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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 computationWed, 16 Dec 2009 06:58:32 -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/Dec/16/t1260972002fzp6swk2qnqd42d.htm/, Retrieved Thu, 31 Oct 2024 23:38:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=68347, Retrieved Thu, 31 Oct 2024 23:38:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact161
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]
-   PD      [Multiple Regression] [] [2009-12-16 13:58:32] [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




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=68347&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=68347&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68347&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 -945.299589603282X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  23033.5348837209 -945.299589603282X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68347&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  23033.5348837209 -945.299589603282X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68347&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68347&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 -945.299589603282X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)23033.5348837209882.98233626.086100
X-945.2995896032821658.834962-0.56990.5709750.285488

\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 & 882.982336 & 26.0861 & 0 & 0 \tabularnewline
X & -945.299589603282 & 1658.834962 & -0.5699 & 0.570975 & 0.285488 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68347&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]882.982336[/C][C]26.0861[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-945.299589603282[/C][C]1658.834962[/C][C]-0.5699[/C][C]0.570975[/C][C]0.285488[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68347&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68347&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)23033.5348837209882.98233626.086100
X-945.2995896032821658.834962-0.56990.5709750.285488







Multiple Linear Regression - Regression Statistics
Multiple R0.0746173622243007
R-squared0.00556775074531249
Adjusted R-squared-0.011577632862527
F-TEST (value)0.324737601249512
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.570975115043695
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5790.10238691632
Sum Squared Residuals1944466567.7565

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0746173622243007 \tabularnewline
R-squared & 0.00556775074531249 \tabularnewline
Adjusted R-squared & -0.011577632862527 \tabularnewline
F-TEST (value) & 0.324737601249512 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.570975115043695 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5790.10238691632 \tabularnewline
Sum Squared Residuals & 1944466567.7565 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68347&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0746173622243007[/C][/ROW]
[ROW][C]R-squared[/C][C]0.00556775074531249[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.011577632862527[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.324737601249512[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.570975115043695[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5790.10238691632[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1944466567.7565[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68347&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68347&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 R0.0746173622243007
R-squared0.00556775074531249
Adjusted R-squared-0.011577632862527
F-TEST (value)0.324737601249512
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.570975115043695
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5790.10238691632
Sum Squared Residuals1944466567.7565







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12036623033.534883721-2667.53488372098
22278223033.5348837209-251.534883720928
31916923033.5348837209-3864.53488372093
