Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 26 Nov 2011 13:16:37 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/26/t1322331420qfph437dev1g6rx.htm/, Retrieved Sun, 05 Feb 2023 13:07:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147433, Retrieved Sun, 05 Feb 2023 13:07:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103
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 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [Multivariate regr...] [2009-11-19 08:45:43] [21324e9cdf3569788a3d630236984d87]
-    D      [Multiple Regression] [] [2010-12-07 12:28:40] [f47feae0308dca73181bb669fbad1c56]
- R             [Multiple Regression] [] [2011-11-26 18:16:37] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- R P             [Multiple Regression] [] [2011-11-27 16:44:08] [3931071255a6f7f4a767409781cc5f7d]
- R P             [Multiple Regression] [] [2011-11-27 16:47:29] [3931071255a6f7f4a767409781cc5f7d]
Feedback Forum

Post a new message
Dataseries X:
112.3	0
117.3	0
111.1	1
102.2	1
104.3	1
122.9	1
107.6	1
121.3	1
131.5	1
89	1
104.4	1
128.9	1
135.9	1
133.3	1
121.3	1
120.5	0
120.4	0
137.9	0
126.1	0
133.2	0
151.1	0
105	0
119	0
140.4	0
156.6	0
137.1	0
122.7	0
125.8	0
139.3	0
134.9	0
149.2	0
132.3	0
149	0
117.2	0
119.6	0
152	0
149.4	0
127.3	0
114.1	0
102.1	0
107.7	0
104.4	0
102.1	0
96	1
109.3	0
90	1
83.9	1
112	1
114.3	1
103.6	1
91.7	1
80.8	1
87.2	1
109.2	1
102.7	1
95.1	1
117.5	1
85.1	1
92.1	1
113.5	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147433&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147433&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147433&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Promet[t] = + 140.995833333333 -19.3930555555556Dummy[t] + 0.461388888888938M1[t] -9.51861111111113M2[t] -17.18M3[t] -26.9586111111111M4[t] -21.4586111111111M5[t] -11.3786111111111M6[t] -15.6986111111111M7[t] -13.78M8[t] -1.55861111111113M9[t] -32.1M10[t] -25.56M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Promet[t] =  +  140.995833333333 -19.3930555555556Dummy[t] +  0.461388888888938M1[t] -9.51861111111113M2[t] -17.18M3[t] -26.9586111111111M4[t] -21.4586111111111M5[t] -11.3786111111111M6[t] -15.6986111111111M7[t] -13.78M8[t] -1.55861111111113M9[t] -32.1M10[t] -25.56M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147433&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Promet[t] =  +  140.995833333333 -19.3930555555556Dummy[t] +  0.461388888888938M1[t] -9.51861111111113M2[t] -17.18M3[t] -26.9586111111111M4[t] -21.4586111111111M5[t] -11.3786111111111M6[t] -15.6986111111111M7[t] -13.78M8[t] -1.55861111111113M9[t] -32.1M10[t] -25.56M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147433&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147433&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
Promet[t] = + 140.995833333333 -19.3930555555556Dummy[t] + 0.461388888888938M1[t] -9.51861111111113M2[t] -17.18M3[t] -26.9586111111111M4[t] -21.4586111111111M5[t] -11.3786111111111M6[t] -15.6986111111111M7[t] -13.78M8[t] -1.55861111111113M9[t] -32.1M10[t] -25.56M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)140.9958333333336.40751422.004800
Dummy-19.39305555555563.55973-5.44792e-061e-06
M10.4613888888889388.5729650.05380.9573070.478654
M2-9.518611111111138.572965-1.11030.2725160.136258
M3-17.188.543352-2.01090.050090.025045
M4-26.95861111111118.572965-3.14460.0028810.001441
M5-21.45861111111118.572965-2.50310.0158460.007923
M6-11.37861111111118.572965-1.32730.1908320.095416
M7-15.69861111111118.572965-1.83120.0734170.036709
M8-13.788.543352-1.6130.1134510.056725
M9-1.558611111111138.572965-0.18180.8565170.428259
M10-32.18.543352-3.75730.0004740.000237
M11-25.568.543352-2.99180.0044070.002204

\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) & 140.995833333333 & 6.407514 & 22.0048 & 0 & 0 \tabularnewline
Dummy & -19.3930555555556 & 3.55973 & -5.4479 & 2e-06 & 1e-06 \tabularnewline
M1 & 0.461388888888938 & 8.572965 & 0.0538 & 0.957307 & 0.478654 \tabularnewline
