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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 19 Nov 2010 13:07:10 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/19/t1290171960e3m7uvo97nlwnm0.htm/, Retrieved Fri, 29 Mar 2024 05:15:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=97956, Retrieved Fri, 29 Mar 2024 05:15:19 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [multicollineariteit] [2010-11-19 13:07:10] [df17410ebb98883e83037e1662207ccb] [Current]
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Dataseries X:
101.82	107.34	93.63	99.85	101.76
101.68	107.34	93.63	99.91	102.37
101.68	107.34	93.63	99.87	102.38
102.45	107.34	96.13	99.86	102.86
102.45	107.34	96.13	100.10	102.87
102.45	107.34	96.13	100.10	102.92
102.45	107.34	96.13	100.12	102.95
102.45	107.34	96.13	99.95	103.02
102.45	112.60	96.13	99.94	104.08
102.52	112.60	96.13	100.18	104.16
102.52	112.60	96.13	100.31	104.24
102.85	112.60	96.13	100.65	104.33
102.85	112.61	96.13	100.65	104.73
102.85	112.61	96.13	100.69	104.86
103.25	112.61	96.13	101.26	105.03
103.25	112.61	98.73	101.26	105.62
103.25	112.61	98.73	101.38	105.63
103.25	112.61	98.73	101.38	105.63
104.45	112.61	98.73	101.38	105.94
104.45	112.61	98.73	101.44	106.61
104.45	118.65	98.73	101.40	107.69
104.80	118.65	98.73	101.40	107.78
104.80	118.65	98.73	100.58	107.93
105.29	118.65	98.73	100.58	108.48
105.29	114.29	98.73	100.58	108.14
105.29	114.29	98.73	100.59	108.48
105.29	114.29	98.73	100.81	108.48
106.04	114.29	101.67	100.75	108.89
105.94	114.29	101.67	100.75	108.93
105.94	114.29	101.67	100.96	109.21
105.94	114.29	101.67	101.31	109.47
106.28	114.29	101.67	101.64	109.80
106.48	123.33	101.67	101.46	111.73
107.19	123.33	101.67	101.73	111.85
108.14	123.33	101.67	101.73	112.12
108.22	123.33	101.67	101.64	112.15
108.22	123.33	101.67	101.77	112.17
108.61	123.33	101.67	101.74	112.67
108.61	123.33	101.67	101.89	112.80
108.61	123.33	107.94	101.89	113.44
108.61	123.33	107.94	101.93	113.53
109.06	123.33	107.94	101.93	114.53
109.06	123.33	107.94	102.32	114.51
112.93	123.33	107.94	102.41	115.05
115.84	129.03	107.94	103.58	116.67
118.57	128.76	107.94	104.12	117.07
118.57	128.76	107.94	104.10	116.92
118.86	128.76	107.94	104.15	117.00
118.98	128.76	107.94	104.15	117.02
119.27	128.76	107.94	104.16	117.35
119.39	128.76	107.94	102.94	117.36
119.49	128.76	110.30	103.07	117.82
119.59	128.76	110.30	103.04	117.88
120.12	128.76	110.30	103.06	118.24
120.14	128.76	110.30	103.05	118.50
120.14	128.76	110.30	102.95	118.80
120.14	132.63	110.30	102.95	119.76
120.14	132.63	110.30	103.05	120.09




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97956&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97956&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97956&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Multiple Linear Regression - Estimated Regression Equation
Eendagsattracties[t] = -81.283726637103 -0.0643130276182469Bioscoop[t] -0.427201176091097Schouwburgabonnement[t] + 0.42473910932952HuurvaneenDVD[t] + 1.8217064126921`And.dienstenrecr.&cultuur`[t] -0.104368938422877t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Eendagsattracties[t] =  -81.283726637103 -0.0643130276182469Bioscoop[t] -0.427201176091097Schouwburgabonnement[t] +  0.42473910932952HuurvaneenDVD[t] +  1.8217064126921`And.dienstenrecr.&cultuur`[t] -0.104368938422877t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97956&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Eendagsattracties[t] =  -81.283726637103 -0.0643130276182469Bioscoop[t] -0.427201176091097Schouwburgabonnement[t] +  0.42473910932952HuurvaneenDVD[t] +  1.8217064126921`And.dienstenrecr.&cultuur`[t] -0.104368938422877t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97956&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97956&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
Eendagsattracties[t] = -81.283726637103 -0.0643130276182469Bioscoop[t] -0.427201176091097Schouwburgabonnement[t] + 0.42473910932952HuurvaneenDVD[t] + 1.8217064126921`And.dienstenrecr.&cultuur`[t] -0.104368938422877t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-81.28372663710345.490792-1.78680.0797970.039898
Bioscoop-0.06431302761824690.096705-0.6650.5089610.254481
Schouwburgabonnement-0.4272011760910970.103081-4.14430.0001266.3e-05
HuurvaneenDVD0.424739109329520.3399721.24930.2171370.108569