41380723033.5348837209-9226.53488372093
52974323033.53488372096709.46511627907
62559123033.53488372092557.46511627907
72909623033.53488372096062.46511627907
82648223033.53488372093448.46511627907
92240523033.5348837209-628.534883720929
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
222369123033.5348837209657.465116279071
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
443193422088.23529411779845.76470588235
452582622088.23529411763737.76470588235
462683522088.23529411764746.76470588235
472020522088.2352941176-1883.23529411765
481778922088.2352941176-4299.23529411765
492052022088.2352941176-1568.23529411765
502251822088.2352941176429.764705882353
511557222088.2352941176-6516.23529411765
521150922088.2352941177-10579.2352941176
532544722088.23529411763358.76470588235
542409022088.23529411762001.76470588235
552778622088.23529411765697.76470588235
562619522088.23529411764106.76470588235
572051622088.2352941176-1572.23529411765
582275922088.2352941176670.764705882353
591902822088.2352941176-3060.23529411765
601697122088.2352941176-5117.23529411765

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 20366 & 23033.534883721 & -2667.53488372098 \tabularnewline
2 & 22782 & 23033.5348837209 & -251.534883720928 \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 & -628.534883720929 \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 & 657.465116279071 \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 & 22088.2352941177 & 9845.76470588235 \tabularnewline
45 & 25826 & 22088.2352941176 & 3737.76470588235 \tabularnewline
46 & 26835 & 22088.2352941176 & 4746.76470588235 \tabularnewline
47 & 20205 & 22088.2352941176 & -1883.23529411765 \tabularnewline
48 & 17789 & 22088.2352941176 & -4299.23529411765 \tabularnewline
49 & 20520 & 22088.2352941176 & -1568.23529411765 \tabularnewline
50 & 22518 & 22088.2352941176 & 429.764705882353 \tabularnewline
51 & 15572 & 22088.2352941176 & -6516.23529411765 \tabularnewline
52 & 11509 & 22088.2352941177 & -10579.2352941176 \tabularnewline
53 & 25447 & 22088.2352941176 & 3358.76470588235 \tabularnewline
54 & 24090 & 22088.2352941176 & 2001.76470588235 \tabularnewline
55 & 27786 & 22088.2352941176 & 5697.76470588235 \tabularnewline
56 & 26195 & 22088.2352941176 & 4106.76470588235 \tabularnewline
57 & 20516 & 22088.2352941176 & -1572.23529411765 \tabularnewline
58 & 22759 & 22088.2352941176 & 670.764705882353 \tabularnewline
59 & 19028 & 22088.2352941176 & -3060.23529411765 \tabularnewline
60 & 16971 & 22088.2352941176 & -5117.23529411765 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68347&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]23033.534883721[/C][C]-2667.53488372098[/C][/ROW]
[ROW][C]2[/C][C]22782[/C][C]23033.5348837209[/C][C]-251.534883720928[/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]-628.534883720929[/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]657.465116279071[/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]22088.2352941177[/C][C]9845.76470588235[/C][/ROW]
[ROW][C]45[/C][C]25826[/C][C]22088.2352941176[/C][C]3737.76470588235[/C][/ROW]
[ROW][C]46[/C][C]26835[/C][C]22088.2352941176[/C][C]4746.76470588235[/C][/ROW]
[ROW][C]47[/C][C]20205[/C][C]22088.2352941176[/C][C]-1883.23529411765[/C][/ROW]
[ROW][C]48[/C][C]17789[/C][C]22088.2352941176[/C][C]-4299.23529411765[/C][/ROW]
[ROW][C]49[/C][C]20520[/C][C]22088.2352941176[/C][C]-1568.23529411765[/C][/ROW]
[ROW][C]50[/C][C]22518[/C][C]22088.2352941176[/C][C]429.764705882353[/C][/ROW]
[ROW][C]51[/C][C]15572[/C][C]22088.2352941176[/C][C]-6516.23529411765[/C][/ROW]
[ROW][C]52[/C][C]11509[/C][C]22088.2352941177[/C][C]-10579.2352941176[/C][/ROW]
[ROW][C]53[/C][C]25447[/C][C]22088.2352941176[/C][C]3358.76470588235[/C][/ROW]
[ROW][C]54[/C][C]24090[/C][C]22088.2352941176[/C][C]2001.76470588235[/C][/ROW]
[ROW][C]55[/C][C]27786[/C][C]22088.2352941176[/C][C]5697.76470588235[/C][/ROW]
[ROW][C]56[/C][C]26195[/C][C]22088.2352941176[/C][C]4106.76470588235[/C][/ROW]
[ROW][C]57[/C][C]20516[/C][C]22088.2352941176[/C][C]-1572.23529411765[/C][/ROW]
[ROW][C]58[/C][C]22759[/C][C]22088.2352941176[/C][C]670.764705882353[/C][/ROW]
