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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 07:16:52 -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/t1258726662wbi452bzmjo8g78.htm/, Retrieved Fri, 29 Mar 2024 10:56:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58190, Retrieved Fri, 29 Mar 2024 10:56:19 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact114
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [] [2009-11-20 14:16:52] [4057bfb3a128b4e91b455d276991f7f0] [Current]
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Dataseries X:
22	0
22	0
20	0
21	0
20	0
21	0
21	0
21	0
19	0
21	0
21	0
22	0
19	0
24	0
22	0
22	0
22	0
24	0
22	0
23	0
24	0
21	0
20	0
22	0
23	0
23	0
22	0
20	0
21	1
21	1
20	1
20	1
17	1
18	1
19	1
19	1
20	1
21	1
20	1
21	1
19	1
22	1
20	1
18	1
16	1
17	1
18	1
19	1
18	1
20	1
21	1
18	1
19	1
19	1
19	1
21	1
19	1
19	1
17	1
16	1
16	1
17	1
16	1
15	1
16	1
16	1
16	1
18	1
19	1
16	1
16	1
16	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58190&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]3 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=58190&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58190&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 22.15 -1.05X[t] -0.149999999999988M1[t] + 1.40833333333333M2[t] + 0.466666666666665M3[t] -0.141666666666668M4[t] + 0.091666666666664M5[t] + 1.15M6[t] + 0.374999999999999M7[t] + 0.93333333333333M8[t] -0.175000000000001M9[t] -0.450000000000001M10[t] -0.558333333333335M11[t] -0.0583333333333334t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  22.15 -1.05X[t] -0.149999999999988M1[t] +  1.40833333333333M2[t] +  0.466666666666665M3[t] -0.141666666666668M4[t] +  0.091666666666664M5[t] +  1.15M6[t] +  0.374999999999999M7[t] +  0.93333333333333M8[t] -0.175000000000001M9[t] -0.450000000000001M10[t] -0.558333333333335M11[t] -0.0583333333333334t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58190&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  22.15 -1.05X[t] -0.149999999999988M1[t] +  1.40833333333333M2[t] +  0.466666666666665M3[t] -0.141666666666668M4[t] +  0.091666666666664M5[t] +  1.15M6[t] +  0.374999999999999M7[t] +  0.93333333333333M8[t] -0.175000000000001M9[t] -0.450000000000001M10[t] -0.558333333333335M11[t] -0.0583333333333334t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58190&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58190&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] = + 22.15 -1.05X[t] -0.149999999999988M1[t] + 1.40833333333333M2[t] + 0.466666666666665M3[t] -0.141666666666668M4[t] + 0.091666666666664M5[t] + 1.15M6[t] + 0.374999999999999M7[t] + 0.93333333333333M8[t] -0.175000000000001M9[t] -0.450000000000001M10[t] -0.558333333333335M11[t] -0.0583333333333334t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)22.150.74571429.703100
X-1.050.711267-1.47620.1452890.072644
M1-0.1499999999999880.902221-0.16630.8685330.434267
M21.408333333333330.9008291.56340.1234050.061702
M30.4666666666666650.8997450.51870.6059680.302984
M4-0.1416666666666680.89897-0.15760.8753290.437665
M50.0916666666666640.9037030.10140.9195550.459778
M61.150.9016961.27540.2072610.103631
M70.3749999999999990.8999930.41670.6784580.339229
M80.933333333333330.8985981.03870.3032760.151638
M9-0.1750000000000010.897511-0.1950.8460870.423044
M10-0.4500000000000010.896734-0.50180.6176930.308847
M11-0.5583333333333350.896268-0.6230.5357570.267878
t-0.05833333333333340.016698-3.49340.000920.00046

\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) & 22.15 & 0.745714 & 29.7031 & 0 & 0 \tabularnewline
X & -1.05 & 0.711267 & -1.4762 & 0.145289 & 0.072644 \tabularnewline
M1 & -0.149999999999988 & 0.902221 & -0.1663 & 0.868533 & 0.434267 \tabularnewline
M2 & 1.40833333333333 & 0.900829 & 1.5634 & 0.123405 & 0.061702 \tabularnewline
M3 & 0.466666666666665 & 0.899745 & 0.5187 & 0.605968 & 0.302984 \tabularnewline
M4 & -0.141666666666668 & 0.89897 & -0.1576 & 0.875329 & 0.437665 \tabularnewline
M5 & 0.091666666666664 & 0.903703 & 0.1014 & 0.919555 & 0.459778 \tabularnewline
M6 & 1.15 & 0.901696 & 1.2754 & 0.207261 & 0.103631 \tabularnewline
M7 & 0.374999999999999 & 0.899993 & 0.4167 & 0.678458 & 0.339229 \tabularnewline
M8 & 0.93333333333333 & 0.898598 & 1.0387 & 0.303276 & 0.151638 \tabularnewline
