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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 02:22:31 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258622718oskyhpdzlqw0jg7.htm/, Retrieved Thu, 28 Mar 2024 13:45:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57664, Retrieved Thu, 28 Mar 2024 13:45:50 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Multivariate regr...] [2009-11-19 09:22:31] [bef26de542bed2eafc60fe4615b06e47] [Current]
-    D        [Multiple Regression] [] [2010-12-07 12:41:22] [f47feae0308dca73181bb669fbad1c56]
- R             [Multiple Regression] [] [2011-11-26 18:25:06] [74be16979710d4c4e7c6647856088456]
- R P             [Multiple Regression] [] [2011-11-27 16:54:17] [3931071255a6f7f4a767409781cc5f7d]
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Dataseries X:
121.6	0
118.8	0
114.0	1
111.5	1
97.2	1
102.5	1
113.4	1
109.8	1
104.9	1
126.1	1
80.0	1
96.8	1
117.2	1
112.3	1
117.3	1
111.1	0
102.2	0
104.3	0
122.9	0
107.6	0
121.3	0
131.5	0
89.0	0
104.4	0
128.9	0
135.9	0
133.3	0
121.3	0
120.5	0
120.4	0
137.9	0
126.1	0
133.2	0
151.1	0
105.0	0
119.0	0
140.4	0
156.6	0
137.1	0
122.7	0
125.8	0
139.3	0
134.9	0
149.2	1
132.3	0
149.0	1
117.2	1
119.6	1
152.0	1
149.4	1
127.3	1
114.1	1
102.1	1
107.7	1
104.4	1
102.1	1
96.0	1
109.3	1
90.0	1
83.9	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=57664&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=57664&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57664&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
Promet[t] = + 101.309677040517 -12.0624779800352Dummy[t] + 28.1271129379527M1[t] + 30.4107848894109M2[t] + 23.7269524368761M3[t] + 11.3581287923273M4[t] + 4.48180074378546M5[t] + 9.4654726952437M6[t] + 17.0291446467019M7[t] + 15.4053121941671M8[t] + 11.2764885496183M9[t] + 29.2526560970836M10[t] -8.2036719514582M11[t] + 0.296328048541789t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Promet[t] =  +  101.309677040517 -12.0624779800352Dummy[t] +  28.1271129379527M1[t] +  30.4107848894109M2[t] +  23.7269524368761M3[t] +  11.3581287923273M4[t] +  4.48180074378546M5[t] +  9.4654726952437M6[t] +  17.0291446467019M7[t] +  15.4053121941671M8[t] +  11.2764885496183M9[t] +  29.2526560970836M10[t] -8.2036719514582M11[t] +  0.296328048541789t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57664&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Promet[t] =  +  101.309677040517 -12.0624779800352Dummy[t] +  28.1271129379527M1[t] +  30.4107848894109M2[t] +  23.7269524368761M3[t] +  11.3581287923273M4[t] +  4.48180074378546M5[t] +  9.4654726952437M6[t] +  17.0291446467019M7[t] +  15.4053121941671M8[t] +  11.2764885496183M9[t] +  29.2526560970836M10[t] -8.2036719514582M11[t] +  0.296328048541789t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57664&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57664&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] = + 101.309677040517 -12.0624779800352Dummy[t] + 28.1271129379527M1[t] + 30.4107848894109M2[t] + 23.7269524368761M3[t] + 11.3581287923273M4[t] + 4.48180074378546M5[t] + 9.4654726952437M6[t] + 17.0291446467019M7[t] + 15.4053121941671M8[t] + 11.2764885496183M9[t] + 29.2526560970836M10[t] -8.2036719514582M11[t] + 0.296328048541789t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)101.3096770405176.90311914.675900
Dummy-12.06247798003523.397488-3.55049e-040.00045
M128.12711293795278.1838033.43690.0012590.000629
M230.41078488941098.1724333.72110.0005390.000269
M323.72695243687618.1427572.91390.0054950.002748
M411.35812879232738.1531861.39310.1702880.085144
M54.481800743785468.1453160.55020.5848230.292412
M69.46547269524378.1386221.1630.2508150.125407
M717.02914464670198.1331042.09380.0418170.020909
M815.40531219416718.1042741.90090.0635930.031796
M911.27648854961838.125611.38780.1718930.085946
M1029.25265609708368.0971493.61270.0007470.000374
M11-8.20367195145828.095367-1.01340.316180.15809
t0.2963280485417890.0980773.02140.0041010.00205

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 101.309677040517 & 6.903119 & 14.6759 & 0 & 0 \tabularnewline
