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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 24 Nov 2009 14:51:11 -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/t12590995393cybi4ez96hzge8.htm/, Retrieved Sat, 12 Oct 2024 11:47:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59294, Retrieved Sat, 12 Oct 2024 11:47:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact253
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [model 1] [2009-11-17 14:36:29] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D      [Multiple Regression] [multiple regression] [2009-11-19 21:38:11] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   P         [Multiple Regression] [monthly dummies] [2009-11-19 22:00:07] [ed603017d2bee8fbd82b6d5ec04e12c3]
-   P           [Multiple Regression] [model3] [2009-11-20 08:47:44] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D            [Multiple Regression] [model 4] [2009-11-20 08:59:37] [ed603017d2bee8fbd82b6d5ec04e12c3]
-    D              [Multiple Regression] [W7: Model 4] [2009-11-22 13:34:45] [03d5b865e91ca35b5a5d21b8d6da5aba]
-    D                  [Multiple Regression] [review 7] [2009-11-24 21:51:11] [6198946fb53eb5eb18db46bb758f7fde] [Current]
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Dataseries X:
6,3	2,3	6,1	6,2	6,3
6,5	1,9	6,3	6,1	6,2
6,6	2	6,5	6,3	6,1
6,5	2,3	6,6	6,5	6,3
6,2	2,8	6,5	6,6	6,5
6,2	2,4	6,2	6,5	6,6
5,9	2,3	6,2	6,2	6,5
6,1	2,7	5,9	6,2	6,2
6,1	2,7	6,1	5,9	6,2
6,1	2,9	6,1	6,1	5,9
6,1	3	6,1	6,1	6,1
6,1	2,2	6,1	6,1	6,1
6,4	2,3	6,1	6,1	6,1
6,7	2,8	6,4	6,1	6,1
6,9	2,8	6,7	6,4	6,1
7	2,8	6,9	6,7	6,4
7	2,2	7	6,9	6,7
6,8	2,6	7	7	6,9
6,4	2,8	6,8	7	7
5,9	2,5	6,4	6,8	7
5,5	2,4	5,9	6,4	6,8
5,5	2,3	5,5	5,9	6,4
5,6	1,9	5,5	5,5	5,9
5,8	1,7	5,6	5,5	5,5
5,9	2	5,8	5,6	5,5
6,1	2,1	5,9	5,8	5,6
6,1	1,7	6,1	5,9	5,8
6	1,8	6,1	6,1	5,9
6	1,8	6	6,1	6,1
5,9	1,8	6	6	6,1
5,5	1,3	5,9	6	6
5,6	1,3	5,5	5,9	6
5,4	1,3	5,6	5,5	5,9
5,2	1,2	5,4	5,6	5,5
5,2	1,4	5,2	5,4	5,6
5,2	2,2	5,2	5,2	5,4
5,5	2,9	5,2	5,2	5,2
5,8	3,1	5,5	5,2	5,2
5,8	3,5	5,8	5,5	5,2
5,5	3,6	5,8	5,8	5,5
5,3	4,4	5,5	5,8	5,8
5,1	4,1	5,3	5,5	5,8
5,2	5,1	5,1	5,3	5,5
5,8	5,8	5,2	5,1	5,3
5,8	5,9	5,8	5,2	5,1
5,5	5,4	5,8	5,8	5,2
5	5,5	5,5	5,8	5,8
4,9	4,8	5	5,5	5,8
5,3	3,2	4,9	5	5,5
6,1	2,7	5,3	4,9	5
6,5	2,1	6,1	5,3	4,9
6,8	1,9	6,5	6,1	5,3
6,6	0,6	6,8	6,5	6,1
6,4	0,7	6,6	6,8	6,5
6,4	-0,2	6,4	6,6	6,8
6,6	-1	6,4	6,4	6,6
6,7	-1,7	6,6	6,4	6,4




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59294&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59294&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59294&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
WMan>25[t] = + 0.735025720431746 -0.0210343049789478Infl[t] + 1.53127463437046`Yt-1`[t] -0.87884453464065`Yt-2`[t] + 0.233796372880887`Yt-3`[t] + 0.189859756972755M1[t] + 0.174504533469675M2[t] -0.0105409561502874M3[t] + 0.0117568591029149M4[t] -0.0614529939498516M5[t] -0.0384070699885562M6[t] -0.143835756622776M7[t] + 0.191929657849674M8[t] -0.237988285451912M9[t] -0.0344461062204082M10[t] -0.0984258641635606M11[t] + 0.000182769755273578t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WMan>25[t] =  +  0.735025720431746 -0.0210343049789478Infl[t] +  1.53127463437046`Yt-1`[t] -0.87884453464065`Yt-2`[t] +  0.233796372880887`Yt-3`[t] +  0.189859756972755M1[t] +  0.174504533469675M2[t] -0.0105409561502874M3[t] +  0.0117568591029149M4[t] -0.0614529939498516M5[t] -0.0384070699885562M6[t] -0.143835756622776M7[t] +  0.191929657849674M8[t] -0.237988285451912M9[t] -0.0344461062204082M10[t] -0.0984258641635606M11[t] +  0.000182769755273578t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59294&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WMan>25[t] =  +  0.735025720431746 -0.0210343049789478Infl[t] +  1.53127463437046`Yt-1`[t] -0.87884453464065`Yt-2`[t] +  0.233796372880887`Yt-3`[t] +  0.189859756972755M1[t] +  0.174504533469675M2[t] -0.0105409561502874M3[t] +  0.0117568591029149M4[t] -0.0614529939498516M5[t] -0.0384070699885562M6[t] -0.143835756622776M7[t] +  0.191929657849674M8[t] -0.237988285451912M9[t] -0.0344461062204082M10[t] -0.0984258641635606M11[t] +  0.000182769755273578t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59294&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59294&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
