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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 07:49:20 -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/t1258728715gzw6nyc1xr6hawk.htm/, Retrieved Thu, 28 Mar 2024 19:04:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58231, Retrieved Thu, 28 Mar 2024 19:04:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact131
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 14:49:20] [54f12ba6dfaf5b88c7c2745223d9c32f] [Current]
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Dataseries X:
20366	0
22782	0
19169	0
13807	0
29743	0
25591	0
29096	0
26482	0
22405	0
27044	0
17970	0
18730	0
19684	0
19785	0
18479	0
10698	0
31956	0
29506	0
34506	0
27165	0
26736	0
23691	0
18157	0
17328	0
18205	0
20995	0
17382	0
9367	0
31124	0
26551	0
30651	0
25859	0
25100	0
25778	0
20418	0
18688	0
20424	0
24776	0
19814	0
12738	0
31566	0
30111	0
30019	0
31934	1
25826	1
26835	1
20205	1
17789	1
20520	1
22518	1
15572	1
11509	1
25447	1
24090	1
27786	1
26195	1
20516	1
22759	1
19028	1
16971	1
20036	1




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=58231&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=58231&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58231&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] = + 23033.5348837209 -1059.31266149871X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  23033.5348837209 -1059.31266149871X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58231&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  23033.5348837209 -1059.31266149871X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58231&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58231&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] = + 23033.5348837209 -1059.31266149871X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)23033.5348837209876.36244626.283100
X-1059.312661498711613.289934-0.65660.5139810.25699

\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) & 23033.5348837209 & 876.362446 & 26.2831 & 0 & 0 \tabularnewline
X & -1059.31266149871 & 1613.289934 & -0.6566 & 0.513981 & 0.25699 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58231&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]23033.5348837209[/C][C]876.362446[/C][C]26.2831[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-1059.31266149871[/C][C]1613.289934[/C][C]-0.6566[/C][C]0.513981[/C][C]0.25699[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58231&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58231&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)23033.5348837209876.36244626.283100
X-1059.312661498711613.289934-0.65660.5139810.25699







Multiple Linear Regression - Regression Statistics
Multiple R0.0851735384717893
R-squared0.00725453165580537
Adjusted R-squared-0.00957166272290966
F-TEST (value)0.431145123640211
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0.513980607497474
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5746.69286738149
Sum Squared Residuals1948444255.80879

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0851735384717893 \tabularnewline
R-squared & 0.00725453165580537 \tabularnewline
Adjusted R-squared & -0.00957166272290966 \tabularnewline
F-TEST (value) & 0.431145123640211 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.513980607497474 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5746.69286738149 \tabularnewline
Sum Squared Residuals & 1948444255.80879 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58231&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0851735384717893[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00725453165580537[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00957166272290966[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.431145123640211[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]0.513980607497474[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5746.69286738149[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1948444255.80879[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58231&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58231&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 R0.0851735384717893