Dummy & -12.0624779800352 & 3.397488 & -3.5504 & 9e-04 & 0.00045 \tabularnewline
M1 & 28.1271129379527 & 8.183803 & 3.4369 & 0.001259 & 0.000629 \tabularnewline
M2 & 30.4107848894109 & 8.172433 & 3.7211 & 0.000539 & 0.000269 \tabularnewline
M3 & 23.7269524368761 & 8.142757 & 2.9139 & 0.005495 & 0.002748 \tabularnewline
M4 & 11.3581287923273 & 8.153186 & 1.3931 & 0.170288 & 0.085144 \tabularnewline
M5 & 4.48180074378546 & 8.145316 & 0.5502 & 0.584823 & 0.292412 \tabularnewline
M6 & 9.4654726952437 & 8.138622 & 1.163 & 0.250815 & 0.125407 \tabularnewline
M7 & 17.0291446467019 & 8.133104 & 2.0938 & 0.041817 & 0.020909 \tabularnewline
M8 & 15.4053121941671 & 8.104274 & 1.9009 & 0.063593 & 0.031796 \tabularnewline
M9 & 11.2764885496183 & 8.12561 & 1.3878 & 0.171893 & 0.085946 \tabularnewline
M10 & 29.2526560970836 & 8.097149 & 3.6127 & 0.000747 & 0.000374 \tabularnewline
M11 & -8.2036719514582 & 8.095367 & -1.0134 & 0.31618 & 0.15809 \tabularnewline
t & 0.296328048541789 & 0.098077 & 3.0214 & 0.004101 & 0.00205 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57664&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]101.309677040517[/C][C]6.903119[/C][C]14.6759[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Dummy[/C][C]-12.0624779800352[/C][C]3.397488[/C][C]-3.5504[/C][C]9e-04[/C][C]0.00045[/C][/ROW]
[ROW][C]M1[/C][C]28.1271129379527[/C][C]8.183803[/C][C]3.4369[/C][C]0.001259[/C][C]0.000629[/C][/ROW]
[ROW][C]M2[/C][C]30.4107848894109[/C][C]8.172433[/C][C]3.7211[/C][C]0.000539[/C][C]0.000269[/C][/ROW]
[ROW][C]M3[/C][C]23.7269524368761[/C][C]8.142757[/C][C]2.9139[/C][C]0.005495[/C][C]0.002748[/C][/ROW]
[ROW][C]M4[/C][C]11.3581287923273[/C][C]8.153186[/C][C]1.3931[/C][C]0.170288[/C][C]0.085144[/C][/ROW]
[ROW][C]M5[/C][C]4.48180074378546[/C][C]8.145316[/C][C]0.5502[/C][C]0.584823[/C][C]0.292412[/C][/ROW]
[ROW][C]M6[/C][C]9.4654726952437[/C][C]8.138622[/C][C]1.163[/C][C]0.250815[/C][C]0.125407[/C][/ROW]
[ROW][C]M7[/C][C]17.0291446467019[/C][C]8.133104[/C][C]2.0938[/C][C]0.041817[/C][C]0.020909[/C][/ROW]
[ROW][C]M8[/C][C]15.4053121941671[/C][C]8.104274[/C][C]1.9009[/C][C]0.063593[/C][C]0.031796[/C][/ROW]
[ROW][C]M9[/C][C]11.2764885496183[/C][C]8.12561[/C][C]1.3878[/C][C]0.171893[/C][C]0.085946[/C][/ROW]
[ROW][C]M10[/C][C]29.2526560970836[/C][C]8.097149[/C][C]3.6127[/C][C]0.000747[/C][C]0.000374[/C][/ROW]
[ROW][C]M11[/C][C]-8.2036719514582[/C][C]8.095367[/C][C]-1.0134[/C][C]0.31618[/C][C]0.15809[/C][/ROW]
[ROW][C]t[/C][C]0.296328048541789[/C][C]0.098077[/C][C]3.0214[/C][C]0.004101[/C][C]0.00205[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57664&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57664&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)101.3096770405176.90311914.675900
Dummy-12.06247798003523.397488-3.55049e-040.00045
M128.12711293795278.1838033.43690.0012590.000629
M230.41078488941098.1724333.72110.0005390.000269
M323.72695243687618.1427572.91390.0054950.002748
M411.35812879232738.1531861.39310.1702880.085144
M54.481800743785468.1453160.55020.5848230.292412
M69.46547269524378.1386221.1630.2508150.125407
M717.02914464670198.1331042.09380.0418170.020909
M815.40531219416718.1042741.90090.0635930.031796
M911.27648854961838.125611.38780.1718930.085946
M1029.25265609708368.0971493.61270.0007470.000374
M11-8.20367195145828.095367-1.01340.316180.15809
t0.2963280485417890.0980773.02140.0041010.00205







Multiple Linear Regression - Regression Statistics
Multiple R0.767100556821748
R-squared0.588443264276236
Adjusted R-squared0.472133752006477
F-TEST (value)5.05928752337509
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value1.90461905464900e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation12.7989599352620
Sum Squared Residuals7535.41526952436

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.767100556821748 \tabularnewline
R-squared & 0.588443264276236 \tabularnewline
Adjusted R-squared & 0.472133752006477 \tabularnewline