WMan>25[t] = + 0.735025720431746 -0.0210343049789478Infl[t] + 1.53127463437046`Yt-1`[t] -0.87884453464065`Yt-2`[t] + 0.233796372880887`Yt-3`[t] + 0.189859756972755M1[t] + 0.174504533469675M2[t] -0.0105409561502874M3[t] + 0.0117568591029149M4[t] -0.0614529939498516M5[t] -0.0384070699885562M6[t] -0.143835756622776M7[t] + 0.191929657849674M8[t] -0.237988285451912M9[t] -0.0344461062204082M10[t] -0.0984258641635606M11[t] + 0.000182769755273578t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.7350257204317460.5792411.26890.2117950.105897
Infl-0.02103430497894780.020598-1.02120.3133030.156652
`Yt-1`1.531274634370460.161459.484500
`Yt-2`-0.878844534640650.260262-3.37680.0016440.000822
`Yt-3`0.2337963728808870.1747671.33780.1885320.094266
M10.1898597569727550.1221221.55470.1279030.063951
M20.1745045334696750.1306471.33570.1892010.0946
M3-0.01054095615028740.1385-0.07610.9397130.469856
M40.01175685910291490.1384980.08490.9327740.466387
M5-0.06145299394985160.133945-0.45880.6488660.324433
M6-0.03840706998855620.132947-0.28890.7741570.387078
M7-0.1438357566227760.131778-1.09150.2815840.140792
M80.1919296578496740.1273431.50720.1396220.069811
M9-0.2379882854519120.135333-1.75850.0863040.043152
M10-0.03444610622040820.13279-0.25940.7966550.398328
M11-0.09842586416356060.12832-0.7670.4475650.223783
t0.0001827697552735780.0019930.09170.9273860.463693

\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) & 0.735025720431746 & 0.579241 & 1.2689 & 0.211795 & 0.105897 \tabularnewline
Infl & -0.0210343049789478 & 0.020598 & -1.0212 & 0.313303 & 0.156652 \tabularnewline
`Yt-1` & 1.53127463437046 & 0.16145 & 9.4845 & 0 & 0 \tabularnewline
`Yt-2` & -0.87884453464065 & 0.260262 & -3.3768 & 0.001644 & 0.000822 \tabularnewline
`Yt-3` & 0.233796372880887 & 0.174767 & 1.3378 & 0.188532 & 0.094266 \tabularnewline
M1 & 0.189859756972755 & 0.122122 & 1.5547 & 0.127903 & 0.063951 \tabularnewline
M2 & 0.174504533469675 & 0.130647 & 1.3357 & 0.189201 & 0.0946 \tabularnewline
M3 & -0.0105409561502874 & 0.1385 & -0.0761 & 0.939713 & 0.469856 \tabularnewline
M4 & 0.0117568591029149 & 0.138498 & 0.0849 & 0.932774 & 0.466387 \tabularnewline
M5 & -0.0614529939498516 & 0.133945 & -0.4588 & 0.648866 & 0.324433 \tabularnewline
M6 & -0.0384070699885562 & 0.132947 & -0.2889 & 0.774157 & 0.387078 \tabularnewline
M7 & -0.143835756622776 & 0.131778 & -1.0915 & 0.281584 & 0.140792 \tabularnewline
M8 & 0.191929657849674 & 0.127343 & 1.5072 & 0.139622 & 0.069811 \tabularnewline
M9 & -0.237988285451912 & 0.135333 & -1.7585 & 0.086304 & 0.043152 \tabularnewline
M10 & -0.0344461062204082 & 0.13279 & -0.2594 & 0.796655 & 0.398328 \tabularnewline
M11 & -0.0984258641635606 & 0.12832 & -0.767 & 0.447565 & 0.223783 \tabularnewline
t & 0.000182769755273578 & 0.001993 & 0.0917 & 0.927386 & 0.463693 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59294&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]0.735025720431746[/C][C]0.579241[/C][C]1.2689[/C][C]0.211795[/C][C]0.105897[/C][/ROW]
[ROW][C]Infl[/C][C]-0.0210343049789478[/C][C]0.020598[/C][C]-1.0212[/C][C]0.313303[/C][C]0.156652[/C][/ROW]
[ROW][C]`Yt-1`[/C][C]1.53127463437046[/C][C]0.16145[/C][C]9.4845[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Yt-2`[/C][C]-0.87884453464065[/C][C]0.260262[/C][C]-3.3768[/C][C]0.001644[/C][C]0.000822[/C][/ROW]