R-squared0.00725453165580537
Adjusted R-squared-0.00957166272290966
F-TEST (value)0.431145123640211
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0.513980607497474
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5746.69286738149
Sum Squared Residuals1948444255.80879







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12036623033.5348837209-2667.53488372095
22278223033.5348837209-251.534883720931
31916923033.5348837209-3864.53488372093
41380723033.5348837209-9226.53488372093
52974323033.53488372096709.46511627907
62559123033.53488372092557.46511627907
72909623033.53488372096062.46511627907
82648223033.53488372093448.46511627907
92240523033.5348837209-628.53488372093
102704423033.53488372094010.46511627907
111797023033.5348837209-5063.53488372093
121873023033.5348837209-4303.53488372093
131968423033.5348837209-3349.53488372093
141978523033.5348837209-3248.53488372093
151847923033.5348837209-4554.53488372093
161069823033.5348837209-12335.5348837209
173195623033.53488372098922.46511627907
182950623033.53488372096472.46511627907
193450623033.534883720911472.4651162791
202716523033.53488372094131.46511627907
212673623033.53488372093702.46511627907
222369123033.5348837209657.46511627907
231815723033.5348837209-4876.53488372093
241732823033.5348837209-5705.53488372093
251820523033.5348837209-4828.53488372093
262099523033.5348837209-2038.53488372093
271738223033.5348837209-5651.53488372093
28936723033.5348837209-13666.5348837209
293112423033.53488372098090.46511627907
302655123033.53488372093517.46511627907
313065123033.53488372097617.46511627907
322585923033.53488372092825.46511627907
332510023033.53488372092066.46511627907
342577823033.53488372092744.46511627907
352041823033.5348837209-2615.53488372093
361868823033.5348837209-4345.53488372093
372042423033.5348837209-2609.53488372093
382477623033.53488372091742.46511627907
391981423033.5348837209-3219.53488372093
401273823033.5348837209-10295.5348837209
413156623033.53488372098532.46511627907
423011123033.53488372097077.46511627907
433001923033.53488372096985.46511627907
443193421974.22222222229959.77777777778
452582621974.22222222223851.77777777778
462683521974.22222222224860.77777777778
472020521974.2222222222-1769.22222222222
481778921974.2222222222-4185.22222222222
492052021974.2222222222-1454.22222222222
502251821974.2222222222543.777777777777
511557221974.2222222222-6402.22222222222
521150921974.2222222222-10465.2222222222
532544721974.22222222223472.77777777778
542409021974.22222222222115.77777777778
552778621974.22222222225811.77777777778
562619521974.22222222224220.77777777778
572051621974.2222222222-1458.22222222222
582275921974.2222222222784.777777777778
591902821974.2222222222-2946.22222222222
601697121974.2222222222-5003.22222222222
612003621974.2222222222-1938.22222222222

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 20366 & 23033.5348837209 & -2667.53488372095 \tabularnewline
2 & 22782 & 23033.5348837209 & -251.534883720931 \tabularnewline
3 & 19169 & 23033.5348837209 & -3864.53488372093 \tabularnewline
4 & 13807 & 23033.5348837209 & -9226.53488372093 \tabularnewline
5 & 29743 & 23033.5348837209 & 6709.46511627907 \tabularnewline
6 & 25591 & 23033.5348837209 & 2557.46511627907 \tabularnewline
7 & 29096 & 23033.5348837209 & 6062.46511627907 \tabularnewline
8 & 26482 & 23033.5348837209 & 3448.46511627907 \tabularnewline
9 & 22405 & 23033.5348837209 & -628.53488372093 \tabularnewline
10 & 27044 & 23033.5348837209 & 4010.46511627907 \tabularnewline
11 & 17970 & 23033.5348837209 & -5063.53488372093 \tabularnewline
12 & 18730 & 23033.5348837209 & -4303.53488372093 \tabularnewline
13 & 19684 & 23033.5348837209 & -3349.53488372093 \tabularnewline
14 & 19785 & 23033.5348837209 & -3248.53488372093 \tabularnewline
15 & 18479 & 23033.5348837209 & -4554.53488372093 \tabularnewline
16 & 10698 & 23033.5348837209 & -12335.5348837209 \tabularnewline
17 & 31956 & 23033.5348837209 & 8922.46511627907 \tabularnewline
18 & 29506 & 23033.5348837209 & 6472.46511627907 \tabularnewline
19 & 34506 & 23033.5348837209 & 11472.4651162791 \tabularnewline
20 & 27165 & 23033.5348837209 & 4131.46511627907 \tabularnewline
21 & 26736 & 23033.5348837209 & 3702.46511627907 \tabularnewline