F-TEST (value) & 5.05928752337509 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 1.90461905464900e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 12.7989599352620 \tabularnewline
Sum Squared Residuals & 7535.41526952436 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57664&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.767100556821748[/C][/ROW]
[ROW][C]R-squared[/C][C]0.588443264276236[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.472133752006477[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.05928752337509[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]1.90461905464900e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]12.7989599352620[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7535.41526952436[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57664&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57664&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 R0.767100556821748
R-squared0.588443264276236
Adjusted R-squared0.472133752006477
F-TEST (value)5.05928752337509
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value1.90461905464900e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation12.7989599352620
Sum Squared Residuals7535.41526952436







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1121.6129.733118027011-8.13311802701108
2118.8132.313118027011-13.5131180270111
3114113.8631356429830.136864357017022
4111.5101.7906400469769.70935995302416
597.295.2106400469761.98935995302406
6102.5100.4906400469762.00935995302408
7113.4108.3506400469765.04935995302407
8109.8107.0231356429832.77686435701701
9104.9103.1906400469761.70935995302405
10126.1121.4631356429834.63686435701701
118084.303135642983-4.30313564298296
1296.892.8031356429833.99686435701701
13117.2121.226576629477-4.02657662947741
14112.3123.806576629477-11.5065766294774
15117.3117.419072225484-0.119072225484445
16111.1117.409054609513-6.30905460951265
17102.2110.829054609513-8.62905460951262
18104.3116.109054609513-11.8090546095126
19122.9123.969054609513-1.06905460951263
20107.6122.641550205520-15.0415502055197
21121.3118.8090546095132.49094539048737
22131.5137.081550205520-5.58155020551967
238999.9215502055197-10.9215502055197
24104.4108.421550205520-4.02155020551968
25128.9136.844991192014-7.94499119201412
26135.9139.424991192014-3.52499119201409
27133.3133.0374867880210.262513211978872
28121.3120.9649911920140.335008807985887
29120.5114.3849911920146.1150088079859
30120.4119.6649911920140.735008807985902
31137.9127.52499119201410.3750088079859
32126.1126.197486788021-0.0974867880211346
33133.2122.36499119201410.8350088079859
34151.1140.63748678802110.4625132119789
35105103.4774867880211.52251321197885
36119111.9774867880217.02251321197885
37140.4140.400927774516-0.000927774515589896
38156.6142.98092777451613.6190722254844
39137.1136.5934233705230.506576629477393
40122.7124.520927774516-1.82092777451557
41125.8117.9409277745167.85907222548444
42139.3123.22092777451616.0790722254844
43134.9131.0809277745163.81907222548444
44149.2117.69094539048731.5090546095126
45132.3125.9209277745166.37907222548446
46149132.13094539048716.8690546095126
47117.294.970945390487422.2290546095126
48119.6103.47094539048716.1290546095126
49152131.89438637698220.1056136230182
50149.4134.47438637698214.9256136230182
51127.3128.086881972989-0.786881972988836
52114.1116.014386376982-1.91438637698181
53102.1109.434386376982-7.3343863769818
54107.7114.714386376982-7.01438637698179
55104.4122.574386376982-18.1743863769818
56102.1121.246881972989-19.1468819729888
5796117.414386376982-21.4143863769818
58109.3135.686881972989-26.3868819729888
599098.5268819729888-8.52688197298884
6083.9107.026881972989-23.1268819729888

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 121.6 & 129.733118027011 & -8.13311802701108 \tabularnewline
2 & 118.8 & 132.313118027011 & -13.5131180270111 \tabularnewline
3 & 114 & 113.863135642983 & 0.136864357017022 \tabularnewline
4 & 111.5 & 101.790640046976 & 9.70935995302416 \tabularnewline
5 & 97.2 & 95.210640046976 & 1.98935995302406 \tabularnewline
6 & 102.5 & 100.490640046976 & 2.00935995302408 \tabularnewline