`And.dienstenrecr.&cultuur`1.82170641269210.4654573.91380.0002650.000133
t-0.1043689384228770.104995-0.9940.3248080.162404

\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) & -81.283726637103 & 45.490792 & -1.7868 & 0.079797 & 0.039898 \tabularnewline
Bioscoop & -0.0643130276182469 & 0.096705 & -0.665 & 0.508961 & 0.254481 \tabularnewline
Schouwburgabonnement & -0.427201176091097 & 0.103081 & -4.1443 & 0.000126 & 6.3e-05 \tabularnewline
HuurvaneenDVD & 0.42473910932952 & 0.339972 & 1.2493 & 0.217137 & 0.108569 \tabularnewline
`And.dienstenrecr.&cultuur` & 1.8217064126921 & 0.465457 & 3.9138 & 0.000265 & 0.000133 \tabularnewline
t & -0.104368938422877 & 0.104995 & -0.994 & 0.324808 & 0.162404 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97956&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]-81.283726637103[/C][C]45.490792[/C][C]-1.7868[/C][C]0.079797[/C][C]0.039898[/C][/ROW]
[ROW][C]Bioscoop[/C][C]-0.0643130276182469[/C][C]0.096705[/C][C]-0.665[/C][C]0.508961[/C][C]0.254481[/C][/ROW]
[ROW][C]Schouwburgabonnement[/C][C]-0.427201176091097[/C][C]0.103081[/C][C]-4.1443[/C][C]0.000126[/C][C]6.3e-05[/C][/ROW]
[ROW][C]HuurvaneenDVD[/C][C]0.42473910932952[/C][C]0.339972[/C][C]1.2493[/C][C]0.217137[/C][C]0.108569[/C][/ROW]
[ROW][C]`And.dienstenrecr.&cultuur`[/C][C]1.8217064126921[/C][C]0.465457[/C][C]3.9138[/C][C]0.000265[/C][C]0.000133[/C][/ROW]
[ROW][C]t[/C][C]-0.104368938422877[/C][C]0.104995[/C][C]-0.994[/C][C]0.324808[/C][C]0.162404[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97956&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97956&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)-81.28372663710345.490792-1.78680.0797970.039898
Bioscoop-0.06431302761824690.096705-0.6650.5089610.254481
Schouwburgabonnement-0.4272011760910970.103081-4.14430.0001266.3e-05
HuurvaneenDVD0.424739109329520.3399721.24930.2171370.108569
`And.dienstenrecr.&cultuur`1.82170641269210.4654573.91380.0002650.000133
t-0.1043689384228770.104995-0.9940.3248080.162404







Multiple Linear Regression - Regression Statistics
Multiple R0.979106475949625
R-squared0.958649491246493
Adjusted R-squared0.954673480789425
F-TEST (value)241.108392846992
F-TEST (DF numerator)5
F-TEST (DF denominator)52
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.14802596690307
Sum Squared Residuals68.5341082755535

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.979106475949625 \tabularnewline
R-squared & 0.958649491246493 \tabularnewline
Adjusted R-squared & 0.954673480789425 \tabularnewline
F-TEST (value) & 241.108392846992 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.14802596690307 \tabularnewline
Sum Squared Residuals & 68.5341082755535 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97956&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.979106475949625[/C][/ROW]
[ROW][C]R-squared[/C][C]0.958649491246493[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.954673480789425[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]241.108392846992[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.14802596690307[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]68.5341082755535[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97956&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97956&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 R0.979106475949625
R-squared0.958649491246493
Adjusted R-squared0.954673480789425
F-TEST (value)241.108392846992
F-TEST (DF numerator)5
F-TEST (DF denominator)52
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.14802596690307
Sum Squared Residuals68.5341082755535







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
193.6393.9948223328667-0.36482233286675
293.6395.0361824766126-1.40618247661258
393.6394.9330410379434-1.30304103794344
496.1395.64932275525340.480677244746571
596.1395.66510826719660.464891732803438
696.1395.65182464940830.478175350591712
796.1395.61060168555280.51939831444723
896.1395.56154654743230.568453452567691
996.1395.13686082913060.993139170869396
1096.1395.27566387802890.854336121971097
1196.1395.37224753683420.757752463165774
1296.1395.55502017361170.574979826388338
1396.1396.1750617885047-0.0450617885047232
1496.1396.324504248105-0.194504248104989
1596.1396.7462014811103-0.616201481110303
1698.7397.71663932617581.01336067382424
1798.7397.68145614499931.04854385500067
1898.7397.57708720657651.15291279342355