22 & 23691 & 23033.5348837209 & 657.46511627907 \tabularnewline
23 & 18157 & 23033.5348837209 & -4876.53488372093 \tabularnewline
24 & 17328 & 23033.5348837209 & -5705.53488372093 \tabularnewline
25 & 18205 & 23033.5348837209 & -4828.53488372093 \tabularnewline
26 & 20995 & 23033.5348837209 & -2038.53488372093 \tabularnewline
27 & 17382 & 23033.5348837209 & -5651.53488372093 \tabularnewline
28 & 9367 & 23033.5348837209 & -13666.5348837209 \tabularnewline
29 & 31124 & 23033.5348837209 & 8090.46511627907 \tabularnewline
30 & 26551 & 23033.5348837209 & 3517.46511627907 \tabularnewline
31 & 30651 & 23033.5348837209 & 7617.46511627907 \tabularnewline
32 & 25859 & 23033.5348837209 & 2825.46511627907 \tabularnewline
33 & 25100 & 23033.5348837209 & 2066.46511627907 \tabularnewline
34 & 25778 & 23033.5348837209 & 2744.46511627907 \tabularnewline
35 & 20418 & 23033.5348837209 & -2615.53488372093 \tabularnewline
36 & 18688 & 23033.5348837209 & -4345.53488372093 \tabularnewline
37 & 20424 & 23033.5348837209 & -2609.53488372093 \tabularnewline
38 & 24776 & 23033.5348837209 & 1742.46511627907 \tabularnewline
39 & 19814 & 23033.5348837209 & -3219.53488372093 \tabularnewline
40 & 12738 & 23033.5348837209 & -10295.5348837209 \tabularnewline
41 & 31566 & 23033.5348837209 & 8532.46511627907 \tabularnewline
42 & 30111 & 23033.5348837209 & 7077.46511627907 \tabularnewline
43 & 30019 & 23033.5348837209 & 6985.46511627907 \tabularnewline
44 & 31934 & 21974.2222222222 & 9959.77777777778 \tabularnewline
45 & 25826 & 21974.2222222222 & 3851.77777777778 \tabularnewline
46 & 26835 & 21974.2222222222 & 4860.77777777778 \tabularnewline
47 & 20205 & 21974.2222222222 & -1769.22222222222 \tabularnewline
48 & 17789 & 21974.2222222222 & -4185.22222222222 \tabularnewline
49 & 20520 & 21974.2222222222 & -1454.22222222222 \tabularnewline
50 & 22518 & 21974.2222222222 & 543.777777777777 \tabularnewline
51 & 15572 & 21974.2222222222 & -6402.22222222222 \tabularnewline
52 & 11509 & 21974.2222222222 & -10465.2222222222 \tabularnewline
53 & 25447 & 21974.2222222222 & 3472.77777777778 \tabularnewline
54 & 24090 & 21974.2222222222 & 2115.77777777778 \tabularnewline
55 & 27786 & 21974.2222222222 & 5811.77777777778 \tabularnewline
56 & 26195 & 21974.2222222222 & 4220.77777777778 \tabularnewline
57 & 20516 & 21974.2222222222 & -1458.22222222222 \tabularnewline
58 & 22759 & 21974.2222222222 & 784.777777777778 \tabularnewline
59 & 19028 & 21974.2222222222 & -2946.22222222222 \tabularnewline
60 & 16971 & 21974.2222222222 & -5003.22222222222 \tabularnewline
61 & 20036 & 21974.2222222222 & -1938.22222222222 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58231&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]20366[/C][C]23033.5348837209[/C][C]-2667.53488372095[/C][/ROW]
[ROW][C]2[/C][C]22782[/C][C]23033.5348837209[/C][C]-251.534883720931[/C][/ROW]
[ROW][C]3[/C][C]19169[/C][C]23033.5348837209[/C][C]-3864.53488372093[/C][/ROW]
[ROW][C]4[/C][C]13807[/C][C]23033.5348837209[/C][C]-9226.53488372093[/C][/ROW]
[ROW][C]5[/C][C]29743[/C][C]23033.5348837209[/C][C]6709.46511627907[/C][/ROW]
[ROW][C]6[/C][C]25591[/C][C]23033.5348837209[/C][C]2557.46511627907[/C][/ROW]
[ROW][C]7[/C][C]29096[/C][C]23033.5348837209[/C][C]6062.46511627907[/C][/ROW]
[ROW][C]8[/C][C]26482[/C][C]23033.5348837209[/C][C]3448.46511627907[/C][/ROW]
[ROW][C]9[/C][C]22405[/C][C]23033.5348837209[/C][C]-628.53488372093[/C][/ROW]
[ROW][C]10[/C][C]27044[/C][C]23033.5348837209[/C][C]4010.46511627907[/C][/ROW]
[ROW][C]11[/C][C]17970[/C][C]23033.5348837209[/C][C]-5063.53488372093[/C][/ROW]
[ROW][C]12[/C][C]18730[/C][C]23033.5348837209[/C][C]-4303.53488372093[/C][/ROW]
[ROW][C]13[/C][C]19684[/C][C]23033.5348837209[/C][C]-3349.53488372093[/C][/ROW]
[ROW][C]14[/C][C]19785[/C][C]23033.5348837209[/C][C]-3248.53488372093[/C][/ROW]
[ROW][C]15[/C][C]18479[/C][C]23033.5348837209[/C][C]-4554.53488372093[/C][/ROW]
[ROW][C]16[/C][C]10698[/C][C]23033.5348837209[/C][C]-12335.5348837209[/C][/ROW]
[ROW][C]17[/C][C]31956[/C][C]23033.5348837209[/C][C]8922.46511627907[/C][/ROW]
[ROW][C]18[/C][C]29506[/C][C]23033.5348837209[/C][C]6472.46511627907[/C][/ROW]
[ROW][C]19[/C][C]34506[/C][C]23033.5348837209[/C][C]11472.4651162791[/C][/ROW]
