## Free Statistics

of Irreproducible Research!

Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 24 Nov 2009 13:28:44 -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/24/t1259094574kr5saqyy8plxbeb.htm/, Retrieved Tue, 05 Dec 2023 20:54:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59267, Retrieved Tue, 05 Dec 2023 20:54:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsJSSHWR1
Estimated Impact133
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] [Ws7] [2009-11-20 12:14:35] [786e067c4f7cec17385c4742b96b6dfa]
-    D        [Multiple Regression] [Revieuw] [2009-11-24 20:28:44] [c8fd62404619100d8e91184019148412] [Current]
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Dataseries X:
132.92	138.04	136.51	131.02	126.51
129.61	132.92	138.04	136.51	131.02
122.96	129.61	132.92	138.04	136.51
124.04	122.96	129.61	132.92	138.04
121.29	124.04	122.96	129.61	132.92
124.56	121.29	124.04	122.96	129.61
118.53	124.56	121.29	124.04	122.96
113.14	118.53	124.56	121.29	124.04
114.15	113.14	118.53	124.56	121.29
122.17	114.15	113.14	118.53	124.56
129.23	122.17	114.15	113.14	118.53
131.19	129.23	122.17	114.15	113.14
129.12	131.19	129.23	122.17	114.15
128.28	129.12	131.19	129.23	122.17
126.83	128.28	129.12	131.19	129.23
138.13	126.83	128.28	129.12	131.19
140.52	138.13	126.83	128.28	129.12
146.83	140.52	138.13	126.83	128.28
135.14	146.83	140.52	138.13	126.83
131.84	135.14	146.83	140.52	138.13
125.7	131.84	135.14	146.83	140.52
128.98	125.7	131.84	135.14	146.83
133.25	128.98	125.7	131.84	135.14
136.76	133.25	128.98	125.7	131.84
133.24	136.76	133.25	128.98	125.7
128.54	133.24	136.76	133.25	128.98
121.08	128.54	133.24	136.76	133.25
120.23	121.08	128.54	133.24	136.76
119.08	120.23	121.08	128.54	133.24
125.75	119.08	120.23	121.08	128.54
126.89	125.75	119.08	120.23	121.08
126.6	126.89	125.75	119.08	120.23
121.89	126.6	126.89	125.75	119.08
123.44	121.89	126.6	126.89	125.75
126.46	123.44	121.89	126.6	126.89
129.49	126.46	123.44	121.89	126.6
127.78	129.49	126.46	123.44	121.89
125.29	127.78	129.49	126.46	123.44
119.02	125.29	127.78	129.49	126.46
119.96	119.02	125.29	127.78	129.49
122.86	119.96	119.02	125.29	127.78
131.89	122.86	119.96	119.02	125.29
132.73	131.89	122.86	119.96	119.02
135.01	132.73	131.89	122.86	119.96
136.71	135.01	132.73	131.89	122.86
142.73	136.71	135.01	132.73	131.89
144.43	142.73	136.71	135.01	132.73
144.93	144.43	142.73	136.71	135.01
138.75	144.93	144.43	142.73	136.71
130.22	138.75	144.93	144.43	142.73
122.19	130.22	138.75	144.93	144.43
128.4	122.19	130.22	138.75	144.93
140.43	128.4	122.19	130.22	138.75
153.5	140.43	128.4	122.19	130.22
149.33	153.5	140.43	128.4	122.19
142.97	149.33	153.5	140.43	128.4


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 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=59267&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=59267&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59267&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135

 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 15.1051730064618 + 1.51203729325094Y[t-1][t] -0.615089404620718Y[t-2][t] -0.323100184191052Y[t-3][t] + 0.296459446705439Y[t-4][t] -2.20171905517736M1[t] + 0.712293154424418M2[t] -2.17351756400950M3[t] + 6.28391154924662M4[t] -0.573360721088252M5[t] + 4.54706471258425M6[t] -6.44161928890922M7[t] + 1.40686131961080M8[t] + 1.31742230283138M9[t] + 4.89880448576374M10[t] + 1.11412070259176M11[t] + 0.037816028123414t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  15.1051730064618 +  1.51203729325094Y[t-1][t] -0.615089404620718Y[t-2][t] -0.323100184191052Y[t-3][t] +  0.296459446705439Y[t-4][t] -2.20171905517736M1[t] +  0.712293154424418M2[t] -2.17351756400950M3[t] +  6.28391154924662M4[t] -0.573360721088252M5[t] +  4.54706471258425M6[t] -6.44161928890922M7[t] +  1.40686131961080M8[t] +  1.31742230283138M9[t] +  4.89880448576374M10[t] +  1.11412070259176M11[t] +  0.037816028123414t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59267&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  15.1051730064618 +  1.51203729325094Y[t-1][t] -0.615089404620718Y[t-2][t] -0.323100184191052Y[t-3][t] +  0.296459446705439Y[t-4][t] -2.20171905517736M1[t] +  0.712293154424418M2[t] -2.17351756400950M3[t] +  6.28391154924662M4[t] -0.573360721088252M5[t] +  4.54706471258425M6[t] -6.44161928890922M7[t] +  1.40686131961080M8[t] +  1.31742230283138M9[t] +  4.89880448576374M10[t] +  1.11412070259176M11[t] +  0.037816028123414t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59267&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59267&T=1