1998.7397.96027162294620.769728377053766
2098.7399.1019303275868-0.371930327586841
2198.7398.3677196469080.362280353091971
2298.7398.4047947259610.325205274038938
2398.7398.22539567979180.5046043202082
2498.7399.0914518848166-0.361451884816633
2598.73100.230299893836-1.50029989383562
2698.73100.749558526821-2.01955852682136
2798.73100.738632192451-2.00863219245097
28101.67101.3074437659580.362556234041603
29101.67101.2823743868050.387625613194959
30101.67101.777278456895-0.107278456895125
31101.67102.295211874038-0.62521187403754
32101.67102.910303528492-1.24030352849159
33101.67102.370613689498-0.700613689498008
34101.67102.553866830508-0.883866830508186
35101.67102.880261247275-1.21026124727486
36101.67102.787171939184-1.11717193918363
37101.67102.774453213227-1.10445321322742
38101.67103.5431132271-1.8731132270996
39101.67103.739276988726-2.06927698872611
40107.94104.8008001544263.13919984557381
41107.94104.8773743575193.06262564248121
42107.94106.565770969361.37422903064019
43107.94106.5906161553221.3493838446784
44107.94107.2593037827090.680696217290513
45107.94107.980846376675-0.0408463766750154
46107.94108.774288874514-0.834288874513695
47107.94108.388169192-0.448169192000423
48107.94108.43212294405-0.4921229440501
49107.94108.356470570567-0.416470570566868
50107.94108.838861361416-0.898861361416381
51107.94108.226810210424-0.286810210424231
52110.3109.0092110032911.29078899670928
53110.3108.9949709735881.30502902641233
54110.3109.5208252212830.779174778717133
55110.3109.8845662985140.415433701485716
56110.3110.2842353729660.0157646270339175
57110.3110.2754360392550.0245639607449071
58110.3110.814704127954-0.514704127953556

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 93.63 & 93.9948223328667 & -0.36482233286675 \tabularnewline
2 & 93.63 & 95.0361824766126 & -1.40618247661258 \tabularnewline
3 & 93.63 & 94.9330410379434 & -1.30304103794344 \tabularnewline
4 & 96.13 & 95.6493227552534 & 0.480677244746571 \tabularnewline
5 & 96.13 & 95.6651082671966 & 0.464891732803438 \tabularnewline
6 & 96.13 & 95.6518246494083 & 0.478175350591712 \tabularnewline
7 & 96.13 & 95.6106016855528 & 0.51939831444723 \tabularnewline
8 & 96.13 & 95.5615465474323 & 0.568453452567691 \tabularnewline
9 & 96.13 & 95.1368608291306 & 0.993139170869396 \tabularnewline
10 & 96.13 & 95.2756638780289 & 0.854336121971097 \tabularnewline
11 & 96.13 & 95.3722475368342 & 0.757752463165774 \tabularnewline
12 & 96.13 & 95.5550201736117 & 0.574979826388338 \tabularnewline
13 & 96.13 & 96.1750617885047 & -0.0450617885047232 \tabularnewline
14 & 96.13 & 96.324504248105 & -0.194504248104989 \tabularnewline
15 & 96.13 & 96.7462014811103 & -0.616201481110303 \tabularnewline
16 & 98.73 & 97.7166393261758 & 1.01336067382424 \tabularnewline
17 & 98.73 & 97.6814561449993 & 1.04854385500067 \tabularnewline
18 & 98.73 & 97.5770872065765 & 1.15291279342355 \tabularnewline
19 & 98.73 & 97.9602716229462 & 0.769728377053766 \tabularnewline
20 & 98.73 & 99.1019303275868 & -0.371930327586841 \tabularnewline
21 & 98.73 & 98.367719646908 & 0.362280353091971 \tabularnewline
22 & 98.73 & 98.404794725961 & 0.325205274038938 \tabularnewline
23 & 98.73 & 98.2253956797918 & 0.5046043202082 \tabularnewline
24 & 98.73 & 99.0914518848166 & -0.361451884816633 \tabularnewline
25 & 98.73 & 100.230299893836 & -1.50029989383562 \tabularnewline
26 & 98.73 & 100.749558526821 & -2.01955852682136 \tabularnewline
27 & 98.73 & 100.738632192451 & -2.00863219245097 \tabularnewline
28 & 101.67 & 101.307443765958 & 0.362556234041603 \tabularnewline
29 & 101.67 & 101.282374386805 & 0.387625613194959 \tabularnewline
30 & 101.67 & 101.777278456895 & -0.107278456895125 \tabularnewline
31 & 101.67 & 102.295211874038 & -0.62521187403754 \tabularnewline
32 & 101.67 & 102.910303528492 & -1.24030352849159 \tabularnewline
33 & 101.67 & 102.370613689498 & -0.700613689498008 \tabularnewline
34 & 101.67 & 102.553866830508 & -0.883866830508186 \tabularnewline
35 & 101.67 & 102.880261247275 & -1.21026124727486 \tabularnewline
36 & 101.67 & 102.787171939184 & -1.11717193918363 \tabularnewline
37 & 101.67 & 102.774453213227 & -1.10445321322742 \tabularnewline
38 & 101.67 & 103.5431132271 & -1.8731132270996 \tabularnewline
39 & 101.67 & 103.739276988726 & -2.06927698872611 \tabularnewline
40 & 107.94 & 104.800800154426 & 3.13919984557381 \tabularnewline
