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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 08:54:08 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322142880u8vi940rudeb9p6.htm/, Retrieved Sat, 20 Apr 2024 00:03:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146812, Retrieved Sat, 20 Apr 2024 00:03:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordskwaliteit aanbevelingsbrief
Estimated Impact117

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-24 13:54:08] [685fae5a377cc1dc51f9f02306b751ce] [Current]
- R       [Multiple Regression] [] [2012-11-12 16:19:22] [391561951b5d7f721cfaa4f5575ab127]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2.04	2.01	1070	5
2.56	3.40	1254	6
3.75	3.68	1466	6
1.10	1.54	706	4
3.00	3.32	1160	5
0.05	0.33	756	3
1.38	0.36	1058	2
1.50	1.97	1008	7
1.38	2.03	1104	4
4.01	2.05	1200	7
1.50	2.13	896	7
1.29	1.34	848	3
1.90	1.51	958	5
3.11	3.12	1246	6
1.92	2.14	1106	4
0.81	2.60	790	5
1.01	1.90	954	4
3.66	3.06	1500	6
2.00	1.60	1046	5
2.05	1.96	1054	4
2.60	1.96	1198	6
2.55	1.56	940	3
0.38	1.60	456	6
2.48	1.92	1150	7
2.74	3.09	636	6
1.77	0.78	744	5
1.61	2.12	644	5
0.99	1.85	842	3
1.62	1.78	852	5
2.03	1.03	1170	3
3.50	3.44	1034	10
3.18	2.42	1202	5
2.39	1.74	1018	5
1.48	1.89	1180	5
1.54	1.43	952	3
1.57	1.64	1038	4
2.46	2.69	1090	6
2.42	1.79	694	5
2.11	2.72	1096	6
2.04	2.15	1114	5
1.68	2.22	1256	6
1.64	1.55	1208	5
2.41	2.34	820	6
2.10	2.92	1222	4
1.40	2.10	1120	5
2.03	1.64	886	4
1.99	2.83	1126	7
2.24	1.76	1158	4
0.45	1.81	676	6
2.31	2.68	1214	7
2.41	2.55	1136	6
2.56	2.70	1264	6
2.50	1.66	1116	3
2.92	2.23	1292	4
2.35	2.01	604	5
2.82	1.24	854	6
1.80	1.95	814	6
1.29	1.73	778	3
1.68	1.08	800	2
3.44	3.46	1424	7
1.90	3.01	950	6
2.06	0.54	1056	3
3.30	3.20	956	8
1.80	1.50	1352	5
2.00	1.71	852	5
1.68	1.99	1168	5
1.94	2.76	970	6
0.97	1.56	776	4
1.12	1.78	854	6
1.31	1.32	1232	5
1.68	0.87	1140	6
3.09	1.75	1084	4
1.87	1.41	954	2
2.00	2.77	1000	4
2.39	1.78	1084	4
1.50	1.34	1058	4
1.82	1.52	816	5
1.80	2.97	1146	7
2.01	1.75	1000	6
1.88	1.64	856	4
1.64	1.80	798	4
2.42	3.37	1324	6
0.22	1.15	704	6
2.31	1.72	1222	5
0.95	2.27	948	6
1.99	2.85	1182	8
1.86	2.21	1000	6
1.79	1.94	910	6
3.02	4.25	1374	9
1.85	1.83	1014	6
1.98	2.75	1420	7
2.15	1.71	400	6
1.46	2.20	998	7
2.29	2.13	776	6
2.39	2.38	1134	7
1.80	1.64	772	4
2.64	1.87	1304	6
2.08	2.53	1212	4
0.70	1.78	818	6
0.89	1.20	864	2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146812&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146812&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146812&T=0

As an alternative you can also use a QR Code:  
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 time9 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Qualityoflettersofrecommendation[t] = + 2.87926918950499 + 0.0907256384999622CollegeGPA[t] + 1.31916985763979HighSchoolGPA[t] -0.000563091457009581SATtotal[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Qualityoflettersofrecommendation[t] =  +  2.87926918950499 +  0.0907256384999622CollegeGPA[t] +  1.31916985763979HighSchoolGPA[t] -0.000563091457009581SATtotal[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146812&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Qualityoflettersofrecommendation[t] =  +  2.87926918950499 +  0.0907256384999622CollegeGPA[t] +  1.31916985763979HighSchoolGPA[t] -0.000563091457009581SATtotal[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146812&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146812&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Qualityoflettersofrecommendation[t] = + 2.87926918950499 + 0.0907256384999622CollegeGPA[t] + 1.31916985763979HighSchoolGPA[t] -0.000563091457009581SATtotal[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.879269189504990.5759574.99913e-061e-06
CollegeGPA0.09072563849996220.2039010.44490.6573580.328679
HighSchoolGPA1.319169857639790.1999756.596700
SATtotal-0.0005630914570095810.000654-0.86160.3910630.195532

\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) & 2.87926918950499 & 0.575957 & 4.9991 & 3e-06 & 1e-06 \tabularnewline
CollegeGPA & 0.0907256384999622 & 0.203901 & 0.4449 & 0.657358 & 0.328679 \tabularnewline
HighSchoolGPA & 1.31916985763979 & 0.199975 & 6.5967 & 0 & 0 \tabularnewline
SATtotal & -0.000563091457009581 & 0.000654 & -0.8616 & 0.391063 & 0.195532 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146812&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]2.87926918950499[/C][C]0.575957[/C][C]4.9991[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]CollegeGPA[/C][C]0.0907256384999622[/C][C]0.203901[/C][C]0.4449[/C][C]0.657358[/C][C]0.328679[/C][/ROW]
[ROW][C]HighSchoolGPA[/C][C]1.31916985763979[/C][C]0.199975[/C][C]6.5967[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]SATtotal[/C][C]-0.000563091457009581[/C][C]0.000654[/C][C]-0.8616[/C][C]0.391063[/C][C]0.195532[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146812&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146812&T=2

As an alternative you can also use a QR Code:  
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)2.879269189504990.5759574.99913e-061e-06
CollegeGPA0.09072563849996220.2039010.44490.6573580.328679
HighSchoolGPA1.319169857639790.1999756.596700
SATtotal-0.0005630914570095810.000654-0.86160.3910630.195532






Multiple Linear Regression - Regression Statistics
Multiple R0.63037199537585
R-squared0.397368852554131
Adjusted R-squared0.378536629196448
F-TEST (value)21.100474702686
F-TEST (DF numerator)3
F-TEST (DF denominator)96
p-value1.39666056497845e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.17887883589416
Sum Squared Residuals133.416509733041

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.63037199537585 \tabularnewline
R-squared & 0.397368852554131 \tabularnewline
Adjusted R-squared & 0.378536629196448 \tabularnewline
F-TEST (value) & 21.100474702686 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 96 \tabularnewline
p-value & 1.39666056497845e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.17887883589416 \tabularnewline
Sum Squared Residuals & 133.416509733041 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146812&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.63037199537585[/C][/ROW]
[ROW][C]R-squared[/C][C]0.397368852554131[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.378536629196448[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.100474702686[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]96[/C][/ROW]
[ROW][C]p-value[/C][C]1.39666056497845e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.17887883589416[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]133.416509733041[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146812&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146812&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.63037199537585