M2 & -9.51861111111113 & 8.572965 & -1.1103 & 0.272516 & 0.136258 \tabularnewline
M3 & -17.18 & 8.543352 & -2.0109 & 0.05009 & 0.025045 \tabularnewline
M4 & -26.9586111111111 & 8.572965 & -3.1446 & 0.002881 & 0.001441 \tabularnewline
M5 & -21.4586111111111 & 8.572965 & -2.5031 & 0.015846 & 0.007923 \tabularnewline
M6 & -11.3786111111111 & 8.572965 & -1.3273 & 0.190832 & 0.095416 \tabularnewline
M7 & -15.6986111111111 & 8.572965 & -1.8312 & 0.073417 & 0.036709 \tabularnewline
M8 & -13.78 & 8.543352 & -1.613 & 0.113451 & 0.056725 \tabularnewline
M9 & -1.55861111111113 & 8.572965 & -0.1818 & 0.856517 & 0.428259 \tabularnewline
M10 & -32.1 & 8.543352 & -3.7573 & 0.000474 & 0.000237 \tabularnewline
M11 & -25.56 & 8.543352 & -2.9918 & 0.004407 & 0.002204 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147433&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]140.995833333333[/C][C]6.407514[/C][C]22.0048[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy[/C][C]-19.3930555555556[/C][C]3.55973[/C][C]-5.4479[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M1[/C][C]0.461388888888938[/C][C]8.572965[/C][C]0.0538[/C][C]0.957307[/C][C]0.478654[/C][/ROW]
[ROW][C]M2[/C][C]-9.51861111111113[/C][C]8.572965[/C][C]-1.1103[/C][C]0.272516[/C][C]0.136258[/C][/ROW]
[ROW][C]M3[/C][C]-17.18[/C][C]8.543352[/C][C]-2.0109[/C][C]0.05009[/C][C]0.025045[/C][/ROW]
[ROW][C]M4[/C][C]-26.9586111111111[/C][C]8.572965[/C][C]-3.1446[/C][C]0.002881[/C][C]0.001441[/C][/ROW]
[ROW][C]M5[/C][C]-21.4586111111111[/C][C]8.572965[/C][C]-2.5031[/C][C]0.015846[/C][C]0.007923[/C][/ROW]
[ROW][C]M6[/C][C]-11.3786111111111[/C][C]8.572965[/C][C]-1.3273[/C][C]0.190832[/C][C]0.095416[/C][/ROW]
[ROW][C]M7[/C][C]-15.6986111111111[/C][C]8.572965[/C][C]-1.8312[/C][C]0.073417[/C][C]0.036709[/C][/ROW]
[ROW][C]M8[/C][C]-13.78[/C][C]8.543352[/C][C]-1.613[/C][C]0.113451[/C][C]0.056725[/C][/ROW]
[ROW][C]M9[/C][C]-1.55861111111113[/C][C]8.572965[/C][C]-0.1818[/C][C]0.856517[/C][C]0.428259[/C][/ROW]
[ROW][C]M10[/C][C]-32.1[/C][C]8.543352[/C][C]-3.7573[/C][C]0.000474[/C][C]0.000237[/C][/ROW]
[ROW][C]M11[/C][C]-25.56[/C][C]8.543352[/C][C]-2.9918[/C][C]0.004407[/C][C]0.002204[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147433&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147433&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)140.9958333333336.40751422.004800
Dummy-19.39305555555563.55973-5.44792e-061e-06
M10.4613888888889388.5729650.05380.9573070.478654
M2-9.518611111111138.572965-1.11030.2725160.136258
M3-17.188.543352-2.01090.050090.025045
M4-26.95861111111118.572965-3.14460.0028810.001441
M5-21.45861111111118.572965-2.50310.0158460.007923
M6-11.37861111111118.572965-1.32730.1908320.095416
M7-15.69861111111118.572965-1.83120.0734170.036709
M8-13.788.543352-1.6130.1134510.056725
M9-1.558611111111138.572965-0.18180.8565170.428259
M10-32.18.543352-3.75730.0004740.000237
M11-25.568.543352-2.99180.0044070.002204







Multiple Linear Regression - Regression Statistics
Multiple R0.772025713756317
R-squared0.596023702700951
Adjusted R-squared0.492880818284172
F-TEST (value)5.77862162834757
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value5.27128218130724e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation13.5082254732941
Sum Squared Residuals8576.19130555554

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.772025713756317 \tabularnewline
R-squared & 0.596023702700951 \tabularnewline
Adjusted R-squared & 0.492880818284172 \tabularnewline
F-TEST (value) & 5.77862162834757 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 5.27128218130724e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 13.5082254732941 \tabularnewline
Sum Squared Residuals & 8576.19130555554 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147433&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.772025713756317[/C][/ROW]
