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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, 20 Nov 2009 00:36:00 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258702732d1k3gyx7m6hzi5f.htm/, Retrieved Fri, 29 Mar 2024 10:46:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57976, Retrieved Fri, 29 Mar 2024 10:46:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact159
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-20 07:36:00] [2b679e8ec54382eeb0ec0b6bb527570a] [Current]
-    D        [Multiple Regression] [Model1] [2009-11-20 22:45:59] [9c2d53170eb755e9ae5fcf19d2174a32]
-   PD        [Multiple Regression] [Model2] [2009-11-20 22:52:44] [9c2d53170eb755e9ae5fcf19d2174a32]
-   PD        [Multiple Regression] [Model3] [2009-11-20 22:56:26] [9c2d53170eb755e9ae5fcf19d2174a32]
-   PD        [Multiple Regression] [Model4] [2009-11-20 23:04:12] [9c2d53170eb755e9ae5fcf19d2174a32]
-   PD        [Multiple Regression] [Model5] [2009-11-20 23:08:43] [9c2d53170eb755e9ae5fcf19d2174a32]
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Dataseries X:
100.03	2
100.25	1.8
99.6	2.7
100.16	2.3
100.49	1.9
99.72	2
100.14	2.3
98.48	2.8
100.38	2.4
101.45	2.3
98.42	2.7
98.6	2.7
100.06	2.9
98.62	3
100.84	2.2
100.02	2.3
97.95	2.8
98.32	2.8
98.27	2.8
97.22	2.2
99.28	2.6
100.38	2.8
99.02	2.5
100.32	2.4
99.81	2.3
100.6	1.9
101.19	1.7
100.47	2
101.77	2.1
102.32	1.7
102.39	1.8
101.16	1.8
100.63	1.8
101.48	1.3
101.44	1.3
100.09	1.3
100.7	1.2
100.78	1.4
99.81	2.2
98.45	2.9
98.49	3.1
97.48	3.5
97.91	3.6
96.94	4.4
98.53	4.1
96.82	5.1
95.76	5.8
95.27	5.9
97.32	5.4
96.68	5.5
97.87	4.8
97.42	3.2
97.94	2.7
99.52	2.1
100.99	1.9
99.92	0.6
101.97	0.7
101.58	-0.2
99.54	-1
100.83	-1.7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57976&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57976&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57976&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 101.656351098474 -0.878501125566274X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  101.656351098474 -0.878501125566274X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57976&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  101.656351098474 -0.878501125566274X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57976&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57976&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
Y[t] = + 101.656351098474 -0.878501125566274X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)101.6563510984740.279527363.67300
X-0.8785011255662740.098678-8.902700

\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) & 101.656351098474 & 0.279527 & 363.673 & 0 & 0 \tabularnewline
X & -0.878501125566274 & 0.098678 & -8.9027 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57976&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]101.656351098474[/C][C]0.279527[/C][C]363.673[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-0.878501125566274[/C][C]0.098678[/C][C]-8.9027[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57976&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57976&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)101.6563510984740.279527363.67300
X-0.8785011255662740.098678-8.902700







Multiple Linear Regression - Regression Statistics
Multiple R0.75989485080661
R-squared0.5774401842824
Adjusted R-squared0.570154670218304
F-TEST (value)79.2586740211045
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value1.91957560957690e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.07801618410123
Sum Squared Residuals67.402895804682

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.75989485080661 \tabularnewline
R-squared & 0.5774401842824 \tabularnewline
Adjusted R-squared & 0.570154670218304 \tabularnewline
F-TEST (value) & 79.2586740211045 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 1.91957560957690e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.07801618410123 \tabularnewline
Sum Squared Residuals & 67.402895804682 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57976&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.75989485080661[/C][/ROW]