[ROW][C]`Yt-3`[/C][C]0.233796372880887[/C][C]0.174767[/C][C]1.3378[/C][C]0.188532[/C][C]0.094266[/C][/ROW]
[ROW][C]M1[/C][C]0.189859756972755[/C][C]0.122122[/C][C]1.5547[/C][C]0.127903[/C][C]0.063951[/C][/ROW]
[ROW][C]M2[/C][C]0.174504533469675[/C][C]0.130647[/C][C]1.3357[/C][C]0.189201[/C][C]0.0946[/C][/ROW]
[ROW][C]M3[/C][C]-0.0105409561502874[/C][C]0.1385[/C][C]-0.0761[/C][C]0.939713[/C][C]0.469856[/C][/ROW]
[ROW][C]M4[/C][C]0.0117568591029149[/C][C]0.138498[/C][C]0.0849[/C][C]0.932774[/C][C]0.466387[/C][/ROW]
[ROW][C]M5[/C][C]-0.0614529939498516[/C][C]0.133945[/C][C]-0.4588[/C][C]0.648866[/C][C]0.324433[/C][/ROW]
[ROW][C]M6[/C][C]-0.0384070699885562[/C][C]0.132947[/C][C]-0.2889[/C][C]0.774157[/C][C]0.387078[/C][/ROW]
[ROW][C]M7[/C][C]-0.143835756622776[/C][C]0.131778[/C][C]-1.0915[/C][C]0.281584[/C][C]0.140792[/C][/ROW]
[ROW][C]M8[/C][C]0.191929657849674[/C][C]0.127343[/C][C]1.5072[/C][C]0.139622[/C][C]0.069811[/C][/ROW]
[ROW][C]M9[/C][C]-0.237988285451912[/C][C]0.135333[/C][C]-1.7585[/C][C]0.086304[/C][C]0.043152[/C][/ROW]
[ROW][C]M10[/C][C]-0.0344461062204082[/C][C]0.13279[/C][C]-0.2594[/C][C]0.796655[/C][C]0.398328[/C][/ROW]
[ROW][C]M11[/C][C]-0.0984258641635606[/C][C]0.12832[/C][C]-0.767[/C][C]0.447565[/C][C]0.223783[/C][/ROW]
[ROW][C]t[/C][C]0.000182769755273578[/C][C]0.001993[/C][C]0.0917[/C][C]0.927386[/C][C]0.463693[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59294&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59294&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)0.7350257204317460.5792411.26890.2117950.105897
Infl-0.02103430497894780.020598-1.02120.3133030.156652
`Yt-1`1.531274634370460.161459.484500
`Yt-2`-0.878844534640650.260262-3.37680.0016440.000822
`Yt-3`0.2337963728808870.1747671.33780.1885320.094266
M10.1898597569727550.1221221.55470.1279030.063951
M20.1745045334696750.1306471.33570.1892010.0946
M3-0.01054095615028740.1385-0.07610.9397130.469856
M40.01175685910291490.1384980.08490.9327740.466387
M5-0.06145299394985160.133945-0.45880.6488660.324433
M6-0.03840706998855620.132947-0.28890.7741570.387078
M7-0.1438357566227760.131778-1.09150.2815840.140792
M80.1919296578496740.1273431.50720.1396220.069811
M9-0.2379882854519120.135333-1.75850.0863040.043152
M10-0.03444610622040820.13279-0.25940.7966550.398328
M11-0.09842586416356060.12832-0.7670.4475650.223783
t0.0001827697552735780.0019930.09170.9273860.463693







Multiple Linear Regression - Regression Statistics
Multiple R0.960363514089563
R-squared0.922298079194454
Adjusted R-squared0.891217310872236
F-TEST (value)29.6742368024135
F-TEST (DF numerator)16
F-TEST (DF denominator)40
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.180375424468064
Sum Squared Residuals1.30141175008137

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.960363514089563 \tabularnewline
R-squared & 0.922298079194454 \tabularnewline
Adjusted R-squared & 0.891217310872236 \tabularnewline
F-TEST (value) & 29.6742368024135 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 40 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.180375424468064 \tabularnewline
Sum Squared Residuals & 1.30141175008137 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59294&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.960363514089563[/C][/ROW]
[ROW][C]R-squared[/C][C]0.922298079194454[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.891217310872236[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]29.6742368024135[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]40[/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]0.180375424468064[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.30141175008137[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59294&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59294&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 R0.960363514089563