[ROW][C]R-squared[/C][C]0.596023702700951[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.492880818284172[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.77862162834757[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]5.27128218130724e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]13.5082254732941[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8576.19130555554[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147433&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147433&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 R0.772025713756317
R-squared0.596023702700951
Adjusted R-squared0.492880818284172
F-TEST (value)5.77862162834757
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value5.27128218130724e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation13.5082254732941
Sum Squared Residuals8576.19130555554







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1112.3141.457222222222-29.157222222222
2117.3131.477222222222-14.1772222222222
3111.1104.4227777777786.67722222222222
4102.294.64416666666677.55583333333335
5104.3100.1441666666674.15583333333334
6122.9110.22416666666712.6758333333333
7107.6105.9041666666671.69583333333333
8121.3107.82277777777813.4772222222222
9131.5120.04416666666711.4558333333333
108989.5027777777778-0.50277777777778
11104.496.04277777777788.35722222222223
12128.9121.6027777777787.29722222222221
13135.9122.06416666666713.8358333333333
14133.3112.08416666666721.2158333333334
15121.3104.42277777777816.8772222222222
16120.5114.0372222222226.46277777777777
17120.4119.5372222222220.862777777777778
18137.9129.6172222222228.28277777777777
19126.1125.2972222222220.802777777777774
20133.2127.2158333333335.98416666666665
21151.1139.43722222222211.6627777777778
22105108.895833333333-3.89583333333333
23119115.4358333333333.56416666666666
24140.4140.995833333333-0.595833333333346
25156.6141.45722222222215.1427777777777
26137.1131.4772222222225.62277777777777
27122.7123.815833333333-1.11583333333334
28125.8114.03722222222211.7627777777778
29139.3119.53722222222219.7627777777778
30134.9129.6172222222225.28277777777777
31149.2125.29722222222223.9027777777778
32132.3127.2158333333335.08416666666667
33149139.4372222222229.56277777777778
34117.2108.8958333333338.30416666666667
35119.6115.4358333333334.16416666666666
36152140.99583333333311.0041666666666
37149.4141.4572222222227.94277777777772
38127.3131.477222222222-4.17722222222222
39114.1123.815833333333-9.71583333333334
40102.1114.037222222222-11.9372222222222
41107.7119.537222222222-11.8372222222222
42104.4129.617222222222-25.2172222222222
43102.1125.297222222222-23.1972222222222
4496107.822777777778-11.8227777777778
45109.3139.437222222222-30.1372222222222
469089.50277777777780.497222222222225
4783.996.0427777777778-12.1427777777778
48112121.602777777778-9.60277777777778
49114.3122.064166666667-7.76416666666672
50103.6112.084166666667-8.48416666666666
5191.7104.422777777778-12.7227777777778
5280.894.6441666666667-13.8441666666667
5387.2100.144166666667-12.9441666666667
54109.2110.224166666667-1.02416666666667
55102.7105.904166666667-3.20416666666665
5695.1107.822777777778-12.7227777777778
57117.5120.044166666667-2.54416666666666
5885.189.5027777777778-4.40277777777778
5992.196.0427777777778-3.94277777777778
60113.5121.602777777778-8.10277777777778

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 112.3 & 141.457222222222 & -29.157222222222 \tabularnewline
2 & 117.3 & 131.477222222222 & -14.1772222222222 \tabularnewline
3 & 111.1 & 104.422777777778 & 6.67722222222222 \tabularnewline
4 & 102.2 & 94.6441666666667 & 7.55583333333335 \tabularnewline
5 & 104.3 & 100.144166666667 & 4.15583333333334 \tabularnewline
6 & 122.9 & 110.224166666667 & 12.6758333333333 \tabularnewline
7 & 107.6 & 105.904166666667 & 1.69583333333333 \tabularnewline
8 & 121.3 & 107.822777777778 & 13.4772222222222 \tabularnewline
9 & 131.5 & 120.044166666667 & 11.4558333333333 \tabularnewline
10 & 89 & 89.5027777777778 & -0.50277777777778 \tabularnewline
11 & 104.4 & 96.0427777777778 & 8.35722222222223 \tabularnewline
12 & 128.9 & 121.602777777778 & 7.29722222222221 \tabularnewline