[ROW][C]59[/C][C]19028[/C][C]22088.2352941176[/C][C]-3060.23529411765[/C][/ROW]
[ROW][C]60[/C][C]16971[/C][C]22088.2352941176[/C][C]-5117.23529411765[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68347&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68347&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
12036623033.534883721-2667.53488372098
22278223033.5348837209-251.534883720928
31916923033.5348837209-3864.53488372093
41380723033.5348837209-9226.53488372093
52974323033.53488372096709.46511627907
62559123033.53488372092557.46511627907
72909623033.53488372096062.46511627907
82648223033.53488372093448.46511627907
92240523033.5348837209-628.534883720929
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
222369123033.5348837209657.465116279071
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
443193422088.23529411779845.76470588235
452582622088.23529411763737.76470588235
462683522088.23529411764746.76470588235
472020522088.2352941176-1883.23529411765
481778922088.2352941176-4299.23529411765
492052022088.2352941176-1568.23529411765
502251822088.2352941176429.764705882353
511557222088.2352941176-6516.23529411765
521150922088.2352941177-10579.2352941176
532544722088.23529411763358.76470588235
542409022088.23529411762001.76470588235
552778622088.23529411765697.76470588235
562619522088.23529411764106.76470588235
572051622088.2352941176-1572.23529411765
582275922088.2352941176670.764705882353
591902822088.2352941176-3060.23529411765
601697122088.2352941176-5117.23529411765







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.7262421488337820.5475157023324350.273757851166218
60.6343191047823650.7313617904352710.365680895217635
70.6485590774142630.7028818451714740.351440922585737
80.5582513760692240.8834972478615530.441748623930776
90.4369430939547230.8738861879094470.563056906045277
100.3678635014242190.7357270028484390.63213649857578
110.3545431444897190.7090862889794380.645456855510281
120.3106106890616900.6212213781233790.68938931093831
130.2490581645311570.4981163290623140.750941835468843
140.1934686660281280.3869373320562570.806531333971872
150.162533988666940.325067977333880.83746601133306
160.3977398701576220.7954797403152440.602260129842378
170.5537753598944610.8924492802110780.446224640105539
180.5829375616410720.8341248767178560.417062438358928
190.7781472546679750.443705490664050.221852745332025
200.7434518709619940.5130962580760130.256548129038006
210.7000772818616260.5998454362767490.299922718138374
220.6281385861177170.7437228277645670.371861413882283
230.6036576150265320.7926847699469350.396342384973468
240.5966874324421340.8066251351157310.403312567557866
250.5701656922720630.8596686154558740.429834307727937
260.502505427584090.994989144831820.49749457241591
270.4973887085629980.9947774171259970.502611291437002
280.8131208496043660.3737583007912690.186879150395634
290.8475433190314720.3049133619370550.152456680968528
300.8130739869904040.3738520260191910.186926013009596
310.8385952549764730.3228094900470550.161404745023527
320.7973573211269850.405285357746030.202642678873015
330.7446580489958030.5106839020083940.255341951004197
340.691959162470080.6160816750598410.308040837529921
350.6365844452018290.7268311095963420.363415554798171
360.6116391609395180.7767216781209630.388360839060482
370.5625330086725850.8749339826548310.437466991327415
380.4863786912558290.9727573825116580.513621308744171
390.460047613363350.92009522672670.53995238663665
400.8312563933245740.3374872133508520.168743606675426
410.8125576107400760.3748847785198470.187442389259924
420.7744646274780270.4510707450439470.225535372521973
430.7278943516870280.5442112966259430.272105648312972
440.8385990559858990.3228018880282030.161400944014101
450.8197399041548950.360520191690210.180260095845105
460.821527800250670.3569443994986590.178472199749330
470.7676147873136920.4647704253726170.232385212686308
480.7292384881957040.5415230236085910.270761511804296
490.6400975681988590.7198048636022820.359902431801141
500.5387046949916760.9225906100166480.461295305008324
510.5424848910898850.9150302178202310.457515108910115