R-squared0.922298079194454
Adjusted R-squared0.891217310872236
F-TEST (value)29.6742368024135
F-TEST (DF numerator)16
F-TEST (DF denominator)40
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.180375424468064
Sum Squared Residuals1.30141175008137







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16.36.241545649745550.0584543502544519
26.56.60554666103937-0.105546661039375
36.66.525686893334660.0743131066653372
46.56.56597501793455-0.0659750179345478
56.26.28817813982265-0.0881781398226484
66.25.971702255971810.228297744028186
75.96.10883349269487-0.208833492694868
86.15.906846652755610.193153347244389
96.16.047019766475580.0529802335244167
106.16.000630035674180.099369964325824
116.15.981488891564580.118511108435421
126.16.096924969466570.00307503053342833
136.46.28486406569670.115135934303295
146.76.71855684977056-0.0185568497705628
156.96.729423159824810.170576840175186
1676.864644223179450.135355776820546
1776.851735191242510.14826480875749
186.86.82542498407961-0.0254249840796129
196.46.43309691661887-0.0330969166188734
205.96.33861444552023-0.43861444552023
215.55.450123924566670.0498760754333326
225.55.389346168471130.110653831528873
235.65.568602529690640.0313974703093559
245.85.731026938889960.0689730611100418
255.96.13312964753433-0.233129647534329
266.16.11659195708563-0.0165919570856334
276.16.20527270719873-0.105272707198725
2866.07326059206927-0.0732605920692655
2965.893865319910900.106134680089095
305.96.00497846709154-0.104978467091538
315.55.73374260197693-0.233742601976932
325.65.545065385920540.0549346140794619
335.45.59661585237944-0.196615852379442
345.25.3147863023736-0.114786302373605
355.25.139676070532060.0603239294679375
365.25.35046689281969-0.150466892819691
375.55.479026131486280.0209738685137202
385.85.91902920705382-0.119029207053820
395.85.92148179511649-0.121481795116494
405.55.74834450109915-0.248344501099146
415.35.269246495371620.0307535046283756
425.15.25618391409998-0.156183914099981
435.24.929278760431860.270721239568141
445.85.532640026963320.267359973036681
455.85.88492247550114-0.084922475501142
465.55.59523749348109-0.0952374934810924
4755.21023250821271-0.210232508212714
484.94.821581198823780.0784188011762216
495.35.261434505537140.0385654944628619
506.15.840275325050610.259724674949391
516.56.5181354445253-0.0181354445253040
526.86.547775665717590.252224334282413
536.66.79697485365231-0.196974853652312
546.46.341710378757060.0582896212429455
556.46.195048228277470.204951771722533
566.66.6768334888403-0.0768334888403017
576.76.521317981077170.178682018922835

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6.3 & 6.24154564974555 & 0.0584543502544519 \tabularnewline
2 & 6.5 & 6.60554666103937 & -0.105546661039375 \tabularnewline
3 & 6.6 & 6.52568689333466 & 0.0743131066653372 \tabularnewline
4 & 6.5 & 6.56597501793455 & -0.0659750179345478 \tabularnewline
5 & 6.2 & 6.28817813982265 & -0.0881781398226484 \tabularnewline
6 & 6.2 & 5.97170225597181 & 0.228297744028186 \tabularnewline
7 & 5.9 & 6.10883349269487 & -0.208833492694868 \tabularnewline
8 & 6.1 & 5.90684665275561 & 0.193153347244389 \tabularnewline
9 & 6.1 & 6.04701976647558 & 0.0529802335244167 \tabularnewline
10 & 6.1 & 6.00063003567418 & 0.099369964325824 \tabularnewline
11 & 6.1 & 5.98148889156458 & 0.118511108435421 \tabularnewline
12 & 6.1 & 6.09692496946657 & 0.00307503053342833 \tabularnewline
13 & 6.4 & 6.2848640656967 & 0.115135934303295 \tabularnewline
14 & 6.7 & 6.71855684977056 & -0.0185568497705628 \tabularnewline
15 & 6.9 & 6.72942315982481 & 0.170576840175186 \tabularnewline
16 & 7 & 6.86464422317945 & 0.135355776820546 \tabularnewline
17 & 7 & 6.85173519124251 & 0.14826480875749 \tabularnewline
18 & 6.8 & 6.82542498407961 & -0.0254249840796129 \tabularnewline