M9 & -0.175000000000001 & 0.897511 & -0.195 & 0.846087 & 0.423044 \tabularnewline
M10 & -0.450000000000001 & 0.896734 & -0.5018 & 0.617693 & 0.308847 \tabularnewline
M11 & -0.558333333333335 & 0.896268 & -0.623 & 0.535757 & 0.267878 \tabularnewline
t & -0.0583333333333334 & 0.016698 & -3.4934 & 0.00092 & 0.00046 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58190&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]22.15[/C][C]0.745714[/C][C]29.7031[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-1.05[/C][C]0.711267[/C][C]-1.4762[/C][C]0.145289[/C][C]0.072644[/C][/ROW]
[ROW][C]M1[/C][C]-0.149999999999988[/C][C]0.902221[/C][C]-0.1663[/C][C]0.868533[/C][C]0.434267[/C][/ROW]
[ROW][C]M2[/C][C]1.40833333333333[/C][C]0.900829[/C][C]1.5634[/C][C]0.123405[/C][C]0.061702[/C][/ROW]
[ROW][C]M3[/C][C]0.466666666666665[/C][C]0.899745[/C][C]0.5187[/C][C]0.605968[/C][C]0.302984[/C][/ROW]
[ROW][C]M4[/C][C]-0.141666666666668[/C][C]0.89897[/C][C]-0.1576[/C][C]0.875329[/C][C]0.437665[/C][/ROW]
[ROW][C]M5[/C][C]0.091666666666664[/C][C]0.903703[/C][C]0.1014[/C][C]0.919555[/C][C]0.459778[/C][/ROW]
[ROW][C]M6[/C][C]1.15[/C][C]0.901696[/C][C]1.2754[/C][C]0.207261[/C][C]0.103631[/C][/ROW]
[ROW][C]M7[/C][C]0.374999999999999[/C][C]0.899993[/C][C]0.4167[/C][C]0.678458[/C][C]0.339229[/C][/ROW]
[ROW][C]M8[/C][C]0.93333333333333[/C][C]0.898598[/C][C]1.0387[/C][C]0.303276[/C][C]0.151638[/C][/ROW]
[ROW][C]M9[/C][C]-0.175000000000001[/C][C]0.897511[/C][C]-0.195[/C][C]0.846087[/C][C]0.423044[/C][/ROW]
[ROW][C]M10[/C][C]-0.450000000000001[/C][C]0.896734[/C][C]-0.5018[/C][C]0.617693[/C][C]0.308847[/C][/ROW]
[ROW][C]M11[/C][C]-0.558333333333335[/C][C]0.896268[/C][C]-0.623[/C][C]0.535757[/C][C]0.267878[/C][/ROW]
[ROW][C]t[/C][C]-0.0583333333333334[/C][C]0.016698[/C][C]-3.4934[/C][C]0.00092[/C][C]0.00046[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58190&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58190&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)22.150.74571429.703100
X-1.050.711267-1.47620.1452890.072644
M1-0.1499999999999880.902221-0.16630.8685330.434267
M21.408333333333330.9008291.56340.1234050.061702
M30.4666666666666650.8997450.51870.6059680.302984
M4-0.1416666666666680.89897-0.15760.8753290.437665
M50.0916666666666640.9037030.10140.9195550.459778
M61.150.9016961.27540.2072610.103631
M70.3749999999999990.8999930.41670.6784580.339229
M80.933333333333330.8985981.03870.3032760.151638
M9-0.1750000000000010.897511-0.1950.8460870.423044
M10-0.4500000000000010.896734-0.50180.6176930.308847
M11-0.5583333333333350.896268-0.6230.5357570.267878
t-0.05833333333333340.016698-3.49340.000920.00046







Multiple Linear Regression - Regression Statistics
Multiple R0.792007423315082
R-squared0.627275758586195
Adjusted R-squared0.543734118269308
F-TEST (value)7.50854012689762
F-TEST (DF numerator)13
F-TEST (DF denominator)58
p-value2.18761772030618e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.55211202048626
Sum Squared Residuals139.725

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.792007423315082 \tabularnewline
R-squared & 0.627275758586195 \tabularnewline
Adjusted R-squared & 0.543734118269308 \tabularnewline
F-TEST (value) & 7.50854012689762 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 2.18761772030618e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.55211202048626 \tabularnewline
Sum Squared Residuals & 139.725 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58190&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.792007423315082[/C][/ROW]
[ROW][C]R-squared[/C][C]0.627275758586195[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.543734118269308[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.50854012689762[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]2.18761772030618e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.55211202048626[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]139.725[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58190&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58190&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 R0.792007423315082
R-squared0.627275758586195
Adjusted R-squared0.543734118269308
F-TEST (value)7.50854012689762
F-TEST (DF numerator)13
F-TEST (DF denominator)58
p-value2.18761772030618e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.55211202048626