13 & 135.9 & 122.064166666667 & 13.8358333333333 \tabularnewline
14 & 133.3 & 112.084166666667 & 21.2158333333334 \tabularnewline
15 & 121.3 & 104.422777777778 & 16.8772222222222 \tabularnewline
16 & 120.5 & 114.037222222222 & 6.46277777777777 \tabularnewline
17 & 120.4 & 119.537222222222 & 0.862777777777778 \tabularnewline
18 & 137.9 & 129.617222222222 & 8.28277777777777 \tabularnewline
19 & 126.1 & 125.297222222222 & 0.802777777777774 \tabularnewline
20 & 133.2 & 127.215833333333 & 5.98416666666665 \tabularnewline
21 & 151.1 & 139.437222222222 & 11.6627777777778 \tabularnewline
22 & 105 & 108.895833333333 & -3.89583333333333 \tabularnewline
23 & 119 & 115.435833333333 & 3.56416666666666 \tabularnewline
24 & 140.4 & 140.995833333333 & -0.595833333333346 \tabularnewline
25 & 156.6 & 141.457222222222 & 15.1427777777777 \tabularnewline
26 & 137.1 & 131.477222222222 & 5.62277777777777 \tabularnewline
27 & 122.7 & 123.815833333333 & -1.11583333333334 \tabularnewline
28 & 125.8 & 114.037222222222 & 11.7627777777778 \tabularnewline
29 & 139.3 & 119.537222222222 & 19.7627777777778 \tabularnewline
30 & 134.9 & 129.617222222222 & 5.28277777777777 \tabularnewline
31 & 149.2 & 125.297222222222 & 23.9027777777778 \tabularnewline
32 & 132.3 & 127.215833333333 & 5.08416666666667 \tabularnewline
33 & 149 & 139.437222222222 & 9.56277777777778 \tabularnewline
34 & 117.2 & 108.895833333333 & 8.30416666666667 \tabularnewline
35 & 119.6 & 115.435833333333 & 4.16416666666666 \tabularnewline
36 & 152 & 140.995833333333 & 11.0041666666666 \tabularnewline
37 & 149.4 & 141.457222222222 & 7.94277777777772 \tabularnewline
38 & 127.3 & 131.477222222222 & -4.17722222222222 \tabularnewline
39 & 114.1 & 123.815833333333 & -9.71583333333334 \tabularnewline
40 & 102.1 & 114.037222222222 & -11.9372222222222 \tabularnewline
41 & 107.7 & 119.537222222222 & -11.8372222222222 \tabularnewline
42 & 104.4 & 129.617222222222 & -25.2172222222222 \tabularnewline
43 & 102.1 & 125.297222222222 & -23.1972222222222 \tabularnewline
44 & 96 & 107.822777777778 & -11.8227777777778 \tabularnewline
45 & 109.3 & 139.437222222222 & -30.1372222222222 \tabularnewline
46 & 90 & 89.5027777777778 & 0.497222222222225 \tabularnewline
47 & 83.9 & 96.0427777777778 & -12.1427777777778 \tabularnewline
48 & 112 & 121.602777777778 & -9.60277777777778 \tabularnewline
49 & 114.3 & 122.064166666667 & -7.76416666666672 \tabularnewline
50 & 103.6 & 112.084166666667 & -8.48416666666666 \tabularnewline
51 & 91.7 & 104.422777777778 & -12.7227777777778 \tabularnewline
52 & 80.8 & 94.6441666666667 & -13.8441666666667 \tabularnewline
53 & 87.2 & 100.144166666667 & -12.9441666666667 \tabularnewline
54 & 109.2 & 110.224166666667 & -1.02416666666667 \tabularnewline
55 & 102.7 & 105.904166666667 & -3.20416666666665 \tabularnewline
56 & 95.1 & 107.822777777778 & -12.7227777777778 \tabularnewline
57 & 117.5 & 120.044166666667 & -2.54416666666666 \tabularnewline
58 & 85.1 & 89.5027777777778 & -4.40277777777778 \tabularnewline
59 & 92.1 & 96.0427777777778 & -3.94277777777778 \tabularnewline
60 & 113.5 & 121.602777777778 & -8.10277777777778 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147433&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]112.3[/C][C]141.457222222222[/C][C]-29.157222222222[/C][/ROW]
[ROW][C]2[/C][C]117.3[/C][C]131.477222222222[/C][C]-14.1772222222222[/C][/ROW]
[ROW][C]3[/C][C]111.1[/C][C]104.422777777778[/C][C]6.67722222222222[/C][/ROW]
[ROW][C]4[/C][C]102.2[/C][C]94.6441666666667[/C][C]7.55583333333335[/C][/ROW]
[ROW][C]5[/C][C]104.3[/C][C]100.144166666667[/C][C]4.15583333333334[/C][/ROW]
[ROW][C]6[/C][C]122.9[/C][C]110.224166666667[/C][C]12.6758333333333[/C][/ROW]
[ROW][C]7[/C][C]107.6[/C][C]105.904166666667[/C][C]1.69583333333333[/C][/ROW]
[ROW][C]8[/C][C]121.3[/C][C]107.822777777778[/C][C]13.4772222222222[/C][/ROW]
[ROW][C]9[/C][C]131.5[/C][C]120.044166666667[/C][C]11.4558333333333[/C][/ROW]
[ROW][C]10[/C][C]89[/C][C]89.5027777777778[/C][C]-0.50277777777778[/C][/ROW]