19 & 6.4 & 6.43309691661887 & -0.0330969166188734 \tabularnewline
20 & 5.9 & 6.33861444552023 & -0.43861444552023 \tabularnewline
21 & 5.5 & 5.45012392456667 & 0.0498760754333326 \tabularnewline
22 & 5.5 & 5.38934616847113 & 0.110653831528873 \tabularnewline
23 & 5.6 & 5.56860252969064 & 0.0313974703093559 \tabularnewline
24 & 5.8 & 5.73102693888996 & 0.0689730611100418 \tabularnewline
25 & 5.9 & 6.13312964753433 & -0.233129647534329 \tabularnewline
26 & 6.1 & 6.11659195708563 & -0.0165919570856334 \tabularnewline
27 & 6.1 & 6.20527270719873 & -0.105272707198725 \tabularnewline
28 & 6 & 6.07326059206927 & -0.0732605920692655 \tabularnewline
29 & 6 & 5.89386531991090 & 0.106134680089095 \tabularnewline
30 & 5.9 & 6.00497846709154 & -0.104978467091538 \tabularnewline
31 & 5.5 & 5.73374260197693 & -0.233742601976932 \tabularnewline
32 & 5.6 & 5.54506538592054 & 0.0549346140794619 \tabularnewline
33 & 5.4 & 5.59661585237944 & -0.196615852379442 \tabularnewline
34 & 5.2 & 5.3147863023736 & -0.114786302373605 \tabularnewline
35 & 5.2 & 5.13967607053206 & 0.0603239294679375 \tabularnewline
36 & 5.2 & 5.35046689281969 & -0.150466892819691 \tabularnewline
37 & 5.5 & 5.47902613148628 & 0.0209738685137202 \tabularnewline
38 & 5.8 & 5.91902920705382 & -0.119029207053820 \tabularnewline
39 & 5.8 & 5.92148179511649 & -0.121481795116494 \tabularnewline
40 & 5.5 & 5.74834450109915 & -0.248344501099146 \tabularnewline
41 & 5.3 & 5.26924649537162 & 0.0307535046283756 \tabularnewline
42 & 5.1 & 5.25618391409998 & -0.156183914099981 \tabularnewline
43 & 5.2 & 4.92927876043186 & 0.270721239568141 \tabularnewline
44 & 5.8 & 5.53264002696332 & 0.267359973036681 \tabularnewline
45 & 5.8 & 5.88492247550114 & -0.084922475501142 \tabularnewline
46 & 5.5 & 5.59523749348109 & -0.0952374934810924 \tabularnewline
47 & 5 & 5.21023250821271 & -0.210232508212714 \tabularnewline
48 & 4.9 & 4.82158119882378 & 0.0784188011762216 \tabularnewline
49 & 5.3 & 5.26143450553714 & 0.0385654944628619 \tabularnewline
50 & 6.1 & 5.84027532505061 & 0.259724674949391 \tabularnewline
51 & 6.5 & 6.5181354445253 & -0.0181354445253040 \tabularnewline
52 & 6.8 & 6.54777566571759 & 0.252224334282413 \tabularnewline
53 & 6.6 & 6.79697485365231 & -0.196974853652312 \tabularnewline
54 & 6.4 & 6.34171037875706 & 0.0582896212429455 \tabularnewline
55 & 6.4 & 6.19504822827747 & 0.204951771722533 \tabularnewline
56 & 6.6 & 6.6768334888403 & -0.0768334888403017 \tabularnewline
57 & 6.7 & 6.52131798107717 & 0.178682018922835 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59294&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]6.3[/C][C]6.24154564974555[/C][C]0.0584543502544519[/C][/ROW]
[ROW][C]2[/C][C]6.5[/C][C]6.60554666103937[/C][C]-0.105546661039375[/C][/ROW]
[ROW][C]3[/C][C]6.6[/C][C]6.52568689333466[/C][C]0.0743131066653372[/C][/ROW]
[ROW][C]4[/C][C]6.5[/C][C]6.56597501793455[/C][C]-0.0659750179345478[/C][/ROW]
[ROW][C]5[/C][C]6.2[/C][C]6.28817813982265[/C][C]-0.0881781398226484[/C][/ROW]
[ROW][C]6[/C][C]6.2[/C][C]5.97170225597181[/C][C]0.228297744028186[/C][/ROW]
[ROW][C]7[/C][C]5.9[/C][C]6.10883349269487[/C][C]-0.208833492694868[/C][/ROW]
[ROW][C]8[/C][C]6.1[/C][C]5.90684665275561[/C][C]0.193153347244389[/C][/ROW]
[ROW][C]9[/C][C]6.1[/C][C]6.04701976647558[/C][C]0.0529802335244167[/C][/ROW]
[ROW][C]10[/C][C]6.1[/C][C]6.00063003567418[/C][C]0.099369964325824[/C][/ROW]
[ROW][C]11[/C][C]6.1[/C][C]5.98148889156458[/C][C]0.118511108435421[/C][/ROW]
[ROW][C]12[/C][C]6.1[/C][C]6.09692496946657[/C][C]0.00307503053342833[/C][/ROW]
[ROW][C]13[/C][C]6.4[/C][C]6.2848640656967[/C][C]0.115135934303295[/C][/ROW]
[ROW][C]14[/C][C]6.7[/C][C]6.71855684977056[/C][C]-0.0185568497705628[/C][/ROW]
[ROW][C]15[/C][C]6.9[/C][C]6.72942315982481[/C][C]0.170576840175186[/C][/ROW]