7 & 113.4 & 108.350640046976 & 5.04935995302407 \tabularnewline
8 & 109.8 & 107.023135642983 & 2.77686435701701 \tabularnewline
9 & 104.9 & 103.190640046976 & 1.70935995302405 \tabularnewline
10 & 126.1 & 121.463135642983 & 4.63686435701701 \tabularnewline
11 & 80 & 84.303135642983 & -4.30313564298296 \tabularnewline
12 & 96.8 & 92.803135642983 & 3.99686435701701 \tabularnewline
13 & 117.2 & 121.226576629477 & -4.02657662947741 \tabularnewline
14 & 112.3 & 123.806576629477 & -11.5065766294774 \tabularnewline
15 & 117.3 & 117.419072225484 & -0.119072225484445 \tabularnewline
16 & 111.1 & 117.409054609513 & -6.30905460951265 \tabularnewline
17 & 102.2 & 110.829054609513 & -8.62905460951262 \tabularnewline
18 & 104.3 & 116.109054609513 & -11.8090546095126 \tabularnewline
19 & 122.9 & 123.969054609513 & -1.06905460951263 \tabularnewline
20 & 107.6 & 122.641550205520 & -15.0415502055197 \tabularnewline
21 & 121.3 & 118.809054609513 & 2.49094539048737 \tabularnewline
22 & 131.5 & 137.081550205520 & -5.58155020551967 \tabularnewline
23 & 89 & 99.9215502055197 & -10.9215502055197 \tabularnewline
24 & 104.4 & 108.421550205520 & -4.02155020551968 \tabularnewline
25 & 128.9 & 136.844991192014 & -7.94499119201412 \tabularnewline
26 & 135.9 & 139.424991192014 & -3.52499119201409 \tabularnewline
27 & 133.3 & 133.037486788021 & 0.262513211978872 \tabularnewline
28 & 121.3 & 120.964991192014 & 0.335008807985887 \tabularnewline
29 & 120.5 & 114.384991192014 & 6.1150088079859 \tabularnewline
30 & 120.4 & 119.664991192014 & 0.735008807985902 \tabularnewline
31 & 137.9 & 127.524991192014 & 10.3750088079859 \tabularnewline
32 & 126.1 & 126.197486788021 & -0.0974867880211346 \tabularnewline
33 & 133.2 & 122.364991192014 & 10.8350088079859 \tabularnewline
34 & 151.1 & 140.637486788021 & 10.4625132119789 \tabularnewline
35 & 105 & 103.477486788021 & 1.52251321197885 \tabularnewline
36 & 119 & 111.977486788021 & 7.02251321197885 \tabularnewline
37 & 140.4 & 140.400927774516 & -0.000927774515589896 \tabularnewline
38 & 156.6 & 142.980927774516 & 13.6190722254844 \tabularnewline
39 & 137.1 & 136.593423370523 & 0.506576629477393 \tabularnewline
40 & 122.7 & 124.520927774516 & -1.82092777451557 \tabularnewline
41 & 125.8 & 117.940927774516 & 7.85907222548444 \tabularnewline
42 & 139.3 & 123.220927774516 & 16.0790722254844 \tabularnewline
43 & 134.9 & 131.080927774516 & 3.81907222548444 \tabularnewline
44 & 149.2 & 117.690945390487 & 31.5090546095126 \tabularnewline
45 & 132.3 & 125.920927774516 & 6.37907222548446 \tabularnewline
46 & 149 & 132.130945390487 & 16.8690546095126 \tabularnewline
47 & 117.2 & 94.9709453904874 & 22.2290546095126 \tabularnewline
48 & 119.6 & 103.470945390487 & 16.1290546095126 \tabularnewline
49 & 152 & 131.894386376982 & 20.1056136230182 \tabularnewline
50 & 149.4 & 134.474386376982 & 14.9256136230182 \tabularnewline
51 & 127.3 & 128.086881972989 & -0.786881972988836 \tabularnewline
52 & 114.1 & 116.014386376982 & -1.91438637698181 \tabularnewline
53 & 102.1 & 109.434386376982 & -7.3343863769818 \tabularnewline
54 & 107.7 & 114.714386376982 & -7.01438637698179 \tabularnewline
55 & 104.4 & 122.574386376982 & -18.1743863769818 \tabularnewline
56 & 102.1 & 121.246881972989 & -19.1468819729888 \tabularnewline
57 & 96 & 117.414386376982 & -21.4143863769818 \tabularnewline
58 & 109.3 & 135.686881972989 & -26.3868819729888 \tabularnewline
59 & 90 & 98.5268819729888 & -8.52688197298884 \tabularnewline
60 & 83.9 & 107.026881972989 & -23.1268819729888 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57664&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]121.6[/C][C]129.733118027011[/C][C]-8.13311802701108[/C][/ROW]
[ROW][C]2[/C][C]118.8[/C][C]132.313118027011[/C][C]-13.5131180270111[/C][/ROW]
[ROW][C]3[/C][C]114[/C][C]113.863135642983[/C][C]0.136864357017022[/C][/ROW]
[ROW][C]4[/C][C]111.5[/C][C]101.790640046976[/C][C]9.70935995302416[/C][/ROW]
[ROW][C]5[/C][C]97.2[/C][C]95.210640046976[/C][C]1.98935995302406[/C][/ROW]