Sum Squared Residuals139.725







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12221.94166666666660.0583333333334018
22223.4416666666667-1.44166666666667
32022.4416666666667-2.44166666666667
42121.775-0.775000000000002
52021.95-1.95000000000000
62122.95-1.95
72122.1166666666667-1.11666666666667
82122.6166666666667-1.61666666666667
91921.45-2.45
102121.1166666666667-0.116666666666669
112120.950.0499999999999965
122221.450.549999999999997
131921.2416666666667-2.24166666666668
142422.74166666666671.25833333333333
152221.74166666666670.258333333333333
162221.0750.924999999999997
172221.250.75
182422.251.75000000000000
192221.41666666666670.583333333333331
202321.91666666666671.08333333333333
212420.753.25
222120.41666666666670.583333333333332
232020.25-0.250000000000001
242220.751.25000000000000
252320.54166666666672.45833333333332
262322.04166666666670.958333333333332
272221.04166666666670.958333333333334
282020.375-0.375000000000001
292119.51.5
302120.50.499999999999999
312019.66666666666670.333333333333332
322020.1666666666667-0.166666666666666
331719-2
341818.6666666666667-0.666666666666668
351918.50.499999999999999
361919-2.55611504185183e-15
372018.79166666666671.20833333333332
382120.29166666666670.708333333333333
392019.29166666666670.708333333333335
402118.6252.375
411918.80.200000000000001
422219.82.2
432018.96666666666671.03333333333333
441819.4666666666667-1.46666666666667
451618.3-2.3
461717.9666666666667-0.966666666666666
471817.80.2
481918.30.699999999999999
491818.0916666666667-0.0916666666666787
502019.59166666666670.408333333333334
512118.59166666666672.40833333333334
521817.9250.0750000000000002
531918.10.900000000000002
541919.1-0.0999999999999987
551918.26666666666670.733333333333334
562118.76666666666672.23333333333334
571917.61.40000000000000
581917.26666666666671.73333333333333
591717.1-0.0999999999999988
601617.6-1.6
611617.3916666666667-1.39166666666668
621718.8916666666667-1.89166666666666
631617.8916666666667-1.89166666666666
641517.225-2.2250
651617.4-1.40000000000000
661618.4-2.40000000000000
671617.5666666666667-1.56666666666666
681818.0666666666667-0.0666666666666627
691916.92.10000000000000
701616.5666666666667-0.566666666666664
711616.4-0.399999999999997
721616.9-0.899999999999998

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 22 & 21.9416666666666 & 0.0583333333334018 \tabularnewline
2 & 22 & 23.4416666666667 & -1.44166666666667 \tabularnewline
3 & 20 & 22.4416666666667 & -2.44166666666667 \tabularnewline
4 & 21 & 21.775 & -0.775000000000002 \tabularnewline
5 & 20 & 21.95 & -1.95000000000000 \tabularnewline
6 & 21 & 22.95 & -1.95 \tabularnewline
7 & 21 & 22.1166666666667 & -1.11666666666667 \tabularnewline
8 & 21 & 22.6166666666667 & -1.61666666666667 \tabularnewline
9 & 19 & 21.45 & -2.45 \tabularnewline
10 & 21 & 21.1166666666667 & -0.116666666666669 \tabularnewline
11 & 21 & 20.95 & 0.0499999999999965 \tabularnewline
12 & 22 & 21.45 & 0.549999999999997 \tabularnewline
13 & 19 & 21.2416666666667 & -2.24166666666668 \tabularnewline
14 & 24 & 22.7416666666667 & 1.25833333333333 \tabularnewline
15 & 22 & 21.7416666666667 & 0.258333333333333 \tabularnewline
16 & 22 & 21.075 & 0.924999999999997 \tabularnewline
17 & 22 & 21.25 & 0.75 \tabularnewline
18 & 24 & 22.25 & 1.75000000000000 \tabularnewline
19 & 22 & 21.4166666666667 & 0.583333333333331 \tabularnewline
20 & 23 & 21.9166666666667 & 1.08333333333333 \tabularnewline
21 & 24 & 20.75 & 3.25 \tabularnewline
22 & 21 & 20.4166666666667 & 0.583333333333332 \tabularnewline
23 & 20 & 20.25 & -0.250000000000001 \tabularnewline
24 & 22 & 20.75 & 1.25000000000000 \tabularnewline
25 & 23 & 20.5416666666667 & 2.45833333333332 \tabularnewline
26 & 23 & 22.0416666666667 & 0.958333333333332 \tabularnewline
27 & 22 & 21.0416666666667 & 0.958333333333334 \tabularnewline
28 & 20 & 20.375 & -0.375000000000001 \tabularnewline
29 & 21 & 19.5 & 1.5 \tabularnewline
30 & 21 & 20.5 & 0.499999999999999 \tabularnewline
31 & 20 & 19.6666666666667 & 0.333333333333332 \tabularnewline
32 & 20 & 20.1666666666667 & -0.166666666666666 \tabularnewline
33 & 17 & 19 & -2 \tabularnewline
34 & 18 & 18.6666666666667 & -0.666666666666668 \tabularnewline
35 & 19 & 18.5 & 0.499999999999999 \tabularnewline