[ROW][C]11[/C][C]104.4[/C][C]96.0427777777778[/C][C]8.35722222222223[/C][/ROW]
[ROW][C]12[/C][C]128.9[/C][C]121.602777777778[/C][C]7.29722222222221[/C][/ROW]
[ROW][C]13[/C][C]135.9[/C][C]122.064166666667[/C][C]13.8358333333333[/C][/ROW]
[ROW][C]14[/C][C]133.3[/C][C]112.084166666667[/C][C]21.2158333333334[/C][/ROW]
[ROW][C]15[/C][C]121.3[/C][C]104.422777777778[/C][C]16.8772222222222[/C][/ROW]
[ROW][C]16[/C][C]120.5[/C][C]114.037222222222[/C][C]6.46277777777777[/C][/ROW]
[ROW][C]17[/C][C]120.4[/C][C]119.537222222222[/C][C]0.862777777777778[/C][/ROW]
[ROW][C]18[/C][C]137.9[/C][C]129.617222222222[/C][C]8.28277777777777[/C][/ROW]
[ROW][C]19[/C][C]126.1[/C][C]125.297222222222[/C][C]0.802777777777774[/C][/ROW]
[ROW][C]20[/C][C]133.2[/C][C]127.215833333333[/C][C]5.98416666666665[/C][/ROW]
[ROW][C]21[/C][C]151.1[/C][C]139.437222222222[/C][C]11.6627777777778[/C][/ROW]
[ROW][C]22[/C][C]105[/C][C]108.895833333333[/C][C]-3.89583333333333[/C][/ROW]
[ROW][C]23[/C][C]119[/C][C]115.435833333333[/C][C]3.56416666666666[/C][/ROW]
[ROW][C]24[/C][C]140.4[/C][C]140.995833333333[/C][C]-0.595833333333346[/C][/ROW]
[ROW][C]25[/C][C]156.6[/C][C]141.457222222222[/C][C]15.1427777777777[/C][/ROW]
[ROW][C]26[/C][C]137.1[/C][C]131.477222222222[/C][C]5.62277777777777[/C][/ROW]
[ROW][C]27[/C][C]122.7[/C][C]123.815833333333[/C][C]-1.11583333333334[/C][/ROW]
[ROW][C]28[/C][C]125.8[/C][C]114.037222222222[/C][C]11.7627777777778[/C][/ROW]
[ROW][C]29[/C][C]139.3[/C][C]119.537222222222[/C][C]19.7627777777778[/C][/ROW]
[ROW][C]30[/C][C]134.9[/C][C]129.617222222222[/C][C]5.28277777777777[/C][/ROW]
[ROW][C]31[/C][C]149.2[/C][C]125.297222222222[/C][C]23.9027777777778[/C][/ROW]
[ROW][C]32[/C][C]132.3[/C][C]127.215833333333[/C][C]5.08416666666667[/C][/ROW]
[ROW][C]33[/C][C]149[/C][C]139.437222222222[/C][C]9.56277777777778[/C][/ROW]
[ROW][C]34[/C][C]117.2[/C][C]108.895833333333[/C][C]8.30416666666667[/C][/ROW]
[ROW][C]35[/C][C]119.6[/C][C]115.435833333333[/C][C]4.16416666666666[/C][/ROW]
[ROW][C]36[/C][C]152[/C][C]140.995833333333[/C][C]11.0041666666666[/C][/ROW]
[ROW][C]37[/C][C]149.4[/C][C]141.457222222222[/C][C]7.94277777777772[/C][/ROW]
[ROW][C]38[/C][C]127.3[/C][C]131.477222222222[/C][C]-4.17722222222222[/C][/ROW]
[ROW][C]39[/C][C]114.1[/C][C]123.815833333333[/C][C]-9.71583333333334[/C][/ROW]
[ROW][C]40[/C][C]102.1[/C][C]114.037222222222[/C][C]-11.9372222222222[/C][/ROW]
[ROW][C]41[/C][C]107.7[/C][C]119.537222222222[/C][C]-11.8372222222222[/C][/ROW]
[ROW][C]42[/C][C]104.4[/C][C]129.617222222222[/C][C]-25.2172222222222[/C][/ROW]
[ROW][C]43[/C][C]102.1[/C][C]125.297222222222[/C][C]-23.1972222222222[/C][/ROW]
[ROW][C]44[/C][C]96[/C][C]107.822777777778[/C][C]-11.8227777777778[/C][/ROW]
[ROW][C]45[/C][C]109.3[/C][C]139.437222222222[/C][C]-30.1372222222222[/C][/ROW]
[ROW][C]46[/C][C]90[/C][C]89.5027777777778[/C][C]0.497222222222225[/C][/ROW]
[ROW][C]47[/C][C]83.9[/C][C]96.0427777777778[/C][C]-12.1427777777778[/C][/ROW]
[ROW][C]48[/C][C]112[/C][C]121.602777777778[/C][C]-9.60277777777778[/C][/ROW]
[ROW][C]49[/C][C]114.3[/C][C]122.064166666667[/C][C]-7.76416666666672[/C][/ROW]
[ROW][C]50[/C][C]103.6[/C][C]112.084166666667[/C][C]-8.48416666666666[/C][/ROW]
[ROW][C]51[/C][C]91.7[/C][C]104.422777777778[/C][C]-12.7227777777778[/C][/ROW]
[ROW][C]52[/C][C]80.8[/C][C]94.6441666666667[/C][C]-13.8441666666667[/C][/ROW]
[ROW][C]53[/C][C]87.2[/C][C]100.144166666667[/C][C]-12.9441666666667[/C][/ROW]
[ROW][C]54[/C][C]109.2[/C][C]110.224166666667[/C][C]-1.02416666666667[/C][/ROW]
[ROW][C]55[/C][C]102.7[/C][C]105.904166666667[/C][C]-3.20416666666665[/C][/ROW]
[ROW][C]56[/C][C]95.1[/C][C]107.822777777778[/C][C]-12.7227777777778[/C][/ROW]
[ROW][C]57[/C][C]117.5[/C][C]120.044166666667[/C][C]-2.54416666666666[/C][/ROW]
[ROW][C]58[/C][C]85.1[/C][C]89.5027777777778[/C][C]-4.40277777777778[/C][/ROW]
[ROW][C]59[/C][C]92.1[/C][C]96.0427777777778[/C][C]-3.94277777777778[/C][/ROW]