R-squared0.397368852554131
Adjusted R-squared0.378536629196448
F-TEST (value)21.100474702686
F-TEST (DF numerator)3
F-TEST (DF denominator)96
p-value1.39666056497845e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.17887883589416
Sum Squared Residuals133.416509733041






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
155.11337304690064-0.11337304690064
266.89058765295017-0.890587652950168
367.24854333401823-1.24854333401823
444.61304640397146-0.613046403971464
556.87790394223787-1.87790394223787
632.893434382951880.106565617048121
722.88362095786913-0.883620957869129
875.046526078139671.95347392186033
945.06073241310514-1.06073241310514
1075.271667459639911.72833254036009
1175.32065949854711.6793405014529
1234.28649131686314-1.28649131686314
1354.504152771875830.495847228124174
1466.57562392564208-0.575623925642083
1545.25370675932148-1.25370675932148
1655.93775633551585-0.937756335515848
1744.94013556391842-0.940135563918416
1866.40334760527824-0.403347605278241
1954.582398574696560.41760142530344
2045.05733127371581-1.05733127371581
2165.026145205081410.973854794918595
2234.63921857600896-1.63921857600896
2364.767646999962271.23235300003773
2474.989519724092282.01048027590772
2566.84596613244375-0.845966132443748
2653.649866014593831.35013398540617
2755.45934666737212-0.459346667372118
2834.9354288014515-1.9354288014515
2954.89461314910160.105386850898404
3033.76337018432769-0.76337018432769
31107.152516667987832.84748333201217
3255.68333184409765-0.683331844097648
3354.818231914577380.181768085422616
3454.842326246152830.157673753847165
3534.36933650214671-1.36933650214671
3644.60065807610324-0.600658076103244
3766.03725148912549-0.0372514891254924
3855.06935380868548-0.0693538086854771
3966.04169406263764-0.0416940626376422
4055.27328080286179-0.273280802861792
4165.253002476141230.74699752385877
4254.392558035919030.607441964080968
4365.723040450419150.276959549580845
4446.23367125419739-2.23367125419739
4555.14587935259777-0.145879352597769
4644.72798177127868-0.727981771278683
4776.159022926647740.840977073352264
4844.75217366197384-0.752173661973844
4964.927143344219521.07285665578048
5075.940627604104911.05937239589509
5165.822129220108480.177870779891517
5265.961537838032220.0384621619677803
5334.66749518341426-1.66749518341426
5445.35842267400524-1.35842267400524
5555.4038986138021-0.403898613802095
5664.290006009262041.70999399073796
5765.156600115196720.843399884803283
5834.84038396333333-1.84038396333333
5924.00591854282824-2.00591854282824
6076.953850858596890.0461491414031065
6166.48741228999159-0.487412289991588
6233.18389114933828-0.183891149338284
6386.861691908101041.13830809189896
6454.260030475387660.739969524612343
6554.83674700169680.163252998303204
6654.999145457100920.000854542899078447
6766.14998702198145-0.149987021981447
6844.58821906612859-0.588219066128595
6964.84812414693761.1518758530624
7054.045695312988660.954304687011337
7163.537441777340622.46255822265938
7244.85776752394112-0.857767523941123
7324.37176638278489-2.37176638278489
7446.15172951515756-2.15172951515756
7544.83383467272034-0.833834672720343
7644.18729449497612-0.187294494976119
7754.590045406267590.409954593732412
7876.315207006262120.684792993737877
7964.807083516749971.19291648325003
8044.73126566921398-0.731265669213976
8144.95321799770291-0.953217997702908
8266.79889456584031-0.79889456584031
8364.0198577805261.980142219474
8454.669719809114640.330280190885363
8565.42616342167720.573836578322802
8686.1538732022081.846126797792
8765.400292805489280.599707194510722
8865.08844438036240.9115556196376
8997.986044850812821.01395514918718
9064.890217722803021.10978227719698
9175.887033193290741.11296680670926
9265.104873186040120.89512681395988
9375.351937034426921.64806296557308
9465.459903727803220.540096272196775
9575.597182014453741.40281798554626
9644.77130730052278-0.771307300522784
9764.851361248990811.14863875100919
9845.72301141151797-1.72301141151797
9964.830290671219961.16970932878004
10024.05650781808143-2.05650781808143

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5 & 5.11337304690064 & -0.11337304690064 \tabularnewline
2 & 6 & 6.89058765295017 & -0.890587652950168 \tabularnewline
3 & 6 & 7.24854333401823 & -1.24854333401823 \tabularnewline
4 & 4 & 4.61304640397146 & -0.613046403971464 \tabularnewline
5 & 5 & 6.87790394223787 & -1.87790394223787 \tabularnewline
6 & 3 & 2.89343438295188 & 0.106565617048121 \tabularnewline
7 & 2 & 2.88362095786913 & -0.883620957869129 \tabularnewline
8 & 7 & 5.04652607813967 & 1.95347392186033 \tabularnewline
9 & 4 & 5.06073241310514 & -1.06073241310514 \tabularnewline
10 & 7 & 5.27166745963991 & 1.72833254036009 \tabularnewline
11 & 7 & 5.3206594985471 & 1.6793405014529 \tabularnewline
12 & 3 & 4.28649131686314 & -1.28649131686314 \tabularnewline
13 & 5 & 4.50415277187583 & 0.495847228124174 \tabularnewline
14 & 6 & 6.57562392564208 & -0.575623925642083 \tabularnewline
15 & 4 & 5.25370675932148 & -1.25370675932148 \tabularnewline
16 & 5 & 5.93775633551585 & -0.937756335515848 \tabularnewline
17 & 4 & 4.94013556391842 & -0.940135563918416 \tabularnewline
18 & 6 & 6.40334760527824 & -0.403347605278241 \tabularnewline
19 & 5 & 4.58239857469656 & 0.41760142530344 \tabularnewline
20 & 4 & 5.05733127371581 & -1.05733127371581 \tabularnewline
21 & 6 & 5.02614520508141 & 0.973854794918595 \tabularnewline
22 & 3 & 4.63921857600896 & -1.63921857600896 \tabularnewline
23 & 6 & 4.76764699996227 & 1.23235300003773 \tabularnewline
24 & 7 & 4.98951972409228 & 2.01048027590772 \tabularnewline
25 & 6 & 6.84596613244375 & -0.845966132443748 \tabularnewline
26 & 5 & 3.64986601459383 & 1.35013398540617 \tabularnewline
27 & 5 & 5.45934666737212 & -0.459346667372118 \tabularnewline
28 & 3 & 4.9354288014515 & -1.9354288014515 \tabularnewline
29 & 5 & 4.8946131491016 & 0.105386850898404 \tabularnewline
30 & 3 & 3.76337018432769 & -0.76337018432769 \tabularnewline
31 & 10 & 7.15251666798783 & 2.84748333201217 \tabularnewline
32 & 5 & 5.68333184409765 & -0.683331844097648 \tabularnewline
33 & 5 & 4.81823191457738 & 0.181768085422616 \tabularnewline
34 & 5 & 4.84232624615283 & 0.157673753847165 \tabularnewline
35 & 3 & 4.36933650214671 & -1.36933650214671 \tabularnewline
36 & 4 & 4.60065807610324 & -0.600658076103244 \tabularnewline
37 & 6 & 6.03725148912549 & -0.0372514891254924 \tabularnewline
38 & 5 & 5.06935380868548 & -0.0693538086854771 \tabularnewline
39 & 6 & 6.04169406263764 & -0.0416940626376422 \tabularnewline
40 & 5 & 5.27328080286179 & -0.273280802861792 \tabularnewline
41 & 6 & 5.25300247614123 & 0.74699752385877 \tabularnewline
42 & 5 & 4.39255803591903 & 0.607441964080968 \tabularnewline