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 15.1051730064618 + 1.51203729325094Y[t-1][t] -0.615089404620718Y[t-2][t] -0.323100184191052Y[t-3][t] + 0.296459446705439Y[t-4][t] -2.20171905517736M1[t] + 0.712293154424418M2[t] -2.17351756400950M3[t] + 6.28391154924662M4[t] -0.573360721088252M5[t] + 4.54706471258425M6[t] -6.44161928890922M7[t] + 1.40686131961080M8[t] + 1.31742230283138M9[t] + 4.89880448576374M10[t] + 1.11412070259176M11[t] + 0.037816028123414t + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 15.1051730064618 9.33752 1.6177 0.113791 0.056896 Y[t-1] 1.51203729325094 0.153906 9.8244 0 0 Y[t-2] -0.615089404620718 0.280843 -2.1902 0.034558 0.017279 Y[t-3] -0.323100184191052 0.28643 -1.128 0.266202 0.133101 Y[t-4] 0.296459446705439 0.160978 1.8416 0.073146 0.036573 M1 -2.20171905517736 2.115769 -1.0406 0.304459 0.152229 M2 0.712293154424418 2.264917 0.3145 0.754825 0.377413 M3 -2.17351756400950 2.441033 -0.8904 0.378706 0.189353 M4 6.28391154924662 2.246145 2.7976 0.007956 0.003978 M5 -0.573360721088252 2.484979 -0.2307 0.81873 0.409365 M6 4.54706471258425 1.943212 2.34 0.024497 0.012248 M7 -6.44161928890922 2.32824 -2.7667 0.008612 0.004306 M8 1.40686131961080 2.335778 0.6023 0.550454 0.275227 M9 1.31742230283138 2.950369 0.4465 0.657686 0.328843 M10 4.89880448576374 2.129084 2.3009 0.026829 0.013415 M11 1.11412070259176 2.324172 0.4794 0.634358 0.317179 t 0.037816028123414 0.025789 1.4664 0.150564 0.075282