[ROW][C]6[/C][C]102.5[/C][C]100.490640046976[/C][C]2.00935995302408[/C][/ROW]
[ROW][C]7[/C][C]113.4[/C][C]108.350640046976[/C][C]5.04935995302407[/C][/ROW]
[ROW][C]8[/C][C]109.8[/C][C]107.023135642983[/C][C]2.77686435701701[/C][/ROW]
[ROW][C]9[/C][C]104.9[/C][C]103.190640046976[/C][C]1.70935995302405[/C][/ROW]
[ROW][C]10[/C][C]126.1[/C][C]121.463135642983[/C][C]4.63686435701701[/C][/ROW]
[ROW][C]11[/C][C]80[/C][C]84.303135642983[/C][C]-4.30313564298296[/C][/ROW]
[ROW][C]12[/C][C]96.8[/C][C]92.803135642983[/C][C]3.99686435701701[/C][/ROW]
[ROW][C]13[/C][C]117.2[/C][C]121.226576629477[/C][C]-4.02657662947741[/C][/ROW]
[ROW][C]14[/C][C]112.3[/C][C]123.806576629477[/C][C]-11.5065766294774[/C][/ROW]
[ROW][C]15[/C][C]117.3[/C][C]117.419072225484[/C][C]-0.119072225484445[/C][/ROW]
[ROW][C]16[/C][C]111.1[/C][C]117.409054609513[/C][C]-6.30905460951265[/C][/ROW]
[ROW][C]17[/C][C]102.2[/C][C]110.829054609513[/C][C]-8.62905460951262[/C][/ROW]
[ROW][C]18[/C][C]104.3[/C][C]116.109054609513[/C][C]-11.8090546095126[/C][/ROW]
[ROW][C]19[/C][C]122.9[/C][C]123.969054609513[/C][C]-1.06905460951263[/C][/ROW]
[ROW][C]20[/C][C]107.6[/C][C]122.641550205520[/C][C]-15.0415502055197[/C][/ROW]
[ROW][C]21[/C][C]121.3[/C][C]118.809054609513[/C][C]2.49094539048737[/C][/ROW]
[ROW][C]22[/C][C]131.5[/C][C]137.081550205520[/C][C]-5.58155020551967[/C][/ROW]
[ROW][C]23[/C][C]89[/C][C]99.9215502055197[/C][C]-10.9215502055197[/C][/ROW]
[ROW][C]24[/C][C]104.4[/C][C]108.421550205520[/C][C]-4.02155020551968[/C][/ROW]
[ROW][C]25[/C][C]128.9[/C][C]136.844991192014[/C][C]-7.94499119201412[/C][/ROW]
[ROW][C]26[/C][C]135.9[/C][C]139.424991192014[/C][C]-3.52499119201409[/C][/ROW]
[ROW][C]27[/C][C]133.3[/C][C]133.037486788021[/C][C]0.262513211978872[/C][/ROW]
[ROW][C]28[/C][C]121.3[/C][C]120.964991192014[/C][C]0.335008807985887[/C][/ROW]
[ROW][C]29[/C][C]120.5[/C][C]114.384991192014[/C][C]6.1150088079859[/C][/ROW]
[ROW][C]30[/C][C]120.4[/C][C]119.664991192014[/C][C]0.735008807985902[/C][/ROW]
[ROW][C]31[/C][C]137.9[/C][C]127.524991192014[/C][C]10.3750088079859[/C][/ROW]
[ROW][C]32[/C][C]126.1[/C][C]126.197486788021[/C][C]-0.0974867880211346[/C][/ROW]
[ROW][C]33[/C][C]133.2[/C][C]122.364991192014[/C][C]10.8350088079859[/C][/ROW]
[ROW][C]34[/C][C]151.1[/C][C]140.637486788021[/C][C]10.4625132119789[/C][/ROW]
[ROW][C]35[/C][C]105[/C][C]103.477486788021[/C][C]1.52251321197885[/C][/ROW]
[ROW][C]36[/C][C]119[/C][C]111.977486788021[/C][C]7.02251321197885[/C][/ROW]
[ROW][C]37[/C][C]140.4[/C][C]140.400927774516[/C][C]-0.000927774515589896[/C][/ROW]
[ROW][C]38[/C][C]156.6[/C][C]142.980927774516[/C][C]13.6190722254844[/C][/ROW]
[ROW][C]39[/C][C]137.1[/C][C]136.593423370523[/C][C]0.506576629477393[/C][/ROW]
[ROW][C]40[/C][C]122.7[/C][C]124.520927774516[/C][C]-1.82092777451557[/C][/ROW]
[ROW][C]41[/C][C]125.8[/C][C]117.940927774516[/C][C]7.85907222548444[/C][/ROW]
[ROW][C]42[/C][C]139.3[/C][C]123.220927774516[/C][C]16.0790722254844[/C][/ROW]
[ROW][C]43[/C][C]134.9[/C][C]131.080927774516[/C][C]3.81907222548444[/C][/ROW]
[ROW][C]44[/C][C]149.2[/C][C]117.690945390487[/C][C]31.5090546095126[/C][/ROW]
[ROW][C]45[/C][C]132.3[/C][C]125.920927774516[/C][C]6.37907222548446[/C][/ROW]
[ROW][C]46[/C][C]149[/C][C]132.130945390487[/C][C]16.8690546095126[/C][/ROW]
[ROW][C]47[/C][C]117.2[/C][C]94.9709453904874[/C][C]22.2290546095126[/C][/ROW]
[ROW][C]48[/C][C]119.6[/C][C]103.470945390487[/C][C]16.1290546095126[/C][/ROW]
[ROW][C]49[/C][C]152[/C][C]131.894386376982[/C][C]20.1056136230182[/C][/ROW]
[ROW][C]50[/C][C]149.4[/C][C]134.474386376982[/C][C]14.9256136230182[/C][/ROW]
[ROW][C]51[/C][C]127.3[/C][C]128.086881972989[/C][C]-0.786881972988836[/C][/ROW]
[ROW][C]52[/C][C]114.1[/C][C]116.014386376982[/C][C]-1.91438637698181[/C][/ROW]
[ROW][C]53[/C][C]102.1[/C][C]109.434386376982[/C][C]-7.3343863769818[/C][/ROW]
[ROW][C]54[/C][C]107.7[/C][C]114.714386376982[/C][C]-7.01438637698179[/C][/ROW]
[ROW][C]55[/C][C]104.4[/C][C]122.574386376982[/C][C]-18.1743863769818[/C][/ROW]
[ROW][C]56[/C][C]102.1[/C][C]121.246881972989[/C][C]-19.1468819729888[/C][/ROW]