[ROW][C]16[/C][C]7[/C][C]6.86464422317945[/C][C]0.135355776820546[/C][/ROW]
[ROW][C]17[/C][C]7[/C][C]6.85173519124251[/C][C]0.14826480875749[/C][/ROW]
[ROW][C]18[/C][C]6.8[/C][C]6.82542498407961[/C][C]-0.0254249840796129[/C][/ROW]
[ROW][C]19[/C][C]6.4[/C][C]6.43309691661887[/C][C]-0.0330969166188734[/C][/ROW]
[ROW][C]20[/C][C]5.9[/C][C]6.33861444552023[/C][C]-0.43861444552023[/C][/ROW]
[ROW][C]21[/C][C]5.5[/C][C]5.45012392456667[/C][C]0.0498760754333326[/C][/ROW]
[ROW][C]22[/C][C]5.5[/C][C]5.38934616847113[/C][C]0.110653831528873[/C][/ROW]
[ROW][C]23[/C][C]5.6[/C][C]5.56860252969064[/C][C]0.0313974703093559[/C][/ROW]
[ROW][C]24[/C][C]5.8[/C][C]5.73102693888996[/C][C]0.0689730611100418[/C][/ROW]
[ROW][C]25[/C][C]5.9[/C][C]6.13312964753433[/C][C]-0.233129647534329[/C][/ROW]
[ROW][C]26[/C][C]6.1[/C][C]6.11659195708563[/C][C]-0.0165919570856334[/C][/ROW]
[ROW][C]27[/C][C]6.1[/C][C]6.20527270719873[/C][C]-0.105272707198725[/C][/ROW]
[ROW][C]28[/C][C]6[/C][C]6.07326059206927[/C][C]-0.0732605920692655[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]5.89386531991090[/C][C]0.106134680089095[/C][/ROW]
[ROW][C]30[/C][C]5.9[/C][C]6.00497846709154[/C][C]-0.104978467091538[/C][/ROW]
[ROW][C]31[/C][C]5.5[/C][C]5.73374260197693[/C][C]-0.233742601976932[/C][/ROW]
[ROW][C]32[/C][C]5.6[/C][C]5.54506538592054[/C][C]0.0549346140794619[/C][/ROW]
[ROW][C]33[/C][C]5.4[/C][C]5.59661585237944[/C][C]-0.196615852379442[/C][/ROW]
[ROW][C]34[/C][C]5.2[/C][C]5.3147863023736[/C][C]-0.114786302373605[/C][/ROW]
[ROW][C]35[/C][C]5.2[/C][C]5.13967607053206[/C][C]0.0603239294679375[/C][/ROW]
[ROW][C]36[/C][C]5.2[/C][C]5.35046689281969[/C][C]-0.150466892819691[/C][/ROW]
[ROW][C]37[/C][C]5.5[/C][C]5.47902613148628[/C][C]0.0209738685137202[/C][/ROW]
[ROW][C]38[/C][C]5.8[/C][C]5.91902920705382[/C][C]-0.119029207053820[/C][/ROW]
[ROW][C]39[/C][C]5.8[/C][C]5.92148179511649[/C][C]-0.121481795116494[/C][/ROW]
[ROW][C]40[/C][C]5.5[/C][C]5.74834450109915[/C][C]-0.248344501099146[/C][/ROW]
[ROW][C]41[/C][C]5.3[/C][C]5.26924649537162[/C][C]0.0307535046283756[/C][/ROW]
[ROW][C]42[/C][C]5.1[/C][C]5.25618391409998[/C][C]-0.156183914099981[/C][/ROW]
[ROW][C]43[/C][C]5.2[/C][C]4.92927876043186[/C][C]0.270721239568141[/C][/ROW]
[ROW][C]44[/C][C]5.8[/C][C]5.53264002696332[/C][C]0.267359973036681[/C][/ROW]
[ROW][C]45[/C][C]5.8[/C][C]5.88492247550114[/C][C]-0.084922475501142[/C][/ROW]
[ROW][C]46[/C][C]5.5[/C][C]5.59523749348109[/C][C]-0.0952374934810924[/C][/ROW]
[ROW][C]47[/C][C]5[/C][C]5.21023250821271[/C][C]-0.210232508212714[/C][/ROW]
[ROW][C]48[/C][C]4.9[/C][C]4.82158119882378[/C][C]0.0784188011762216[/C][/ROW]
[ROW][C]49[/C][C]5.3[/C][C]5.26143450553714[/C][C]0.0385654944628619[/C][/ROW]
[ROW][C]50[/C][C]6.1[/C][C]5.84027532505061[/C][C]0.259724674949391[/C][/ROW]
[ROW][C]51[/C][C]6.5[/C][C]6.5181354445253[/C][C]-0.0181354445253040[/C][/ROW]
[ROW][C]52[/C][C]6.8[/C][C]6.54777566571759[/C][C]0.252224334282413[/C][/ROW]
[ROW][C]53[/C][C]6.6[/C][C]6.79697485365231[/C][C]-0.196974853652312[/C][/ROW]
[ROW][C]54[/C][C]6.4[/C][C]6.34171037875706[/C][C]0.0582896212429455[/C][/ROW]
[ROW][C]55[/C][C]6.4[/C][C]6.19504822827747[/C][C]0.204951771722533[/C][/ROW]
[ROW][C]56[/C][C]6.6[/C][C]6.6768334888403[/C][C]-0.0768334888403017[/C][/ROW]
[ROW][C]57[/C][C]6.7[/C][C]6.52131798107717[/C][C]0.178682018922835[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59294&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59294&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
16.36.241545649745550.0584543502544519
26.56.60554666103937-0.105546661039375
36.66.525686893334660.0743131066653372
46.56.56597501793455-0.0659750179345478
56.26.28817813982265-0.0881781398226484
66.25.971702255971810.228297744028186
75.96.10883349269487-0.208833492694868
86.15.906846652755610.193153347244389
96.16.047019766475580.0529802335244167