\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) & 15.1051730064618 & 9.33752 & 1.6177 & 0.113791 & 0.056896 \tabularnewline
Y[t-1] & 1.51203729325094 & 0.153906 & 9.8244 & 0 & 0 \tabularnewline
Y[t-2] & -0.615089404620718 & 0.280843 & -2.1902 & 0.034558 & 0.017279 \tabularnewline
Y[t-3] & -0.323100184191052 & 0.28643 & -1.128 & 0.266202 & 0.133101 \tabularnewline
Y[t-4] & 0.296459446705439 & 0.160978 & 1.8416 & 0.073146 & 0.036573 \tabularnewline
M1 & -2.20171905517736 & 2.115769 & -1.0406 & 0.304459 & 0.152229 \tabularnewline
M2 & 0.712293154424418 & 2.264917 & 0.3145 & 0.754825 & 0.377413 \tabularnewline
M3 & -2.17351756400950 & 2.441033 & -0.8904 & 0.378706 & 0.189353 \tabularnewline
M4 & 6.28391154924662 & 2.246145 & 2.7976 & 0.007956 & 0.003978 \tabularnewline
M5 & -0.573360721088252 & 2.484979 & -0.2307 & 0.81873 & 0.409365 \tabularnewline
M6 & 4.54706471258425 & 1.943212 & 2.34 & 0.024497 & 0.012248 \tabularnewline
M7 & -6.44161928890922 & 2.32824 & -2.7667 & 0.008612 & 0.004306 \tabularnewline
M8 & 1.40686131961080 & 2.335778 & 0.6023 & 0.550454 & 0.275227 \tabularnewline
M9 & 1.31742230283138 & 2.950369 & 0.4465 & 0.657686 & 0.328843 \tabularnewline
M10 & 4.89880448576374 & 2.129084 & 2.3009 & 0.026829 & 0.013415 \tabularnewline
M11 & 1.11412070259176 & 2.324172 & 0.4794 & 0.634358 & 0.317179 \tabularnewline
t & 0.037816028123414 & 0.025789 & 1.4664 & 0.150564 & 0.075282 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59267&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]15.1051730064618[/C][C]9.33752[/C][C]1.6177[/C][C]0.113791[/C][C]0.056896[/C][/ROW]
[ROW][C]Y[t-1][/C][C]1.51203729325094[/C][C]0.153906[/C][C]9.8244[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y[t-2][/C][C]-0.615089404620718[/C][C]0.280843[/C][C]-2.1902[/C][C]0.034558[/C][C]0.017279[/C][/ROW]
[ROW][C]Y[t-3][/C][C]-0.323100184191052[/C][C]0.28643[/C][C]-1.128[/C][C]0.266202[/C][C]0.133101[/C][/ROW]
[ROW][C]Y[t-4][/C][C]0.296459446705439[/C][C]0.160978[/C][C]1.8416[/C][C]0.073146[/C][C]0.036573[/C][/ROW]
[ROW][C]M1[/C][C]-2.20171905517736[/C][C]2.115769[/C][C]-1.0406[/C][C]0.304459[/C][C]0.152229[/C][/ROW]
[ROW][C]M2[/C][C]0.712293154424418[/C][C]2.264917[/C][C]0.3145[/C][C]0.754825[/C][C]0.377413[/C][/ROW]
[ROW][C]M3[/C][C]-2.17351756400950[/C][C]2.441033[/C][C]-0.8904[/C][C]0.378706[/C][C]0.189353[/C][/ROW]
[ROW][C]M4[/C][C]6.28391154924662[/C][C]2.246145[/C][C]2.7976[/C][C]0.007956[/C][C]0.003978[/C][/ROW]
[ROW][C]M5[/C][C]-0.573360721088252[/C][C]2.484979[/C][C]-0.2307[/C][C]0.81873[/C][C]0.409365[/C][/ROW]
[ROW][C]M6[/C][C]4.54706471258425[/C][C]1.943212[/C][C]2.34[/C][C]0.024497[/C][C]0.012248[/C][/ROW]
[ROW][C]M7[/C][C]-6.44161928890922[/C][C]2.32824[/C][C]-2.7667[/C][C]0.008612[/C][C]0.004306[/C][/ROW]
[ROW][C]M8[/C][C]1.40686131961080[/C][C]2.335778[/C][C]0.6023[/C][C]0.550454[/C][C]0.275227[/C][/ROW]
[ROW][C]M9[/C][C]1.31742230283138[/C][C]2.950369[/C][C]0.4465[/C][C]0.657686[/C][C]0.328843[/C][/ROW]
[ROW][C]M10[/C][C]4.89880448576374[/C][C]2.129084[/C][C]2.3009[/C][C]0.026829[/C][C]0.013415[/C][/ROW]
[ROW][C]M11[/C][C]1.11412070259176[/C][C]2.324172[/C][C]0.4794[/C][C]0.634358[/C][C]0.317179[/C][/ROW]
[ROW][C]t[/C][C]0.037816028123414[/C][C]0.025789[/C][C]1.4664[/C][C]0.150564[/C][C]0.075282[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59267&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59267&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 15.1051730064618 9.33752 1.6177 0.113791 0.056896 Y[t-1] 1.51203729325094 0.153906 9.8244 0 0 Y[t-2] -0.615089404620718 0.280843 -2.1902 0.034558 0.017279 Y[t-3] -0.323100184191052 0.28643 -1.128 0.266202 0.133101 Y[t-4] 0.296459446705439 0.160978 1.8416 0.073146 0.036573 M1 -2.20171905517736 2.115769 -1.0406 0.304459 0.152229 M2 0.712293154424418 2.264917 0.3145 0.754825 0.377413 M3 -2.17351756400950 2.441033 -0.8904 0.378706 0.189353 M4 6.28391154924662 2.246145 2.7976 0.007956 0.003978 M5 -0.573360721088252 2.484979 -0.2307 0.81873 0.409365 M6 4.54706471258425 1.943212 2.34 0.024497 0.012248 M7 -6.44161928890922 2.32824 -2.7667 0.008612 0.004306 M8 1.40686131961080 2.335778 0.6023 0.550454 0.275227 M9 1.31742230283138 2.950369 0.4465 0.657686 0.328843 M10 4.89880448576374 2.129084 2.3009 0.026829 0.013415 M11 1.11412070259176 2.324172 0.4794 0.634358 0.317179 t 0.037816028123414 0.025789 1.4664 0.150564 0.075282

 Multiple Linear Regression - Regression Statistics Multiple R 0.964424788006357 R-squared 0.930115171721107 Adjusted R-squared 0.901444472940023 F-TEST (value) 32.4413150451275 F-TEST (DF numerator) 16 F-TEST (DF denominator) 39 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.77372047228213 Sum Squared Residuals 300.047485075922

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.964424788006357 \tabularnewline
R-squared & 0.930115171721107 \tabularnewline
F-TEST (value) & 32.4413150451275 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 39 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.77372047228213 \tabularnewline
Sum Squared Residuals & 300.047485075922 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59267&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.964424788006357[/C][/ROW]
[ROW][C]R-squared[/C][C]0.930115171721107[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]32.4413150451275[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]39[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.77372047228213[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]300.047485075922[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59267&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59267&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 R 0.964424788006357 R-squared 0.930115171721107 Adjusted R-squared 0.901444472940023 F-TEST (value) 32.4413150451275 F-TEST (DF numerator) 16 F-TEST (DF denominator) 39 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.77372047228213 Sum Squared Residuals 300.047485075922