[ROW][C]57[/C][C]96[/C][C]117.414386376982[/C][C]-21.4143863769818[/C][/ROW]
[ROW][C]58[/C][C]109.3[/C][C]135.686881972989[/C][C]-26.3868819729888[/C][/ROW]
[ROW][C]59[/C][C]90[/C][C]98.5268819729888[/C][C]-8.52688197298884[/C][/ROW]
[ROW][C]60[/C][C]83.9[/C][C]107.026881972989[/C][C]-23.1268819729888[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57664&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57664&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
1121.6129.733118027011-8.13311802701108
2118.8132.313118027011-13.5131180270111
3114113.8631356429830.136864357017022
4111.5101.7906400469769.70935995302416
597.295.2106400469761.98935995302406
6102.5100.4906400469762.00935995302408
7113.4108.3506400469765.04935995302407
8109.8107.0231356429832.77686435701701
9104.9103.1906400469761.70935995302405
10126.1121.4631356429834.63686435701701
118084.303135642983-4.30313564298296
1296.892.8031356429833.99686435701701
13117.2121.226576629477-4.02657662947741
14112.3123.806576629477-11.5065766294774
15117.3117.419072225484-0.119072225484445
16111.1117.409054609513-6.30905460951265
17102.2110.829054609513-8.62905460951262
18104.3116.109054609513-11.8090546095126
19122.9123.969054609513-1.06905460951263
20107.6122.641550205520-15.0415502055197
21121.3118.8090546095132.49094539048737
22131.5137.081550205520-5.58155020551967
238999.9215502055197-10.9215502055197
24104.4108.421550205520-4.02155020551968
25128.9136.844991192014-7.94499119201412
26135.9139.424991192014-3.52499119201409
27133.3133.0374867880210.262513211978872
28121.3120.9649911920140.335008807985887
29120.5114.3849911920146.1150088079859
30120.4119.6649911920140.735008807985902
31137.9127.52499119201410.3750088079859
32126.1126.197486788021-0.0974867880211346
33133.2122.36499119201410.8350088079859
34151.1140.63748678802110.4625132119789
35105103.4774867880211.52251321197885
36119111.9774867880217.02251321197885
37140.4140.400927774516-0.000927774515589896
38156.6142.98092777451613.6190722254844
39137.1136.5934233705230.506576629477393
40122.7124.520927774516-1.82092777451557
41125.8117.9409277745167.85907222548444
42139.3123.22092777451616.0790722254844
43134.9131.0809277745163.81907222548444
44149.2117.69094539048731.5090546095126
45132.3125.9209277745166.37907222548446
46149132.13094539048716.8690546095126
47117.294.970945390487422.2290546095126
48119.6103.47094539048716.1290546095126
49152131.89438637698220.1056136230182
50149.4134.47438637698214.9256136230182
51127.3128.086881972989-0.786881972988836
52114.1116.014386376982-1.91438637698181
53102.1109.434386376982-7.3343863769818
54107.7114.714386376982-7.01438637698179
55104.4122.574386376982-18.1743863769818
56102.1121.246881972989-19.1468819729888
5796117.414386376982-21.4143863769818
58109.3135.686881972989-26.3868819729888
599098.5268819729888-8.52688197298884
6083.9107.026881972989-23.1268819729888







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.006034782572791550.01206956514558310.993965217427208
180.0009157532951238460.001831506590247690.999084246704876
190.0005420654709184890.001084130941836980.999457934529081
200.0003249422776722440.0006498845553444890.999675057722328
210.001017362415029180.002034724830058360.99898263758497
220.000274315769261430.000548631538522860.999725684230738
230.0001160249865856880.0002320499731713760.999883975013414
243.50899353468321e-057.01798706936641e-050.999964910064653
253.0052660052094e-056.0105320104188e-050.999969947339948
260.0005191945190066280.001038389038013260.999480805480993
270.000445796220045860.000891592440091720.999554203779954
280.0002249051077205630.0004498102154411250.99977509489228
290.0004240159329074890.0008480318658149780.999575984067093
300.0008352922866109380.001670584573221880.99916470771339
310.0007497152936836880.001499430587367380.999250284706316
320.001025513936206510.002051027872413020.998974486063793
330.001126051386002810.002252102772005620.998873948613997
340.000897299069212310.001794598138424620.999102700930788
350.004544618541999470.009089237083998940.995455381458