520.843927078317570.3121458433648580.156072921682429
530.7749163822217550.4501672355564900.225083617778245
540.6543295453139740.6913409093720510.345670454686026
550.7032303341754680.5935393316490630.296769665824532

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.726242148833782 & 0.547515702332435 & 0.273757851166218 \tabularnewline
6 & 0.634319104782365 & 0.731361790435271 & 0.365680895217635 \tabularnewline
7 & 0.648559077414263 & 0.702881845171474 & 0.351440922585737 \tabularnewline
8 & 0.558251376069224 & 0.883497247861553 & 0.441748623930776 \tabularnewline
9 & 0.436943093954723 & 0.873886187909447 & 0.563056906045277 \tabularnewline
10 & 0.367863501424219 & 0.735727002848439 & 0.63213649857578 \tabularnewline
11 & 0.354543144489719 & 0.709086288979438 & 0.645456855510281 \tabularnewline
12 & 0.310610689061690 & 0.621221378123379 & 0.68938931093831 \tabularnewline
13 & 0.249058164531157 & 0.498116329062314 & 0.750941835468843 \tabularnewline
14 & 0.193468666028128 & 0.386937332056257 & 0.806531333971872 \tabularnewline
15 & 0.16253398866694 & 0.32506797733388 & 0.83746601133306 \tabularnewline
16 & 0.397739870157622 & 0.795479740315244 & 0.602260129842378 \tabularnewline
17 & 0.553775359894461 & 0.892449280211078 & 0.446224640105539 \tabularnewline
18 & 0.582937561641072 & 0.834124876717856 & 0.417062438358928 \tabularnewline
19 & 0.778147254667975 & 0.44370549066405 & 0.221852745332025 \tabularnewline
20 & 0.743451870961994 & 0.513096258076013 & 0.256548129038006 \tabularnewline
21 & 0.700077281861626 & 0.599845436276749 & 0.299922718138374 \tabularnewline
22 & 0.628138586117717 & 0.743722827764567 & 0.371861413882283 \tabularnewline
23 & 0.603657615026532 & 0.792684769946935 & 0.396342384973468 \tabularnewline
24 & 0.596687432442134 & 0.806625135115731 & 0.403312567557866 \tabularnewline
25 & 0.570165692272063 & 0.859668615455874 & 0.429834307727937 \tabularnewline
26 & 0.50250542758409 & 0.99498914483182 & 0.49749457241591 \tabularnewline
27 & 0.497388708562998 & 0.994777417125997 & 0.502611291437002 \tabularnewline
28 & 0.813120849604366 & 0.373758300791269 & 0.186879150395634 \tabularnewline
29 & 0.847543319031472 & 0.304913361937055 & 0.152456680968528 \tabularnewline
30 & 0.813073986990404 & 0.373852026019191 & 0.186926013009596 \tabularnewline
31 & 0.838595254976473 & 0.322809490047055 & 0.161404745023527 \tabularnewline
32 & 0.797357321126985 & 0.40528535774603 & 0.202642678873015 \tabularnewline
33 & 0.744658048995803 & 0.510683902008394 & 0.255341951004197 \tabularnewline
34 & 0.69195916247008 & 0.616081675059841 & 0.308040837529921 \tabularnewline
35 & 0.636584445201829 & 0.726831109596342 & 0.363415554798171 \tabularnewline
36 & 0.611639160939518 & 0.776721678120963 & 0.388360839060482 \tabularnewline
37 & 0.562533008672585 & 0.874933982654831 & 0.437466991327415 \tabularnewline
38 & 0.486378691255829 & 0.972757382511658 & 0.513621308744171 \tabularnewline
39 & 0.46004761336335 & 0.9200952267267 & 0.53995238663665 \tabularnewline
40 & 0.831256393324574 & 0.337487213350852 & 0.168743606675426 \tabularnewline
41 & 0.812557610740076 & 0.374884778519847 & 0.187442389259924 \tabularnewline
42 & 0.774464627478027 & 0.451070745043947 & 0.225535372521973 \tabularnewline
43 & 0.727894351687028 & 0.544211296625943 & 0.272105648312972 \tabularnewline
44 & 0.838599055985899 & 0.322801888028203 & 0.161400944014101 \tabularnewline
45 & 0.819739904154895 & 0.36052019169021 & 0.180260095845105 \tabularnewline
46 & 0.82152780025067 & 0.356944399498659 & 0.178472199749330 \tabularnewline
47 & 0.767614787313692 & 0.464770425372617 & 0.232385212686308 \tabularnewline
48 & 0.729238488195704 & 0.541523023608591 & 0.270761511804296 \tabularnewline
49 & 0.640097568198859 & 0.719804863602282 & 0.359902431801141 \tabularnewline
50 & 0.538704694991676 & 0.922590610016648 & 0.461295305008324 \tabularnewline
51 & 0.542484891089885 & 0.915030217820231 & 0.457515108910115 \tabularnewline
52 & 0.84392707831757 & 0.312145843364858 & 0.156072921682429 \tabularnewline
53 & 0.774916382221755 & 0.450167235556490 & 0.225083617778245 \tabularnewline