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 132.92 132.869541784987 0.0504582150127606 2 129.61 126.701864385630 2.90813561436963 3 122.96 123.131503086918 -0.171503086917884 4 124.04 125.715502053991 -1.67550205399069 5 121.29 124.170979871759 -2.88097987175857 6 124.56 125.674157676400 -1.11415767639953 7 118.53 119.038743995150 -0.508743995149505 8 113.14 117.004815109347 -3.86481510934728 9 114.15 110.640499139187 3.50950086081307 10 122.17 122.019903408731 0.150096591269331 11 129.23 129.732193976044 -0.502193976043713 12 131.19 132.473607963094 -1.28360796309354 13 129.12 126.638927398149 2.48107260185056 14 128.28 125.351780667977 2.92821933202262 15 126.83 123.966637051627 2.86336294837310 16 138.13 132.035981114492 6.09401888550788 17 140.52 142.852159022757 -2.33215902275655 18 146.83 144.893128675053 1.93687132494742 19 135.14 137.932254065971 -2.7922540659707 20 131.84 126.839402908909 5.00059709109134 21 125.7 127.658227907921 -1.95822790792126 22 128.98 129.671012435569 -0.691012435569362 23 133.25 132.260895622599 0.989104377401063 24 136.76 136.629015899961 0.130984100038700 25 133.24 134.265902407570 -1.02590240756961 26 128.54 129.329144761531 -0.789144761530856 27 121.08 121.671489688127 -0.591489688127453 28 120.23 123.955742129861 -3.72574212986093 29 119.08 120.914654760152 -1.83465476015155 30 125.75 125.873847303186 -0.12384730318616 31 126.89 123.778668575254 3.11133142474646 32 126.6 129.405616079503 -2.80561607950294 33 121.89 125.718293762271 -3.82829376227096 34 123.44 124.003222549002 -0.5632225490023 35 126.46 125.928746516916 0.531253483084114 36 129.49 129.901234518899 -0.41123451889852 37 127.78 128.564105208962 -0.784105208961647 38 125.29 126.550378365363 -1.26037836536338 39 119.02 120.905527667711 -1.88552766771102 40 119.96 122.902645036397 -2.9426450363969 41 122.86 121.658688221583 1.20131177841736 42 131.89 131.911307926044 -0.0213079260441995 43 132.73 130.667862533347 2.06213746665265 44 135.01 133.611694518346 1.39830548165445 45 136.71 134.432979190621 2.27702080937915 46 142.73 141.625861606698 1.10413839330233 47 144.43 145.448163884441 -1.01816388444147 48 144.93 143.366141618047 1.56385838195336 49 138.75 139.471523200332 -0.721523200332063 50 130.22 134.006831819498 -3.78683181949801 51 122.19 122.404842505617 -0.214842505616743 52 128.4 126.150129665259 2.24987033474065 53 140.43 134.583518123751 5.84648187624932 54 153.5 154.177558419318 -0.677558419317529 55 149.33 151.202470830279 -1.87247083027891 56 142.97 142.698471383896 0.271528616104431