360.005868643903702770.01173728780740550.994131356096297
370.05387338654398540.1077467730879710.946126613456015
380.1536301040542210.3072602081084430.846369895945779
390.2223602553093700.4447205106187410.77763974469063
400.6624845899968210.6750308200063580.337515410003179
410.6495157899933870.7009684200132260.350484210006613
420.5471073899220180.9057852201559640.452892610077982
430.4345204482122090.8690408964244170.565479551787791

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.00603478257279155 & 0.0120695651455831 & 0.993965217427208 \tabularnewline
18 & 0.000915753295123846 & 0.00183150659024769 & 0.999084246704876 \tabularnewline
19 & 0.000542065470918489 & 0.00108413094183698 & 0.999457934529081 \tabularnewline
20 & 0.000324942277672244 & 0.000649884555344489 & 0.999675057722328 \tabularnewline
21 & 0.00101736241502918 & 0.00203472483005836 & 0.99898263758497 \tabularnewline
22 & 0.00027431576926143 & 0.00054863153852286 & 0.999725684230738 \tabularnewline
23 & 0.000116024986585688 & 0.000232049973171376 & 0.999883975013414 \tabularnewline
24 & 3.50899353468321e-05 & 7.01798706936641e-05 & 0.999964910064653 \tabularnewline
25 & 3.0052660052094e-05 & 6.0105320104188e-05 & 0.999969947339948 \tabularnewline
26 & 0.000519194519006628 & 0.00103838903801326 & 0.999480805480993 \tabularnewline
27 & 0.00044579622004586 & 0.00089159244009172 & 0.999554203779954 \tabularnewline
28 & 0.000224905107720563 & 0.000449810215441125 & 0.99977509489228 \tabularnewline
29 & 0.000424015932907489 & 0.000848031865814978 & 0.999575984067093 \tabularnewline
30 & 0.000835292286610938 & 0.00167058457322188 & 0.99916470771339 \tabularnewline
31 & 0.000749715293683688 & 0.00149943058736738 & 0.999250284706316 \tabularnewline
32 & 0.00102551393620651 & 0.00205102787241302 & 0.998974486063793 \tabularnewline
33 & 0.00112605138600281 & 0.00225210277200562 & 0.998873948613997 \tabularnewline
34 & 0.00089729906921231 & 0.00179459813842462 & 0.999102700930788 \tabularnewline
35 & 0.00454461854199947 & 0.00908923708399894 & 0.995455381458 \tabularnewline
36 & 0.00586864390370277 & 0.0117372878074055 & 0.994131356096297 \tabularnewline
37 & 0.0538733865439854 & 0.107746773087971 & 0.946126613456015 \tabularnewline
38 & 0.153630104054221 & 0.307260208108443 & 0.846369895945779 \tabularnewline
39 & 0.222360255309370 & 0.444720510618741 & 0.77763974469063 \tabularnewline
40 & 0.662484589996821 & 0.675030820006358 & 0.337515410003179 \tabularnewline
41 & 0.649515789993387 & 0.700968420013226 & 0.350484210006613 \tabularnewline
42 & 0.547107389922018 & 0.905785220155964 & 0.452892610077982 \tabularnewline
43 & 0.434520448212209 & 0.869040896424417 & 0.565479551787791 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57664&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]17[/C][C]0.00603478257279155[/C][C]0.0120695651455831[/C][C]0.993965217427208[/C][/ROW]
[ROW][C]18[/C][C]0.000915753295123846[/C][C]0.00183150659024769[/C][C]0.999084246704876[/C][/ROW]
[ROW][C]19[/C][C]0.000542065470918489[/C][C]0.00108413094183698[/C][C]0.999457934529081[/C][/ROW]
[ROW][C]20[/C][C]0.000324942277672244[/C][C]0.000649884555344489[/C][C]0.999675057722328[/C][/ROW]
[ROW][C]21[/C][C]0.00101736241502918[/C][C]0.00203472483005836[/C][C]0.99898263758497[/C][/ROW]
[ROW][C]22[/C][C]0.00027431576926143[/C][C]0.00054863153852286[/C][C]0.999725684230738[/C][/ROW]
[ROW][C]23[/C][C]0.000116024986585688[/C][C]0.000232049973171376[/C][C]0.999883975013414[/C][/ROW]
[ROW][C]24[/C][C]3.50899353468321e-05[/C][C]7.01798706936641e-05[/C][C]0.999964910064653[/C][/ROW]
[ROW][C]25[/C][C]3.0052660052094e-05[/C][C]6.0105320104188e-05[/C][C]0.999969947339948[/C][/ROW]
[ROW][C]26[/C][C]0.000519194519006628[/C][C]0.00103838903801326[/C][C]0.999480805480993[/C][/ROW]
[ROW][C]27[/C][C]0.00044579622004586[/C][C]0.00089159244009172[/C][C]0.999554203779954[/C][/ROW]
[ROW][C]28[/C][C]0.000224905107720563[/C][C]0.000449810215441125[/C][C]0.99977509489228[/C][/ROW]
[ROW][C]29[/C][C]0.000424015932907489[/C][C]0.000848031865814978[/C][C]0.999575984067093[/C][/ROW]