[ROW][C]R-squared[/C][C]0.5774401842824[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.570154670218304[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]79.2586740211045[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]1.91957560957690e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.07801618410123[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]67.402895804682[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57976&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57976&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 R0.75989485080661
R-squared0.5774401842824
Adjusted R-squared0.570154670218304
F-TEST (value)79.2586740211045
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value1.91957560957690e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.07801618410123
Sum Squared Residuals67.402895804682







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1100.0399.8993488473420.130651152658071
2100.25100.0750490724550.174950927544810
399.699.28439805944550.315601940554456
4100.1699.6357985096720.524201490327948
5100.4999.98719895989860.502801040101436
699.7299.899348847342-0.179348847341932
7100.1499.6357985096720.504201490327952
898.4899.196547946889-0.716547946888908
9100.3899.54794839711540.832051602884574
10101.4599.6357985096721.81420149032795
1198.4299.2843980594455-0.864398059445537
1298.699.2843980594455-0.684398059445544
13100.0699.10869783433230.951302165667718
1498.6299.0208477217757-0.400847721775652
15100.8499.72364862222871.11635137777133
16100.0299.6357985096720.384201490327947
1797.9599.196547946889-1.24654794688891
1898.3299.196547946889-0.876547946888918
1998.2799.196547946889-0.926547946888916
2097.2299.7236486222287-2.50364862222868
2199.2899.3722481720022-0.092248172002165
22100.3899.1965479468891.18345205311108
2399.0299.4600982845588-0.440098284558798
24100.3299.54794839711540.772051602884572
2599.8199.6357985096720.174201490327954
26100.699.98719895989860.612801040101436
27101.19100.1628991850121.02710081498818
28100.4799.8993488473420.570651152658068
29101.7799.81149873478531.95850126521469
30102.32100.1628991850122.15710081498818
31102.39100.0750490724552.31495092754481
32101.16100.0750490724551.08495092754481
33100.63100.0750490724550.55495092754481
34101.48100.5142996352380.96570036476168
35101.44100.5142996352380.925700364761675
36100.09100.514299635238-0.42429963523832
37100.7100.6021497477950.0978502522050524
38100.78100.4264495226820.353550477318306
3999.8199.72364862222870.0863513777713264
4098.4599.1086978343323-0.658697834332281
4198.4998.932997609219-0.442997609219034
4297.4898.5815971589925-1.10159715899252
4397.9198.493747046436-0.583747046435895
4496.9497.7909461459829-0.850946145982874
4598.5398.05449648365280.475503516347246
4696.8297.1759953580865-0.355995358086487
4795.7696.56104457019-0.801044570190083
4895.2796.4731944576335-1.20319445763346
4997.3296.91244502041660.407554979583396
5096.6896.82459490786-0.144594907859964
5197.8797.43954569575640.430454304243642
5297.4298.8451474966624-1.4251474966624
5397.9499.2843980594455-1.34439805944554
5499.5299.8114987347853-0.291498734785307
55100.9999.98719895989861.00280104010144
5699.92101.129250423135-1.20925042313471
57101.97101.0414003105780.928599689421911
58101.58101.832051323588-0.252051323587737
5999.54102.534852224041-2.99485222404075
60100.83103.149803011937-2.31980301193715

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100.03 & 99.899348847342 & 0.130651152658071 \tabularnewline
2 & 100.25 & 100.075049072455 & 0.174950927544810 \tabularnewline
3 & 99.6 & 99.2843980594455 & 0.315601940554456 \tabularnewline
4 & 100.16 & 99.635798509672 & 0.524201490327948 \tabularnewline
5 & 100.49 & 99.9871989598986 & 0.502801040101436 \tabularnewline
6 & 99.72 & 99.899348847342 & -0.179348847341932 \tabularnewline
7 & 100.14 & 99.635798509672 & 0.504201490327952 \tabularnewline
8 & 98.48 & 99.196547946889 & -0.716547946888908 \tabularnewline
9 & 100.38 & 99.5479483971154 & 0.832051602884574 \tabularnewline
10 & 101.45 & 99.635798509672 & 1.81420149032795 \tabularnewline
11 & 98.42 & 99.2843980594455 & -0.864398059445537 \tabularnewline
12 & 98.6 & 99.2843980594455 & -0.684398059445544 \tabularnewline
13 & 100.06 & 99.1086978343323 & 0.951302165667718 \tabularnewline
14 & 98.62 & 99.0208477217757 & -0.400847721775652 \tabularnewline
15 & 100.84 & 99.7236486222287 & 1.11635137777133 \tabularnewline
16 & 100.02 & 99.635798509672 & 0.384201490327947 \tabularnewline
17 & 97.95 & 99.196547946889 & -1.24654794688891 \tabularnewline