[ROW][C]20[/C][C]27165[/C][C]23033.5348837209[/C][C]4131.46511627907[/C][/ROW]
[ROW][C]21[/C][C]26736[/C][C]23033.5348837209[/C][C]3702.46511627907[/C][/ROW]
[ROW][C]22[/C][C]23691[/C][C]23033.5348837209[/C][C]657.46511627907[/C][/ROW]
[ROW][C]23[/C][C]18157[/C][C]23033.5348837209[/C][C]-4876.53488372093[/C][/ROW]
[ROW][C]24[/C][C]17328[/C][C]23033.5348837209[/C][C]-5705.53488372093[/C][/ROW]
[ROW][C]25[/C][C]18205[/C][C]23033.5348837209[/C][C]-4828.53488372093[/C][/ROW]
[ROW][C]26[/C][C]20995[/C][C]23033.5348837209[/C][C]-2038.53488372093[/C][/ROW]
[ROW][C]27[/C][C]17382[/C][C]23033.5348837209[/C][C]-5651.53488372093[/C][/ROW]
[ROW][C]28[/C][C]9367[/C][C]23033.5348837209[/C][C]-13666.5348837209[/C][/ROW]
[ROW][C]29[/C][C]31124[/C][C]23033.5348837209[/C][C]8090.46511627907[/C][/ROW]
[ROW][C]30[/C][C]26551[/C][C]23033.5348837209[/C][C]3517.46511627907[/C][/ROW]
[ROW][C]31[/C][C]30651[/C][C]23033.5348837209[/C][C]7617.46511627907[/C][/ROW]
[ROW][C]32[/C][C]25859[/C][C]23033.5348837209[/C][C]2825.46511627907[/C][/ROW]
[ROW][C]33[/C][C]25100[/C][C]23033.5348837209[/C][C]2066.46511627907[/C][/ROW]
[ROW][C]34[/C][C]25778[/C][C]23033.5348837209[/C][C]2744.46511627907[/C][/ROW]
[ROW][C]35[/C][C]20418[/C][C]23033.5348837209[/C][C]-2615.53488372093[/C][/ROW]
[ROW][C]36[/C][C]18688[/C][C]23033.5348837209[/C][C]-4345.53488372093[/C][/ROW]
[ROW][C]37[/C][C]20424[/C][C]23033.5348837209[/C][C]-2609.53488372093[/C][/ROW]
[ROW][C]38[/C][C]24776[/C][C]23033.5348837209[/C][C]1742.46511627907[/C][/ROW]
[ROW][C]39[/C][C]19814[/C][C]23033.5348837209[/C][C]-3219.53488372093[/C][/ROW]
[ROW][C]40[/C][C]12738[/C][C]23033.5348837209[/C][C]-10295.5348837209[/C][/ROW]
[ROW][C]41[/C][C]31566[/C][C]23033.5348837209[/C][C]8532.46511627907[/C][/ROW]
[ROW][C]42[/C][C]30111[/C][C]23033.5348837209[/C][C]7077.46511627907[/C][/ROW]
[ROW][C]43[/C][C]30019[/C][C]23033.5348837209[/C][C]6985.46511627907[/C][/ROW]
[ROW][C]44[/C][C]31934[/C][C]21974.2222222222[/C][C]9959.77777777778[/C][/ROW]
[ROW][C]45[/C][C]25826[/C][C]21974.2222222222[/C][C]3851.77777777778[/C][/ROW]
[ROW][C]46[/C][C]26835[/C][C]21974.2222222222[/C][C]4860.77777777778[/C][/ROW]
[ROW][C]47[/C][C]20205[/C][C]21974.2222222222[/C][C]-1769.22222222222[/C][/ROW]
[ROW][C]48[/C][C]17789[/C][C]21974.2222222222[/C][C]-4185.22222222222[/C][/ROW]
[ROW][C]49[/C][C]20520[/C][C]21974.2222222222[/C][C]-1454.22222222222[/C][/ROW]
[ROW][C]50[/C][C]22518[/C][C]21974.2222222222[/C][C]543.777777777777[/C][/ROW]
[ROW][C]51[/C][C]15572[/C][C]21974.2222222222[/C][C]-6402.22222222222[/C][/ROW]
[ROW][C]52[/C][C]11509[/C][C]21974.2222222222[/C][C]-10465.2222222222[/C][/ROW]
[ROW][C]53[/C][C]25447[/C][C]21974.2222222222[/C][C]3472.77777777778[/C][/ROW]
[ROW][C]54[/C][C]24090[/C][C]21974.2222222222[/C][C]2115.77777777778[/C][/ROW]
[ROW][C]55[/C][C]27786[/C][C]21974.2222222222[/C][C]5811.77777777778[/C][/ROW]
[ROW][C]56[/C][C]26195[/C][C]21974.2222222222[/C][C]4220.77777777778[/C][/ROW]
[ROW][C]57[/C][C]20516[/C][C]21974.2222222222[/C][C]-1458.22222222222[/C][/ROW]
[ROW][C]58[/C][C]22759[/C][C]21974.2222222222[/C][C]784.777777777778[/C][/ROW]
[ROW][C]59[/C][C]19028[/C][C]21974.2222222222[/C][C]-2946.22222222222[/C][/ROW]
[ROW][C]60[/C][C]16971[/C][C]21974.2222222222[/C][C]-5003.22222222222[/C][/ROW]
[ROW][C]61[/C][C]20036[/C][C]21974.2222222222[/C][C]-1938.22222222222[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58231&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58231&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
12036623033.5348837209-2667.53488372095
22278223033.5348837209-251.534883720931
31916923033.5348837209-3864.53488372093
41380723033.5348837209-9226.53488372093
52974323033.53488372096709.46511627907
62559123033.53488372092557.46511627907
72909623033.53488372096062.46511627907
82648223033.53488372093448.46511627907
92240523033.5348837209-628.53488372093
102704423033.53488372094010.46511627907
111797023033.5348837209-5063.53488372093
121873023033.5348837209-4303.53488372093
131968423033.5348837209-3349.53488372093
141978523033.5348837209-3248.53488372093
151847923033.5348837209-4554.53488372093
161069823033.5348837209-12335.5348837209
173195623033.53488372098922.46511627907
182950623033.53488372096472.46511627907