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 132.92 & 132.869541784987 & 0.0504582150127606 \tabularnewline
2 & 129.61 & 126.701864385630 & 2.90813561436963 \tabularnewline
3 & 122.96 & 123.131503086918 & -0.171503086917884 \tabularnewline
4 & 124.04 & 125.715502053991 & -1.67550205399069 \tabularnewline
5 & 121.29 & 124.170979871759 & -2.88097987175857 \tabularnewline
6 & 124.56 & 125.674157676400 & -1.11415767639953 \tabularnewline
7 & 118.53 & 119.038743995150 & -0.508743995149505 \tabularnewline
8 & 113.14 & 117.004815109347 & -3.86481510934728 \tabularnewline
9 & 114.15 & 110.640499139187 & 3.50950086081307 \tabularnewline
10 & 122.17 & 122.019903408731 & 0.150096591269331 \tabularnewline
11 & 129.23 & 129.732193976044 & -0.502193976043713 \tabularnewline
12 & 131.19 & 132.473607963094 & -1.28360796309354 \tabularnewline
13 & 129.12 & 126.638927398149 & 2.48107260185056 \tabularnewline
14 & 128.28 & 125.351780667977 & 2.92821933202262 \tabularnewline
15 & 126.83 & 123.966637051627 & 2.86336294837310 \tabularnewline
16 & 138.13 & 132.035981114492 & 6.09401888550788 \tabularnewline
17 & 140.52 & 142.852159022757 & -2.33215902275655 \tabularnewline
18 & 146.83 & 144.893128675053 & 1.93687132494742 \tabularnewline
19 & 135.14 & 137.932254065971 & -2.7922540659707 \tabularnewline
20 & 131.84 & 126.839402908909 & 5.00059709109134 \tabularnewline
21 & 125.7 & 127.658227907921 & -1.95822790792126 \tabularnewline
22 & 128.98 & 129.671012435569 & -0.691012435569362 \tabularnewline
23 & 133.25 & 132.260895622599 & 0.989104377401063 \tabularnewline
24 & 136.76 & 136.629015899961 & 0.130984100038700 \tabularnewline
25 & 133.24 & 134.265902407570 & -1.02590240756961 \tabularnewline
26 & 128.54 & 129.329144761531 & -0.789144761530856 \tabularnewline
27 & 121.08 & 121.671489688127 & -0.591489688127453 \tabularnewline
28 & 120.23 & 123.955742129861 & -3.72574212986093 \tabularnewline
29 & 119.08 & 120.914654760152 & -1.83465476015155 \tabularnewline
30 & 125.75 & 125.873847303186 & -0.12384730318616 \tabularnewline
31 & 126.89 & 123.778668575254 & 3.11133142474646 \tabularnewline
32 & 126.6 & 129.405616079503 & -2.80561607950294 \tabularnewline
33 & 121.89 & 125.718293762271 & -3.82829376227096 \tabularnewline
34 & 123.44 & 124.003222549002 & -0.5632225490023 \tabularnewline
35 & 126.46 & 125.928746516916 & 0.531253483084114 \tabularnewline
36 & 129.49 & 129.901234518899 & -0.41123451889852 \tabularnewline
37 & 127.78 & 128.564105208962 & -0.784105208961647 \tabularnewline
38 & 125.29 & 126.550378365363 & -1.26037836536338 \tabularnewline
39 & 119.02 & 120.905527667711 & -1.88552766771102 \tabularnewline
40 & 119.96 & 122.902645036397 & -2.9426450363969 \tabularnewline
41 & 122.86 & 121.658688221583 & 1.20131177841736 \tabularnewline
42 & 131.89 & 131.911307926044 & -0.0213079260441995 \tabularnewline
43 & 132.73 & 130.667862533347 & 2.06213746665265 \tabularnewline
44 & 135.01 & 133.611694518346 & 1.39830548165445 \tabularnewline
45 & 136.71 & 134.432979190621 & 2.27702080937915 \tabularnewline
46 & 142.73 & 141.625861606698 & 1.10413839330233 \tabularnewline
47 & 144.43 & 145.448163884441 & -1.01816388444147 \tabularnewline
48 & 144.93 & 143.366141618047 & 1.56385838195336 \tabularnewline
49 & 138.75 & 139.471523200332 & -0.721523200332063 \tabularnewline
50 & 130.22 & 134.006831819498 & -3.78683181949801 \tabularnewline
51 & 122.19 & 122.404842505617 & -0.214842505616743 \tabularnewline
52 & 128.4 & 126.150129665259 & 2.24987033474065 \tabularnewline
53 & 140.43 & 134.583518123751 & 5.84648187624932 \tabularnewline
54 & 153.5 & 154.177558419318 & -0.677558419317529 \tabularnewline