18 & 98.32 & 99.196547946889 & -0.876547946888918 \tabularnewline
19 & 98.27 & 99.196547946889 & -0.926547946888916 \tabularnewline
20 & 97.22 & 99.7236486222287 & -2.50364862222868 \tabularnewline
21 & 99.28 & 99.3722481720022 & -0.092248172002165 \tabularnewline
22 & 100.38 & 99.196547946889 & 1.18345205311108 \tabularnewline
23 & 99.02 & 99.4600982845588 & -0.440098284558798 \tabularnewline
24 & 100.32 & 99.5479483971154 & 0.772051602884572 \tabularnewline
25 & 99.81 & 99.635798509672 & 0.174201490327954 \tabularnewline
26 & 100.6 & 99.9871989598986 & 0.612801040101436 \tabularnewline
27 & 101.19 & 100.162899185012 & 1.02710081498818 \tabularnewline
28 & 100.47 & 99.899348847342 & 0.570651152658068 \tabularnewline
29 & 101.77 & 99.8114987347853 & 1.95850126521469 \tabularnewline
30 & 102.32 & 100.162899185012 & 2.15710081498818 \tabularnewline
31 & 102.39 & 100.075049072455 & 2.31495092754481 \tabularnewline
32 & 101.16 & 100.075049072455 & 1.08495092754481 \tabularnewline
33 & 100.63 & 100.075049072455 & 0.55495092754481 \tabularnewline
34 & 101.48 & 100.514299635238 & 0.96570036476168 \tabularnewline
35 & 101.44 & 100.514299635238 & 0.925700364761675 \tabularnewline
36 & 100.09 & 100.514299635238 & -0.42429963523832 \tabularnewline
37 & 100.7 & 100.602149747795 & 0.0978502522050524 \tabularnewline
38 & 100.78 & 100.426449522682 & 0.353550477318306 \tabularnewline
39 & 99.81 & 99.7236486222287 & 0.0863513777713264 \tabularnewline
40 & 98.45 & 99.1086978343323 & -0.658697834332281 \tabularnewline
41 & 98.49 & 98.932997609219 & -0.442997609219034 \tabularnewline
42 & 97.48 & 98.5815971589925 & -1.10159715899252 \tabularnewline
43 & 97.91 & 98.493747046436 & -0.583747046435895 \tabularnewline
44 & 96.94 & 97.7909461459829 & -0.850946145982874 \tabularnewline
45 & 98.53 & 98.0544964836528 & 0.475503516347246 \tabularnewline
46 & 96.82 & 97.1759953580865 & -0.355995358086487 \tabularnewline
47 & 95.76 & 96.56104457019 & -0.801044570190083 \tabularnewline
48 & 95.27 & 96.4731944576335 & -1.20319445763346 \tabularnewline
49 & 97.32 & 96.9124450204166 & 0.407554979583396 \tabularnewline
50 & 96.68 & 96.82459490786 & -0.144594907859964 \tabularnewline
51 & 97.87 & 97.4395456957564 & 0.430454304243642 \tabularnewline
52 & 97.42 & 98.8451474966624 & -1.4251474966624 \tabularnewline
53 & 97.94 & 99.2843980594455 & -1.34439805944554 \tabularnewline
54 & 99.52 & 99.8114987347853 & -0.291498734785307 \tabularnewline
55 & 100.99 & 99.9871989598986 & 1.00280104010144 \tabularnewline
56 & 99.92 & 101.129250423135 & -1.20925042313471 \tabularnewline
57 & 101.97 & 101.041400310578 & 0.928599689421911 \tabularnewline
58 & 101.58 & 101.832051323588 & -0.252051323587737 \tabularnewline
59 & 99.54 & 102.534852224041 & -2.99485222404075 \tabularnewline
60 & 100.83 & 103.149803011937 & -2.31980301193715 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57976&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]100.03[/C][C]99.899348847342[/C][C]0.130651152658071[/C][/ROW]
[ROW][C]2[/C][C]100.25[/C][C]100.075049072455[/C][C]0.174950927544810[/C][/ROW]
[ROW][C]3[/C][C]99.6[/C][C]99.2843980594455[/C][C]0.315601940554456[/C][/ROW]
[ROW][C]4[/C][C]100.16[/C][C]99.635798509672[/C][C]0.524201490327948[/C][/ROW]
[ROW][C]5[/C][C]100.49[/C][C]99.9871989598986[/C][C]0.502801040101436[/C][/ROW]
[ROW][C]6[/C][C]99.72[/C][C]99.899348847342[/C][C]-0.179348847341932[/C][/ROW]
[ROW][C]7[/C][C]100.14[/C][C]99.635798509672[/C][C]0.504201490327952[/C][/ROW]
[ROW][C]8[/C][C]98.48[/C][C]99.196547946889[/C][C]-0.716547946888908[/C][/ROW]
[ROW][C]9[/C][C]100.38[/C][C]99.5479483971154[/C][C]0.832051602884574[/C][/ROW]
[ROW][C]10[/C][C]101.45[/C][C]99.635798509672[/C][C]1.81420149032795[/C][/ROW]
[ROW][C]11[/C][C]98.42[/C][C]99.2843980594455[/C][C]-0.864398059445537[/C][/ROW]
[ROW][C]12[/C][C]98.6[/C][C]99.2843980594455[/C][C]-0.684398059445544[/C][/ROW]
[ROW][C]13[/C][C]100.06[/C][C]99.1086978343323[/C][C]0.951302165667718[/C][/ROW]
[ROW][C]14[/C][C]98.62[/C][C]99.0208477217757[/C][C]-0.400847721775652[/C][/ROW]
[ROW][C]15[/C][C]100.84[/C][C]99.7236486222287[/C][C]1.11635137777133[/C][/ROW]
[ROW][C]16[/C][C]100.02[/C][C]99.635798509672[/C][C]0.384201490327947[/C][/ROW]
[ROW][C]17[/C][C]97.95[/C][C]99.196547946889[/C][C]-1.24654794688891[/C][/ROW]
[ROW][C]18[/C][C]98.32[/C][C]99.196547946889[/C][C]-0.876547946888918[/C][/ROW]