106.16.000630035674180.099369964325824
116.15.981488891564580.118511108435421
126.16.096924969466570.00307503053342833
136.46.28486406569670.115135934303295
146.76.71855684977056-0.0185568497705628
156.96.729423159824810.170576840175186
1676.864644223179450.135355776820546
1776.851735191242510.14826480875749
186.86.82542498407961-0.0254249840796129
196.46.43309691661887-0.0330969166188734
205.96.33861444552023-0.43861444552023
215.55.450123924566670.0498760754333326
225.55.389346168471130.110653831528873
235.65.568602529690640.0313974703093559
245.85.731026938889960.0689730611100418
255.96.13312964753433-0.233129647534329
266.16.11659195708563-0.0165919570856334
276.16.20527270719873-0.105272707198725
2866.07326059206927-0.0732605920692655
2965.893865319910900.106134680089095
305.96.00497846709154-0.104978467091538
315.55.73374260197693-0.233742601976932
325.65.545065385920540.0549346140794619
335.45.59661585237944-0.196615852379442
345.25.3147863023736-0.114786302373605
355.25.139676070532060.0603239294679375
365.25.35046689281969-0.150466892819691
375.55.479026131486280.0209738685137202
385.85.91902920705382-0.119029207053820
395.85.92148179511649-0.121481795116494
405.55.74834450109915-0.248344501099146
415.35.269246495371620.0307535046283756
425.15.25618391409998-0.156183914099981
435.24.929278760431860.270721239568141
445.85.532640026963320.267359973036681
455.85.88492247550114-0.084922475501142
465.55.59523749348109-0.0952374934810924
4755.21023250821271-0.210232508212714
484.94.821581198823780.0784188011762216
495.35.261434505537140.0385654944628619
506.15.840275325050610.259724674949391
516.56.5181354445253-0.0181354445253040
526.86.547775665717590.252224334282413
536.66.79697485365231-0.196974853652312
546.46.341710378757060.0582896212429455
556.46.195048228277470.204951771722533
566.66.6768334888403-0.0768334888403017
576.76.521317981077170.178682018922835







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.4925795427513540.9851590855027070.507420457248646
210.3285048936164330.6570097872328670.671495106383567
220.4846086313078980.9692172626157960.515391368692102
230.4851203097681210.9702406195362420.514879690231879
240.4779502892563080.9559005785126160.522049710743692
250.6404475594121290.7191048811757420.359552440587871
260.5271900489672930.9456199020654130.472809951032707
270.4671906268002160.9343812536004330.532809373199784
280.3972570614990410.7945141229980830.602742938500959
290.5922799264673270.8154401470653470.407720073532673
300.8446817290366440.3106365419267120.155318270963356
310.8207144592974930.3585710814050150.179285540702507
320.8352122884036510.3295754231926980.164787711596349
330.7428782102798510.5142435794402980.257121789720149
340.637755585755430.7244888284891390.362244414244570
350.6233667645946520.7532664708106960.376633235405348
360.5620527744810220.8758944510379560.437947225518978
370.4140517546130090.8281035092260170.585948245386991

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.492579542751354 & 0.985159085502707 & 0.507420457248646 \tabularnewline
21 & 0.328504893616433 & 0.657009787232867 & 0.671495106383567 \tabularnewline
22 & 0.484608631307898 & 0.969217262615796 & 0.515391368692102 \tabularnewline
23 & 0.485120309768121 & 0.970240619536242 & 0.514879690231879 \tabularnewline
24 & 0.477950289256308 & 0.955900578512616 & 0.522049710743692 \tabularnewline
25 & 0.640447559412129 & 0.719104881175742 & 0.359552440587871 \tabularnewline
26 & 0.527190048967293 & 0.945619902065413 & 0.472809951032707 \tabularnewline
27 & 0.467190626800216 & 0.934381253600433 & 0.532809373199784 \tabularnewline
28 & 0.397257061499041 & 0.794514122998083 & 0.602742938500959 \tabularnewline
29 & 0.592279926467327 & 0.815440147065347 & 0.407720073532673 \tabularnewline