43 & 6 & 5.72304045041915 & 0.276959549580845 \tabularnewline
44 & 4 & 6.23367125419739 & -2.23367125419739 \tabularnewline
45 & 5 & 5.14587935259777 & -0.145879352597769 \tabularnewline
46 & 4 & 4.72798177127868 & -0.727981771278683 \tabularnewline
47 & 7 & 6.15902292664774 & 0.840977073352264 \tabularnewline
48 & 4 & 4.75217366197384 & -0.752173661973844 \tabularnewline
49 & 6 & 4.92714334421952 & 1.07285665578048 \tabularnewline
50 & 7 & 5.94062760410491 & 1.05937239589509 \tabularnewline
51 & 6 & 5.82212922010848 & 0.177870779891517 \tabularnewline
52 & 6 & 5.96153783803222 & 0.0384621619677803 \tabularnewline
53 & 3 & 4.66749518341426 & -1.66749518341426 \tabularnewline
54 & 4 & 5.35842267400524 & -1.35842267400524 \tabularnewline
55 & 5 & 5.4038986138021 & -0.403898613802095 \tabularnewline
56 & 6 & 4.29000600926204 & 1.70999399073796 \tabularnewline
57 & 6 & 5.15660011519672 & 0.843399884803283 \tabularnewline
58 & 3 & 4.84038396333333 & -1.84038396333333 \tabularnewline
59 & 2 & 4.00591854282824 & -2.00591854282824 \tabularnewline
60 & 7 & 6.95385085859689 & 0.0461491414031065 \tabularnewline
61 & 6 & 6.48741228999159 & -0.487412289991588 \tabularnewline
62 & 3 & 3.18389114933828 & -0.183891149338284 \tabularnewline
63 & 8 & 6.86169190810104 & 1.13830809189896 \tabularnewline
64 & 5 & 4.26003047538766 & 0.739969524612343 \tabularnewline
65 & 5 & 4.8367470016968 & 0.163252998303204 \tabularnewline
66 & 5 & 4.99914545710092 & 0.000854542899078447 \tabularnewline
67 & 6 & 6.14998702198145 & -0.149987021981447 \tabularnewline
68 & 4 & 4.58821906612859 & -0.588219066128595 \tabularnewline
69 & 6 & 4.8481241469376 & 1.1518758530624 \tabularnewline
70 & 5 & 4.04569531298866 & 0.954304687011337 \tabularnewline
71 & 6 & 3.53744177734062 & 2.46255822265938 \tabularnewline
72 & 4 & 4.85776752394112 & -0.857767523941123 \tabularnewline
73 & 2 & 4.37176638278489 & -2.37176638278489 \tabularnewline
74 & 4 & 6.15172951515756 & -2.15172951515756 \tabularnewline
75 & 4 & 4.83383467272034 & -0.833834672720343 \tabularnewline
76 & 4 & 4.18729449497612 & -0.187294494976119 \tabularnewline
77 & 5 & 4.59004540626759 & 0.409954593732412 \tabularnewline
78 & 7 & 6.31520700626212 & 0.684792993737877 \tabularnewline
79 & 6 & 4.80708351674997 & 1.19291648325003 \tabularnewline
80 & 4 & 4.73126566921398 & -0.731265669213976 \tabularnewline
81 & 4 & 4.95321799770291 & -0.953217997702908 \tabularnewline
82 & 6 & 6.79889456584031 & -0.79889456584031 \tabularnewline
83 & 6 & 4.019857780526 & 1.980142219474 \tabularnewline
84 & 5 & 4.66971980911464 & 0.330280190885363 \tabularnewline
85 & 6 & 5.4261634216772 & 0.573836578322802 \tabularnewline
86 & 8 & 6.153873202208 & 1.846126797792 \tabularnewline
87 & 6 & 5.40029280548928 & 0.599707194510722 \tabularnewline
88 & 6 & 5.0884443803624 & 0.9115556196376 \tabularnewline
89 & 9 & 7.98604485081282 & 1.01395514918718 \tabularnewline
90 & 6 & 4.89021772280302 & 1.10978227719698 \tabularnewline
91 & 7 & 5.88703319329074 & 1.11296680670926 \tabularnewline
92 & 6 & 5.10487318604012 & 0.89512681395988 \tabularnewline
93 & 7 & 5.35193703442692 & 1.64806296557308 \tabularnewline
94 & 6 & 5.45990372780322 & 0.540096272196775 \tabularnewline
95 & 7 & 5.59718201445374 & 1.40281798554626 \tabularnewline
96 & 4 & 4.77130730052278 & -0.771307300522784 \tabularnewline
97 & 6 & 4.85136124899081 & 1.14863875100919 \tabularnewline
98 & 4 & 5.72301141151797 & -1.72301141151797 \tabularnewline
99 & 6 & 4.83029067121996 & 1.16970932878004 \tabularnewline
100 & 2 & 4.05650781808143 & -2.05650781808143 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146812&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]5[/C][C]5.11337304690064[/C][C]-0.11337304690064[/C][/ROW]
[ROW][C]2[/C][C]6[/C][C]6.89058765295017[/C][C]-0.890587652950168[/C][/ROW]
[ROW][C]3[/C][C]6[/C][C]7.24854333401823[/C][C]-1.24854333401823[/C][/ROW]
[ROW][C]4[/C][C]4[/C][C]4.61304640397146[/C][C]-0.613046403971464[/C][/ROW]
[ROW][C]5[/C][C]5[/C][C]6.87790394223787[/C][C]-1.87790394223787[/C][/ROW]
[ROW][C]6[/C][C]3[/C][C]2.89343438295188[/C][C]0.106565617048121[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]2.88362095786913[/C][C]-0.883620957869129[/C][/ROW]
[ROW][C]8[/C][C]7[/C][C]5.04652607813967[/C][C]1.95347392186033[/C][/ROW]
[ROW][C]9[/C][C]4[/C][C]5.06073241310514[/C][C]-1.06073241310514[/C][/ROW]
[ROW][C]10[/C][C]7[/C][C]5.27166745963991[/C][C]1.72833254036009[/C][/ROW]
[ROW][C]11[/C][C]7[/C][C]5.3206594985471[/C][C]1.6793405014529[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]4.28649131686314[/C][C]-1.28649131686314[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]4.50415277187583[/C][C]0.495847228124174[/C][/ROW]
[ROW][C]14[/C][C]6[/C][C]6.57562392564208[/C][C]-0.575623925642083[/C][/ROW]
[ROW][C]15[/C][C]4[/C][C]5.25370675932148[/C][C]-1.25370675932148[/C][/ROW]
[ROW][C]16[/C][C]5[/C][C]5.93775633551585[/C][C]-0.937756335515848[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]4.94013556391842[/C][C]-0.940135563918416[/C][/ROW]
[ROW][C]18[/C][C]6[/C][C]6.40334760527824[/C][C]-0.403347605278241[/C][/ROW]
[ROW][C]19[/C][C]5[/C][C]4.58239857469656[/C][C]0.41760142530344[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]5.05733127371581[/C][C]-1.05733127371581[/C][/ROW]
[ROW][C]21[/C][C]6[/C][C]5.02614520508141[/C][C]0.973854794918595[/C][/ROW]
[ROW][C]22[/C][C]3[/C][C]4.63921857600896[/C][C]-1.63921857600896[/C][/ROW]
[ROW][C]23[/C][C]6[/C][C]4.76764699996227[/C][C]1.23235300003773[/C][/ROW]
[ROW][C]24[/C][C]7[/C][C]4.98951972409228[/C][C]2.01048027590772[/C][/ROW]
[ROW][C]25[/C][C]6[/C][C]6.84596613244375[/C][C]-0.845966132443748[/C][/ROW]
[ROW][C]26[/C][C]5[/C][C]3.64986601459383[/C][C]1.35013398540617[/C][/ROW]
[ROW][C]27[/C][C]5[/C][C]5.45934666737212[/C][C]-0.459346667372118[/C][/ROW]
[ROW][C]28[/C][C]3[/C][C]4.9354288014515[/C][C]-1.9354288014515[/C][/ROW]
[ROW][C]29[/C][C]5[/C][C]4.8946131491016[/C][C]0.105386850898404[/C][/ROW]
[ROW][C]30[/C][C]3[/C][C]3.76337018432769[/C][C]-0.76337018432769[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]7.15251666798783[/C][C]2.84748333201217[/C][/ROW]
[ROW][C]32[/C][C]5[/C][C]5.68333184409765[/C][C]-0.683331844097648[/C][/ROW]
[ROW][C]33[/C][C]5[/C][C]4.81823191457738[/C][C]0.181768085422616[/C][/ROW]
[ROW][C]34[/C][C]5[/C][C]4.84232624615283[/C][C]0.157673753847165[/C][/ROW]
[ROW][C]35[/C][C]3[/C][C]4.36933650214671[/C][C]-1.36933650214671[/C][/ROW]
[ROW][C]36[/C][C]4[/C][C]4.60065807610324[/C][C]-0.600658076103244[/C][/ROW]
[ROW][C]37[/C][C]6[/C][C]6.03725148912549[/C][C]-0.0372514891254924[/C][/ROW]
[ROW][C]38[/C][C]5[/C][C]5.06935380868548[/C][C]-0.0693538086854771[/C][/ROW]
[ROW][C]39[/C][C]6[/C][C]6.04169406263764[/C][C]-0.0416940626376422[/C][/ROW]