[ROW][C]19[/C][C]98.27[/C][C]99.196547946889[/C][C]-0.926547946888916[/C][/ROW]
[ROW][C]20[/C][C]97.22[/C][C]99.7236486222287[/C][C]-2.50364862222868[/C][/ROW]
[ROW][C]21[/C][C]99.28[/C][C]99.3722481720022[/C][C]-0.092248172002165[/C][/ROW]
[ROW][C]22[/C][C]100.38[/C][C]99.196547946889[/C][C]1.18345205311108[/C][/ROW]
[ROW][C]23[/C][C]99.02[/C][C]99.4600982845588[/C][C]-0.440098284558798[/C][/ROW]
[ROW][C]24[/C][C]100.32[/C][C]99.5479483971154[/C][C]0.772051602884572[/C][/ROW]
[ROW][C]25[/C][C]99.81[/C][C]99.635798509672[/C][C]0.174201490327954[/C][/ROW]
[ROW][C]26[/C][C]100.6[/C][C]99.9871989598986[/C][C]0.612801040101436[/C][/ROW]
[ROW][C]27[/C][C]101.19[/C][C]100.162899185012[/C][C]1.02710081498818[/C][/ROW]
[ROW][C]28[/C][C]100.47[/C][C]99.899348847342[/C][C]0.570651152658068[/C][/ROW]
[ROW][C]29[/C][C]101.77[/C][C]99.8114987347853[/C][C]1.95850126521469[/C][/ROW]
[ROW][C]30[/C][C]102.32[/C][C]100.162899185012[/C][C]2.15710081498818[/C][/ROW]
[ROW][C]31[/C][C]102.39[/C][C]100.075049072455[/C][C]2.31495092754481[/C][/ROW]
[ROW][C]32[/C][C]101.16[/C][C]100.075049072455[/C][C]1.08495092754481[/C][/ROW]
[ROW][C]33[/C][C]100.63[/C][C]100.075049072455[/C][C]0.55495092754481[/C][/ROW]
[ROW][C]34[/C][C]101.48[/C][C]100.514299635238[/C][C]0.96570036476168[/C][/ROW]
[ROW][C]35[/C][C]101.44[/C][C]100.514299635238[/C][C]0.925700364761675[/C][/ROW]
[ROW][C]36[/C][C]100.09[/C][C]100.514299635238[/C][C]-0.42429963523832[/C][/ROW]
[ROW][C]37[/C][C]100.7[/C][C]100.602149747795[/C][C]0.0978502522050524[/C][/ROW]
[ROW][C]38[/C][C]100.78[/C][C]100.426449522682[/C][C]0.353550477318306[/C][/ROW]
[ROW][C]39[/C][C]99.81[/C][C]99.7236486222287[/C][C]0.0863513777713264[/C][/ROW]
[ROW][C]40[/C][C]98.45[/C][C]99.1086978343323[/C][C]-0.658697834332281[/C][/ROW]
[ROW][C]41[/C][C]98.49[/C][C]98.932997609219[/C][C]-0.442997609219034[/C][/ROW]
[ROW][C]42[/C][C]97.48[/C][C]98.5815971589925[/C][C]-1.10159715899252[/C][/ROW]
[ROW][C]43[/C][C]97.91[/C][C]98.493747046436[/C][C]-0.583747046435895[/C][/ROW]
[ROW][C]44[/C][C]96.94[/C][C]97.7909461459829[/C][C]-0.850946145982874[/C][/ROW]
[ROW][C]45[/C][C]98.53[/C][C]98.0544964836528[/C][C]0.475503516347246[/C][/ROW]
[ROW][C]46[/C][C]96.82[/C][C]97.1759953580865[/C][C]-0.355995358086487[/C][/ROW]
[ROW][C]47[/C][C]95.76[/C][C]96.56104457019[/C][C]-0.801044570190083[/C][/ROW]
[ROW][C]48[/C][C]95.27[/C][C]96.4731944576335[/C][C]-1.20319445763346[/C][/ROW]
[ROW][C]49[/C][C]97.32[/C][C]96.9124450204166[/C][C]0.407554979583396[/C][/ROW]
[ROW][C]50[/C][C]96.68[/C][C]96.82459490786[/C][C]-0.144594907859964[/C][/ROW]
[ROW][C]51[/C][C]97.87[/C][C]97.4395456957564[/C][C]0.430454304243642[/C][/ROW]
[ROW][C]52[/C][C]97.42[/C][C]98.8451474966624[/C][C]-1.4251474966624[/C][/ROW]
[ROW][C]53[/C][C]97.94[/C][C]99.2843980594455[/C][C]-1.34439805944554[/C][/ROW]
[ROW][C]54[/C][C]99.52[/C][C]99.8114987347853[/C][C]-0.291498734785307[/C][/ROW]
[ROW][C]55[/C][C]100.99[/C][C]99.9871989598986[/C][C]1.00280104010144[/C][/ROW]
[ROW][C]56[/C][C]99.92[/C][C]101.129250423135[/C][C]-1.20925042313471[/C][/ROW]
[ROW][C]57[/C][C]101.97[/C][C]101.041400310578[/C][C]0.928599689421911[/C][/ROW]
[ROW][C]58[/C][C]101.58[/C][C]101.832051323588[/C][C]-0.252051323587737[/C][/ROW]
[ROW][C]59[/C][C]99.54[/C][C]102.534852224041[/C][C]-2.99485222404075[/C][/ROW]
[ROW][C]60[/C][C]100.83[/C][C]103.149803011937[/C][C]-2.31980301193715[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57976&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57976&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
1100.0399.8993488473420.130651152658071
2100.25100.0750490724550.174950927544810
399.699.28439805944550.315601940554456
4100.1699.6357985096720.524201490327948
5100.4999.98719895989860.502801040101436
699.7299.899348847342-0.179348847341932
7100.1499.6357985096720.504201490327952
898.4899.196547946889-0.716547946888908
9100.3899.54794839711540.832051602884574
10101.4599.6357985096721.81420149032795
1198.4299.2843980594455-0.864398059445537
1298.699.2843980594455-0.684398059445544
13100.0699.10869783433230.951302165667718
1498.6299.0208477217757-0.400847721775652
15100.8499.72364862222871.11635137777133
16100.0299.6357985096720.384201490327947
1797.9599.196547946889-1.24654794688891
1898.3299.196547946889-0.876547946888918
1998.2799.196547946889-0.926547946888916
2097.2299.7236486222287-2.50364862222868
