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]
Feedback Forum

Post a new message
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)75.904616775723212.2299836.206400
InvoerAM0.4697852794165740.0935445.02215e-063e-06
t-0.141797251099940.159358-0.88980.3772490.188624

\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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)75.904616775723212.2299836.206400
InvoerAM0.4697852794165740.0935445.02215e-063e-06
t-0.141797251099940.159358-0.88980.3772490.188624







Multiple Linear Regression - Regression Statistics
Multiple R0.6678574402613
R-squared0.446033560512376
Adjusted R-squared0.426931269495561
F-TEST (value)23.3497416681464
F-TEST (DF numerator)2
F-TEST (DF denominator)58
p-value3.63931959013186e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation14.0324313472979
Sum Squared Residuals11420.7295119645

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.6678574402613 \tabularnewline
R-squared & 0.446033560512376 \tabularnewline
Adjusted R-squared & 0.426931269495561 \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]Adjusted R-squared[/C][C]0.426931269495561[/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 R0.6678574402613
R-squared0.446033560512376
Adjusted R-squared0.426931269495561
F-TEST (value)23.3497416681464
F-TEST (DF numerator)2
F-TEST (DF denominator)58
p-value3.63931959013186e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation14.0324313472979
Sum Squared Residuals11420.7295119645







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1114.08139.883812312192-25.8038123121919
2112.95142.621798823915-29.6717988239151
3135.31142.052496968546-6.74249696854607
4134.31145.575024896895-11.2650248968954
5133.03144.437282853432-11.4072828534323
6140.11137.0466987409353.06330125906536
7124.69138.981352424856-14.2913524248560
8131.68134.592696247830-2.9126962478302
9150.95137.50920116573213.4407988342678
10137.26133.2755741309143.98442586908613
11130.51140.702017731215-10.1920177312149
12143.15144.351387685007-1.20138768500673
13118.01130.867688498476-12.8576884984761
14122.56137.270000189649-14.7100001896490
15147.97135.75642992265312.2135700773473
16135.74136.690440961417-0.9504409614167
17151.62140.70624343315310.9137565668466
18154.82141.64495232471213.1750476752884
19145.59136.3120277360599.27797226394146
20147.12132.40725039683214.7127496031682
21175.86149.13074467678726.7292553232131
22140.66131.0807325743279.57926742567281
23152.69144.9479323554297.74206764457053
24154.38145.0081427744799.37185722552134
25132.45137.518903753304-5.06890375330351
26136.44143.146069733439-6.70606973343908
27153.24140.84795804981712.3920419501829
28154.11155.6970090649-1.58700906490000
29155.93150.8244740500755.10552594992484
30142.53148.000202853507-5.47020285350659
31148.73146.1389914797422.59100852025799
32147.73142.8543307093454.87566929065482
33166.79151.56328812245315.2267118775465
34144.3151.646987805474-7.34698780547349
35156.07160.022397670196-3.95239767019605
36161.7154.1680114213917.53198857860942
37152.1155.210073074420-3.11007307442039
38140.45144.253818691151-3.80381869115093
39155.56149.2467745440746.31322545592588
40174.53161.73750345648612.7924965435141
41167.16160.6561356465536.50386435344722
42159.48147.49189045002511.9881095499746
43173.22162.16712091172411.0528790882758
44176.13140.90377749805535.2262225019449
45180.31157.38298343271422.9270165672865
46185.84168.50193932922917.3380606707711
47169.43172.160704988609-2.73070498860903
48195.25179.81734337582415.4326566241758
49174.99182.381509334164-7.39150933416373
50156.42156.880702700157-0.460702700157158
51182.08176.5215635652895.55843643471088
52182166.45320336011715.5467966398830
53153.28169.435478217137-16.1554782171373
54136.72170.190970849723-33.470970849723
55130.19148.476633567814-18.2866335678140
56132.04151.778362414838-19.7383624148375
57143.89160.059815223677-16.1698152236768
58133.38148.535120652313-15.1551206523132
59127.98140.322412300837-12.3424123008365
60150.45153.019846736192-2.56984673619157
61133.55152.633761139795-19.083761139795