41 & 107.94 & 104.877374357519 & 3.06262564248121 \tabularnewline
42 & 107.94 & 106.56577096936 & 1.37422903064019 \tabularnewline
43 & 107.94 & 106.590616155322 & 1.3493838446784 \tabularnewline
44 & 107.94 & 107.259303782709 & 0.680696217290513 \tabularnewline
45 & 107.94 & 107.980846376675 & -0.0408463766750154 \tabularnewline
46 & 107.94 & 108.774288874514 & -0.834288874513695 \tabularnewline
47 & 107.94 & 108.388169192 & -0.448169192000423 \tabularnewline
48 & 107.94 & 108.43212294405 & -0.4921229440501 \tabularnewline
49 & 107.94 & 108.356470570567 & -0.416470570566868 \tabularnewline
50 & 107.94 & 108.838861361416 & -0.898861361416381 \tabularnewline
51 & 107.94 & 108.226810210424 & -0.286810210424231 \tabularnewline
52 & 110.3 & 109.009211003291 & 1.29078899670928 \tabularnewline
53 & 110.3 & 108.994970973588 & 1.30502902641233 \tabularnewline
54 & 110.3 & 109.520825221283 & 0.779174778717133 \tabularnewline
55 & 110.3 & 109.884566298514 & 0.415433701485716 \tabularnewline
56 & 110.3 & 110.284235372966 & 0.0157646270339175 \tabularnewline
57 & 110.3 & 110.275436039255 & 0.0245639607449071 \tabularnewline
58 & 110.3 & 110.814704127954 & -0.514704127953556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97956&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]93.63[/C][C]93.9948223328667[/C][C]-0.36482233286675[/C][/ROW]
[ROW][C]2[/C][C]93.63[/C][C]95.0361824766126[/C][C]-1.40618247661258[/C][/ROW]
[ROW][C]3[/C][C]93.63[/C][C]94.9330410379434[/C][C]-1.30304103794344[/C][/ROW]
[ROW][C]4[/C][C]96.13[/C][C]95.6493227552534[/C][C]0.480677244746571[/C][/ROW]
[ROW][C]5[/C][C]96.13[/C][C]95.6651082671966[/C][C]0.464891732803438[/C][/ROW]
[ROW][C]6[/C][C]96.13[/C][C]95.6518246494083[/C][C]0.478175350591712[/C][/ROW]
[ROW][C]7[/C][C]96.13[/C][C]95.6106016855528[/C][C]0.51939831444723[/C][/ROW]
[ROW][C]8[/C][C]96.13[/C][C]95.5615465474323[/C][C]0.568453452567691[/C][/ROW]
[ROW][C]9[/C][C]96.13[/C][C]95.1368608291306[/C][C]0.993139170869396[/C][/ROW]
[ROW][C]10[/C][C]96.13[/C][C]95.2756638780289[/C][C]0.854336121971097[/C][/ROW]
[ROW][C]11[/C][C]96.13[/C][C]95.3722475368342[/C][C]0.757752463165774[/C][/ROW]
[ROW][C]12[/C][C]96.13[/C][C]95.5550201736117[/C][C]0.574979826388338[/C][/ROW]
[ROW][C]13[/C][C]96.13[/C][C]96.1750617885047[/C][C]-0.0450617885047232[/C][/ROW]
[ROW][C]14[/C][C]96.13[/C][C]96.324504248105[/C][C]-0.194504248104989[/C][/ROW]
[ROW][C]15[/C][C]96.13[/C][C]96.7462014811103[/C][C]-0.616201481110303[/C][/ROW]
[ROW][C]16[/C][C]98.73[/C][C]97.7166393261758[/C][C]1.01336067382424[/C][/ROW]
[ROW][C]17[/C][C]98.73[/C][C]97.6814561449993[/C][C]1.04854385500067[/C][/ROW]
[ROW][C]18[/C][C]98.73[/C][C]97.5770872065765[/C][C]1.15291279342355[/C][/ROW]
[ROW][C]19[/C][C]98.73[/C][C]97.9602716229462[/C][C]0.769728377053766[/C][/ROW]
[ROW][C]20[/C][C]98.73[/C][C]99.1019303275868[/C][C]-0.371930327586841[/C][/ROW]
[ROW][C]21[/C][C]98.73[/C][C]98.367719646908[/C][C]0.362280353091971[/C][/ROW]
[ROW][C]22[/C][C]98.73[/C][C]98.404794725961[/C][C]0.325205274038938[/C][/ROW]
[ROW][C]23[/C][C]98.73[/C][C]98.2253956797918[/C][C]0.5046043202082[/C][/ROW]
[ROW][C]24[/C][C]98.73[/C][C]99.0914518848166[/C][C]-0.361451884816633[/C][/ROW]
[ROW][C]25[/C][C]98.73[/C][C]100.230299893836[/C][C]-1.50029989383562[/C][/ROW]
[ROW][C]26[/C][C]98.73[/C][C]100.749558526821[/C][C]-2.01955852682136[/C][/ROW]
[ROW][C]27[/C][C]98.73[/C][C]100.738632192451[/C][C]-2.00863219245097[/C][/ROW]
[ROW][C]28[/C][C]101.67[/C][C]101.307443765958[/C][C]0.362556234041603[/C][/ROW]
[ROW][C]29[/C][C]101.67[/C][C]101.282374386805[/C][C]0.387625613194959[/C][/ROW]
[ROW][C]30[/C][C]101.67[/C][C]101.777278456895[/C][C]-0.107278456895125[/C][/ROW]
[ROW][C]31[/C][C]101.67[/C][C]102.295211874038[/C][C]-0.62521187403754[/C][/ROW]
[ROW][C]32[/C][C]101.67[/C][C]102.910303528492[/C][C]-1.24030352849159[/C][/ROW]
[ROW][C]33[/C][C]101.67[/C][C]102.370613689498[/C][C]-0.700613689498008[/C][/ROW]
[ROW][C]34[/C][C]101.67[/C][C]102.553866830508[/C][C]-0.883866830508186[/C][/ROW]
[ROW][C]35[/C][C]101.67[/C][C]102.880261247275[/C][C]-1.21026124727486[/C][/ROW]
[ROW][C]36[/C][C]101.67[/C][C]102.787171939184[/C][C]-1.11717193918363[/C][/ROW]
[ROW][C]37[/C][C]101.67[/C][C]102.774453213227[/C][C]-1.10445321322742[/C][/ROW]
[ROW][C]38[/C][C]101.67[/C][C]103.5431132271[/C][C]-1.8731132270996[/C][/ROW]