2199.2899.3722481720022-0.092248172002165
22100.3899.1965479468891.18345205311108
2399.0299.4600982845588-0.440098284558798
24100.3299.54794839711540.772051602884572
2599.8199.6357985096720.174201490327954
26100.699.98719895989860.612801040101436
27101.19100.1628991850121.02710081498818
28100.4799.8993488473420.570651152658068
29101.7799.81149873478531.95850126521469
30102.32100.1628991850122.15710081498818
31102.39100.0750490724552.31495092754481
32101.16100.0750490724551.08495092754481
33100.63100.0750490724550.55495092754481
34101.48100.5142996352380.96570036476168
35101.44100.5142996352380.925700364761675
36100.09100.514299635238-0.42429963523832
37100.7100.6021497477950.0978502522050524
38100.78100.4264495226820.353550477318306
3999.8199.72364862222870.0863513777713264
4098.4599.1086978343323-0.658697834332281
4198.4998.932997609219-0.442997609219034
4297.4898.5815971589925-1.10159715899252
4397.9198.493747046436-0.583747046435895
4496.9497.7909461459829-0.850946145982874
4598.5398.05449648365280.475503516347246
4696.8297.1759953580865-0.355995358086487
4795.7696.56104457019-0.801044570190083
4895.2796.4731944576335-1.20319445763346
4997.3296.91244502041660.407554979583396
5096.6896.82459490786-0.144594907859964
5197.8797.43954569575640.430454304243642
5297.4298.8451474966624-1.4251474966624
5397.9499.2843980594455-1.34439805944554
5499.5299.8114987347853-0.291498734785307
55100.9999.98719895989861.00280104010144
5699.92101.129250423135-1.20925042313471
57101.97101.0414003105780.928599689421911
58101.58101.832051323588-0.252051323587737
5999.54102.534852224041-2.99485222404075
60100.83103.149803011937-2.31980301193715







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.00811869832002560.01623739664005120.991881301679974
60.00742412833767670.01484825667535340.992575871662323
70.002058310019838730.004116620039677470.997941689980161
80.009640839255795640.01928167851159130.990359160744204
90.009979283698224850.01995856739644970.990020716301775
100.07286156809888560.1457231361977710.927138431901114
110.08169195499748660.1633839099949730.918308045002513
120.06113224880812680.1222644976162540.938867751191873
130.07198351427201850.1439670285440370.928016485727981
140.04551473506381370.09102947012762750.954485264936186
150.04023923392069050.08047846784138110.95976076607931
160.02347179009702720.04694358019405440.976528209902973
170.03333310618453560.06666621236907120.966666893815464
180.02671003212162120.05342006424324240.973289967878379
190.02118902382506890.04237804765013770.978810976174931
200.2451509918516170.4903019837032350.754849008148383
210.1848466227266370.3696932454532750.815153377273363
220.2241540158992690.4483080317985390.77584598410073
230.1762685212815380.3525370425630760.823731478718462
240.1510728277590100.3021456555180200.84892717224099
250.1095171418434360.2190342836868710.890482858156564
260.0809975374855420.1619950749710840.919002462514458
270.06620037328585580.1324007465717120.933799626714144
280.04719658516671970.09439317033343950.95280341483328
290.09582134243644170.1916426848728830.904178657563558
300.1776589308020600.3553178616041210.82234106919794
310.3701493620225990.7402987240451970.629850637977401
320.380146933782060.760293867564120.61985306621794
330.3546910855408450.709382171081690.645308914459155
340.3922729678153020.7845459356306040.607727032184698
350.4457766189762640.8915532379525290.554223381023736
360.4747239532636630.9494479065273250.525276046736337
370.4707480386876980.9414960773753970.529251961312302
380.4724039330144260.9448078660288520.527596066985574
390.4311264563785740.8622529127571480.568873543621426
400.3596278111846100.7192556223692190.64037218881539
410.2872544502542950.5745089005085890.712745549745705
420.2405688546331880.4811377092663750.759431145366812
430.1813562518570440.3627125037140880.818643748142956
440.1434982295310640.2869964590621270.856501770468936
450.1503357552605230.3006715105210470.849664244739477
460.1143114563578020.2286229127156030.885688543642199
470.09294970190854350.1858994038170870.907050298091457
480.1137018603213750.227403720642750.886298139678625
490.09047047682982620.1809409536596520.909529523170174
500.06681548796553380.1336309759310680.933184512034466
510.0432908540793090.0865817081586180.95670914592069
520.0752054419999860.1504108839999720.924794558000014