193450623033.534883720911472.4651162791
202716523033.53488372094131.46511627907
212673623033.53488372093702.46511627907
222369123033.5348837209657.46511627907
231815723033.5348837209-4876.53488372093
241732823033.5348837209-5705.53488372093
251820523033.5348837209-4828.53488372093
262099523033.5348837209-2038.53488372093
271738223033.5348837209-5651.53488372093
28936723033.5348837209-13666.5348837209
293112423033.53488372098090.46511627907
302655123033.53488372093517.46511627907
313065123033.53488372097617.46511627907
322585923033.53488372092825.46511627907
332510023033.53488372092066.46511627907
342577823033.53488372092744.46511627907
352041823033.5348837209-2615.53488372093
361868823033.5348837209-4345.53488372093
372042423033.5348837209-2609.53488372093
382477623033.53488372091742.46511627907
391981423033.5348837209-3219.53488372093
401273823033.5348837209-10295.5348837209
413156623033.53488372098532.46511627907
423011123033.53488372097077.46511627907
433001923033.53488372096985.46511627907
443193421974.22222222229959.77777777778
452582621974.22222222223851.77777777778
462683521974.22222222224860.77777777778
472020521974.2222222222-1769.22222222222
481778921974.2222222222-4185.22222222222
492052021974.2222222222-1454.22222222222
502251821974.2222222222543.777777777777
511557221974.2222222222-6402.22222222222
521150921974.2222222222-10465.2222222222
532544721974.22222222223472.77777777778
542409021974.22222222222115.77777777778
552778621974.22222222225811.77777777778
562619521974.22222222224220.77777777778
572051621974.2222222222-1458.22222222222
582275921974.2222222222784.777777777778
591902821974.2222222222-2946.22222222222
601697121974.2222222222-5003.22222222222
612003621974.2222222222-1938.22222222222







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.7333862665898990.5332274668202020.266613733410101
60.6432218297480060.7135563405039890.356778170251994
70.6585950800854260.6828098398291480.341404919914574
80.5696105889275380.8607788221449250.430389411072462
90.4487656328711160.8975312657422330.551234367128884
100.379831396097110.759662792194220.62016860390289
110.3672502090878890.7345004181757780.632749790912111
120.3233369106486480.6466738212972960.676663089351352
130.2609772326080450.521954465216090.739022767391955
140.2041892843092250.408378568618450.795810715690775
150.1725787975981900.3451575951963790.82742120240181
160.4157099163937830.8314198327875660.584290083606217
170.5739002491934620.8521995016130760.426099750806538
180.6038801774488030.7922396451023950.396119822551198
190.7955324849918360.4089350300163270.204467515008164
200.7628774109338720.4742451781322560.237122589066128
210.7216776737168250.556644652566350.278322326283175
220.6522454826230070.6955090347539860.347754517376993
230.6290858383590260.7418283232819490.370914161640974
240.6231818759919510.7536362480160970.376818124008049
250.5979434879742710.8041130240514580.402056512025729
260.531508271306850.93698345738630.46849172869315
270.527434139221150.94513172155770.47256586077885
280.8347845747658430.3304308504683150.165215425234157
290.8673359284380790.2653281431238420.132664071561921
300.8363261310829820.3273477378340350.163673868917017
310.8605793438464540.2788413123070930.139420656153546
320.8234853217437230.3530293565125540.176514678256277
330.7753333774195320.4493332451609360.224666622580468
340.7266281066894740.5467437866210520.273371893310526
350.6747947506928820.6504104986142360.325205249307118
360.652033379560480.695933240879040.34796662043952
370.6055303445517120.7889393108965760.394469655448288
380.5314785570622130.9370428858755740.468521442937787
390.506663349316870.986673301366260.49333665068313
400.8617790242556410.2764419514887180.138220975744359
410.8467160383343380.3065679233313250.153283961665662
420.8143856289566540.3712287420866920.185614371043346
430.7741869787900060.4516260424199880.225813021209994
440.876525833818030.246948332363940.12347416618197
450.8634936559047220.2730126881905570.136506344095278
460.8683134353085050.2633731293829890.131686564691495
470.8253820746615880.3492358506768250.174617925338412
480.7947928226656530.4104143546686950.205207177334347
490.7191419129406750.561716174118650.280858087059325