[ROW][C]39[/C][C]101.67[/C][C]103.739276988726[/C][C]-2.06927698872611[/C][/ROW]
[ROW][C]40[/C][C]107.94[/C][C]104.800800154426[/C][C]3.13919984557381[/C][/ROW]
[ROW][C]41[/C][C]107.94[/C][C]104.877374357519[/C][C]3.06262564248121[/C][/ROW]
[ROW][C]42[/C][C]107.94[/C][C]106.56577096936[/C][C]1.37422903064019[/C][/ROW]
[ROW][C]43[/C][C]107.94[/C][C]106.590616155322[/C][C]1.3493838446784[/C][/ROW]
[ROW][C]44[/C][C]107.94[/C][C]107.259303782709[/C][C]0.680696217290513[/C][/ROW]
[ROW][C]45[/C][C]107.94[/C][C]107.980846376675[/C][C]-0.0408463766750154[/C][/ROW]
[ROW][C]46[/C][C]107.94[/C][C]108.774288874514[/C][C]-0.834288874513695[/C][/ROW]
[ROW][C]47[/C][C]107.94[/C][C]108.388169192[/C][C]-0.448169192000423[/C][/ROW]
[ROW][C]48[/C][C]107.94[/C][C]108.43212294405[/C][C]-0.4921229440501[/C][/ROW]
[ROW][C]49[/C][C]107.94[/C][C]108.356470570567[/C][C]-0.416470570566868[/C][/ROW]
[ROW][C]50[/C][C]107.94[/C][C]108.838861361416[/C][C]-0.898861361416381[/C][/ROW]
[ROW][C]51[/C][C]107.94[/C][C]108.226810210424[/C][C]-0.286810210424231[/C][/ROW]
[ROW][C]52[/C][C]110.3[/C][C]109.009211003291[/C][C]1.29078899670928[/C][/ROW]
[ROW][C]53[/C][C]110.3[/C][C]108.994970973588[/C][C]1.30502902641233[/C][/ROW]
[ROW][C]54[/C][C]110.3[/C][C]109.520825221283[/C][C]0.779174778717133[/C][/ROW]
[ROW][C]55[/C][C]110.3[/C][C]109.884566298514[/C][C]0.415433701485716[/C][/ROW]
[ROW][C]56[/C][C]110.3[/C][C]110.284235372966[/C][C]0.0157646270339175[/C][/ROW]
[ROW][C]57[/C][C]110.3[/C][C]110.275436039255[/C][C]0.0245639607449071[/C][/ROW]
[ROW][C]58[/C][C]110.3[/C][C]110.814704127954[/C][C]-0.514704127953556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97956&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97956&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
193.6393.9948223328667-0.36482233286675
293.6395.0361824766126-1.40618247661258
393.6394.9330410379434-1.30304103794344
496.1395.64932275525340.480677244746571
596.1395.66510826719660.464891732803438
696.1395.65182464940830.478175350591712
796.1395.61060168555280.51939831444723
896.1395.56154654743230.568453452567691
996.1395.13686082913060.993139170869396
1096.1395.27566387802890.854336121971097
1196.1395.37224753683420.757752463165774
1296.1395.55502017361170.574979826388338
1396.1396.1750617885047-0.0450617885047232
1496.1396.324504248105-0.194504248104989
1596.1396.7462014811103-0.616201481110303
1698.7397.71663932617581.01336067382424
1798.7397.68145614499931.04854385500067
1898.7397.57708720657651.15291279342355
1998.7397.96027162294620.769728377053766
2098.7399.1019303275868-0.371930327586841
2198.7398.3677196469080.362280353091971
2298.7398.4047947259610.325205274038938
2398.7398.22539567979180.5046043202082
2498.7399.0914518848166-0.361451884816633
2598.73100.230299893836-1.50029989383562
2698.73100.749558526821-2.01955852682136
2798.73100.738632192451-2.00863219245097
28101.67101.3074437659580.362556234041603
29101.67101.2823743868050.387625613194959
30101.67101.777278456895-0.107278456895125
31101.67102.295211874038-0.62521187403754
32101.67102.910303528492-1.24030352849159
33101.67102.370613689498-0.700613689498008
34101.67102.553866830508-0.883866830508186
35101.67102.880261247275-1.21026124727486
36101.67102.787171939184-1.11717193918363
37101.67102.774453213227-1.10445321322742
38101.67103.5431132271-1.8731132270996
39101.67103.739276988726-2.06927698872611
40107.94104.8008001544263.13919984557381
41107.94104.8773743575193.06262564248121
42107.94106.565770969361.37422903064019
43107.94106.5906161553221.3493838446784
44107.94107.2593037827090.680696217290513
45107.94107.980846376675-0.0408463766750154
46107.94108.774288874514-0.834288874513695
47107.94108.388169192-0.448169192000423
48107.94108.43212294405-0.4921229440501
49107.94108.356470570567-0.416470570566868
50107.94108.838861361416-0.898861361416381
51107.94108.226810210424-0.286810210424231
52110.3109.0092110032911.29078899670928
53110.3108.9949709735881.30502902641233
54110.3109.5208252212830.779174778717133
55110.3109.8845662985140.415433701485716
56110.3110.2842353729660.0157646270339175
57110.3110.2754360392550.0245639607449071
58110.3110.814704127954-0.514704127953556







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
91.0932548537229e-062.18650970744581e-060.999998906745146
102.72627639846001e-055.45255279692002e-050.999972737236015
111.94194921276017e-063.88389842552035e-060.999998058050787