[ROW][C]60[/C][C]113.5[/C][C]121.602777777778[/C][C]-8.10277777777778[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147433&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147433&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
1112.3141.457222222222-29.157222222222
2117.3131.477222222222-14.1772222222222
3111.1104.4227777777786.67722222222222
4102.294.64416666666677.55583333333335
5104.3100.1441666666674.15583333333334
6122.9110.22416666666712.6758333333333
7107.6105.9041666666671.69583333333333
8121.3107.82277777777813.4772222222222
9131.5120.04416666666711.4558333333333
108989.5027777777778-0.50277777777778
11104.496.04277777777788.35722222222223
12128.9121.6027777777787.29722222222221
13135.9122.06416666666713.8358333333333
14133.3112.08416666666721.2158333333334
15121.3104.42277777777816.8772222222222
16120.5114.0372222222226.46277777777777
17120.4119.5372222222220.862777777777778
18137.9129.6172222222228.28277777777777
19126.1125.2972222222220.802777777777774
20133.2127.2158333333335.98416666666665
21151.1139.43722222222211.6627777777778
22105108.895833333333-3.89583333333333
23119115.4358333333333.56416666666666
24140.4140.995833333333-0.595833333333346
25156.6141.45722222222215.1427777777777
26137.1131.4772222222225.62277777777777
27122.7123.815833333333-1.11583333333334
28125.8114.03722222222211.7627777777778
29139.3119.53722222222219.7627777777778
30134.9129.6172222222225.28277777777777
31149.2125.29722222222223.9027777777778
32132.3127.2158333333335.08416666666667
33149139.4372222222229.56277777777778
34117.2108.8958333333338.30416666666667
35119.6115.4358333333334.16416666666666
36152140.99583333333311.0041666666666
37149.4141.4572222222227.94277777777772
38127.3131.477222222222-4.17722222222222
39114.1123.815833333333-9.71583333333334
40102.1114.037222222222-11.9372222222222
41107.7119.537222222222-11.8372222222222
42104.4129.617222222222-25.2172222222222
43102.1125.297222222222-23.1972222222222
4496107.822777777778-11.8227777777778
45109.3139.437222222222-30.1372222222222
469089.50277777777780.497222222222225
4783.996.0427777777778-12.1427777777778
48112121.602777777778-9.60277777777778
49114.3122.064166666667-7.76416666666672
50103.6112.084166666667-8.48416666666666
5191.7104.422777777778-12.7227777777778
5280.894.6441666666667-13.8441666666667
5387.2100.144166666667-12.9441666666667
54109.2110.224166666667-1.02416666666667
55102.7105.904166666667-3.20416666666665
5695.1107.822777777778-12.7227777777778
57117.5120.044166666667-2.54416666666666
5885.189.5027777777778-4.40277777777778
5992.196.0427777777778-3.94277777777778
60113.5121.602777777778-8.10277777777778







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.7017533110235990.5964933779528030.298246688976401
170.6966478325619540.6067043348760930.303352167438046
180.6428453705775650.7143092588448710.357154629422435
190.5839705426026090.8320589147947820.416029457397391
200.4739414179997220.9478828359994450.526058582000278
210.4356281215695850.871256243139170.564371878430415
220.3565611946156020.7131223892312040.643438805384398
230.2653313686350990.5306627372701990.734668631364901
240.1852946119099980.3705892238199950.814705388090002
250.3226466313044170.6452932626088350.677353368695583
260.250026996211290.500053992422580.74997300378871
270.1823196215100350.364639243020070.817680378489965
280.1704629682268150.3409259364536310.829537031773185
290.288537409978380.577074819956760.71146259002162
300.243385512686790.4867710253735790.75661448731321
310.554423181353810.8911536372923810.44557681864619
320.5113893574429210.9772212851141590.488610642557079
330.6081243357607590.7837513284784830.391875664239241
340.5792548389273340.8414903221453310.420745161072666
350.5437630082327410.9124739835345180.456236991767259
360.7008218309056880.5983563381886250.299178169094312
370.8098598142104310.3802803715791390.190140185789569
380.8094360763832180.3811278472335640.190563923616782
390.8430874274977620.3138251450044760.156912572502238
400.9166796444153520.1666407111692960.0833203555846481
410.997359736638150.005280526723699790.0026402633618499