530.2422545660766180.4845091321532370.757745433923382
540.3087945765658600.6175891531317210.69120542343414
550.1952412876910610.3904825753821220.804758712308939

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0081186983200256 & 0.0162373966400512 & 0.991881301679974 \tabularnewline
6 & 0.0074241283376767 & 0.0148482566753534 & 0.992575871662323 \tabularnewline
7 & 0.00205831001983873 & 0.00411662003967747 & 0.997941689980161 \tabularnewline
8 & 0.00964083925579564 & 0.0192816785115913 & 0.990359160744204 \tabularnewline
9 & 0.00997928369822485 & 0.0199585673964497 & 0.990020716301775 \tabularnewline
10 & 0.0728615680988856 & 0.145723136197771 & 0.927138431901114 \tabularnewline
11 & 0.0816919549974866 & 0.163383909994973 & 0.918308045002513 \tabularnewline
12 & 0.0611322488081268 & 0.122264497616254 & 0.938867751191873 \tabularnewline
13 & 0.0719835142720185 & 0.143967028544037 & 0.928016485727981 \tabularnewline
14 & 0.0455147350638137 & 0.0910294701276275 & 0.954485264936186 \tabularnewline
15 & 0.0402392339206905 & 0.0804784678413811 & 0.95976076607931 \tabularnewline
16 & 0.0234717900970272 & 0.0469435801940544 & 0.976528209902973 \tabularnewline
17 & 0.0333331061845356 & 0.0666662123690712 & 0.966666893815464 \tabularnewline
18 & 0.0267100321216212 & 0.0534200642432424 & 0.973289967878379 \tabularnewline
19 & 0.0211890238250689 & 0.0423780476501377 & 0.978810976174931 \tabularnewline
20 & 0.245150991851617 & 0.490301983703235 & 0.754849008148383 \tabularnewline
21 & 0.184846622726637 & 0.369693245453275 & 0.815153377273363 \tabularnewline
22 & 0.224154015899269 & 0.448308031798539 & 0.77584598410073 \tabularnewline
23 & 0.176268521281538 & 0.352537042563076 & 0.823731478718462 \tabularnewline
24 & 0.151072827759010 & 0.302145655518020 & 0.84892717224099 \tabularnewline
25 & 0.109517141843436 & 0.219034283686871 & 0.890482858156564 \tabularnewline
26 & 0.080997537485542 & 0.161995074971084 & 0.919002462514458 \tabularnewline
27 & 0.0662003732858558 & 0.132400746571712 & 0.933799626714144 \tabularnewline
28 & 0.0471965851667197 & 0.0943931703334395 & 0.95280341483328 \tabularnewline
29 & 0.0958213424364417 & 0.191642684872883 & 0.904178657563558 \tabularnewline
30 & 0.177658930802060 & 0.355317861604121 & 0.82234106919794 \tabularnewline
31 & 0.370149362022599 & 0.740298724045197 & 0.629850637977401 \tabularnewline
32 & 0.38014693378206 & 0.76029386756412 & 0.61985306621794 \tabularnewline
33 & 0.354691085540845 & 0.70938217108169 & 0.645308914459155 \tabularnewline
34 & 0.392272967815302 & 0.784545935630604 & 0.607727032184698 \tabularnewline
35 & 0.445776618976264 & 0.891553237952529 & 0.554223381023736 \tabularnewline
36 & 0.474723953263663 & 0.949447906527325 & 0.525276046736337 \tabularnewline
37 & 0.470748038687698 & 0.941496077375397 & 0.529251961312302 \tabularnewline
38 & 0.472403933014426 & 0.944807866028852 & 0.527596066985574 \tabularnewline
39 & 0.431126456378574 & 0.862252912757148 & 0.568873543621426 \tabularnewline
40 & 0.359627811184610 & 0.719255622369219 & 0.64037218881539 \tabularnewline
41 & 0.287254450254295 & 0.574508900508589 & 0.712745549745705 \tabularnewline
42 & 0.240568854633188 & 0.481137709266375 & 0.759431145366812 \tabularnewline
43 & 0.181356251857044 & 0.362712503714088 & 0.818643748142956 \tabularnewline
44 & 0.143498229531064 & 0.286996459062127 & 0.856501770468936 \tabularnewline
45 & 0.150335755260523 & 0.300671510521047 & 0.849664244739477 \tabularnewline
46 & 0.114311456357802 & 0.228622912715603 & 0.885688543642199 \tabularnewline
47 & 0.0929497019085435 & 0.185899403817087 & 0.907050298091457 \tabularnewline
48 & 0.113701860321375 & 0.22740372064275 & 0.886298139678625 \tabularnewline
49 & 0.0904704768298262 & 0.180940953659652 & 0.909529523170174 \tabularnewline
50 & 0.0668154879655338 & 0.133630975931068 & 0.933184512034466 \tabularnewline
51 & 0.043290854079309 & 0.086581708158618 & 0.95670914592069 \tabularnewline
52 & 0.075205441999986 & 0.150410883999972 & 0.924794558000014 \tabularnewline
53 & 0.242254566076618 & 0.484509132153237 & 0.757745433923382 \tabularnewline
54 & 0.308794576565860 & 0.617589153131721 & 0.69120542343414 \tabularnewline
55 & 0.195241287691061 & 0.390482575382122 & 0.804758712308939 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57976&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]5[/C][C]0.0081186983200256[/C][C]0.0162373966400512[/C][C]0.991881301679974[/C][/ROW]