36 & 19 & 19 & -2.55611504185183e-15 \tabularnewline
37 & 20 & 18.7916666666667 & 1.20833333333332 \tabularnewline
38 & 21 & 20.2916666666667 & 0.708333333333333 \tabularnewline
39 & 20 & 19.2916666666667 & 0.708333333333335 \tabularnewline
40 & 21 & 18.625 & 2.375 \tabularnewline
41 & 19 & 18.8 & 0.200000000000001 \tabularnewline
42 & 22 & 19.8 & 2.2 \tabularnewline
43 & 20 & 18.9666666666667 & 1.03333333333333 \tabularnewline
44 & 18 & 19.4666666666667 & -1.46666666666667 \tabularnewline
45 & 16 & 18.3 & -2.3 \tabularnewline
46 & 17 & 17.9666666666667 & -0.966666666666666 \tabularnewline
47 & 18 & 17.8 & 0.2 \tabularnewline
48 & 19 & 18.3 & 0.699999999999999 \tabularnewline
49 & 18 & 18.0916666666667 & -0.0916666666666787 \tabularnewline
50 & 20 & 19.5916666666667 & 0.408333333333334 \tabularnewline
51 & 21 & 18.5916666666667 & 2.40833333333334 \tabularnewline
52 & 18 & 17.925 & 0.0750000000000002 \tabularnewline
53 & 19 & 18.1 & 0.900000000000002 \tabularnewline
54 & 19 & 19.1 & -0.0999999999999987 \tabularnewline
55 & 19 & 18.2666666666667 & 0.733333333333334 \tabularnewline
56 & 21 & 18.7666666666667 & 2.23333333333334 \tabularnewline
57 & 19 & 17.6 & 1.40000000000000 \tabularnewline
58 & 19 & 17.2666666666667 & 1.73333333333333 \tabularnewline
59 & 17 & 17.1 & -0.0999999999999988 \tabularnewline
60 & 16 & 17.6 & -1.6 \tabularnewline
61 & 16 & 17.3916666666667 & -1.39166666666668 \tabularnewline
62 & 17 & 18.8916666666667 & -1.89166666666666 \tabularnewline
63 & 16 & 17.8916666666667 & -1.89166666666666 \tabularnewline
64 & 15 & 17.225 & -2.2250 \tabularnewline
65 & 16 & 17.4 & -1.40000000000000 \tabularnewline
66 & 16 & 18.4 & -2.40000000000000 \tabularnewline
67 & 16 & 17.5666666666667 & -1.56666666666666 \tabularnewline
68 & 18 & 18.0666666666667 & -0.0666666666666627 \tabularnewline
69 & 19 & 16.9 & 2.10000000000000 \tabularnewline
70 & 16 & 16.5666666666667 & -0.566666666666664 \tabularnewline
71 & 16 & 16.4 & -0.399999999999997 \tabularnewline
72 & 16 & 16.9 & -0.899999999999998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58190&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]22[/C][C]21.9416666666666[/C][C]0.0583333333334018[/C][/ROW]
[ROW][C]2[/C][C]22[/C][C]23.4416666666667[/C][C]-1.44166666666667[/C][/ROW]
[ROW][C]3[/C][C]20[/C][C]22.4416666666667[/C][C]-2.44166666666667[/C][/ROW]
[ROW][C]4[/C][C]21[/C][C]21.775[/C][C]-0.775000000000002[/C][/ROW]
[ROW][C]5[/C][C]20[/C][C]21.95[/C][C]-1.95000000000000[/C][/ROW]
[ROW][C]6[/C][C]21[/C][C]22.95[/C][C]-1.95[/C][/ROW]
[ROW][C]7[/C][C]21[/C][C]22.1166666666667[/C][C]-1.11666666666667[/C][/ROW]
[ROW][C]8[/C][C]21[/C][C]22.6166666666667[/C][C]-1.61666666666667[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]21.45[/C][C]-2.45[/C][/ROW]
[ROW][C]10[/C][C]21[/C][C]21.1166666666667[/C][C]-0.116666666666669[/C][/ROW]
[ROW][C]11[/C][C]21[/C][C]20.95[/C][C]0.0499999999999965[/C][/ROW]
[ROW][C]12[/C][C]22[/C][C]21.45[/C][C]0.549999999999997[/C][/ROW]
[ROW][C]13[/C][C]19[/C][C]21.2416666666667[/C][C]-2.24166666666668[/C][/ROW]
[ROW][C]14[/C][C]24[/C][C]22.7416666666667[/C][C]1.25833333333333[/C][/ROW]
[ROW][C]15[/C][C]22[/C][C]21.7416666666667[/C][C]0.258333333333333[/C][/ROW]
[ROW][C]16[/C][C]22[/C][C]21.075[/C][C]0.924999999999997[/C][/ROW]
[ROW][C]17[/C][C]22[/C][C]21.25[/C][C]0.75[/C][/ROW]
[ROW][C]18[/C][C]24[/C][C]22.25[/C][C]1.75000000000000[/C][/ROW]
[ROW][C]19[/C][C]22[/C][C]21.4166666666667[/C][C]0.583333333333331[/C][/ROW]
[ROW][C]20[/C][C]23[/C][C]21.9166666666667[/C][C]1.08333333333333[/C][/ROW]
[ROW][C]21[/C][C]24[/C][C]20.75[/C][C]3.25[/C][/ROW]
[ROW][C]22[/C][C]21[/C][C]20.4166666666667[/C][C]0.583333333333332[/C][/ROW]
[ROW][C]23[/C][C]20[/C][C]20.25[/C][C]-0.250000000000001[/C][/ROW]
[ROW][C]24[/C][C]22[/C][C]20.75[/C][C]1.25000000000000[/C][/ROW]
[ROW][C]25[/C][C]23[/C][C]20.5416666666667[/C][C]2.45833333333332[/C][/ROW]
[ROW][C]26[/C][C]23[/C][C]22.0416666666667[/C][C]0.958333333333332[/C][/ROW]
[ROW][C]27[/C][C]22[/C][C]21.0416666666667[/C][C]0.958333333333334[/C][/ROW]
[ROW][C]28[/C][C]20[/C][C]20.375[/C][C]-0.375000000000001[/C][/ROW]
[ROW][C]29[/C][C]21[/C][C]19.5[/C][C]1.5[/C][/ROW]
[ROW][C]30[/C][C]21[/C][C]20.5[/C][C]0.499999999999999[/C][/ROW]
[ROW][C]31[/C][C]20[/C][C]19.6666666666667[/C][C]0.333333333333332[/C][/ROW]