[ROW][C]40[/C][C]5[/C][C]5.27328080286179[/C][C]-0.273280802861792[/C][/ROW]
[ROW][C]41[/C][C]6[/C][C]5.25300247614123[/C][C]0.74699752385877[/C][/ROW]
[ROW][C]42[/C][C]5[/C][C]4.39255803591903[/C][C]0.607441964080968[/C][/ROW]
[ROW][C]43[/C][C]6[/C][C]5.72304045041915[/C][C]0.276959549580845[/C][/ROW]
[ROW][C]44[/C][C]4[/C][C]6.23367125419739[/C][C]-2.23367125419739[/C][/ROW]
[ROW][C]45[/C][C]5[/C][C]5.14587935259777[/C][C]-0.145879352597769[/C][/ROW]
[ROW][C]46[/C][C]4[/C][C]4.72798177127868[/C][C]-0.727981771278683[/C][/ROW]
[ROW][C]47[/C][C]7[/C][C]6.15902292664774[/C][C]0.840977073352264[/C][/ROW]
[ROW][C]48[/C][C]4[/C][C]4.75217366197384[/C][C]-0.752173661973844[/C][/ROW]
[ROW][C]49[/C][C]6[/C][C]4.92714334421952[/C][C]1.07285665578048[/C][/ROW]
[ROW][C]50[/C][C]7[/C][C]5.94062760410491[/C][C]1.05937239589509[/C][/ROW]
[ROW][C]51[/C][C]6[/C][C]5.82212922010848[/C][C]0.177870779891517[/C][/ROW]
[ROW][C]52[/C][C]6[/C][C]5.96153783803222[/C][C]0.0384621619677803[/C][/ROW]
[ROW][C]53[/C][C]3[/C][C]4.66749518341426[/C][C]-1.66749518341426[/C][/ROW]
[ROW][C]54[/C][C]4[/C][C]5.35842267400524[/C][C]-1.35842267400524[/C][/ROW]
[ROW][C]55[/C][C]5[/C][C]5.4038986138021[/C][C]-0.403898613802095[/C][/ROW]
[ROW][C]56[/C][C]6[/C][C]4.29000600926204[/C][C]1.70999399073796[/C][/ROW]
[ROW][C]57[/C][C]6[/C][C]5.15660011519672[/C][C]0.843399884803283[/C][/ROW]
[ROW][C]58[/C][C]3[/C][C]4.84038396333333[/C][C]-1.84038396333333[/C][/ROW]
[ROW][C]59[/C][C]2[/C][C]4.00591854282824[/C][C]-2.00591854282824[/C][/ROW]
[ROW][C]60[/C][C]7[/C][C]6.95385085859689[/C][C]0.0461491414031065[/C][/ROW]
[ROW][C]61[/C][C]6[/C][C]6.48741228999159[/C][C]-0.487412289991588[/C][/ROW]
[ROW][C]62[/C][C]3[/C][C]3.18389114933828[/C][C]-0.183891149338284[/C][/ROW]
[ROW][C]63[/C][C]8[/C][C]6.86169190810104[/C][C]1.13830809189896[/C][/ROW]
[ROW][C]64[/C][C]5[/C][C]4.26003047538766[/C][C]0.739969524612343[/C][/ROW]
[ROW][C]65[/C][C]5[/C][C]4.8367470016968[/C][C]0.163252998303204[/C][/ROW]
[ROW][C]66[/C][C]5[/C][C]4.99914545710092[/C][C]0.000854542899078447[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]6.14998702198145[/C][C]-0.149987021981447[/C][/ROW]
[ROW][C]68[/C][C]4[/C][C]4.58821906612859[/C][C]-0.588219066128595[/C][/ROW]
[ROW][C]69[/C][C]6[/C][C]4.8481241469376[/C][C]1.1518758530624[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]4.04569531298866[/C][C]0.954304687011337[/C][/ROW]
[ROW][C]71[/C][C]6[/C][C]3.53744177734062[/C][C]2.46255822265938[/C][/ROW]
[ROW][C]72[/C][C]4[/C][C]4.85776752394112[/C][C]-0.857767523941123[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]4.37176638278489[/C][C]-2.37176638278489[/C][/ROW]
[ROW][C]74[/C][C]4[/C][C]6.15172951515756[/C][C]-2.15172951515756[/C][/ROW]
[ROW][C]75[/C][C]4[/C][C]4.83383467272034[/C][C]-0.833834672720343[/C][/ROW]
[ROW][C]76[/C][C]4[/C][C]4.18729449497612[/C][C]-0.187294494976119[/C][/ROW]
[ROW][C]77[/C][C]5[/C][C]4.59004540626759[/C][C]0.409954593732412[/C][/ROW]
[ROW][C]78[/C][C]7[/C][C]6.31520700626212[/C][C]0.684792993737877[/C][/ROW]
[ROW][C]79[/C][C]6[/C][C]4.80708351674997[/C][C]1.19291648325003[/C][/ROW]
[ROW][C]80[/C][C]4[/C][C]4.73126566921398[/C][C]-0.731265669213976[/C][/ROW]
[ROW][C]81[/C][C]4[/C][C]4.95321799770291[/C][C]-0.953217997702908[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]6.79889456584031[/C][C]-0.79889456584031[/C][/ROW]
[ROW][C]83[/C][C]6[/C][C]4.019857780526[/C][C]1.980142219474[/C][/ROW]
[ROW][C]84[/C][C]5[/C][C]4.66971980911464[/C][C]0.330280190885363[/C][/ROW]
[ROW][C]85[/C][C]6[/C][C]5.4261634216772[/C][C]0.573836578322802[/C][/ROW]
[ROW][C]86[/C][C]8[/C][C]6.153873202208[/C][C]1.846126797792[/C][/ROW]
[ROW][C]87[/C][C]6[/C][C]5.40029280548928[/C][C]0.599707194510722[/C][/ROW]
[ROW][C]88[/C][C]6[/C][C]5.0884443803624[/C][C]0.9115556196376[/C][/ROW]
[ROW][C]89[/C][C]9[/C][C]7.98604485081282[/C][C]1.01395514918718[/C][/ROW]
[ROW][C]90[/C][C]6[/C][C]4.89021772280302[/C][C]1.10978227719698[/C][/ROW]
[ROW][C]91[/C][C]7[/C][C]5.88703319329074[/C][C]1.11296680670926[/C][/ROW]
[ROW][C]92[/C][C]6[/C][C]5.10487318604012[/C][C]0.89512681395988[/C][/ROW]
[ROW][C]93[/C][C]7[/C][C]5.35193703442692[/C][C]1.64806296557308[/C][/ROW]
[ROW][C]94[/C][C]6[/C][C]5.45990372780322[/C][C]0.540096272196775[/C][/ROW]
[ROW][C]95[/C][C]7[/C][C]5.59718201445374[/C][C]1.40281798554626[/C][/ROW]
[ROW][C]96[/C][C]4[/C][C]4.77130730052278[/C][C]-0.771307300522784[/C][/ROW]
[ROW][C]97[/C][C]6[/C][C]4.85136124899081[/C][C]1.14863875100919[/C][/ROW]
[ROW][C]98[/C][C]4[/C][C]5.72301141151797[/C][C]-1.72301141151797[/C][/ROW]
[ROW][C]99[/C][C]6[/C][C]4.83029067121996[/C][C]1.16970932878004[/C][/ROW]
[ROW][C]100[/C][C]2[/C][C]4.05650781808143[/C][C]-2.05650781808143[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146812&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146812&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
155.11337304690064-0.11337304690064
266.89058765295017-0.890587652950168
367.24854333401823-1.24854333401823
444.61304640397146-0.613046403971464
556.87790394223787-1.87790394223787
632.893434382951880.106565617048121
722.88362095786913-0.883620957869129
875.046526078139671.95347392186033
945.06073241310514-1.06073241310514
1075.271667459639911.72833254036009
1175.32065949854711.6793405014529
1234.28649131686314-1.28649131686314
1354.504152771875830.495847228124174
1466.57562392564208-0.575623925642083
1545.25370675932148-1.25370675932148
1655.93775633551585-0.937756335515848
1744.94013556391842-0.940135563918416
1866.40334760527824-0.403347605278241
1954.582398574696560.41760142530344
2045.05733127371581-1.05733127371581
2165.026145205081410.973854794918595
2234.63921857600896-1.63921857600896
2364.767646999962271.23235300003773
2474.989519724092282.01048027590772
2566.84596613244375-0.845966132443748
2653.649866014593831.35013398540617
2755.45934666737212-0.459346667372118
2834.9354288014515-1.9354288014515
2954.89461314910160.105386850898404
3033.76337018432769-0.76337018432769
31107.152516667987832.84748333201217
3255.68333184409765-0.683331844097648
3354.818231914577380.181768085422616
3454.842326246152830.157673753847165
3534.36933650214671-1.36933650214671
3644.60065807610324-0.600658076103244
3766.03725148912549-0.0372514891254924
3855.06935380868548-0.0693538086854771
3966.04169406263764-0.0416940626376422
4055.27328080286179-0.273280802861792
4165.253002476141230.74699752385877
4254.392558035919030.607441964080968
4365.723040450419150.276959549580845
4446.23367125419739-2.23367125419739
4555.14587935259777-0.145879352597769
4644.72798177127868-0.727981771278683
4776.159022926647740.840977073352264
4844.75217366197384-0.752173661973844
4964.927143344219521.07285665578048
5075.940627604104911.05937239589509
5165.822129220108480.177870779891517