120.0001296285636676120.0002592571273352250.999870371436332
130.0001359962843323590.0002719925686647170.999864003715668
144.43212445887518e-058.86424891775037e-050.999955678755411
151.20419053190271e-052.40838106380542e-050.99998795809468
160.001895650218439340.003791300436878670.99810434978156
170.003363979174775580.006727958349551170.996636020825224
180.003574566350145680.007149132700291370.996425433649854
190.008395920422990050.01679184084598010.99160407957701
200.02118095251790490.04236190503580970.978819047482095
210.01429258819429790.02858517638859570.985707411805702
220.01204341435481360.02408682870962720.987956585645186
230.01936085262310590.03872170524621190.980639147376894
240.02687578937222520.05375157874445040.973124210627775
250.03582424376132170.07164848752264330.964175756238678
260.03452724904201840.06905449808403680.965472750957982
270.03448919190895430.06897838381790870.965510808091046
280.06141329999126790.1228265999825360.938586700008732
290.07392290867735150.1478458173547030.926077091322648
300.06018117857207650.1203623571441530.939818821427924
310.03914113077074810.07828226154149620.960858869229252
320.04815338152572140.09630676305144280.951846618474279
330.03434736845962180.06869473691924360.965652631540378
340.02175870371093140.04351740742186290.978241296289069
350.01507516714504380.03015033429008760.984924832854956
360.009456309863495380.01891261972699080.990543690136505
370.006000072161643750.01200014432328750.993999927838356
380.01441885642934220.02883771285868430.985581143570658
390.8912035346375220.2175929307249560.108796465362478
400.9902483131360870.01950337372782630.00975168686391314
410.9967415003035980.00651699939280330.00325849969640165
420.993580777278590.01283844544282150.00641922272141073
430.9890819137328020.02183617253439560.0109180862671978
440.9833935972285270.03321280554294670.0166064027714734
450.964378922460740.07124215507851910.0356210775392595
460.9296296322648590.1407407354702830.0703703677351414
470.8636050690691710.2727898618616580.136394930930829
480.7538184796734160.4923630406531690.246181520326584
490.5985979973265020.8028040053469950.401402002673498

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 1.0932548537229e-06 & 2.18650970744581e-06 & 0.999998906745146 \tabularnewline
10 & 2.72627639846001e-05 & 5.45255279692002e-05 & 0.999972737236015 \tabularnewline
11 & 1.94194921276017e-06 & 3.88389842552035e-06 & 0.999998058050787 \tabularnewline
12 & 0.000129628563667612 & 0.000259257127335225 & 0.999870371436332 \tabularnewline
13 & 0.000135996284332359 & 0.000271992568664717 & 0.999864003715668 \tabularnewline
14 & 4.43212445887518e-05 & 8.86424891775037e-05 & 0.999955678755411 \tabularnewline
15 & 1.20419053190271e-05 & 2.40838106380542e-05 & 0.99998795809468 \tabularnewline
16 & 0.00189565021843934 & 0.00379130043687867 & 0.99810434978156 \tabularnewline
17 & 0.00336397917477558 & 0.00672795834955117 & 0.996636020825224 \tabularnewline
18 & 0.00357456635014568 & 0.00714913270029137 & 0.996425433649854 \tabularnewline
19 & 0.00839592042299005 & 0.0167918408459801 & 0.99160407957701 \tabularnewline
20 & 0.0211809525179049 & 0.0423619050358097 & 0.978819047482095 \tabularnewline
21 & 0.0142925881942979 & 0.0285851763885957 & 0.985707411805702 \tabularnewline
22 & 0.0120434143548136 & 0.0240868287096272 & 0.987956585645186 \tabularnewline
23 & 0.0193608526231059 & 0.0387217052462119 & 0.980639147376894 \tabularnewline
24 & 0.0268757893722252 & 0.0537515787444504 & 0.973124210627775 \tabularnewline
25 & 0.0358242437613217 & 0.0716484875226433 & 0.964175756238678 \tabularnewline
26 & 0.0345272490420184 & 0.0690544980840368 & 0.965472750957982 \tabularnewline
27 & 0.0344891919089543 & 0.0689783838179087 & 0.965510808091046 \tabularnewline
28 & 0.0614132999912679 & 0.122826599982536 & 0.938586700008732 \tabularnewline
29 & 0.0739229086773515 & 0.147845817354703 & 0.926077091322648 \tabularnewline
30 & 0.0601811785720765 & 0.120362357144153 & 0.939818821427924 \tabularnewline
31 & 0.0391411307707481 & 0.0782822615414962 & 0.960858869229252 \tabularnewline
32 & 0.0481533815257214 & 0.0963067630514428 & 0.951846618474279 \tabularnewline
33 & 0.0343473684596218 & 0.0686947369192436 & 0.965652631540378 \tabularnewline
34 & 0.0217587037109314 & 0.0435174074218629 & 0.978241296289069 \tabularnewline