[ROW][C]30[/C][C]0.000835292286610938[/C][C]0.00167058457322188[/C][C]0.99916470771339[/C][/ROW]
[ROW][C]31[/C][C]0.000749715293683688[/C][C]0.00149943058736738[/C][C]0.999250284706316[/C][/ROW]
[ROW][C]32[/C][C]0.00102551393620651[/C][C]0.00205102787241302[/C][C]0.998974486063793[/C][/ROW]
[ROW][C]33[/C][C]0.00112605138600281[/C][C]0.00225210277200562[/C][C]0.998873948613997[/C][/ROW]
[ROW][C]34[/C][C]0.00089729906921231[/C][C]0.00179459813842462[/C][C]0.999102700930788[/C][/ROW]
[ROW][C]35[/C][C]0.00454461854199947[/C][C]0.00908923708399894[/C][C]0.995455381458[/C][/ROW]
[ROW][C]36[/C][C]0.00586864390370277[/C][C]0.0117372878074055[/C][C]0.994131356096297[/C][/ROW]
[ROW][C]37[/C][C]0.0538733865439854[/C][C]0.107746773087971[/C][C]0.946126613456015[/C][/ROW]
[ROW][C]38[/C][C]0.153630104054221[/C][C]0.307260208108443[/C][C]0.846369895945779[/C][/ROW]
[ROW][C]39[/C][C]0.222360255309370[/C][C]0.444720510618741[/C][C]0.77763974469063[/C][/ROW]
[ROW][C]40[/C][C]0.662484589996821[/C][C]0.675030820006358[/C][C]0.337515410003179[/C][/ROW]
[ROW][C]41[/C][C]0.649515789993387[/C][C]0.700968420013226[/C][C]0.350484210006613[/C][/ROW]
[ROW][C]42[/C][C]0.547107389922018[/C][C]0.905785220155964[/C][C]0.452892610077982[/C][/ROW]
[ROW][C]43[/C][C]0.434520448212209[/C][C]0.869040896424417[/C][C]0.565479551787791[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57664&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57664&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.006034782572791550.01206956514558310.993965217427208
180.0009157532951238460.001831506590247690.999084246704876
190.0005420654709184890.001084130941836980.999457934529081
200.0003249422776722440.0006498845553444890.999675057722328
210.001017362415029180.002034724830058360.99898263758497
220.000274315769261430.000548631538522860.999725684230738
230.0001160249865856880.0002320499731713760.999883975013414
243.50899353468321e-057.01798706936641e-050.999964910064653
253.0052660052094e-056.0105320104188e-050.999969947339948
260.0005191945190066280.001038389038013260.999480805480993
270.000445796220045860.000891592440091720.999554203779954
280.0002249051077205630.0004498102154411250.99977509489228
290.0004240159329074890.0008480318658149780.999575984067093
300.0008352922866109380.001670584573221880.99916470771339
310.0007497152936836880.001499430587367380.999250284706316
320.001025513936206510.002051027872413020.998974486063793
330.001126051386002810.002252102772005620.998873948613997
340.000897299069212310.001794598138424620.999102700930788
350.004544618541999470.009089237083998940.995455381458
360.005868643903702770.01173728780740550.994131356096297
370.05387338654398540.1077467730879710.946126613456015
380.1536301040542210.3072602081084430.846369895945779
390.2223602553093700.4447205106187410.77763974469063
400.6624845899968210.6750308200063580.337515410003179
410.6495157899933870.7009684200132260.350484210006613
420.5471073899220180.9057852201559640.452892610077982
430.4345204482122090.8690408964244170.565479551787791







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level180.666666666666667NOK
5% type I error level200.740740740740741NOK
10% type I error level200.740740740740741NOK

\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 & 18 & 0.666666666666667 & NOK \tabularnewline
5% type I error level & 20 & 0.740740740740741 & NOK \tabularnewline
10% type I error level & 20 & 0.740740740740741 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57664&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]18[/C][C]0.666666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]20[/C][C]0.740740740740741[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.740740740740741[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57664&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57664&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 level180.666666666666667NOK
5% type I error level200.740740740740741NOK
10% type I error level200.740740740740741NOK



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