[ROW][C]6[/C][C]0.0074241283376767[/C][C]0.0148482566753534[/C][C]0.992575871662323[/C][/ROW]
[ROW][C]7[/C][C]0.00205831001983873[/C][C]0.00411662003967747[/C][C]0.997941689980161[/C][/ROW]
[ROW][C]8[/C][C]0.00964083925579564[/C][C]0.0192816785115913[/C][C]0.990359160744204[/C][/ROW]
[ROW][C]9[/C][C]0.00997928369822485[/C][C]0.0199585673964497[/C][C]0.990020716301775[/C][/ROW]
[ROW][C]10[/C][C]0.0728615680988856[/C][C]0.145723136197771[/C][C]0.927138431901114[/C][/ROW]
[ROW][C]11[/C][C]0.0816919549974866[/C][C]0.163383909994973[/C][C]0.918308045002513[/C][/ROW]
[ROW][C]12[/C][C]0.0611322488081268[/C][C]0.122264497616254[/C][C]0.938867751191873[/C][/ROW]
[ROW][C]13[/C][C]0.0719835142720185[/C][C]0.143967028544037[/C][C]0.928016485727981[/C][/ROW]
[ROW][C]14[/C][C]0.0455147350638137[/C][C]0.0910294701276275[/C][C]0.954485264936186[/C][/ROW]
[ROW][C]15[/C][C]0.0402392339206905[/C][C]0.0804784678413811[/C][C]0.95976076607931[/C][/ROW]
[ROW][C]16[/C][C]0.0234717900970272[/C][C]0.0469435801940544[/C][C]0.976528209902973[/C][/ROW]
[ROW][C]17[/C][C]0.0333331061845356[/C][C]0.0666662123690712[/C][C]0.966666893815464[/C][/ROW]
[ROW][C]18[/C][C]0.0267100321216212[/C][C]0.0534200642432424[/C][C]0.973289967878379[/C][/ROW]
[ROW][C]19[/C][C]0.0211890238250689[/C][C]0.0423780476501377[/C][C]0.978810976174931[/C][/ROW]
[ROW][C]20[/C][C]0.245150991851617[/C][C]0.490301983703235[/C][C]0.754849008148383[/C][/ROW]
[ROW][C]21[/C][C]0.184846622726637[/C][C]0.369693245453275[/C][C]0.815153377273363[/C][/ROW]
[ROW][C]22[/C][C]0.224154015899269[/C][C]0.448308031798539[/C][C]0.77584598410073[/C][/ROW]
[ROW][C]23[/C][C]0.176268521281538[/C][C]0.352537042563076[/C][C]0.823731478718462[/C][/ROW]
[ROW][C]24[/C][C]0.151072827759010[/C][C]0.302145655518020[/C][C]0.84892717224099[/C][/ROW]
[ROW][C]25[/C][C]0.109517141843436[/C][C]0.219034283686871[/C][C]0.890482858156564[/C][/ROW]
[ROW][C]26[/C][C]0.080997537485542[/C][C]0.161995074971084[/C][C]0.919002462514458[/C][/ROW]
[ROW][C]27[/C][C]0.0662003732858558[/C][C]0.132400746571712[/C][C]0.933799626714144[/C][/ROW]
[ROW][C]28[/C][C]0.0471965851667197[/C][C]0.0943931703334395[/C][C]0.95280341483328[/C][/ROW]
[ROW][C]29[/C][C]0.0958213424364417[/C][C]0.191642684872883[/C][C]0.904178657563558[/C][/ROW]
[ROW][C]30[/C][C]0.177658930802060[/C][C]0.355317861604121[/C][C]0.82234106919794[/C][/ROW]
[ROW][C]31[/C][C]0.370149362022599[/C][C]0.740298724045197[/C][C]0.629850637977401[/C][/ROW]
[ROW][C]32[/C][C]0.38014693378206[/C][C]0.76029386756412[/C][C]0.61985306621794[/C][/ROW]
[ROW][C]33[/C][C]0.354691085540845[/C][C]0.70938217108169[/C][C]0.645308914459155[/C][/ROW]
[ROW][C]34[/C][C]0.392272967815302[/C][C]0.784545935630604[/C][C]0.607727032184698[/C][/ROW]
[ROW][C]35[/C][C]0.445776618976264[/C][C]0.891553237952529[/C][C]0.554223381023736[/C][/ROW]
[ROW][C]36[/C][C]0.474723953263663[/C][C]0.949447906527325[/C][C]0.525276046736337[/C][/ROW]
[ROW][C]37[/C][C]0.470748038687698[/C][C]0.941496077375397[/C][C]0.529251961312302[/C][/ROW]
[ROW][C]38[/C][C]0.472403933014426[/C][C]0.944807866028852[/C][C]0.527596066985574[/C][/ROW]
[ROW][C]39[/C][C]0.431126456378574[/C][C]0.862252912757148[/C][C]0.568873543621426[/C][/ROW]
[ROW][C]40[/C][C]0.359627811184610[/C][C]0.719255622369219[/C][C]0.64037218881539[/C][/ROW]
[ROW][C]41[/C][C]0.287254450254295[/C][C]0.574508900508589[/C][C]0.712745549745705[/C][/ROW]
[ROW][C]42[/C][C]0.240568854633188[/C][C]0.481137709266375[/C][C]0.759431145366812[/C][/ROW]
[ROW][C]43[/C][C]0.181356251857044[/C][C]0.362712503714088[/C][C]0.818643748142956[/C][/ROW]
[ROW][C]44[/C][C]0.143498229531064[/C][C]0.286996459062127[/C][C]0.856501770468936[/C][/ROW]
[ROW][C]45[/C][C]0.150335755260523[/C][C]0.300671510521047[/C][C]0.849664244739477[/C][/ROW]
[ROW][C]46[/C][C]0.114311456357802[/C][C]0.228622912715603[/C][C]0.885688543642199[/C][/ROW]
[ROW][C]47[/C][C]0.0929497019085435[/C][C]0.185899403817087[/C][C]0.907050298091457[/C][/ROW]
[ROW][C]48[/C][C]0.113701860321375[/C][C]0.22740372064275[/C][C]0.886298139678625[/C][/ROW]
[ROW][C]49[/C][C]0.0904704768298262[/C][C]0.180940953659652[/C][C]0.909529523170174[/C][/ROW]
[ROW][C]50[/C][C]0.0668154879655338[/C][C]0.133630975931068[/C][C]0.933184512034466[/C][/ROW]
[ROW][C]51[/C][C]0.043290854079309[/C][C]0.086581708158618[/C][C]0.95670914592069[/C][/ROW]