30 & 0.844681729036644 & 0.310636541926712 & 0.155318270963356 \tabularnewline
31 & 0.820714459297493 & 0.358571081405015 & 0.179285540702507 \tabularnewline
32 & 0.835212288403651 & 0.329575423192698 & 0.164787711596349 \tabularnewline
33 & 0.742878210279851 & 0.514243579440298 & 0.257121789720149 \tabularnewline
34 & 0.63775558575543 & 0.724488828489139 & 0.362244414244570 \tabularnewline
35 & 0.623366764594652 & 0.753266470810696 & 0.376633235405348 \tabularnewline
36 & 0.562052774481022 & 0.875894451037956 & 0.437947225518978 \tabularnewline
37 & 0.414051754613009 & 0.828103509226017 & 0.585948245386991 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59294&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]0.492579542751354[/C][C]0.985159085502707[/C][C]0.507420457248646[/C][/ROW]
[ROW][C]21[/C][C]0.328504893616433[/C][C]0.657009787232867[/C][C]0.671495106383567[/C][/ROW]
[ROW][C]22[/C][C]0.484608631307898[/C][C]0.969217262615796[/C][C]0.515391368692102[/C][/ROW]
[ROW][C]23[/C][C]0.485120309768121[/C][C]0.970240619536242[/C][C]0.514879690231879[/C][/ROW]
[ROW][C]24[/C][C]0.477950289256308[/C][C]0.955900578512616[/C][C]0.522049710743692[/C][/ROW]
[ROW][C]25[/C][C]0.640447559412129[/C][C]0.719104881175742[/C][C]0.359552440587871[/C][/ROW]
[ROW][C]26[/C][C]0.527190048967293[/C][C]0.945619902065413[/C][C]0.472809951032707[/C][/ROW]
[ROW][C]27[/C][C]0.467190626800216[/C][C]0.934381253600433[/C][C]0.532809373199784[/C][/ROW]
[ROW][C]28[/C][C]0.397257061499041[/C][C]0.794514122998083[/C][C]0.602742938500959[/C][/ROW]
[ROW][C]29[/C][C]0.592279926467327[/C][C]0.815440147065347[/C][C]0.407720073532673[/C][/ROW]
[ROW][C]30[/C][C]0.844681729036644[/C][C]0.310636541926712[/C][C]0.155318270963356[/C][/ROW]
[ROW][C]31[/C][C]0.820714459297493[/C][C]0.358571081405015[/C][C]0.179285540702507[/C][/ROW]
[ROW][C]32[/C][C]0.835212288403651[/C][C]0.329575423192698[/C][C]0.164787711596349[/C][/ROW]
[ROW][C]33[/C][C]0.742878210279851[/C][C]0.514243579440298[/C][C]0.257121789720149[/C][/ROW]
[ROW][C]34[/C][C]0.63775558575543[/C][C]0.724488828489139[/C][C]0.362244414244570[/C][/ROW]
[ROW][C]35[/C][C]0.623366764594652[/C][C]0.753266470810696[/C][C]0.376633235405348[/C][/ROW]
[ROW][C]36[/C][C]0.562052774481022[/C][C]0.875894451037956[/C][C]0.437947225518978[/C][/ROW]
[ROW][C]37[/C][C]0.414051754613009[/C][C]0.828103509226017[/C][C]0.585948245386991[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59294&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59294&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
200.4925795427513540.9851590855027070.507420457248646
210.3285048936164330.6570097872328670.671495106383567
220.4846086313078980.9692172626157960.515391368692102
230.4851203097681210.9702406195362420.514879690231879
240.4779502892563080.9559005785126160.522049710743692
250.6404475594121290.7191048811757420.359552440587871
260.5271900489672930.9456199020654130.472809951032707
270.4671906268002160.9343812536004330.532809373199784
280.3972570614990410.7945141229980830.602742938500959
290.5922799264673270.8154401470653470.407720073532673
300.8446817290366440.3106365419267120.155318270963356
310.8207144592974930.3585710814050150.179285540702507
320.8352122884036510.3295754231926980.164787711596349
330.7428782102798510.5142435794402980.257121789720149
340.637755585755430.7244888284891390.362244414244570
350.6233667645946520.7532664708106960.376633235405348
360.5620527744810220.8758944510379560.437947225518978
370.4140517546130090.8281035092260170.585948245386991







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59294&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59294&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59294&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
par1 = 1 ; par2 = 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')
}