500.6299553330454920.7400893339090170.370044666954508
510.6370607839291470.7258784321417060.362939216070853
520.8915347339459170.2169305321081650.108465266054083
530.8482817404808540.3034365190382930.151718259519146
540.7644184700924180.4711630598151650.235581529907582
550.8356669121682450.328666175663510.164333087831755
560.9164213707358260.1671572585283490.0835786292641744

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.733386266589899 & 0.533227466820202 & 0.266613733410101 \tabularnewline
6 & 0.643221829748006 & 0.713556340503989 & 0.356778170251994 \tabularnewline
7 & 0.658595080085426 & 0.682809839829148 & 0.341404919914574 \tabularnewline
8 & 0.569610588927538 & 0.860778822144925 & 0.430389411072462 \tabularnewline
9 & 0.448765632871116 & 0.897531265742233 & 0.551234367128884 \tabularnewline
10 & 0.37983139609711 & 0.75966279219422 & 0.62016860390289 \tabularnewline
11 & 0.367250209087889 & 0.734500418175778 & 0.632749790912111 \tabularnewline
12 & 0.323336910648648 & 0.646673821297296 & 0.676663089351352 \tabularnewline
13 & 0.260977232608045 & 0.52195446521609 & 0.739022767391955 \tabularnewline
14 & 0.204189284309225 & 0.40837856861845 & 0.795810715690775 \tabularnewline
15 & 0.172578797598190 & 0.345157595196379 & 0.82742120240181 \tabularnewline
16 & 0.415709916393783 & 0.831419832787566 & 0.584290083606217 \tabularnewline
17 & 0.573900249193462 & 0.852199501613076 & 0.426099750806538 \tabularnewline
18 & 0.603880177448803 & 0.792239645102395 & 0.396119822551198 \tabularnewline
19 & 0.795532484991836 & 0.408935030016327 & 0.204467515008164 \tabularnewline
20 & 0.762877410933872 & 0.474245178132256 & 0.237122589066128 \tabularnewline
21 & 0.721677673716825 & 0.55664465256635 & 0.278322326283175 \tabularnewline
22 & 0.652245482623007 & 0.695509034753986 & 0.347754517376993 \tabularnewline
23 & 0.629085838359026 & 0.741828323281949 & 0.370914161640974 \tabularnewline
24 & 0.623181875991951 & 0.753636248016097 & 0.376818124008049 \tabularnewline
25 & 0.597943487974271 & 0.804113024051458 & 0.402056512025729 \tabularnewline
26 & 0.53150827130685 & 0.9369834573863 & 0.46849172869315 \tabularnewline
27 & 0.52743413922115 & 0.9451317215577 & 0.47256586077885 \tabularnewline
28 & 0.834784574765843 & 0.330430850468315 & 0.165215425234157 \tabularnewline
29 & 0.867335928438079 & 0.265328143123842 & 0.132664071561921 \tabularnewline
30 & 0.836326131082982 & 0.327347737834035 & 0.163673868917017 \tabularnewline
31 & 0.860579343846454 & 0.278841312307093 & 0.139420656153546 \tabularnewline
32 & 0.823485321743723 & 0.353029356512554 & 0.176514678256277 \tabularnewline
33 & 0.775333377419532 & 0.449333245160936 & 0.224666622580468 \tabularnewline
34 & 0.726628106689474 & 0.546743786621052 & 0.273371893310526 \tabularnewline
35 & 0.674794750692882 & 0.650410498614236 & 0.325205249307118 \tabularnewline
36 & 0.65203337956048 & 0.69593324087904 & 0.34796662043952 \tabularnewline
37 & 0.605530344551712 & 0.788939310896576 & 0.394469655448288 \tabularnewline
38 & 0.531478557062213 & 0.937042885875574 & 0.468521442937787 \tabularnewline
39 & 0.50666334931687 & 0.98667330136626 & 0.49333665068313 \tabularnewline
40 & 0.861779024255641 & 0.276441951488718 & 0.138220975744359 \tabularnewline
41 & 0.846716038334338 & 0.306567923331325 & 0.153283961665662 \tabularnewline
42 & 0.814385628956654 & 0.371228742086692 & 0.185614371043346 \tabularnewline
43 & 0.774186978790006 & 0.451626042419988 & 0.225813021209994 \tabularnewline
44 & 0.87652583381803 & 0.24694833236394 & 0.12347416618197 \tabularnewline
45 & 0.863493655904722 & 0.273012688190557 & 0.136506344095278 \tabularnewline
46 & 0.868313435308505 & 0.263373129382989 & 0.131686564691495 \tabularnewline
47 & 0.825382074661588 & 0.349235850676825 & 0.174617925338412 \tabularnewline
48 & 0.794792822665653 & 0.410414354668695 & 0.205207177334347 \tabularnewline
49 & 0.719141912940675 & 0.56171617411865 & 0.280858087059325 \tabularnewline
50 & 0.629955333045492 & 0.740089333909017 & 0.370044666954508 \tabularnewline
51 & 0.637060783929147 & 0.725878432141706 & 0.362939216070853 \tabularnewline
52 & 0.891534733945917 & 0.216930532108165 & 0.108465266054083 \tabularnewline