[ROW][C]32[/C][C]20[/C][C]20.1666666666667[/C][C]-0.166666666666666[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]19[/C][C]-2[/C][/ROW]
[ROW][C]34[/C][C]18[/C][C]18.6666666666667[/C][C]-0.666666666666668[/C][/ROW]
[ROW][C]35[/C][C]19[/C][C]18.5[/C][C]0.499999999999999[/C][/ROW]
[ROW][C]36[/C][C]19[/C][C]19[/C][C]-2.55611504185183e-15[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]18.7916666666667[/C][C]1.20833333333332[/C][/ROW]
[ROW][C]38[/C][C]21[/C][C]20.2916666666667[/C][C]0.708333333333333[/C][/ROW]
[ROW][C]39[/C][C]20[/C][C]19.2916666666667[/C][C]0.708333333333335[/C][/ROW]
[ROW][C]40[/C][C]21[/C][C]18.625[/C][C]2.375[/C][/ROW]
[ROW][C]41[/C][C]19[/C][C]18.8[/C][C]0.200000000000001[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]19.8[/C][C]2.2[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]18.9666666666667[/C][C]1.03333333333333[/C][/ROW]
[ROW][C]44[/C][C]18[/C][C]19.4666666666667[/C][C]-1.46666666666667[/C][/ROW]
[ROW][C]45[/C][C]16[/C][C]18.3[/C][C]-2.3[/C][/ROW]
[ROW][C]46[/C][C]17[/C][C]17.9666666666667[/C][C]-0.966666666666666[/C][/ROW]
[ROW][C]47[/C][C]18[/C][C]17.8[/C][C]0.2[/C][/ROW]
[ROW][C]48[/C][C]19[/C][C]18.3[/C][C]0.699999999999999[/C][/ROW]
[ROW][C]49[/C][C]18[/C][C]18.0916666666667[/C][C]-0.0916666666666787[/C][/ROW]
[ROW][C]50[/C][C]20[/C][C]19.5916666666667[/C][C]0.408333333333334[/C][/ROW]
[ROW][C]51[/C][C]21[/C][C]18.5916666666667[/C][C]2.40833333333334[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]17.925[/C][C]0.0750000000000002[/C][/ROW]
[ROW][C]53[/C][C]19[/C][C]18.1[/C][C]0.900000000000002[/C][/ROW]
[ROW][C]54[/C][C]19[/C][C]19.1[/C][C]-0.0999999999999987[/C][/ROW]
[ROW][C]55[/C][C]19[/C][C]18.2666666666667[/C][C]0.733333333333334[/C][/ROW]
[ROW][C]56[/C][C]21[/C][C]18.7666666666667[/C][C]2.23333333333334[/C][/ROW]
[ROW][C]57[/C][C]19[/C][C]17.6[/C][C]1.40000000000000[/C][/ROW]
[ROW][C]58[/C][C]19[/C][C]17.2666666666667[/C][C]1.73333333333333[/C][/ROW]
[ROW][C]59[/C][C]17[/C][C]17.1[/C][C]-0.0999999999999988[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]17.6[/C][C]-1.6[/C][/ROW]
[ROW][C]61[/C][C]16[/C][C]17.3916666666667[/C][C]-1.39166666666668[/C][/ROW]
[ROW][C]62[/C][C]17[/C][C]18.8916666666667[/C][C]-1.89166666666666[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]17.8916666666667[/C][C]-1.89166666666666[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]17.225[/C][C]-2.2250[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]17.4[/C][C]-1.40000000000000[/C][/ROW]
[ROW][C]66[/C][C]16[/C][C]18.4[/C][C]-2.40000000000000[/C][/ROW]
[ROW][C]67[/C][C]16[/C][C]17.5666666666667[/C][C]-1.56666666666666[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]18.0666666666667[/C][C]-0.0666666666666627[/C][/ROW]
[ROW][C]69[/C][C]19[/C][C]16.9[/C][C]2.10000000000000[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]16.5666666666667[/C][C]-0.566666666666664[/C][/ROW]
[ROW][C]71[/C][C]16[/C][C]16.4[/C][C]-0.399999999999997[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]16.9[/C][C]-0.899999999999998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58190&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58190&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
12221.94166666666660.0583333333334018
22223.4416666666667-1.44166666666667
32022.4416666666667-2.44166666666667
42121.775-0.775000000000002
52021.95-1.95000000000000
62122.95-1.95
72122.1166666666667-1.11666666666667
82122.6166666666667-1.61666666666667
91921.45-2.45
102121.1166666666667-0.116666666666669
112120.950.0499999999999965
122221.450.549999999999997
131921.2416666666667-2.24166666666668
142422.74166666666671.25833333333333
152221.74166666666670.258333333333333
162221.0750.924999999999997
172221.250.75
182422.251.75000000000000
192221.41666666666670.583333333333331
202321.91666666666671.08333333333333
212420.753.25
222120.41666666666670.583333333333332
232020.25-0.250000000000001
242220.751.25000000000000
252320.54166666666672.45833333333332
262322.04166666666670.958333333333332
272221.04166666666670.958333333333334
282020.375-0.375000000000001
292119.51.5
302120.50.499999999999999
312019.66666666666670.333333333333332
322020.1666666666667-0.166666666666666
331719-2
341818.6666666666667-0.666666666666668
351918.50.499999999999999
361919-2.55611504185183e-15
372018.79166666666671.20833333333332