35 & 0.0150751671450438 & 0.0301503342900876 & 0.984924832854956 \tabularnewline
36 & 0.00945630986349538 & 0.0189126197269908 & 0.990543690136505 \tabularnewline
37 & 0.00600007216164375 & 0.0120001443232875 & 0.993999927838356 \tabularnewline
38 & 0.0144188564293422 & 0.0288377128586843 & 0.985581143570658 \tabularnewline
39 & 0.891203534637522 & 0.217592930724956 & 0.108796465362478 \tabularnewline
40 & 0.990248313136087 & 0.0195033737278263 & 0.00975168686391314 \tabularnewline
41 & 0.996741500303598 & 0.0065169993928033 & 0.00325849969640165 \tabularnewline
42 & 0.99358077727859 & 0.0128384454428215 & 0.00641922272141073 \tabularnewline
43 & 0.989081913732802 & 0.0218361725343956 & 0.0109180862671978 \tabularnewline
44 & 0.983393597228527 & 0.0332128055429467 & 0.0166064027714734 \tabularnewline
45 & 0.96437892246074 & 0.0712421550785191 & 0.0356210775392595 \tabularnewline
46 & 0.929629632264859 & 0.140740735470283 & 0.0703703677351414 \tabularnewline
47 & 0.863605069069171 & 0.272789861861658 & 0.136394930930829 \tabularnewline
48 & 0.753818479673416 & 0.492363040653169 & 0.246181520326584 \tabularnewline
49 & 0.598597997326502 & 0.802804005346995 & 0.401402002673498 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97956&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]9[/C][C]1.0932548537229e-06[/C][C]2.18650970744581e-06[/C][C]0.999998906745146[/C][/ROW]
[ROW][C]10[/C][C]2.72627639846001e-05[/C][C]5.45255279692002e-05[/C][C]0.999972737236015[/C][/ROW]
[ROW][C]11[/C][C]1.94194921276017e-06[/C][C]3.88389842552035e-06[/C][C]0.999998058050787[/C][/ROW]
[ROW][C]12[/C][C]0.000129628563667612[/C][C]0.000259257127335225[/C][C]0.999870371436332[/C][/ROW]
[ROW][C]13[/C][C]0.000135996284332359[/C][C]0.000271992568664717[/C][C]0.999864003715668[/C][/ROW]
[ROW][C]14[/C][C]4.43212445887518e-05[/C][C]8.86424891775037e-05[/C][C]0.999955678755411[/C][/ROW]
[ROW][C]15[/C][C]1.20419053190271e-05[/C][C]2.40838106380542e-05[/C][C]0.99998795809468[/C][/ROW]
[ROW][C]16[/C][C]0.00189565021843934[/C][C]0.00379130043687867[/C][C]0.99810434978156[/C][/ROW]
[ROW][C]17[/C][C]0.00336397917477558[/C][C]0.00672795834955117[/C][C]0.996636020825224[/C][/ROW]
[ROW][C]18[/C][C]0.00357456635014568[/C][C]0.00714913270029137[/C][C]0.996425433649854[/C][/ROW]
[ROW][C]19[/C][C]0.00839592042299005[/C][C]0.0167918408459801[/C][C]0.99160407957701[/C][/ROW]
[ROW][C]20[/C][C]0.0211809525179049[/C][C]0.0423619050358097[/C][C]0.978819047482095[/C][/ROW]
[ROW][C]21[/C][C]0.0142925881942979[/C][C]0.0285851763885957[/C][C]0.985707411805702[/C][/ROW]
[ROW][C]22[/C][C]0.0120434143548136[/C][C]0.0240868287096272[/C][C]0.987956585645186[/C][/ROW]
[ROW][C]23[/C][C]0.0193608526231059[/C][C]0.0387217052462119[/C][C]0.980639147376894[/C][/ROW]
[ROW][C]24[/C][C]0.0268757893722252[/C][C]0.0537515787444504[/C][C]0.973124210627775[/C][/ROW]
[ROW][C]25[/C][C]0.0358242437613217[/C][C]0.0716484875226433[/C][C]0.964175756238678[/C][/ROW]
[ROW][C]26[/C][C]0.0345272490420184[/C][C]0.0690544980840368[/C][C]0.965472750957982[/C][/ROW]
[ROW][C]27[/C][C]0.0344891919089543[/C][C]0.0689783838179087[/C][C]0.965510808091046[/C][/ROW]
[ROW][C]28[/C][C]0.0614132999912679[/C][C]0.122826599982536[/C][C]0.938586700008732[/C][/ROW]
[ROW][C]29[/C][C]0.0739229086773515[/C][C]0.147845817354703[/C][C]0.926077091322648[/C][/ROW]
[ROW][C]30[/C][C]0.0601811785720765[/C][C]0.120362357144153[/C][C]0.939818821427924[/C][/ROW]
[ROW][C]31[/C][C]0.0391411307707481[/C][C]0.0782822615414962[/C][C]0.960858869229252[/C][/ROW]
[ROW][C]32[/C][C]0.0481533815257214[/C][C]0.0963067630514428[/C][C]0.951846618474279[/C][/ROW]
[ROW][C]33[/C][C]0.0343473684596218[/C][C]0.0686947369192436[/C][C]0.965652631540378[/C][/ROW]
[ROW][C]34[/C][C]0.0217587037109314[/C][C]0.0435174074218629[/C][C]0.978241296289069[/C][/ROW]
[ROW][C]35[/C][C]0.0150751671450438[/C][C]0.0301503342900876[/C][C]0.984924832854956[/C][/ROW]
[ROW][C]36[/C][C]0.00945630986349538[/C][C]0.0189126197269908[/C][C]0.990543690136505[/C][/ROW]
[ROW][C]37[/C][C]0.00600007216164375[/C][C]0.0120001443232875[/C][C]0.993999927838356[/C][/ROW]
[ROW][C]38[/C][C]0.0144188564293422[/C][C]0.0288377128586843[/C][C]0.985581143570658[/C][/ROW]
[ROW][C]39[/C][C]0.891203534637522[/C][C]0.217592930724956[/C][C]0.108796465362478[/C][/ROW]
[ROW][C]40[/C][C]0.990248313136087[/C][C]0.0195033737278263[/C][C]0.00975168686391314[/C][/ROW]