5265.961537838032220.0384621619677803
5334.66749518341426-1.66749518341426
5445.35842267400524-1.35842267400524
5555.4038986138021-0.403898613802095
5664.290006009262041.70999399073796
5765.156600115196720.843399884803283
5834.84038396333333-1.84038396333333
5924.00591854282824-2.00591854282824
6076.953850858596890.0461491414031065
6166.48741228999159-0.487412289991588
6233.18389114933828-0.183891149338284
6386.861691908101041.13830809189896
6454.260030475387660.739969524612343
6554.83674700169680.163252998303204
6654.999145457100920.000854542899078447
6766.14998702198145-0.149987021981447
6844.58821906612859-0.588219066128595
6964.84812414693761.1518758530624
7054.045695312988660.954304687011337
7163.537441777340622.46255822265938
7244.85776752394112-0.857767523941123
7324.37176638278489-2.37176638278489
7446.15172951515756-2.15172951515756
7544.83383467272034-0.833834672720343
7644.18729449497612-0.187294494976119
7754.590045406267590.409954593732412
7876.315207006262120.684792993737877
7964.807083516749971.19291648325003
8044.73126566921398-0.731265669213976
8144.95321799770291-0.953217997702908
8266.79889456584031-0.79889456584031
8364.0198577805261.980142219474
8454.669719809114640.330280190885363
8565.42616342167720.573836578322802
8686.1538732022081.846126797792
8765.400292805489280.599707194510722
8865.08844438036240.9115556196376
8997.986044850812821.01395514918718
9064.890217722803021.10978227719698
9175.887033193290741.11296680670926
9265.104873186040120.89512681395988
9375.351937034426921.64806296557308
9465.459903727803220.540096272196775
9575.597182014453741.40281798554626
9644.77130730052278-0.771307300522784
9764.851361248990811.14863875100919
9845.72301141151797-1.72301141151797
9964.830290671219961.16970932878004
10024.05650781808143-2.05650781808143






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.1281993131577270.2563986263154550.871800686842273
80.5784038786354780.8431922427290440.421596121364522
90.6027115078004390.7945769843991220.397288492199561
100.734281208917110.531437582165780.26571879108289
110.8072767655126830.3854464689746350.192723234487317
120.8335766730599480.3328466538801040.166423326940052
130.7685151935812280.4629696128375440.231484806418772
140.692926673329380.614146653341240.30707332667062
150.6479459303494560.7041081393010880.352054069650544
160.5769573676831830.8460852646336340.423042632316817
170.5023180768136540.9953638463726910.497681923186346
180.4263274984453440.8526549968906890.573672501554656
190.3569794425761680.7139588851523360.643020557423832
200.3293218268734280.6586436537468560.670678173126572
210.3206927042455520.6413854084911050.679307295754448
220.4694562807420410.9389125614840830.530543719257959
230.4736737709045640.9473475418091290.526326229095435
240.6266590734376250.7466818531247510.373340926562375
250.6026442176317080.7947115647365840.397355782368292
260.5801386500018960.8397226999962080.419861349998104
270.5212736295270150.957452740945970.478726370472985
280.5919734909593490.8160530180813020.408026509040651
290.5271457117634480.9457085764731050.472854288236552
300.4994413022015490.9988826044030980.500558697798451
310.7781325166710330.4437349666579350.221867483328967
320.7468407563322070.5063184873355860.253159243667793
330.6943072655487660.6113854689024690.305692734451234
340.658434924956880.6831301500862410.34156507504312
350.6712118834280030.6575762331439950.328788116571997
360.6233385584734710.7533228830530580.376661441526529
370.5648812062642720.8702375874714560.435118793735728
380.51703834110480.96592331779040.4829616588952
390.4617602605576940.9235205211153880.538239739442306
400.4043246565109410.8086493130218810.595675343489059
410.4018940776783870.8037881553567740.598105922321613
420.3692138921555940.7384277843111870.630786107844406
430.3161142459918790.6322284919837570.683885754008121
440.4316630969035130.8633261938070270.568336903096487
450.3833396885863340.7666793771726690.616660311413666
460.3522461403628150.704492280725630.647753859637185
470.3435831741380990.6871663482761980.656416825861901
480.3108493699520790.6216987399041580.689150630047921
490.3084905934747440.6169811869494880.691509406525256
500.3069847511133820.6139695022267630.693015248886618
510.2591508151357880.5183016302715760.740849184864212
520.2152051634517940.4304103269035890.784794836548206
530.2615254174849190.5230508349698370.738474582515081
540.2811472286712140.5622944573424280.718852771328786
550.2414610988399420.4829221976798840.758538901160058
560.2914812431784250.5829624863568490.708518756821575
570.2669966069540550.533993213908110.733003393045945
580.3439692613284180.6879385226568350.656030738671582
590.4569576874714860.9139153749429730.543042312528514
600.3996942032069810.7993884064139610.60030579679302
610.3586695912541340.7173391825082670.641330408745866
620.3050403213712570.6100806427425140.694959678628743
630.307561158530070.615122317060140.69243884146993
640.2758731765956220.5517463531912430.724126823404378
650.2273337204803820.4546674409607640.772666279519618
660.1885619615860960.3771239231721920.811438038413904
670.151215044561540.3024300891230790.84878495543846
680.1329125767592660.2658251535185320.867087423240734
690.1229289845269840.2458579690539670.877071015473016
700.1057631651706030.2115263303412060.894236834829397
710.2230830973319470.4461661946638940.776916902668053
720.1898397203545380.3796794407090760.810160279645462
730.3444680400433620.6889360800867240.655531959956638
740.5802439663217980.8395120673564040.419756033678202
750.5555454556941010.8889090886117990.444454544305899
760.4923862060936150.984772412187230.507613793906385
770.4228444538077210.8456889076154410.577155546192279
780.3630432780173420.7260865560346840.636956721982658
790.34134604551430.6826920910286010.6586539544857
800.315753588274410.631507176548820.68424641172559
810.3363993467432110.6727986934864220.663600653256789
820.385551796114260.771103592228520.61444820388574
830.4997128625153430.9994257250306860.500287137484657
840.4139991323965240.8279982647930480.586000867603476
850.3340727190107670.6681454380215350.665927280989233
860.3342823448295680.6685646896591360.665717655170432
870.2545003059596220.5090006119192440.745499694040378
880.19604042102510.3920808420501990.8039595789749
890.1650862352751550.3301724705503090.834913764724845
900.1372930519749710.2745861039499410.86270694802503
910.08737291340182170.1747458268036430.912627086598178
920.0501234419003810.1002468838007620.949876558099619
930.04888221645664010.09776443291328010.95111778354336

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.128199313157727 & 0.256398626315455 & 0.871800686842273 \tabularnewline
8 & 0.578403878635478 & 0.843192242729044 & 0.421596121364522 \tabularnewline
9 & 0.602711507800439 & 0.794576984399122 & 0.397288492199561 \tabularnewline