[ROW][C]52[/C][C]0.075205441999986[/C][C]0.150410883999972[/C][C]0.924794558000014[/C][/ROW]
[ROW][C]53[/C][C]0.242254566076618[/C][C]0.484509132153237[/C][C]0.757745433923382[/C][/ROW]
[ROW][C]54[/C][C]0.308794576565860[/C][C]0.617589153131721[/C][C]0.69120542343414[/C][/ROW]
[ROW][C]55[/C][C]0.195241287691061[/C][C]0.390482575382122[/C][C]0.804758712308939[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57976&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57976&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
50.00811869832002560.01623739664005120.991881301679974
60.00742412833767670.01484825667535340.992575871662323
70.002058310019838730.004116620039677470.997941689980161
80.009640839255795640.01928167851159130.990359160744204
90.009979283698224850.01995856739644970.990020716301775
100.07286156809888560.1457231361977710.927138431901114
110.08169195499748660.1633839099949730.918308045002513
120.06113224880812680.1222644976162540.938867751191873
130.07198351427201850.1439670285440370.928016485727981
140.04551473506381370.09102947012762750.954485264936186
150.04023923392069050.08047846784138110.95976076607931
160.02347179009702720.04694358019405440.976528209902973
170.03333310618453560.06666621236907120.966666893815464
180.02671003212162120.05342006424324240.973289967878379
190.02118902382506890.04237804765013770.978810976174931
200.2451509918516170.4903019837032350.754849008148383
210.1848466227266370.3696932454532750.815153377273363
220.2241540158992690.4483080317985390.77584598410073
230.1762685212815380.3525370425630760.823731478718462
240.1510728277590100.3021456555180200.84892717224099
250.1095171418434360.2190342836868710.890482858156564
260.0809975374855420.1619950749710840.919002462514458
270.06620037328585580.1324007465717120.933799626714144
280.04719658516671970.09439317033343950.95280341483328
290.09582134243644170.1916426848728830.904178657563558
300.1776589308020600.3553178616041210.82234106919794
310.3701493620225990.7402987240451970.629850637977401
320.380146933782060.760293867564120.61985306621794
330.3546910855408450.709382171081690.645308914459155
340.3922729678153020.7845459356306040.607727032184698
350.4457766189762640.8915532379525290.554223381023736
360.4747239532636630.9494479065273250.525276046736337
370.4707480386876980.9414960773753970.529251961312302
380.4724039330144260.9448078660288520.527596066985574
390.4311264563785740.8622529127571480.568873543621426
400.3596278111846100.7192556223692190.64037218881539
410.2872544502542950.5745089005085890.712745549745705
420.2405688546331880.4811377092663750.759431145366812
430.1813562518570440.3627125037140880.818643748142956
440.1434982295310640.2869964590621270.856501770468936
450.1503357552605230.3006715105210470.849664244739477
460.1143114563578020.2286229127156030.885688543642199
470.09294970190854350.1858994038170870.907050298091457
480.1137018603213750.227403720642750.886298139678625
490.09047047682982620.1809409536596520.909529523170174
500.06681548796553380.1336309759310680.933184512034466
510.0432908540793090.0865817081586180.95670914592069
520.0752054419999860.1504108839999720.924794558000014
530.2422545660766180.4845091321532370.757745433923382
540.3087945765658600.6175891531317210.69120542343414
550.1952412876910610.3904825753821220.804758712308939







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0196078431372549NOK
5% type I error level70.137254901960784NOK
10% type I error level130.254901960784314NOK

\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 & 1 & 0.0196078431372549 & NOK \tabularnewline
5% type I error level & 7 & 0.137254901960784 & NOK \tabularnewline
10% type I error level & 13 & 0.254901960784314 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57976&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]1[/C][C]0.0196078431372549[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]7[/C][C]0.137254901960784[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.254901960784314[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57976&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57976&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 level10.0196078431372549NOK
5% type I error level70.137254901960784NOK
10% type I error level130.254901960784314NOK



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