382120.29166666666670.708333333333333
392019.29166666666670.708333333333335
402118.6252.375
411918.80.200000000000001
422219.82.2
432018.96666666666671.03333333333333
441819.4666666666667-1.46666666666667
451618.3-2.3
461717.9666666666667-0.966666666666666
471817.80.2
481918.30.699999999999999
491818.0916666666667-0.0916666666666787
502019.59166666666670.408333333333334
512118.59166666666672.40833333333334
521817.9250.0750000000000002
531918.10.900000000000002
541919.1-0.0999999999999987
551918.26666666666670.733333333333334
562118.76666666666672.23333333333334
571917.61.40000000000000
581917.26666666666671.73333333333333
591717.1-0.0999999999999988
601617.6-1.6
611617.3916666666667-1.39166666666668
621718.8916666666667-1.89166666666666
631617.8916666666667-1.89166666666666
641517.225-2.2250
651617.4-1.40000000000000
661618.4-2.40000000000000
671617.5666666666667-1.56666666666666
681818.0666666666667-0.0666666666666627
691916.92.10000000000000
701616.5666666666667-0.566666666666664
711616.4-0.399999999999997
721616.9-0.899999999999998







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.7879622663749680.4240754672500650.212037733625032
180.747192519687570.5056149606248590.252807480312429
190.6218528169924590.7562943660150820.378147183007541
200.5056557660324020.9886884679351950.494344233967598
210.6920851208812210.6158297582375570.307914879118779
220.6309699620329120.7380600759341760.369030037967088
230.6389809011566410.7220381976867180.361019098843359
240.5632758060820320.8734483878359350.436724193917968
250.4977814707639630.9955629415279250.502218529236037
260.4723678465837050.944735693167410.527632153416295
270.3869496070385440.7738992140770880.613050392961456
280.4480853734184540.8961707468369090.551914626581546
290.3647279790195660.7294559580391310.635272020980434
300.3001143452242980.6002286904485970.699885654775702
310.2324752486769970.4649504973539930.767524751323003
320.1864350079746260.3728700159492520.813564992025374
330.3205236004665730.6410472009331450.679476399533427
340.2937237125331040.5874474250662080.706276287466896
350.2310520343391390.4621040686782780.768947965660861
360.1838226995733050.367645399146610.816177300426695
370.1377757707962010.2755515415924030.862224229203799
380.09696070277045060.1939214055409010.90303929722955
390.06648899274851480.1329779854970300.933511007251485
400.07611473568811030.1522294713762210.92388526431189
410.05940448485177820.1188089697035560.940595515148222
420.06182112868423820.1236422573684760.938178871315762
430.04192000667903940.08384001335807880.95807999332096
440.09284947569604450.1856989513920890.907150524303955
450.5451372188407920.9097255623184160.454862781159208
460.8020195386855070.3959609226289860.197980461314493
470.824509087882260.3509818242354790.175490912117740
480.7618577692757340.4762844614485310.238142230724265
490.6968802103792760.6062395792414480.303119789620724
500.6094312920055240.7811374159889520.390568707994476
510.7466041272271320.5067917455457370.253395872772868
520.6798785999971440.6402428000057130.320121400002857
530.5943407484177040.8113185031645920.405659251582296
540.5313780179070290.9372439641859420.468621982092971
550.4571573157996150.9143146315992310.542842684200385

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.787962266374968 & 0.424075467250065 & 0.212037733625032 \tabularnewline
18 & 0.74719251968757 & 0.505614960624859 & 0.252807480312429 \tabularnewline
19 & 0.621852816992459 & 0.756294366015082 & 0.378147183007541 \tabularnewline
20 & 0.505655766032402 & 0.988688467935195 & 0.494344233967598 \tabularnewline
21 & 0.692085120881221 & 0.615829758237557 & 0.307914879118779 \tabularnewline
22 & 0.630969962032912 & 0.738060075934176 & 0.369030037967088 \tabularnewline
23 & 0.638980901156641 & 0.722038197686718 & 0.361019098843359 \tabularnewline
24 & 0.563275806082032 & 0.873448387835935 & 0.436724193917968 \tabularnewline
25 & 0.497781470763963 & 0.995562941527925 & 0.502218529236037 \tabularnewline
26 & 0.472367846583705 & 0.94473569316741 & 0.527632153416295 \tabularnewline
27 & 0.386949607038544 & 0.773899214077088 & 0.613050392961456 \tabularnewline
28 & 0.448085373418454 & 0.896170746836909 & 0.551914626581546 \tabularnewline
29 & 0.364727979019566 & 0.729455958039131 & 0.635272020980434 \tabularnewline