53 & 0.848281740480854 & 0.303436519038293 & 0.151718259519146 \tabularnewline
54 & 0.764418470092418 & 0.471163059815165 & 0.235581529907582 \tabularnewline
55 & 0.835666912168245 & 0.32866617566351 & 0.164333087831755 \tabularnewline
56 & 0.916421370735826 & 0.167157258528349 & 0.0835786292641744 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58231&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]0.733386266589899[/C][C]0.533227466820202[/C][C]0.266613733410101[/C][/ROW]
[ROW][C]6[/C][C]0.643221829748006[/C][C]0.713556340503989[/C][C]0.356778170251994[/C][/ROW]
[ROW][C]7[/C][C]0.658595080085426[/C][C]0.682809839829148[/C][C]0.341404919914574[/C][/ROW]
[ROW][C]8[/C][C]0.569610588927538[/C][C]0.860778822144925[/C][C]0.430389411072462[/C][/ROW]
[ROW][C]9[/C][C]0.448765632871116[/C][C]0.897531265742233[/C][C]0.551234367128884[/C][/ROW]
[ROW][C]10[/C][C]0.37983139609711[/C][C]0.75966279219422[/C][C]0.62016860390289[/C][/ROW]
[ROW][C]11[/C][C]0.367250209087889[/C][C]0.734500418175778[/C][C]0.632749790912111[/C][/ROW]
[ROW][C]12[/C][C]0.323336910648648[/C][C]0.646673821297296[/C][C]0.676663089351352[/C][/ROW]
[ROW][C]13[/C][C]0.260977232608045[/C][C]0.52195446521609[/C][C]0.739022767391955[/C][/ROW]
[ROW][C]14[/C][C]0.204189284309225[/C][C]0.40837856861845[/C][C]0.795810715690775[/C][/ROW]
[ROW][C]15[/C][C]0.172578797598190[/C][C]0.345157595196379[/C][C]0.82742120240181[/C][/ROW]
[ROW][C]16[/C][C]0.415709916393783[/C][C]0.831419832787566[/C][C]0.584290083606217[/C][/ROW]
[ROW][C]17[/C][C]0.573900249193462[/C][C]0.852199501613076[/C][C]0.426099750806538[/C][/ROW]
[ROW][C]18[/C][C]0.603880177448803[/C][C]0.792239645102395[/C][C]0.396119822551198[/C][/ROW]
[ROW][C]19[/C][C]0.795532484991836[/C][C]0.408935030016327[/C][C]0.204467515008164[/C][/ROW]
[ROW][C]20[/C][C]0.762877410933872[/C][C]0.474245178132256[/C][C]0.237122589066128[/C][/ROW]
[ROW][C]21[/C][C]0.721677673716825[/C][C]0.55664465256635[/C][C]0.278322326283175[/C][/ROW]
[ROW][C]22[/C][C]0.652245482623007[/C][C]0.695509034753986[/C][C]0.347754517376993[/C][/ROW]
[ROW][C]23[/C][C]0.629085838359026[/C][C]0.741828323281949[/C][C]0.370914161640974[/C][/ROW]
[ROW][C]24[/C][C]0.623181875991951[/C][C]0.753636248016097[/C][C]0.376818124008049[/C][/ROW]
[ROW][C]25[/C][C]0.597943487974271[/C][C]0.804113024051458[/C][C]0.402056512025729[/C][/ROW]
[ROW][C]26[/C][C]0.53150827130685[/C][C]0.9369834573863[/C][C]0.46849172869315[/C][/ROW]
[ROW][C]27[/C][C]0.52743413922115[/C][C]0.9451317215577[/C][C]0.47256586077885[/C][/ROW]
[ROW][C]28[/C][C]0.834784574765843[/C][C]0.330430850468315[/C][C]0.165215425234157[/C][/ROW]
[ROW][C]29[/C][C]0.867335928438079[/C][C]0.265328143123842[/C][C]0.132664071561921[/C][/ROW]
[ROW][C]30[/C][C]0.836326131082982[/C][C]0.327347737834035[/C][C]0.163673868917017[/C][/ROW]
[ROW][C]31[/C][C]0.860579343846454[/C][C]0.278841312307093[/C][C]0.139420656153546[/C][/ROW]
[ROW][C]32[/C][C]0.823485321743723[/C][C]0.353029356512554[/C][C]0.176514678256277[/C][/ROW]
[ROW][C]33[/C][C]0.775333377419532[/C][C]0.449333245160936[/C][C]0.224666622580468[/C][/ROW]
[ROW][C]34[/C][C]0.726628106689474[/C][C]0.546743786621052[/C][C]0.273371893310526[/C][/ROW]
[ROW][C]35[/C][C]0.674794750692882[/C][C]0.650410498614236[/C][C]0.325205249307118[/C][/ROW]
[ROW][C]36[/C][C]0.65203337956048[/C][C]0.69593324087904[/C][C]0.34796662043952[/C][/ROW]
[ROW][C]37[/C][C]0.605530344551712[/C][C]0.788939310896576[/C][C]0.394469655448288[/C][/ROW]
[ROW][C]38[/C][C]0.531478557062213[/C][C]0.937042885875574[/C][C]0.468521442937787[/C][/ROW]
[ROW][C]39[/C][C]0.50666334931687[/C][C]0.98667330136626[/C][C]0.49333665068313[/C][/ROW]
[ROW][C]40[/C][C]0.861779024255641[/C][C]0.276441951488718[/C][C]0.138220975744359[/C][/ROW]
[ROW][C]41[/C][C]0.846716038334338[/C][C]0.306567923331325[/C][C]0.153283961665662[/C][/ROW]
[ROW][C]42[/C][C]0.814385628956654[/C][C]0.371228742086692[/C][C]0.185614371043346[/C][/ROW]
[ROW][C]43[/C][C]0.774186978790006[/C][C]0.451626042419988[/C][C]0.225813021209994[/C][/ROW]
[ROW][C]44[/C][C]0.87652583381803[/C][C]0.24694833236394[/C][C]0.12347416618197[/C][/ROW]
[ROW][C]45[/C][C]0.863493655904722[/C][C]0.273012688190557[/C][C]0.136506344095278[/C][/ROW]
[ROW][C]46[/C][C]0.868313435308505[/C][C]0.263373129382989[/C][C]0.131686564691495[/C][/ROW]