[ROW][C]41[/C][C]0.996741500303598[/C][C]0.0065169993928033[/C][C]0.00325849969640165[/C][/ROW]
[ROW][C]42[/C][C]0.99358077727859[/C][C]0.0128384454428215[/C][C]0.00641922272141073[/C][/ROW]
[ROW][C]43[/C][C]0.989081913732802[/C][C]0.0218361725343956[/C][C]0.0109180862671978[/C][/ROW]
[ROW][C]44[/C][C]0.983393597228527[/C][C]0.0332128055429467[/C][C]0.0166064027714734[/C][/ROW]
[ROW][C]45[/C][C]0.96437892246074[/C][C]0.0712421550785191[/C][C]0.0356210775392595[/C][/ROW]
[ROW][C]46[/C][C]0.929629632264859[/C][C]0.140740735470283[/C][C]0.0703703677351414[/C][/ROW]
[ROW][C]47[/C][C]0.863605069069171[/C][C]0.272789861861658[/C][C]0.136394930930829[/C][/ROW]
[ROW][C]48[/C][C]0.753818479673416[/C][C]0.492363040653169[/C][C]0.246181520326584[/C][/ROW]
[ROW][C]49[/C][C]0.598597997326502[/C][C]0.802804005346995[/C][C]0.401402002673498[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97956&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97956&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
91.0932548537229e-062.18650970744581e-060.999998906745146
102.72627639846001e-055.45255279692002e-050.999972737236015
111.94194921276017e-063.88389842552035e-060.999998058050787
120.0001296285636676120.0002592571273352250.999870371436332
130.0001359962843323590.0002719925686647170.999864003715668
144.43212445887518e-058.86424891775037e-050.999955678755411
151.20419053190271e-052.40838106380542e-050.99998795809468
160.001895650218439340.003791300436878670.99810434978156
170.003363979174775580.006727958349551170.996636020825224
180.003574566350145680.007149132700291370.996425433649854
190.008395920422990050.01679184084598010.99160407957701
200.02118095251790490.04236190503580970.978819047482095
210.01429258819429790.02858517638859570.985707411805702
220.01204341435481360.02408682870962720.987956585645186
230.01936085262310590.03872170524621190.980639147376894
240.02687578937222520.05375157874445040.973124210627775
250.03582424376132170.07164848752264330.964175756238678
260.03452724904201840.06905449808403680.965472750957982
270.03448919190895430.06897838381790870.965510808091046
280.06141329999126790.1228265999825360.938586700008732
290.07392290867735150.1478458173547030.926077091322648
300.06018117857207650.1203623571441530.939818821427924
310.03914113077074810.07828226154149620.960858869229252
320.04815338152572140.09630676305144280.951846618474279
330.03434736845962180.06869473691924360.965652631540378
340.02175870371093140.04351740742186290.978241296289069
350.01507516714504380.03015033429008760.984924832854956
360.009456309863495380.01891261972699080.990543690136505
370.006000072161643750.01200014432328750.993999927838356
380.01441885642934220.02883771285868430.985581143570658
390.8912035346375220.2175929307249560.108796465362478
400.9902483131360870.01950337372782630.00975168686391314
410.9967415003035980.00651699939280330.00325849969640165
420.993580777278590.01283844544282150.00641922272141073
430.9890819137328020.02183617253439560.0109180862671978
440.9833935972285270.03321280554294670.0166064027714734
450.964378922460740.07124215507851910.0356210775392595
460.9296296322648590.1407407354702830.0703703677351414
470.8636050690691710.2727898618616580.136394930930829
480.7538184796734160.4923630406531690.246181520326584
490.5985979973265020.8028040053469950.401402002673498







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level110.268292682926829NOK
5% type I error level250.609756097560976NOK
10% type I error level330.804878048780488NOK

\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 & 11 & 0.268292682926829 & NOK \tabularnewline
5% type I error level & 25 & 0.609756097560976 & NOK \tabularnewline
10% type I error level & 33 & 0.804878048780488 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97956&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]11[/C][C]0.268292682926829[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]25[/C][C]0.609756097560976[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]33[/C][C]0.804878048780488[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97956&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97956&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 level110.268292682926829NOK
5% type I error level250.609756097560976NOK
10% type I error level330.804878048780488NOK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}