30 & 0.300114345224298 & 0.600228690448597 & 0.699885654775702 \tabularnewline
31 & 0.232475248676997 & 0.464950497353993 & 0.767524751323003 \tabularnewline
32 & 0.186435007974626 & 0.372870015949252 & 0.813564992025374 \tabularnewline
33 & 0.320523600466573 & 0.641047200933145 & 0.679476399533427 \tabularnewline
34 & 0.293723712533104 & 0.587447425066208 & 0.706276287466896 \tabularnewline
35 & 0.231052034339139 & 0.462104068678278 & 0.768947965660861 \tabularnewline
36 & 0.183822699573305 & 0.36764539914661 & 0.816177300426695 \tabularnewline
37 & 0.137775770796201 & 0.275551541592403 & 0.862224229203799 \tabularnewline
38 & 0.0969607027704506 & 0.193921405540901 & 0.90303929722955 \tabularnewline
39 & 0.0664889927485148 & 0.132977985497030 & 0.933511007251485 \tabularnewline
40 & 0.0761147356881103 & 0.152229471376221 & 0.92388526431189 \tabularnewline
41 & 0.0594044848517782 & 0.118808969703556 & 0.940595515148222 \tabularnewline
42 & 0.0618211286842382 & 0.123642257368476 & 0.938178871315762 \tabularnewline
43 & 0.0419200066790394 & 0.0838400133580788 & 0.95807999332096 \tabularnewline
44 & 0.0928494756960445 & 0.185698951392089 & 0.907150524303955 \tabularnewline
45 & 0.545137218840792 & 0.909725562318416 & 0.454862781159208 \tabularnewline
46 & 0.802019538685507 & 0.395960922628986 & 0.197980461314493 \tabularnewline
47 & 0.82450908788226 & 0.350981824235479 & 0.175490912117740 \tabularnewline
48 & 0.761857769275734 & 0.476284461448531 & 0.238142230724265 \tabularnewline
49 & 0.696880210379276 & 0.606239579241448 & 0.303119789620724 \tabularnewline
50 & 0.609431292005524 & 0.781137415988952 & 0.390568707994476 \tabularnewline
51 & 0.746604127227132 & 0.506791745545737 & 0.253395872772868 \tabularnewline
52 & 0.679878599997144 & 0.640242800005713 & 0.320121400002857 \tabularnewline
53 & 0.594340748417704 & 0.811318503164592 & 0.405659251582296 \tabularnewline
54 & 0.531378017907029 & 0.937243964185942 & 0.468621982092971 \tabularnewline
55 & 0.457157315799615 & 0.914314631599231 & 0.542842684200385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58190&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]17[/C][C]0.787962266374968[/C][C]0.424075467250065[/C][C]0.212037733625032[/C][/ROW]
[ROW][C]18[/C][C]0.74719251968757[/C][C]0.505614960624859[/C][C]0.252807480312429[/C][/ROW]
[ROW][C]19[/C][C]0.621852816992459[/C][C]0.756294366015082[/C][C]0.378147183007541[/C][/ROW]
[ROW][C]20[/C][C]0.505655766032402[/C][C]0.988688467935195[/C][C]0.494344233967598[/C][/ROW]
[ROW][C]21[/C][C]0.692085120881221[/C][C]0.615829758237557[/C][C]0.307914879118779[/C][/ROW]
[ROW][C]22[/C][C]0.630969962032912[/C][C]0.738060075934176[/C][C]0.369030037967088[/C][/ROW]
[ROW][C]23[/C][C]0.638980901156641[/C][C]0.722038197686718[/C][C]0.361019098843359[/C][/ROW]
[ROW][C]24[/C][C]0.563275806082032[/C][C]0.873448387835935[/C][C]0.436724193917968[/C][/ROW]
[ROW][C]25[/C][C]0.497781470763963[/C][C]0.995562941527925[/C][C]0.502218529236037[/C][/ROW]
[ROW][C]26[/C][C]0.472367846583705[/C][C]0.94473569316741[/C][C]0.527632153416295[/C][/ROW]
[ROW][C]27[/C][C]0.386949607038544[/C][C]0.773899214077088[/C][C]0.613050392961456[/C][/ROW]
[ROW][C]28[/C][C]0.448085373418454[/C][C]0.896170746836909[/C][C]0.551914626581546[/C][/ROW]
[ROW][C]29[/C][C]0.364727979019566[/C][C]0.729455958039131[/C][C]0.635272020980434[/C][/ROW]
[ROW][C]30[/C][C]0.300114345224298[/C][C]0.600228690448597[/C][C]0.699885654775702[/C][/ROW]
[ROW][C]31[/C][C]0.232475248676997[/C][C]0.464950497353993[/C][C]0.767524751323003[/C][/ROW]
[ROW][C]32[/C][C]0.186435007974626[/C][C]0.372870015949252[/C][C]0.813564992025374[/C][/ROW]
[ROW][C]33[/C][C]0.320523600466573[/C][C]0.641047200933145[/C][C]0.679476399533427[/C][/ROW]
[ROW][C]34[/C][C]0.293723712533104[/C][C]0.587447425066208[/C][C]0.706276287466896[/C][/ROW]
[ROW][C]35[/C][C]0.231052034339139[/C][C]0.462104068678278[/C][C]0.768947965660861[/C][/ROW]
[ROW][C]36[/C][C]0.183822699573305[/C][C]0.36764539914661[/C][C]0.816177300426695[/C][/ROW]
[ROW][C]37[/C][C]0.137775770796201[/C][C]0.275551541592403[/C][C]0.862224229203799[/C][/ROW]
[ROW][C]38[/C][C]0.0969607027704506[/C][C]0.193921405540901[/C][C]0.90303929722955[/C][/ROW]
[ROW][C]39[/C][C]0.0664889927485148[/C][C]0.132977985497030[/C][C]0.933511007251485[/C][/ROW]