10 & 0.73428120891711 & 0.53143758216578 & 0.26571879108289 \tabularnewline
11 & 0.807276765512683 & 0.385446468974635 & 0.192723234487317 \tabularnewline
12 & 0.833576673059948 & 0.332846653880104 & 0.166423326940052 \tabularnewline
13 & 0.768515193581228 & 0.462969612837544 & 0.231484806418772 \tabularnewline
14 & 0.69292667332938 & 0.61414665334124 & 0.30707332667062 \tabularnewline
15 & 0.647945930349456 & 0.704108139301088 & 0.352054069650544 \tabularnewline
16 & 0.576957367683183 & 0.846085264633634 & 0.423042632316817 \tabularnewline
17 & 0.502318076813654 & 0.995363846372691 & 0.497681923186346 \tabularnewline
18 & 0.426327498445344 & 0.852654996890689 & 0.573672501554656 \tabularnewline
19 & 0.356979442576168 & 0.713958885152336 & 0.643020557423832 \tabularnewline
20 & 0.329321826873428 & 0.658643653746856 & 0.670678173126572 \tabularnewline
21 & 0.320692704245552 & 0.641385408491105 & 0.679307295754448 \tabularnewline
22 & 0.469456280742041 & 0.938912561484083 & 0.530543719257959 \tabularnewline
23 & 0.473673770904564 & 0.947347541809129 & 0.526326229095435 \tabularnewline
24 & 0.626659073437625 & 0.746681853124751 & 0.373340926562375 \tabularnewline
25 & 0.602644217631708 & 0.794711564736584 & 0.397355782368292 \tabularnewline
26 & 0.580138650001896 & 0.839722699996208 & 0.419861349998104 \tabularnewline
27 & 0.521273629527015 & 0.95745274094597 & 0.478726370472985 \tabularnewline
28 & 0.591973490959349 & 0.816053018081302 & 0.408026509040651 \tabularnewline
29 & 0.527145711763448 & 0.945708576473105 & 0.472854288236552 \tabularnewline
30 & 0.499441302201549 & 0.998882604403098 & 0.500558697798451 \tabularnewline
31 & 0.778132516671033 & 0.443734966657935 & 0.221867483328967 \tabularnewline
32 & 0.746840756332207 & 0.506318487335586 & 0.253159243667793 \tabularnewline
33 & 0.694307265548766 & 0.611385468902469 & 0.305692734451234 \tabularnewline
34 & 0.65843492495688 & 0.683130150086241 & 0.34156507504312 \tabularnewline
35 & 0.671211883428003 & 0.657576233143995 & 0.328788116571997 \tabularnewline
36 & 0.623338558473471 & 0.753322883053058 & 0.376661441526529 \tabularnewline
37 & 0.564881206264272 & 0.870237587471456 & 0.435118793735728 \tabularnewline
38 & 0.5170383411048 & 0.9659233177904 & 0.4829616588952 \tabularnewline
39 & 0.461760260557694 & 0.923520521115388 & 0.538239739442306 \tabularnewline
40 & 0.404324656510941 & 0.808649313021881 & 0.595675343489059 \tabularnewline
41 & 0.401894077678387 & 0.803788155356774 & 0.598105922321613 \tabularnewline
42 & 0.369213892155594 & 0.738427784311187 & 0.630786107844406 \tabularnewline
43 & 0.316114245991879 & 0.632228491983757 & 0.683885754008121 \tabularnewline
44 & 0.431663096903513 & 0.863326193807027 & 0.568336903096487 \tabularnewline
45 & 0.383339688586334 & 0.766679377172669 & 0.616660311413666 \tabularnewline
46 & 0.352246140362815 & 0.70449228072563 & 0.647753859637185 \tabularnewline
47 & 0.343583174138099 & 0.687166348276198 & 0.656416825861901 \tabularnewline
48 & 0.310849369952079 & 0.621698739904158 & 0.689150630047921 \tabularnewline
49 & 0.308490593474744 & 0.616981186949488 & 0.691509406525256 \tabularnewline
50 & 0.306984751113382 & 0.613969502226763 & 0.693015248886618 \tabularnewline
51 & 0.259150815135788 & 0.518301630271576 & 0.740849184864212 \tabularnewline
52 & 0.215205163451794 & 0.430410326903589 & 0.784794836548206 \tabularnewline
53 & 0.261525417484919 & 0.523050834969837 & 0.738474582515081 \tabularnewline
54 & 0.281147228671214 & 0.562294457342428 & 0.718852771328786 \tabularnewline
55 & 0.241461098839942 & 0.482922197679884 & 0.758538901160058 \tabularnewline
56 & 0.291481243178425 & 0.582962486356849 & 0.708518756821575 \tabularnewline
57 & 0.266996606954055 & 0.53399321390811 & 0.733003393045945 \tabularnewline
58 & 0.343969261328418 & 0.687938522656835 & 0.656030738671582 \tabularnewline
59 & 0.456957687471486 & 0.913915374942973 & 0.543042312528514 \tabularnewline
60 & 0.399694203206981 & 0.799388406413961 & 0.60030579679302 \tabularnewline
61 & 0.358669591254134 & 0.717339182508267 & 0.641330408745866 \tabularnewline
62 & 0.305040321371257 & 0.610080642742514 & 0.694959678628743 \tabularnewline
63 & 0.30756115853007 & 0.61512231706014 & 0.69243884146993 \tabularnewline
64 & 0.275873176595622 & 0.551746353191243 & 0.724126823404378 \tabularnewline
65 & 0.227333720480382 & 0.454667440960764 & 0.772666279519618 \tabularnewline
66 & 0.188561961586096 & 0.377123923172192 & 0.811438038413904 \tabularnewline
67 & 0.15121504456154 & 0.302430089123079 & 0.84878495543846 \tabularnewline
68 & 0.132912576759266 & 0.265825153518532 & 0.867087423240734 \tabularnewline
69 & 0.122928984526984 & 0.245857969053967 & 0.877071015473016 \tabularnewline
70 & 0.105763165170603 & 0.211526330341206 & 0.894236834829397 \tabularnewline
71 & 0.223083097331947 & 0.446166194663894 & 0.776916902668053 \tabularnewline
72 & 0.189839720354538 & 0.379679440709076 & 0.810160279645462 \tabularnewline
73 & 0.344468040043362 & 0.688936080086724 & 0.655531959956638 \tabularnewline
74 & 0.580243966321798 & 0.839512067356404 & 0.419756033678202 \tabularnewline
75 & 0.555545455694101 & 0.888909088611799 & 0.444454544305899 \tabularnewline
76 & 0.492386206093615 & 0.98477241218723 & 0.507613793906385 \tabularnewline
77 & 0.422844453807721 & 0.845688907615441 & 0.577155546192279 \tabularnewline
78 & 0.363043278017342 & 0.726086556034684 & 0.636956721982658 \tabularnewline
79 & 0.3413460455143 & 0.682692091028601 & 0.6586539544857 \tabularnewline
80 & 0.31575358827441 & 0.63150717654882 & 0.68424641172559 \tabularnewline
81 & 0.336399346743211 & 0.672798693486422 & 0.663600653256789 \tabularnewline
82 & 0.38555179611426 & 0.77110359222852 & 0.61444820388574 \tabularnewline
83 & 0.499712862515343 & 0.999425725030686 & 0.500287137484657 \tabularnewline
84 & 0.413999132396524 & 0.827998264793048 & 0.586000867603476 \tabularnewline
85 & 0.334072719010767 & 0.668145438021535 & 0.665927280989233 \tabularnewline
86 & 0.334282344829568 & 0.668564689659136 & 0.665717655170432 \tabularnewline
87 & 0.254500305959622 & 0.509000611919244 & 0.745499694040378 \tabularnewline
88 & 0.1960404210251 & 0.392080842050199 & 0.8039595789749 \tabularnewline
89 & 0.165086235275155 & 0.330172470550309 & 0.834913764724845 \tabularnewline
90 & 0.137293051974971 & 0.274586103949941 & 0.86270694802503 \tabularnewline
91 & 0.0873729134018217 & 0.174745826803643 & 0.912627086598178 \tabularnewline
92 & 0.050123441900381 & 0.100246883800762 & 0.949876558099619 \tabularnewline
93 & 0.0488822164566401 & 0.0977644329132801 & 0.95111778354336 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146812&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.128199313157727[/C][C]0.256398626315455[/C][C]0.871800686842273[/C][/ROW]