54 & 0.654329545313974 & 0.691340909372051 & 0.345670454686026 \tabularnewline
55 & 0.703230334175468 & 0.593539331649063 & 0.296769665824532 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=68347&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]0.726242148833782[/C][C]0.547515702332435[/C][C]0.273757851166218[/C][/ROW]
[ROW][C]6[/C][C]0.634319104782365[/C][C]0.731361790435271[/C][C]0.365680895217635[/C][/ROW]
[ROW][C]7[/C][C]0.648559077414263[/C][C]0.702881845171474[/C][C]0.351440922585737[/C][/ROW]
[ROW][C]8[/C][C]0.558251376069224[/C][C]0.883497247861553[/C][C]0.441748623930776[/C][/ROW]
[ROW][C]9[/C][C]0.436943093954723[/C][C]0.873886187909447[/C][C]0.563056906045277[/C][/ROW]
[ROW][C]10[/C][C]0.367863501424219[/C][C]0.735727002848439[/C][C]0.63213649857578[/C][/ROW]
[ROW][C]11[/C][C]0.354543144489719[/C][C]0.709086288979438[/C][C]0.645456855510281[/C][/ROW]
[ROW][C]12[/C][C]0.310610689061690[/C][C]0.621221378123379[/C][C]0.68938931093831[/C][/ROW]
[ROW][C]13[/C][C]0.249058164531157[/C][C]0.498116329062314[/C][C]0.750941835468843[/C][/ROW]
[ROW][C]14[/C][C]0.193468666028128[/C][C]0.386937332056257[/C][C]0.806531333971872[/C][/ROW]
[ROW][C]15[/C][C]0.16253398866694[/C][C]0.32506797733388[/C][C]0.83746601133306[/C][/ROW]
[ROW][C]16[/C][C]0.397739870157622[/C][C]0.795479740315244[/C][C]0.602260129842378[/C][/ROW]
[ROW][C]17[/C][C]0.553775359894461[/C][C]0.892449280211078[/C][C]0.446224640105539[/C][/ROW]
[ROW][C]18[/C][C]0.582937561641072[/C][C]0.834124876717856[/C][C]0.417062438358928[/C][/ROW]
[ROW][C]19[/C][C]0.778147254667975[/C][C]0.44370549066405[/C][C]0.221852745332025[/C][/ROW]
[ROW][C]20[/C][C]0.743451870961994[/C][C]0.513096258076013[/C][C]0.256548129038006[/C][/ROW]
[ROW][C]21[/C][C]0.700077281861626[/C][C]0.599845436276749[/C][C]0.299922718138374[/C][/ROW]
[ROW][C]22[/C][C]0.628138586117717[/C][C]0.743722827764567[/C][C]0.371861413882283[/C][/ROW]
[ROW][C]23[/C][C]0.603657615026532[/C][C]0.792684769946935[/C][C]0.396342384973468[/C][/ROW]
[ROW][C]24[/C][C]0.596687432442134[/C][C]0.806625135115731[/C][C]0.403312567557866[/C][/ROW]
[ROW][C]25[/C][C]0.570165692272063[/C][C]0.859668615455874[/C][C]0.429834307727937[/C][/ROW]
[ROW][C]26[/C][C]0.50250542758409[/C][C]0.99498914483182[/C][C]0.49749457241591[/C][/ROW]
[ROW][C]27[/C][C]0.497388708562998[/C][C]0.994777417125997[/C][C]0.502611291437002[/C][/ROW]
[ROW][C]28[/C][C]0.813120849604366[/C][C]0.373758300791269[/C][C]0.186879150395634[/C][/ROW]
[ROW][C]29[/C][C]0.847543319031472[/C][C]0.304913361937055[/C][C]0.152456680968528[/C][/ROW]
[ROW][C]30[/C][C]0.813073986990404[/C][C]0.373852026019191[/C][C]0.186926013009596[/C][/ROW]
[ROW][C]31[/C][C]0.838595254976473[/C][C]0.322809490047055[/C][C]0.161404745023527[/C][/ROW]
[ROW][C]32[/C][C]0.797357321126985[/C][C]0.40528535774603[/C][C]0.202642678873015[/C][/ROW]
[ROW][C]33[/C][C]0.744658048995803[/C][C]0.510683902008394[/C][C]0.255341951004197[/C][/ROW]
[ROW][C]34[/C][C]0.69195916247008[/C][C]0.616081675059841[/C][C]0.308040837529921[/C][/ROW]
[ROW][C]35[/C][C]0.636584445201829[/C][C]0.726831109596342[/C][C]0.363415554798171[/C][/ROW]
[ROW][C]36[/C][C]0.611639160939518[/C][C]0.776721678120963[/C][C]0.388360839060482[/C][/ROW]
[ROW][C]37[/C][C]0.562533008672585[/C][C]0.874933982654831[/C][C]0.437466991327415[/C][/ROW]
[ROW][C]38[/C][C]0.486378691255829[/C][C]0.972757382511658[/C][C]0.513621308744171[/C][/ROW]
[ROW][C]39[/C][C]0.46004761336335[/C][C]0.9200952267267[/C][C]0.53995238663665[/C][/ROW]
[ROW][C]40[/C][C]0.831256393324574[/C][C]0.337487213350852[/C][C]0.168743606675426[/C][/ROW]
[ROW][C]41[/C][C]0.812557610740076[/C][C]0.374884778519847[/C][C]0.187442389259924[/C][/ROW]
[ROW][C]42[/C][C]0.774464627478027[/C][C]0.451070745043947[/C][C]0.225535372521973[/C][/ROW]
[ROW][C]43[/C][C]0.727894351687028[/C][C]0.544211296625943[/C][C]0.272105648312972[/C][/ROW]
[ROW][C]44[/C][C]0.838599055985899[/C][C]0.322801888028203[/C][C]0.161400944014101[/C][/ROW]
[ROW][C]45[/C][C]0.819739904154895[/C][C]0.36052019169021[/C][C]0.180260095845105[/C][/ROW]