55 & 149.33 & 151.202470830279 & -1.87247083027891 \tabularnewline
56 & 142.97 & 142.698471383896 & 0.271528616104431 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59267&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]132.92[/C][C]132.869541784987[/C][C]0.0504582150127606[/C][/ROW]
[ROW][C]2[/C][C]129.61[/C][C]126.701864385630[/C][C]2.90813561436963[/C][/ROW]
[ROW][C]3[/C][C]122.96[/C][C]123.131503086918[/C][C]-0.171503086917884[/C][/ROW]
[ROW][C]4[/C][C]124.04[/C][C]125.715502053991[/C][C]-1.67550205399069[/C][/ROW]
[ROW][C]5[/C][C]121.29[/C][C]124.170979871759[/C][C]-2.88097987175857[/C][/ROW]
[ROW][C]6[/C][C]124.56[/C][C]125.674157676400[/C][C]-1.11415767639953[/C][/ROW]
[ROW][C]7[/C][C]118.53[/C][C]119.038743995150[/C][C]-0.508743995149505[/C][/ROW]
[ROW][C]8[/C][C]113.14[/C][C]117.004815109347[/C][C]-3.86481510934728[/C][/ROW]
[ROW][C]9[/C][C]114.15[/C][C]110.640499139187[/C][C]3.50950086081307[/C][/ROW]
[ROW][C]10[/C][C]122.17[/C][C]122.019903408731[/C][C]0.150096591269331[/C][/ROW]
[ROW][C]11[/C][C]129.23[/C][C]129.732193976044[/C][C]-0.502193976043713[/C][/ROW]
[ROW][C]12[/C][C]131.19[/C][C]132.473607963094[/C][C]-1.28360796309354[/C][/ROW]
[ROW][C]13[/C][C]129.12[/C][C]126.638927398149[/C][C]2.48107260185056[/C][/ROW]
[ROW][C]14[/C][C]128.28[/C][C]125.351780667977[/C][C]2.92821933202262[/C][/ROW]
[ROW][C]15[/C][C]126.83[/C][C]123.966637051627[/C][C]2.86336294837310[/C][/ROW]
[ROW][C]16[/C][C]138.13[/C][C]132.035981114492[/C][C]6.09401888550788[/C][/ROW]
[ROW][C]17[/C][C]140.52[/C][C]142.852159022757[/C][C]-2.33215902275655[/C][/ROW]
[ROW][C]18[/C][C]146.83[/C][C]144.893128675053[/C][C]1.93687132494742[/C][/ROW]
[ROW][C]19[/C][C]135.14[/C][C]137.932254065971[/C][C]-2.7922540659707[/C][/ROW]
[ROW][C]20[/C][C]131.84[/C][C]126.839402908909[/C][C]5.00059709109134[/C][/ROW]
[ROW][C]21[/C][C]125.7[/C][C]127.658227907921[/C][C]-1.95822790792126[/C][/ROW]
[ROW][C]22[/C][C]128.98[/C][C]129.671012435569[/C][C]-0.691012435569362[/C][/ROW]
[ROW][C]23[/C][C]133.25[/C][C]132.260895622599[/C][C]0.989104377401063[/C][/ROW]
[ROW][C]24[/C][C]136.76[/C][C]136.629015899961[/C][C]0.130984100038700[/C][/ROW]
[ROW][C]25[/C][C]133.24[/C][C]134.265902407570[/C][C]-1.02590240756961[/C][/ROW]
[ROW][C]26[/C][C]128.54[/C][C]129.329144761531[/C][C]-0.789144761530856[/C][/ROW]
[ROW][C]27[/C][C]121.08[/C][C]121.671489688127[/C][C]-0.591489688127453[/C][/ROW]
[ROW][C]28[/C][C]120.23[/C][C]123.955742129861[/C][C]-3.72574212986093[/C][/ROW]
[ROW][C]29[/C][C]119.08[/C][C]120.914654760152[/C][C]-1.83465476015155[/C][/ROW]
[ROW][C]30[/C][C]125.75[/C][C]125.873847303186[/C][C]-0.12384730318616[/C][/ROW]
[ROW][C]31[/C][C]126.89[/C][C]123.778668575254[/C][C]3.11133142474646[/C][/ROW]
[ROW][C]32[/C][C]126.6[/C][C]129.405616079503[/C][C]-2.80561607950294[/C][/ROW]
[ROW][C]33[/C][C]121.89[/C][C]125.718293762271[/C][C]-3.82829376227096[/C][/ROW]
[ROW][C]34[/C][C]123.44[/C][C]124.003222549002[/C][C]-0.5632225490023[/C][/ROW]
[ROW][C]35[/C][C]126.46[/C][C]125.928746516916[/C][C]0.531253483084114[/C][/ROW]
[ROW][C]36[/C][C]129.49[/C][C]129.901234518899[/C][C]-0.41123451889852[/C][/ROW]
[ROW][C]37[/C][C]127.78[/C][C]128.564105208962[/C][C]-0.784105208961647[/C][/ROW]
[ROW][C]38[/C][C]125.29[/C][C]126.550378365363[/C][C]-1.26037836536338[/C][/ROW]
[ROW][C]39[/C][C]119.02[/C][C]120.905527667711[/C][C]-1.88552766771102[/C][/ROW]
[ROW][C]40[/C][C]119.96[/C][C]122.902645036397[/C][C]-2.9426450363969[/C][/ROW]
[ROW][C]41[/C][C]122.86[/C][C]121.658688221583[/C][C]1.20131177841736[/C][/ROW]
[ROW][C]42[/C][C]131.89[/C][C]131.911307926044[/C][C]-0.0213079260441995[/C][/ROW]
[ROW][C]43[/C][C]132.73[/C][C]130.667862533347[/C][C]2.06213746665265[/C][/ROW]