[ROW][C]8[/C][C]0.578403878635478[/C][C]0.843192242729044[/C][C]0.421596121364522[/C][/ROW]
[ROW][C]9[/C][C]0.602711507800439[/C][C]0.794576984399122[/C][C]0.397288492199561[/C][/ROW]
[ROW][C]10[/C][C]0.73428120891711[/C][C]0.53143758216578[/C][C]0.26571879108289[/C][/ROW]
[ROW][C]11[/C][C]0.807276765512683[/C][C]0.385446468974635[/C][C]0.192723234487317[/C][/ROW]
[ROW][C]12[/C][C]0.833576673059948[/C][C]0.332846653880104[/C][C]0.166423326940052[/C][/ROW]
[ROW][C]13[/C][C]0.768515193581228[/C][C]0.462969612837544[/C][C]0.231484806418772[/C][/ROW]
[ROW][C]14[/C][C]0.69292667332938[/C][C]0.61414665334124[/C][C]0.30707332667062[/C][/ROW]
[ROW][C]15[/C][C]0.647945930349456[/C][C]0.704108139301088[/C][C]0.352054069650544[/C][/ROW]
[ROW][C]16[/C][C]0.576957367683183[/C][C]0.846085264633634[/C][C]0.423042632316817[/C][/ROW]
[ROW][C]17[/C][C]0.502318076813654[/C][C]0.995363846372691[/C][C]0.497681923186346[/C][/ROW]
[ROW][C]18[/C][C]0.426327498445344[/C][C]0.852654996890689[/C][C]0.573672501554656[/C][/ROW]
[ROW][C]19[/C][C]0.356979442576168[/C][C]0.713958885152336[/C][C]0.643020557423832[/C][/ROW]
[ROW][C]20[/C][C]0.329321826873428[/C][C]0.658643653746856[/C][C]0.670678173126572[/C][/ROW]
[ROW][C]21[/C][C]0.320692704245552[/C][C]0.641385408491105[/C][C]0.679307295754448[/C][/ROW]
[ROW][C]22[/C][C]0.469456280742041[/C][C]0.938912561484083[/C][C]0.530543719257959[/C][/ROW]
[ROW][C]23[/C][C]0.473673770904564[/C][C]0.947347541809129[/C][C]0.526326229095435[/C][/ROW]
[ROW][C]24[/C][C]0.626659073437625[/C][C]0.746681853124751[/C][C]0.373340926562375[/C][/ROW]
[ROW][C]25[/C][C]0.602644217631708[/C][C]0.794711564736584[/C][C]0.397355782368292[/C][/ROW]
[ROW][C]26[/C][C]0.580138650001896[/C][C]0.839722699996208[/C][C]0.419861349998104[/C][/ROW]
[ROW][C]27[/C][C]0.521273629527015[/C][C]0.95745274094597[/C][C]0.478726370472985[/C][/ROW]
[ROW][C]28[/C][C]0.591973490959349[/C][C]0.816053018081302[/C][C]0.408026509040651[/C][/ROW]
[ROW][C]29[/C][C]0.527145711763448[/C][C]0.945708576473105[/C][C]0.472854288236552[/C][/ROW]
[ROW][C]30[/C][C]0.499441302201549[/C][C]0.998882604403098[/C][C]0.500558697798451[/C][/ROW]
[ROW][C]31[/C][C]0.778132516671033[/C][C]0.443734966657935[/C][C]0.221867483328967[/C][/ROW]
[ROW][C]32[/C][C]0.746840756332207[/C][C]0.506318487335586[/C][C]0.253159243667793[/C][/ROW]
[ROW][C]33[/C][C]0.694307265548766[/C][C]0.611385468902469[/C][C]0.305692734451234[/C][/ROW]
[ROW][C]34[/C][C]0.65843492495688[/C][C]0.683130150086241[/C][C]0.34156507504312[/C][/ROW]
[ROW][C]35[/C][C]0.671211883428003[/C][C]0.657576233143995[/C][C]0.328788116571997[/C][/ROW]
[ROW][C]36[/C][C]0.623338558473471[/C][C]0.753322883053058[/C][C]0.376661441526529[/C][/ROW]
[ROW][C]37[/C][C]0.564881206264272[/C][C]0.870237587471456[/C][C]0.435118793735728[/C][/ROW]
[ROW][C]38[/C][C]0.5170383411048[/C][C]0.9659233177904[/C][C]0.4829616588952[/C][/ROW]
[ROW][C]39[/C][C]0.461760260557694[/C][C]0.923520521115388[/C][C]0.538239739442306[/C][/ROW]
[ROW][C]40[/C][C]0.404324656510941[/C][C]0.808649313021881[/C][C]0.595675343489059[/C][/ROW]
[ROW][C]41[/C][C]0.401894077678387[/C][C]0.803788155356774[/C][C]0.598105922321613[/C][/ROW]
[ROW][C]42[/C][C]0.369213892155594[/C][C]0.738427784311187[/C][C]0.630786107844406[/C][/ROW]
[ROW][C]43[/C][C]0.316114245991879[/C][C]0.632228491983757[/C][C]0.683885754008121[/C][/ROW]
[ROW][C]44[/C][C]0.431663096903513[/C][C]0.863326193807027[/C][C]0.568336903096487[/C][/ROW]
[ROW][C]45[/C][C]0.383339688586334[/C][C]0.766679377172669[/C][C]0.616660311413666[/C][/ROW]
[ROW][C]46[/C][C]0.352246140362815[/C][C]0.70449228072563[/C][C]0.647753859637185[/C][/ROW]
[ROW][C]47[/C][C]0.343583174138099[/C][C]0.687166348276198[/C][C]0.656416825861901[/C][/ROW]
[ROW][C]48[/C][C]0.310849369952079[/C][C]0.621698739904158[/C][C]0.689150630047921[/C][/ROW]
[ROW][C]49[/C][C]0.308490593474744[/C][C]0.616981186949488[/C][C]0.691509406525256[/C][/ROW]
[ROW][C]50[/C][C]0.306984751113382[/C][C]0.613969502226763[/C][C]0.693015248886618[/C][/ROW]
[ROW][C]51[/C][C]0.259150815135788[/C][C]0.518301630271576[/C][C]0.740849184864212[/C][/ROW]
[ROW][C]52[/C][C]0.215205163451794[/C][C]0.430410326903589[/C][C]0.784794836548206[/C][/ROW]
[ROW][C]53[/C][C]0.261525417484919[/C][C]0.523050834969837[/C][C]0.738474582515081[/C][/ROW]
[ROW][C]54[/C][C]0.281147228671214[/C][C]0.562294457342428[/C][C]0.718852771328786[/C][/ROW]
[ROW][C]55[/C][C]0.241461098839942[/C][C]0.482922197679884[/C][C]0.758538901160058[/C][/ROW]
[ROW][C]56[/C][C]0.291481243178425[/C][C]0.582962486356849[/C][C]0.708518756821575[/C][/ROW]
[ROW][C]57[/C][C]0.266996606954055[/C][C]0.53399321390811[/C][C]0.733003393045945[/C][/ROW]
[ROW][C]58[/C][C]0.343969261328418[/C][C]0.687938522656835[/C][C]0.656030738671582[/C][/ROW]
[ROW][C]59[/C][C]0.456957687471486[/C][C]0.913915374942973[/C][C]0.543042312528514[/C][/ROW]
[ROW][C]60[/C][C]0.399694203206981[/C][C]0.799388406413961[/C][C]0.60030579679302[/C][/ROW]
[ROW][C]61[/C][C]0.358669591254134[/C][C]0.717339182508267[/C][C]0.641330408745866[/C][/ROW]
[ROW][C]62[/C][C]0.305040321371257[/C][C]0.610080642742514[/C][C]0.694959678628743[/C][/ROW]
[ROW][C]63[/C][C]0.30756115853007[/C][C]0.61512231706014[/C][C]0.69243884146993[/C][/ROW]
[ROW][C]64[/C][C]0.275873176595622[/C][C]0.551746353191243[/C][C]0.724126823404378[/C][/ROW]
[ROW][C]65[/C][C]0.227333720480382[/C][C]0.454667440960764[/C][C]0.772666279519618[/C][/ROW]
[ROW][C]66[/C][C]0.188561961586096[/C][C]0.377123923172192[/C][C]0.811438038413904[/C][/ROW]
[ROW][C]67[/C][C]0.15121504456154[/C][C]0.302430089123079[/C][C]0.84878495543846[/C][/ROW]
[ROW][C]68[/C][C]0.132912576759266[/C][C]0.265825153518532[/C][C]0.867087423240734[/C][/ROW]
[ROW][C]69[/C][C]0.122928984526984[/C][C]0.245857969053967[/C][C]0.877071015473016[/C][/ROW]
[ROW][C]70[/C][C]0.105763165170603[/C][C]0.211526330341206[/C][C]0.894236834829397[/C][/ROW]
[ROW][C]71[/C][C]0.223083097331947[/C][C]0.446166194663894[/C][C]0.776916902668053[/C][/ROW]
[ROW][C]72[/C][C]0.189839720354538[/C][C]0.379679440709076[/C][C]0.810160279645462[/C][/ROW]
[ROW][C]73[/C][C]0.344468040043362[/C][C]0.688936080086724[/C][C]0.655531959956638[/C][/ROW]
[ROW][C]74[/C][C]0.580243966321798[/C][C]0.839512067356404[/C][C]0.419756033678202[/C][/ROW]
[ROW][C]75[/C][C]0.555545455694101[/C][C]0.888909088611799[/C][C]0.444454544305899[/C][/ROW]
[ROW][C]76[/C][C]0.492386206093615[/C][C]0.98477241218723[/C][C]0.507613793906385[/C][/ROW]
[ROW][C]77[/C][C]0.422844453807721[/C][C]0.845688907615441[/C][C]0.577155546192279[/C][/ROW]
[ROW][C]78[/C][C]0.363043278017342[/C][C]0.726086556034684[/C][C]0.636956721982658[/C][/ROW]
[ROW][C]79[/C][C]0.3413460455143[/C][C]0.682692091028601[/C][C]0.6586539544857[/C][/ROW]