420.9938939084754510.01221218304909860.00610609152454931
430.989625922461720.02074815507656080.0103740775382804
440.9641405346407130.07171893071857450.0358594653592873

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.701753311023599 & 0.596493377952803 & 0.298246688976401 \tabularnewline
17 & 0.696647832561954 & 0.606704334876093 & 0.303352167438046 \tabularnewline
18 & 0.642845370577565 & 0.714309258844871 & 0.357154629422435 \tabularnewline
19 & 0.583970542602609 & 0.832058914794782 & 0.416029457397391 \tabularnewline
20 & 0.473941417999722 & 0.947882835999445 & 0.526058582000278 \tabularnewline
21 & 0.435628121569585 & 0.87125624313917 & 0.564371878430415 \tabularnewline
22 & 0.356561194615602 & 0.713122389231204 & 0.643438805384398 \tabularnewline
23 & 0.265331368635099 & 0.530662737270199 & 0.734668631364901 \tabularnewline
24 & 0.185294611909998 & 0.370589223819995 & 0.814705388090002 \tabularnewline
25 & 0.322646631304417 & 0.645293262608835 & 0.677353368695583 \tabularnewline
26 & 0.25002699621129 & 0.50005399242258 & 0.74997300378871 \tabularnewline
27 & 0.182319621510035 & 0.36463924302007 & 0.817680378489965 \tabularnewline
28 & 0.170462968226815 & 0.340925936453631 & 0.829537031773185 \tabularnewline
29 & 0.28853740997838 & 0.57707481995676 & 0.71146259002162 \tabularnewline
30 & 0.24338551268679 & 0.486771025373579 & 0.75661448731321 \tabularnewline
31 & 0.55442318135381 & 0.891153637292381 & 0.44557681864619 \tabularnewline
32 & 0.511389357442921 & 0.977221285114159 & 0.488610642557079 \tabularnewline
33 & 0.608124335760759 & 0.783751328478483 & 0.391875664239241 \tabularnewline
34 & 0.579254838927334 & 0.841490322145331 & 0.420745161072666 \tabularnewline
35 & 0.543763008232741 & 0.912473983534518 & 0.456236991767259 \tabularnewline
36 & 0.700821830905688 & 0.598356338188625 & 0.299178169094312 \tabularnewline
37 & 0.809859814210431 & 0.380280371579139 & 0.190140185789569 \tabularnewline
38 & 0.809436076383218 & 0.381127847233564 & 0.190563923616782 \tabularnewline
39 & 0.843087427497762 & 0.313825145004476 & 0.156912572502238 \tabularnewline
40 & 0.916679644415352 & 0.166640711169296 & 0.0833203555846481 \tabularnewline
41 & 0.99735973663815 & 0.00528052672369979 & 0.0026402633618499 \tabularnewline
42 & 0.993893908475451 & 0.0122121830490986 & 0.00610609152454931 \tabularnewline
43 & 0.98962592246172 & 0.0207481550765608 & 0.0103740775382804 \tabularnewline
44 & 0.964140534640713 & 0.0717189307185745 & 0.0358594653592873 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147433&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]16[/C][C]0.701753311023599[/C][C]0.596493377952803[/C][C]0.298246688976401[/C][/ROW]
[ROW][C]17[/C][C]0.696647832561954[/C][C]0.606704334876093[/C][C]0.303352167438046[/C][/ROW]
[ROW][C]18[/C][C]0.642845370577565[/C][C]0.714309258844871[/C][C]0.357154629422435[/C][/ROW]
[ROW][C]19[/C][C]0.583970542602609[/C][C]0.832058914794782[/C][C]0.416029457397391[/C][/ROW]
[ROW][C]20[/C][C]0.473941417999722[/C][C]0.947882835999445[/C][C]0.526058582000278[/C][/ROW]
[ROW][C]21[/C][C]0.435628121569585[/C][C]0.87125624313917[/C][C]0.564371878430415[/C][/ROW]
[ROW][C]22[/C][C]0.356561194615602[/C][C]0.713122389231204[/C][C]0.643438805384398[/C][/ROW]
[ROW][C]23[/C][C]0.265331368635099[/C][C]0.530662737270199[/C][C]0.734668631364901[/C][/ROW]
[ROW][C]24[/C][C]0.185294611909998[/C][C]0.370589223819995[/C][C]0.814705388090002[/C][/ROW]
[ROW][C]25[/C][C]0.322646631304417[/C][C]0.645293262608835[/C][C]0.677353368695583[/C][/ROW]
[ROW][C]26[/C][C]0.25002699621129[/C][C]0.50005399242258[/C][C]0.74997300378871[/C][/ROW]
[ROW][C]27[/C][C]0.182319621510035[/C][C]0.36463924302007[/C][C]0.817680378489965[/C][/ROW]
[ROW][C]28[/C][C]0.170462968226815[/C][C]0.340925936453631[/C][C]0.829537031773185[/C][/ROW]
[ROW][C]29[/C][C]0.28853740997838[/C][C]0.57707481995676[/C][C]0.71146259002162[/C][/ROW]
[ROW][C]30[/C][C]0.24338551268679[/C][C]0.486771025373579[/C][C]0.75661448731321[/C][/ROW]