[ROW][C]44[/C][C]135.01[/C][C]133.611694518346[/C][C]1.39830548165445[/C][/ROW]
[ROW][C]45[/C][C]136.71[/C][C]134.432979190621[/C][C]2.27702080937915[/C][/ROW]
[ROW][C]46[/C][C]142.73[/C][C]141.625861606698[/C][C]1.10413839330233[/C][/ROW]
[ROW][C]47[/C][C]144.43[/C][C]145.448163884441[/C][C]-1.01816388444147[/C][/ROW]
[ROW][C]48[/C][C]144.93[/C][C]143.366141618047[/C][C]1.56385838195336[/C][/ROW]
[ROW][C]49[/C][C]138.75[/C][C]139.471523200332[/C][C]-0.721523200332063[/C][/ROW]
[ROW][C]50[/C][C]130.22[/C][C]134.006831819498[/C][C]-3.78683181949801[/C][/ROW]
[ROW][C]51[/C][C]122.19[/C][C]122.404842505617[/C][C]-0.214842505616743[/C][/ROW]
[ROW][C]52[/C][C]128.4[/C][C]126.150129665259[/C][C]2.24987033474065[/C][/ROW]
[ROW][C]53[/C][C]140.43[/C][C]134.583518123751[/C][C]5.84648187624932[/C][/ROW]
[ROW][C]54[/C][C]153.5[/C][C]154.177558419318[/C][C]-0.677558419317529[/C][/ROW]
[ROW][C]55[/C][C]149.33[/C][C]151.202470830279[/C][C]-1.87247083027891[/C][/ROW]
[ROW][C]56[/C][C]142.97[/C][C]142.698471383896[/C][C]0.271528616104431[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59267&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59267&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 132.92 132.869541784987 0.0504582150127606 2 129.61 126.701864385630 2.90813561436963 3 122.96 123.131503086918 -0.171503086917884 4 124.04 125.715502053991 -1.67550205399069 5 121.29 124.170979871759 -2.88097987175857 6 124.56 125.674157676400 -1.11415767639953 7 118.53 119.038743995150 -0.508743995149505 8 113.14 117.004815109347 -3.86481510934728 9 114.15 110.640499139187 3.50950086081307 10 122.17 122.019903408731 0.150096591269331 11 129.23 129.732193976044 -0.502193976043713 12 131.19 132.473607963094 -1.28360796309354 13 129.12 126.638927398149 2.48107260185056 14 128.28 125.351780667977 2.92821933202262 15 126.83 123.966637051627 2.86336294837310 16 138.13 132.035981114492 6.09401888550788 17 140.52 142.852159022757 -2.33215902275655 18 146.83 144.893128675053 1.93687132494742 19 135.14 137.932254065971 -2.7922540659707 20 131.84 126.839402908909 5.00059709109134 21 125.7 127.658227907921 -1.95822790792126 22 128.98 129.671012435569 -0.691012435569362 23 133.25 132.260895622599 0.989104377401063 24 136.76 136.629015899961 0.130984100038700 25 133.24 134.265902407570 -1.02590240756961 26 128.54 129.329144761531 -0.789144761530856 27 121.08 121.671489688127 -0.591489688127453 28 120.23 123.955742129861 -3.72574212986093 29 119.08 120.914654760152 -1.83465476015155 30 125.75 125.873847303186 -0.12384730318616 31 126.89 123.778668575254 3.11133142474646 32 126.6 129.405616079503 -2.80561607950294 33 121.89 125.718293762271 -3.82829376227096 34 123.44 124.003222549002 -0.5632225490023 35 126.46 125.928746516916 0.531253483084114 36 129.49 129.901234518899 -0.41123451889852 37 127.78 128.564105208962 -0.784105208961647 38 125.29 126.550378365363 -1.26037836536338 39 119.02 120.905527667711 -1.88552766771102 40 119.96 122.902645036397 -2.9426450363969 41 122.86 121.658688221583 1.20131177841736 42 131.89 131.911307926044 -0.0213079260441995 43 132.73 130.667862533347 2.06213746665265 44 135.01 133.611694518346 1.39830548165445 45 136.71 134.432979190621 2.27702080937915 46 142.73 141.625861606698 1.10413839330233 47 144.43 145.448163884441 -1.01816388444147 48 144.93 143.366141618047 1.56385838195336 49 138.75 139.471523200332 -0.721523200332063 50 130.22 134.006831819498 -3.78683181949801 51 122.19 122.404842505617 -0.214842505616743 52 128.4 126.150129665259 2.24987033474065 53 140.43 134.583518123751 5.84648187624932 54 153.5 154.177558419318 -0.677558419317529 55 149.33 151.202470830279 -1.87247083027891 56 142.97 142.698471383896 0.271528616104431