[ROW][C]80[/C][C]0.31575358827441[/C][C]0.63150717654882[/C][C]0.68424641172559[/C][/ROW]
[ROW][C]81[/C][C]0.336399346743211[/C][C]0.672798693486422[/C][C]0.663600653256789[/C][/ROW]
[ROW][C]82[/C][C]0.38555179611426[/C][C]0.77110359222852[/C][C]0.61444820388574[/C][/ROW]
[ROW][C]83[/C][C]0.499712862515343[/C][C]0.999425725030686[/C][C]0.500287137484657[/C][/ROW]
[ROW][C]84[/C][C]0.413999132396524[/C][C]0.827998264793048[/C][C]0.586000867603476[/C][/ROW]
[ROW][C]85[/C][C]0.334072719010767[/C][C]0.668145438021535[/C][C]0.665927280989233[/C][/ROW]
[ROW][C]86[/C][C]0.334282344829568[/C][C]0.668564689659136[/C][C]0.665717655170432[/C][/ROW]
[ROW][C]87[/C][C]0.254500305959622[/C][C]0.509000611919244[/C][C]0.745499694040378[/C][/ROW]
[ROW][C]88[/C][C]0.1960404210251[/C][C]0.392080842050199[/C][C]0.8039595789749[/C][/ROW]
[ROW][C]89[/C][C]0.165086235275155[/C][C]0.330172470550309[/C][C]0.834913764724845[/C][/ROW]
[ROW][C]90[/C][C]0.137293051974971[/C][C]0.274586103949941[/C][C]0.86270694802503[/C][/ROW]
[ROW][C]91[/C][C]0.0873729134018217[/C][C]0.174745826803643[/C][C]0.912627086598178[/C][/ROW]
[ROW][C]92[/C][C]0.050123441900381[/C][C]0.100246883800762[/C][C]0.949876558099619[/C][/ROW]
[ROW][C]93[/C][C]0.0488822164566401[/C][C]0.0977644329132801[/C][C]0.95111778354336[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146812&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146812&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.1281993131577270.2563986263154550.871800686842273
80.5784038786354780.8431922427290440.421596121364522
90.6027115078004390.7945769843991220.397288492199561
100.734281208917110.531437582165780.26571879108289
110.8072767655126830.3854464689746350.192723234487317
120.8335766730599480.3328466538801040.166423326940052
130.7685151935812280.4629696128375440.231484806418772
140.692926673329380.614146653341240.30707332667062
150.6479459303494560.7041081393010880.352054069650544
160.5769573676831830.8460852646336340.423042632316817
170.5023180768136540.9953638463726910.497681923186346
180.4263274984453440.8526549968906890.573672501554656
190.3569794425761680.7139588851523360.643020557423832
200.3293218268734280.6586436537468560.670678173126572
210.3206927042455520.6413854084911050.679307295754448
220.4694562807420410.9389125614840830.530543719257959
230.4736737709045640.9473475418091290.526326229095435
240.6266590734376250.7466818531247510.373340926562375
250.6026442176317080.7947115647365840.397355782368292
260.5801386500018960.8397226999962080.419861349998104
270.5212736295270150.957452740945970.478726370472985
280.5919734909593490.8160530180813020.408026509040651
290.5271457117634480.9457085764731050.472854288236552
300.4994413022015490.9988826044030980.500558697798451
310.7781325166710330.4437349666579350.221867483328967
320.7468407563322070.5063184873355860.253159243667793
330.6943072655487660.6113854689024690.305692734451234
340.658434924956880.6831301500862410.34156507504312
350.6712118834280030.6575762331439950.328788116571997
360.6233385584734710.7533228830530580.376661441526529
370.5648812062642720.8702375874714560.435118793735728
380.51703834110480.96592331779040.4829616588952
390.4617602605576940.9235205211153880.538239739442306
400.4043246565109410.8086493130218810.595675343489059
410.4018940776783870.8037881553567740.598105922321613
420.3692138921555940.7384277843111870.630786107844406
430.3161142459918790.6322284919837570.683885754008121
440.4316630969035130.8633261938070270.568336903096487
450.3833396885863340.7666793771726690.616660311413666
460.3522461403628150.704492280725630.647753859637185
470.3435831741380990.6871663482761980.656416825861901
480.3108493699520790.6216987399041580.689150630047921
490.3084905934747440.6169811869494880.691509406525256
500.3069847511133820.6139695022267630.693015248886618
510.2591508151357880.5183016302715760.740849184864212
520.2152051634517940.4304103269035890.784794836548206
530.2615254174849190.5230508349698370.738474582515081
540.2811472286712140.5622944573424280.718852771328786
550.2414610988399420.4829221976798840.758538901160058
560.2914812431784250.5829624863568490.708518756821575
570.2669966069540550.533993213908110.733003393045945
580.3439692613284180.6879385226568350.656030738671582
590.4569576874714860.9139153749429730.543042312528514
600.3996942032069810.7993884064139610.60030579679302
610.3586695912541340.7173391825082670.641330408745866
620.3050403213712570.6100806427425140.694959678628743
630.307561158530070.615122317060140.69243884146993
640.2758731765956220.5517463531912430.724126823404378
650.2273337204803820.4546674409607640.772666279519618
660.1885619615860960.3771239231721920.811438038413904
670.151215044561540.3024300891230790.84878495543846
680.1329125767592660.2658251535185320.867087423240734
690.1229289845269840.2458579690539670.877071015473016
700.1057631651706030.2115263303412060.894236834829397
710.2230830973319470.4461661946638940.776916902668053
720.1898397203545380.3796794407090760.810160279645462
730.3444680400433620.6889360800867240.655531959956638
740.5802439663217980.8395120673564040.419756033678202
750.5555454556941010.8889090886117990.444454544305899
760.4923862060936150.984772412187230.507613793906385
770.4228444538077210.8456889076154410.577155546192279
780.3630432780173420.7260865560346840.636956721982658
790.34134604551430.6826920910286010.6586539544857
800.315753588274410.631507176548820.68424641172559
810.3363993467432110.6727986934864220.663600653256789
820.385551796114260.771103592228520.61444820388574
830.4997128625153430.9994257250306860.500287137484657
840.4139991323965240.8279982647930480.586000867603476
850.3340727190107670.6681454380215350.665927280989233
860.3342823448295680.6685646896591360.665717655170432
870.2545003059596220.5090006119192440.745499694040378
880.19604042102510.3920808420501990.8039595789749
890.1650862352751550.3301724705503090.834913764724845
900.1372930519749710.2745861039499410.86270694802503
910.08737291340182170.1747458268036430.912627086598178
920.0501234419003810.1002468838007620.949876558099619
930.04888221645664010.09776443291328010.95111778354336






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 level10.0114942528735632OK

\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 & 1 & 0.0114942528735632 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146812&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]1[/C][C]0.0114942528735632[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146812&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146812&T=6

As an alternative you can also use a QR Code:  
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 level10.0114942528735632OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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