also use a QR Code:

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

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

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}