[ROW][C]40[/C][C]0.0761147356881103[/C][C]0.152229471376221[/C][C]0.92388526431189[/C][/ROW]
[ROW][C]41[/C][C]0.0594044848517782[/C][C]0.118808969703556[/C][C]0.940595515148222[/C][/ROW]
[ROW][C]42[/C][C]0.0618211286842382[/C][C]0.123642257368476[/C][C]0.938178871315762[/C][/ROW]
[ROW][C]43[/C][C]0.0419200066790394[/C][C]0.0838400133580788[/C][C]0.95807999332096[/C][/ROW]
[ROW][C]44[/C][C]0.0928494756960445[/C][C]0.185698951392089[/C][C]0.907150524303955[/C][/ROW]
[ROW][C]45[/C][C]0.545137218840792[/C][C]0.909725562318416[/C][C]0.454862781159208[/C][/ROW]
[ROW][C]46[/C][C]0.802019538685507[/C][C]0.395960922628986[/C][C]0.197980461314493[/C][/ROW]
[ROW][C]47[/C][C]0.82450908788226[/C][C]0.350981824235479[/C][C]0.175490912117740[/C][/ROW]
[ROW][C]48[/C][C]0.761857769275734[/C][C]0.476284461448531[/C][C]0.238142230724265[/C][/ROW]
[ROW][C]49[/C][C]0.696880210379276[/C][C]0.606239579241448[/C][C]0.303119789620724[/C][/ROW]
[ROW][C]50[/C][C]0.609431292005524[/C][C]0.781137415988952[/C][C]0.390568707994476[/C][/ROW]
[ROW][C]51[/C][C]0.746604127227132[/C][C]0.506791745545737[/C][C]0.253395872772868[/C][/ROW]
[ROW][C]52[/C][C]0.679878599997144[/C][C]0.640242800005713[/C][C]0.320121400002857[/C][/ROW]
[ROW][C]53[/C][C]0.594340748417704[/C][C]0.811318503164592[/C][C]0.405659251582296[/C][/ROW]
[ROW][C]54[/C][C]0.531378017907029[/C][C]0.937243964185942[/C][C]0.468621982092971[/C][/ROW]
[ROW][C]55[/C][C]0.457157315799615[/C][C]0.914314631599231[/C][C]0.542842684200385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58190&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58190&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
170.7879622663749680.4240754672500650.212037733625032
180.747192519687570.5056149606248590.252807480312429
190.6218528169924590.7562943660150820.378147183007541
200.5056557660324020.9886884679351950.494344233967598
210.6920851208812210.6158297582375570.307914879118779
220.6309699620329120.7380600759341760.369030037967088
230.6389809011566410.7220381976867180.361019098843359
240.5632758060820320.8734483878359350.436724193917968
250.4977814707639630.9955629415279250.502218529236037
260.4723678465837050.944735693167410.527632153416295
270.3869496070385440.7738992140770880.613050392961456
280.4480853734184540.8961707468369090.551914626581546
290.3647279790195660.7294559580391310.635272020980434
300.3001143452242980.6002286904485970.699885654775702
310.2324752486769970.4649504973539930.767524751323003
320.1864350079746260.3728700159492520.813564992025374
330.3205236004665730.6410472009331450.679476399533427
340.2937237125331040.5874474250662080.706276287466896
350.2310520343391390.4621040686782780.768947965660861
360.1838226995733050.367645399146610.816177300426695
370.1377757707962010.2755515415924030.862224229203799
380.09696070277045060.1939214055409010.90303929722955
390.06648899274851480.1329779854970300.933511007251485
400.07611473568811030.1522294713762210.92388526431189
410.05940448485177820.1188089697035560.940595515148222
420.06182112868423820.1236422573684760.938178871315762
430.04192000667903940.08384001335807880.95807999332096
440.09284947569604450.1856989513920890.907150524303955
450.5451372188407920.9097255623184160.454862781159208
460.8020195386855070.3959609226289860.197980461314493
470.824509087882260.3509818242354790.175490912117740
480.7618577692757340.4762844614485310.238142230724265
490.6968802103792760.6062395792414480.303119789620724
500.6094312920055240.7811374159889520.390568707994476
510.7466041272271320.5067917455457370.253395872772868
520.6798785999971440.6402428000057130.320121400002857
530.5943407484177040.8113185031645920.405659251582296
540.5313780179070290.9372439641859420.468621982092971
550.4571573157996150.9143146315992310.542842684200385







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 level10.0256410256410256OK

\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 & 0.0256410256410256 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58190&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]0.0256410256410256[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58190&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58190&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level10.0256410256410256OK



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