[ROW][C]46[/C][C]0.82152780025067[/C][C]0.356944399498659[/C][C]0.178472199749330[/C][/ROW]
[ROW][C]47[/C][C]0.767614787313692[/C][C]0.464770425372617[/C][C]0.232385212686308[/C][/ROW]
[ROW][C]48[/C][C]0.729238488195704[/C][C]0.541523023608591[/C][C]0.270761511804296[/C][/ROW]
[ROW][C]49[/C][C]0.640097568198859[/C][C]0.719804863602282[/C][C]0.359902431801141[/C][/ROW]
[ROW][C]50[/C][C]0.538704694991676[/C][C]0.922590610016648[/C][C]0.461295305008324[/C][/ROW]
[ROW][C]51[/C][C]0.542484891089885[/C][C]0.915030217820231[/C][C]0.457515108910115[/C][/ROW]
[ROW][C]52[/C][C]0.84392707831757[/C][C]0.312145843364858[/C][C]0.156072921682429[/C][/ROW]
[ROW][C]53[/C][C]0.774916382221755[/C][C]0.450167235556490[/C][C]0.225083617778245[/C][/ROW]
[ROW][C]54[/C][C]0.654329545313974[/C][C]0.691340909372051[/C][C]0.345670454686026[/C][/ROW]
[ROW][C]55[/C][C]0.703230334175468[/C][C]0.593539331649063[/C][C]0.296769665824532[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=68347&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68347&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
50.7262421488337820.5475157023324350.273757851166218
60.6343191047823650.7313617904352710.365680895217635
70.6485590774142630.7028818451714740.351440922585737
80.5582513760692240.8834972478615530.441748623930776
90.4369430939547230.8738861879094470.563056906045277
100.3678635014242190.7357270028484390.63213649857578
110.3545431444897190.7090862889794380.645456855510281
120.3106106890616900.6212213781233790.68938931093831
130.2490581645311570.4981163290623140.750941835468843
140.1934686660281280.3869373320562570.806531333971872
150.162533988666940.325067977333880.83746601133306
160.3977398701576220.7954797403152440.602260129842378
170.5537753598944610.8924492802110780.446224640105539
180.5829375616410720.8341248767178560.417062438358928
190.7781472546679750.443705490664050.221852745332025
200.7434518709619940.5130962580760130.256548129038006
210.7000772818616260.5998454362767490.299922718138374
220.6281385861177170.7437228277645670.371861413882283
230.6036576150265320.7926847699469350.396342384973468
240.5966874324421340.8066251351157310.403312567557866
250.5701656922720630.8596686154558740.429834307727937
260.502505427584090.994989144831820.49749457241591
270.4973887085629980.9947774171259970.502611291437002
280.8131208496043660.3737583007912690.186879150395634
290.8475433190314720.3049133619370550.152456680968528
300.8130739869904040.3738520260191910.186926013009596
310.8385952549764730.3228094900470550.161404745023527
320.7973573211269850.405285357746030.202642678873015
330.7446580489958030.5106839020083940.255341951004197
340.691959162470080.6160816750598410.308040837529921
350.6365844452018290.7268311095963420.363415554798171
360.6116391609395180.7767216781209630.388360839060482
370.5625330086725850.8749339826548310.437466991327415
380.4863786912558290.9727573825116580.513621308744171
390.460047613363350.92009522672670.53995238663665
400.8312563933245740.3374872133508520.168743606675426
410.8125576107400760.3748847785198470.187442389259924
420.7744646274780270.4510707450439470.225535372521973
430.7278943516870280.5442112966259430.272105648312972
440.8385990559858990.3228018880282030.161400944014101
450.8197399041548950.360520191690210.180260095845105
460.821527800250670.3569443994986590.178472199749330
470.7676147873136920.4647704253726170.232385212686308
480.7292384881957040.5415230236085910.270761511804296
490.6400975681988590.7198048636022820.359902431801141
500.5387046949916760.9225906100166480.461295305008324
510.5424848910898850.9150302178202310.457515108910115
520.843927078317570.3121458433648580.156072921682429
530.7749163822217550.4501672355564900.225083617778245
540.6543295453139740.6913409093720510.345670454686026
550.7032303341754680.5935393316490630.296769665824532







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=68347&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=68347&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=68347&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 = 0 ; par2 = 36 ;
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')
}