[ROW][C]47[/C][C]0.825382074661588[/C][C]0.349235850676825[/C][C]0.174617925338412[/C][/ROW]
[ROW][C]48[/C][C]0.794792822665653[/C][C]0.410414354668695[/C][C]0.205207177334347[/C][/ROW]
[ROW][C]49[/C][C]0.719141912940675[/C][C]0.56171617411865[/C][C]0.280858087059325[/C][/ROW]
[ROW][C]50[/C][C]0.629955333045492[/C][C]0.740089333909017[/C][C]0.370044666954508[/C][/ROW]
[ROW][C]51[/C][C]0.637060783929147[/C][C]0.725878432141706[/C][C]0.362939216070853[/C][/ROW]
[ROW][C]52[/C][C]0.891534733945917[/C][C]0.216930532108165[/C][C]0.108465266054083[/C][/ROW]
[ROW][C]53[/C][C]0.848281740480854[/C][C]0.303436519038293[/C][C]0.151718259519146[/C][/ROW]
[ROW][C]54[/C][C]0.764418470092418[/C][C]0.471163059815165[/C][C]0.235581529907582[/C][/ROW]
[ROW][C]55[/C][C]0.835666912168245[/C][C]0.32866617566351[/C][C]0.164333087831755[/C][/ROW]
[ROW][C]56[/C][C]0.916421370735826[/C][C]0.167157258528349[/C][C]0.0835786292641744[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58231&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58231&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
50.7333862665898990.5332274668202020.266613733410101
60.6432218297480060.7135563405039890.356778170251994
70.6585950800854260.6828098398291480.341404919914574
80.5696105889275380.8607788221449250.430389411072462
90.4487656328711160.8975312657422330.551234367128884
100.379831396097110.759662792194220.62016860390289
110.3672502090878890.7345004181757780.632749790912111
120.3233369106486480.6466738212972960.676663089351352
130.2609772326080450.521954465216090.739022767391955
140.2041892843092250.408378568618450.795810715690775
150.1725787975981900.3451575951963790.82742120240181
160.4157099163937830.8314198327875660.584290083606217
170.5739002491934620.8521995016130760.426099750806538
180.6038801774488030.7922396451023950.396119822551198
190.7955324849918360.4089350300163270.204467515008164
200.7628774109338720.4742451781322560.237122589066128
210.7216776737168250.556644652566350.278322326283175
220.6522454826230070.6955090347539860.347754517376993
230.6290858383590260.7418283232819490.370914161640974
240.6231818759919510.7536362480160970.376818124008049
250.5979434879742710.8041130240514580.402056512025729
260.531508271306850.93698345738630.46849172869315
270.527434139221150.94513172155770.47256586077885
280.8347845747658430.3304308504683150.165215425234157
290.8673359284380790.2653281431238420.132664071561921
300.8363261310829820.3273477378340350.163673868917017
310.8605793438464540.2788413123070930.139420656153546
320.8234853217437230.3530293565125540.176514678256277
330.7753333774195320.4493332451609360.224666622580468
340.7266281066894740.5467437866210520.273371893310526
350.6747947506928820.6504104986142360.325205249307118
360.652033379560480.695933240879040.34796662043952
370.6055303445517120.7889393108965760.394469655448288
380.5314785570622130.9370428858755740.468521442937787
390.506663349316870.986673301366260.49333665068313
400.8617790242556410.2764419514887180.138220975744359
410.8467160383343380.3065679233313250.153283961665662
420.8143856289566540.3712287420866920.185614371043346
430.7741869787900060.4516260424199880.225813021209994
440.876525833818030.246948332363940.12347416618197
450.8634936559047220.2730126881905570.136506344095278
460.8683134353085050.2633731293829890.131686564691495
470.8253820746615880.3492358506768250.174617925338412
480.7947928226656530.4104143546686950.205207177334347
490.7191419129406750.561716174118650.280858087059325
500.6299553330454920.7400893339090170.370044666954508
510.6370607839291470.7258784321417060.362939216070853
520.8915347339459170.2169305321081650.108465266054083
530.8482817404808540.3034365190382930.151718259519146
540.7644184700924180.4711630598151650.235581529907582
550.8356669121682450.328666175663510.164333087831755
560.9164213707358260.1671572585283490.0835786292641744







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58231&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58231&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58231&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
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')
}