[ROW][C]31[/C][C]0.55442318135381[/C][C]0.891153637292381[/C][C]0.44557681864619[/C][/ROW]
[ROW][C]32[/C][C]0.511389357442921[/C][C]0.977221285114159[/C][C]0.488610642557079[/C][/ROW]
[ROW][C]33[/C][C]0.608124335760759[/C][C]0.783751328478483[/C][C]0.391875664239241[/C][/ROW]
[ROW][C]34[/C][C]0.579254838927334[/C][C]0.841490322145331[/C][C]0.420745161072666[/C][/ROW]
[ROW][C]35[/C][C]0.543763008232741[/C][C]0.912473983534518[/C][C]0.456236991767259[/C][/ROW]
[ROW][C]36[/C][C]0.700821830905688[/C][C]0.598356338188625[/C][C]0.299178169094312[/C][/ROW]
[ROW][C]37[/C][C]0.809859814210431[/C][C]0.380280371579139[/C][C]0.190140185789569[/C][/ROW]
[ROW][C]38[/C][C]0.809436076383218[/C][C]0.381127847233564[/C][C]0.190563923616782[/C][/ROW]
[ROW][C]39[/C][C]0.843087427497762[/C][C]0.313825145004476[/C][C]0.156912572502238[/C][/ROW]
[ROW][C]40[/C][C]0.916679644415352[/C][C]0.166640711169296[/C][C]0.0833203555846481[/C][/ROW]
[ROW][C]41[/C][C]0.99735973663815[/C][C]0.00528052672369979[/C][C]0.0026402633618499[/C][/ROW]
[ROW][C]42[/C][C]0.993893908475451[/C][C]0.0122121830490986[/C][C]0.00610609152454931[/C][/ROW]
[ROW][C]43[/C][C]0.98962592246172[/C][C]0.0207481550765608[/C][C]0.0103740775382804[/C][/ROW]
[ROW][C]44[/C][C]0.964140534640713[/C][C]0.0717189307185745[/C][C]0.0358594653592873[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147433&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147433&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
160.7017533110235990.5964933779528030.298246688976401
170.6966478325619540.6067043348760930.303352167438046
180.6428453705775650.7143092588448710.357154629422435
190.5839705426026090.8320589147947820.416029457397391
200.4739414179997220.9478828359994450.526058582000278
210.4356281215695850.871256243139170.564371878430415
220.3565611946156020.7131223892312040.643438805384398
230.2653313686350990.5306627372701990.734668631364901
240.1852946119099980.3705892238199950.814705388090002
250.3226466313044170.6452932626088350.677353368695583
260.250026996211290.500053992422580.74997300378871
270.1823196215100350.364639243020070.817680378489965
280.1704629682268150.3409259364536310.829537031773185
290.288537409978380.577074819956760.71146259002162
300.243385512686790.4867710253735790.75661448731321
310.554423181353810.8911536372923810.44557681864619
320.5113893574429210.9772212851141590.488610642557079
330.6081243357607590.7837513284784830.391875664239241
340.5792548389273340.8414903221453310.420745161072666
350.5437630082327410.9124739835345180.456236991767259
360.7008218309056880.5983563381886250.299178169094312
370.8098598142104310.3802803715791390.190140185789569
380.8094360763832180.3811278472335640.190563923616782
390.8430874274977620.3138251450044760.156912572502238
400.9166796444153520.1666407111692960.0833203555846481
410.997359736638150.005280526723699790.0026402633618499
420.9938939084754510.01221218304909860.00610609152454931
430.989625922461720.02074815507656080.0103740775382804
440.9641405346407130.07171893071857450.0358594653592873







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0344827586206897NOK
5% type I error level30.103448275862069NOK
10% type I error level40.137931034482759NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 1 & 0.0344827586206897 & NOK \tabularnewline
5% type I error level & 3 & 0.103448275862069 & NOK \tabularnewline
10% type I error level & 4 & 0.137931034482759 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147433&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]1[/C][C]0.0344827586206897[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.103448275862069[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.137931034482759[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147433&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147433&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0344827586206897NOK
5% type I error level30.103448275862069NOK
10% type I error level40.137931034482759NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}