\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 IndexActualsInterpolationForecastResidualsPrediction Error
1114.08139.883812312192-25.8038123121919
2112.95142.621798823915-29.6717988239151
3135.31142.052496968546-6.74249696854607
4134.31145.575024896895-11.2650248968954
5133.03144.437282853432-11.4072828534323
6140.11137.0466987409353.06330125906536
7124.69138.981352424856-14.2913524248560
8131.68134.592696247830-2.9126962478302
9150.95137.50920116573213.4407988342678
10137.26133.2755741309143.98442586908613
11130.51140.702017731215-10.1920177312149
12143.15144.351387685007-1.20138768500673
13118.01130.867688498476-12.8576884984761
14122.56137.270000189649-14.7100001896490
15147.97135.75642992265312.2135700773473
16135.74136.690440961417-0.9504409614167
17151.62140.70624343315310.9137565668466
18154.82141.64495232471213.1750476752884
19145.59136.3120277360599.27797226394146
20147.12132.40725039683214.7127496031682
21175.86149.13074467678726.7292553232131
22140.66131.0807325743279.57926742567281
23152.69144.9479323554297.74206764457053
24154.38145.0081427744799.37185722552134
25132.45137.518903753304-5.06890375330351
26136.44143.146069733439-6.70606973343908
27153.24140.84795804981712.3920419501829
28154.11155.6970090649-1.58700906490000
29155.93150.8244740500755.10552594992484
30142.53148.000202853507-5.47020285350659
31148.73146.1389914797422.59100852025799
32147.73142.8543307093454.87566929065482
33166.79151.56328812245315.2267118775465
34144.3151.646987805474-7.34698780547349
35156.07160.022397670196-3.95239767019605
36161.7154.1680114213917.53198857860942
37152.1155.210073074420-3.11007307442039
38140.45144.253818691151-3.80381869115093
39155.56149.2467745440746.31322545592588
40174.53161.73750345648612.7924965435141
41167.16160.6561356465536.50386435344722
42159.48147.49189045002511.9881095499746
43173.22162.16712091172411.0528790882758
44176.13140.90377749805535.2262225019449
45180.31157.38298343271422.9270165672865
46185.84168.50193932922917.3380606707711
47169.43172.160704988609-2.73070498860903
48195.25179.81734337582415.4326566241758
49174.99182.381509334164-7.39150933416373
50156.42156.880702700157-0.460702700157158
51182.08176.5215635652895.55843643471088
52182166.45320336011715.5467966398830
53153.28169.435478217137-16.1554782171373
54136.72170.190970849723-33.470970849723
55130.19148.476633567814-18.2866335678140
56132.04151.778362414838-19.7383624148375
57143.89160.059815223677-16.1698152236768
58133.38148.535120652313-15.1551206523132
59127.98140.322412300837-12.3424123008365
60150.45153.019846736192-2.56984673619157
61133.55152.633761139795-19.083761139795