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.87639302704956 0.247213945900879 0.123606972950440 21 0.935867501611208 0.128264996777583 0.0641324983887917 22 0.979552272868494 0.0408954542630127 0.0204477271315063 23 0.962888159266092 0.074223681467817 0.0371118407339085 24 0.947881345209814 0.104237309580372 0.0521186547901859 25 0.948436608588008 0.103126782823983 0.0515633914119915 26 0.986385761098432 0.0272284778031352 0.0136142389015676 27 0.986405177605871 0.0271896447882573 0.0135948223941287 28 0.98590624518674 0.0281875096265189 0.0140937548132594 29 0.971569119500679 0.0568617609986418 0.0284308804993209 30 0.984689094124772 0.0306218117504565 0.0153109058752282 31 0.997119704128138 0.00576059174372455 0.00288029587186228 32 0.995218892634077 0.00956221473184612 0.00478110736592306 33 0.99336851226078 0.0132629754784413 0.00663148773922064 34 0.98150546531639 0.0369890693672174 0.0184945346836087 35 0.946318451535634 0.107363096928732 0.0536815484643659 36 0.994954098083264 0.0100918038334716 0.00504590191673582

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.87639302704956 & 0.247213945900879 & 0.123606972950440 \tabularnewline
21 & 0.935867501611208 & 0.128264996777583 & 0.0641324983887917 \tabularnewline
22 & 0.979552272868494 & 0.0408954542630127 & 0.0204477271315063 \tabularnewline
23 & 0.962888159266092 & 0.074223681467817 & 0.0371118407339085 \tabularnewline
24 & 0.947881345209814 & 0.104237309580372 & 0.0521186547901859 \tabularnewline
25 & 0.948436608588008 & 0.103126782823983 & 0.0515633914119915 \tabularnewline
26 & 0.986385761098432 & 0.0272284778031352 & 0.0136142389015676 \tabularnewline
27 & 0.986405177605871 & 0.0271896447882573 & 0.0135948223941287 \tabularnewline
28 & 0.98590624518674 & 0.0281875096265189 & 0.0140937548132594 \tabularnewline
29 & 0.971569119500679 & 0.0568617609986418 & 0.0284308804993209 \tabularnewline
30 & 0.984689094124772 & 0.0306218117504565 & 0.0153109058752282 \tabularnewline
31 & 0.997119704128138 & 0.00576059174372455 & 0.00288029587186228 \tabularnewline
32 & 0.995218892634077 & 0.00956221473184612 & 0.00478110736592306 \tabularnewline
33 & 0.99336851226078 & 0.0132629754784413 & 0.00663148773922064 \tabularnewline
34 & 0.98150546531639 & 0.0369890693672174 & 0.0184945346836087 \tabularnewline
35 & 0.946318451535634 & 0.107363096928732 & 0.0536815484643659 \tabularnewline
36 & 0.994954098083264 & 0.0100918038334716 & 0.00504590191673582 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59267&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]20[/C][C]0.87639302704956[/C][C]0.247213945900879[/C][C]0.123606972950440[/C][/ROW]
[ROW][C]21[/C][C]0.935867501611208[/C][C]0.128264996777583[/C][C]0.0641324983887917[/C][/ROW]
[ROW][C]22[/C][C]0.979552272868494[/C][C]0.0408954542630127[/C][C]0.0204477271315063[/C][/ROW]
[ROW][C]23[/C][C]0.962888159266092[/C][C]0.074223681467817[/C][C]0.0371118407339085[/C][/ROW]
[ROW][C]24[/C][C]0.947881345209814[/C][C]0.104237309580372[/C][C]0.0521186547901859[/C][/ROW]
[ROW][C]25[/C][C]0.948436608588008[/C][C]0.103126782823983[/C][C]0.0515633914119915[/C][/ROW]
[ROW][C]26[/C][C]0.986385761098432[/C][C]0.0272284778031352[/C][C]0.0136142389015676[/C][/ROW]
[ROW][C]27[/C][C]0.986405177605871[/C][C]0.0271896447882573[/C][C]0.0135948223941287[/C][/ROW]
[ROW][C]28[/C][C]0.98590624518674[/C][C]0.0281875096265189[/C][C]0.0140937548132594[/C][/ROW]
[ROW][C]29[/C][C]0.971569119500679[/C][C]0.0568617609986418[/C][C]0.0284308804993209[/C][/ROW]
[ROW][C]30[/C][C]0.984689094124772[/C][C]0.0306218117504565[/C][C]0.0153109058752282[/C][/ROW]
[ROW][C]31[/C][C]0.997119704128138[/C][C]0.00576059174372455[/C][C]0.00288029587186228[/C][/ROW]
[ROW][C]32[/C][C]0.995218892634077[/C][C]0.00956221473184612[/C][C]0.00478110736592306[/C][/ROW]
[ROW][C]33[/C][C]0.99336851226078[/C][C]0.0132629754784413[/C][C]0.00663148773922064[/C][/ROW]
[ROW][C]34[/C][C]0.98150546531639[/C][C]0.0369890693672174[/C][C]0.0184945346836087[/C][/ROW]
[ROW][C]35[/C][C]0.946318451535634[/C][C]0.107363096928732[/C][C]0.0536815484643659[/C][/ROW]
[ROW][C]36[/C][C]0.994954098083264[/C][C]0.0100918038334716[/C][C]0.00504590191673582[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59267&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59267&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-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.87639302704956 0.247213945900879 0.123606972950440 21 0.935867501611208 0.128264996777583 0.0641324983887917 22 0.979552272868494 0.0408954542630127 0.0204477271315063 23 0.962888159266092 0.074223681467817 0.0371118407339085 24 0.947881345209814 0.104237309580372 0.0521186547901859 25 0.948436608588008 0.103126782823983 0.0515633914119915 26 0.986385761098432 0.0272284778031352 0.0136142389015676 27 0.986405177605871 0.0271896447882573 0.0135948223941287 28 0.98590624518674 0.0281875096265189 0.0140937548132594 29 0.971569119500679 0.0568617609986418 0.0284308804993209 30 0.984689094124772 0.0306218117504565 0.0153109058752282 31 0.997119704128138 0.00576059174372455 0.00288029587186228 32 0.995218892634077 0.00956221473184612 0.00478110736592306 33 0.99336851226078 0.0132629754784413 0.00663148773922064 34 0.98150546531639 0.0369890693672174 0.0184945346836087 35 0.946318451535634 0.107363096928732 0.0536815484643659 36 0.994954098083264 0.0100918038334716 0.00504590191673582

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.117647058823529 NOK 5% type I error level 10 0.588235294117647 NOK 10% type I error level 12 0.705882352941177 NOK

\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 & 2 & 0.117647058823529 & NOK \tabularnewline
5% type I error level & 10 & 0.588235294117647 & NOK \tabularnewline
10% type I error level & 12 & 0.705882352941177 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59267&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]2[/C][C]0.117647058823529[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.588235294117647[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.705882352941177[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59267&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59267&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 tests OK/NOK 1% type I error level 2 0.117647058823529 NOK 5% type I error level 10 0.588235294117647 NOK 10% type I error level 12 0.705882352941177 NOK

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 testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}