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.2004095206986520.4008190413973050.799590479301348
70.3613691664896720.7227383329793440.638630833510328
80.2368785520605530.4737571041211060.763121447939447
90.1987995835054570.3975991670109130.801200416494543
100.1307590532119620.2615181064239230.869240946788038
110.2246721585982070.4493443171964140.775327841401793
120.1551575283709190.3103150567418390.84484247162908
130.3256650272332710.6513300544665430.674334972766729
140.4363247137323860.8726494274647720.563675286267614
150.4063824606524490.8127649213048980.593617539347551
160.3478858978768890.6957717957537790.65211410212311
170.2897047881629170.5794095763258330.710295211837083
180.2304857651144610.4609715302289220.769514234885539
190.1690814297902420.3381628595804830.830918570209758
200.1231646160282180.2463292320564360.876835383971782
210.1230430223037120.2460860446074250.876956977696288
220.09089491612943610.1817898322588720.909105083870564
230.08486204712794660.1697240942558930.915137952872053
240.06850901915867740.1370180383173550.931490980841323
250.1268078052104440.2536156104208890.873192194789556
260.2075527775502950.415105555100590.792447222449705
270.1566468800587180.3132937601174360.843353119941282
280.1583212752189870.3166425504379740.841678724781013
290.1226965857419720.2453931714839430.877303414258028
300.1529855311755280.3059710623510550.847014468824473
310.1295414932585890.2590829865171780.870458506741411
320.1036723935659070.2073447871318140.896327606434093
330.07825406338420720.1565081267684140.921745936615793
340.1151078823458660.2302157646917310.884892117654134
350.1267407183551950.2534814367103900.873259281644805
360.09812754215681350.1962550843136270.901872457843186
370.1220385045346040.2440770090692090.877961495465395
380.2212434338707770.4424868677415550.778756566129223
390.2350521930558710.4701043861117430.764947806944129
400.2105757829329600.4211515658659210.78942421706704
410.2149430200573610.4298860401147220.785056979942639
420.2204076911471490.4408153822942990.779592308852851
430.2067980534816860.4135961069633720.793201946518314
440.2115896922627260.4231793845254530.788410307737274
450.1888576293910070.3777152587820150.811142370608993
460.1759385574308070.3518771148616140.824061442569193
470.1428155009413970.2856310018827930.857184499058603
480.1689560949920290.3379121899840580.831043905007971
490.1291817734770790.2583635469541580.870818226522921
500.101364905501920.202729811003840.89863509449808
510.1000323801728890.2000647603457780.89996761982711
520.7859771330830170.4280457338339670.214022866916983
530.874015636806530.251968726386940.12598436319347
540.8921512306898060.2156975386203880.107848769310194
550.807088958972550.38582208205490.19291104102745

\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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.2004095206986520.4008190413973050.799590479301348
70.3613691664896720.7227383329793440.638630833510328
80.2368785520605530.4737571041211060.763121447939447
90.1987995835054570.3975991670109130.801200416494543
100.1307590532119620.2615181064239230.869240946788038
110.2246721585982070.4493443171964140.775327841401793
120.1551575283709190.3103150567418390.84484247162908
130.3256650272332710.6513300544665430.674334972766729
140.4363247137323860.8726494274647720.563675286267614
150.4063824606524490.8127649213048980.593617539347551
160.3478858978768890.6957717957537790.65211410212311
170.2897047881629170.5794095763258330.710295211837083
180.2304857651144610.4609715302289220.769514234885539
190.1690814297902420.3381628595804830.830918570209758
200.1231646160282180.2463292320564360.876835383971782
210.1230430223037120.2460860446074250.876956977696288
220.09089491612943610.1817898322588720.909105083870564
230.08486204712794660.1697240942558930.915137952872053
240.06850901915867740.1370180383173550.931490980841323
250.1268078052104440.2536156104208890.873192194789556
260.2075527775502950.415105555100590.792447222449705
270.1566468800587180.3132937601174360.843353119941282
280.1583212752189870.3166425504379740.841678724781013
290.1226965857419720.2453931714839430.877303414258028
300.1529855311755280.3059710623510550.847014468824473
310.1295414932585890.2590829865171780.870458506741411
320.1036723935659070.2073447871318140.896327606434093
330.07825406338420720.1565081267684140.921745936615793
340.1151078823458660.2302157646917310.884892117654134
350.1267407183551950.2534814367103900.873259281644805
360.09812754215681350.1962550843136270.901872457843186
370.1220385045346040.2440770090692090.877961495465395
380.2212434338707770.4424868677415550.778756566129223
390.2350521930558710.4701043861117430.764947806944129
400.2105757829329600.4211515658659210.78942421706704
410.2149430200573610.4298860401147220.785056979942639
420.2204076911471490.4408153822942990.779592308852851
430.2067980534816860.4135961069633720.793201946518314
440.2115896922627260.4231793845254530.788410307737274
450.1888576293910070.3777152587820150.811142370608993
460.1759385574308070.3518771148616140.824061442569193
470.1428155009413970.2856310018827930.857184499058603
480.1689560949920290.3379121899840580.831043905007971
490.1291817734770790.2583635469541580.870818226522921
500.101364905501920.202729811003840.89863509449808
510.1000323801728890.2000647603457780.89996761982711
520.7859771330830170.4280457338339670.214022866916983
530.874015636806530.251968726386940.12598436319347
540.8921512306898060.2156975386203880.107848769310194
550.807088958972550.38582208205490.19291104102745







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\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 testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK



Parameters (Session):
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}