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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 computationFri, 20 Nov 2009 10:15:11 -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/20/t1258737517vvf1iqf0ew7iwpl.htm/, Retrieved Fri, 29 Mar 2024 15:47:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58343, Retrieved Fri, 29 Mar 2024 15:47:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact180
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Notched Boxplots] [3/11/2009] [2009-11-02 21:10:41] [b98453cac15ba1066b407e146608df68]
-    D  [Notched Boxplots] [] [2009-11-09 10:28:17] [023d83ebdf42a2acf423907b4076e8a1]
- RMP     [Kendall tau Correlation Matrix] [] [2009-11-09 11:33:31] [023d83ebdf42a2acf423907b4076e8a1]
- RMPD      [Multiple Regression] [] [2009-11-20 15:57:46] [023d83ebdf42a2acf423907b4076e8a1]
-   PD        [Multiple Regression] [] [2009-11-20 17:10:41] [023d83ebdf42a2acf423907b4076e8a1]
-    D            [Multiple Regression] [] [2009-11-20 17:15:11] [9f6463b67b1eb7bae5c03a796abf0348] [Current]
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Dataseries X:
100,00	100,00	91,12	94,05	97,82	100,00
97,82	99,87	93,13	91,12	94,05	97,82
94,05	99,54	93,88	93,13	91,12	94,05
91,12	99,81	92,55	93,88	93,13	91,12
93,13	100,49	94,43	92,55	93,88	93,13
93,88	101,14	96,25	94,43	92,55	93,88
92,55	101,37	100,44	96,25	94,43	92,55
94,43	101,51	101,50	100,44	96,25	94,43
96,25	101,82	99,40	101,50	100,44	96,25
100,44	102,44	99,69	99,40	101,50	100,44
101,50	102,53	101,69	99,69	99,40	101,50
99,40	102,65	103,67	101,69	99,69	99,40
99,69	102,47	103,05	103,67	101,69	99,69
101,69	102,44	100,95	103,05	103,67	101,69
103,67	102,42	102,35	100,95	103,05	103,67
103,05	102,45	101,65	102,35	100,95	103,05
100,95	102,89	99,57	101,65	102,35	100,95
102,35	102,85	95,68	99,57	101,65	102,35
101,65	103,36	96,58	95,68	99,57	101,65
99,57	103,74	96,33	96,58	95,68	99,57
95,68	103,72	95,37	96,33	96,58	95,68
96,58	104,08	96,00	95,37	96,33	96,58
96,33	104,21	96,88	96,00	95,37	96,33
95,37	103,91	94,85	96,88	96,00	95,37
96,00	103,70	92,47	94,85	96,88	96,00
96,88	103,96	93,99	92,47	94,85	96,88
94,85	104,10	93,45	93,99	92,47	94,85
92,47	104,15	92,27	93,45	93,99	92,47
93,99	104,71	90,40	92,27	93,45	93,99
93,45	104,72	90,43	90,40	92,27	93,45
92,27	105,20	91,05	90,43	90,40	92,27
90,40	105,07	89,08	91,05	90,43	90,40
90,43	105,06	89,69	89,08	91,05	90,43
91,05	105,50	87,92	89,69	89,08	91,05
89,08	105,38	85,88	87,92	89,69	89,08
89,69	105,47	83,21	85,88	87,92	89,69
87,92	106,03	83,86	83,21	85,88	87,92
85,88	107,02	83,01	83,86	83,21	85,88
83,21	107,32	82,85	83,01	83,86	83,21
83,86	107,75	78,69	82,85	83,01	83,86
83,01	108,52	77,57	78,69	82,85	83,01
82,85	109,32	78,54	77,57	78,69	82,85
78,69	109,56	78,56	78,54	77,57	78,69
77,57	110,54	77,48	78,56	78,54	77,57
78,54	111,16	81,59	77,48	78,56	78,54
78,56	111,74	85,02	81,59	77,48	78,56
77,48	111,06	91,71	85,02	81,59	77,48
81,59	111,24	95,96	91,71	85,02	81,59
85,02	111,04	90,85	95,96	91,71	85,02
91,71	110,38	92,29	90,85	95,96	91,71
95,96	110,14	95,57	92,29	90,85	95,96
90,85	110,25	93,62	95,57	92,29	90,85
92,29	110,62	92,63	93,62	95,57	92,29
95,57	109,99	89,51	92,63	93,62	95,57
93,62	110,22	87,17	89,51	92,63	93,62
92,63	110,14	86,73	87,17	89,51	92,63
89,51	109,93	85,63	86,73	87,17	89,51




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58343&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]6 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=58343&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58343&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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
wisselkoers[t] = + 8.46828148995742e-15 -2.46149433910034e-17consumptieprijzen[t] -4.29728240716339e-17`Yt-1`[t] + 4.15699978358523e-17`Yt-2`[t] -2.25683481723919e-16`Yt-3`[t] + 1`Yt-4`[t] -8.47467574596834e-17M1[t] -8.20573878030883e-17M2[t] -7.1915385565832e-17M3[t] -2.74872398299474e-16M4[t] -4.20111500162079e-16M5[t] -1.30278565279883e-15M6[t] -2.20954492161706e-16M7[t] -1.98972849676541e-16M8[t] -1.22427412989515e-16M9[t] -1.63618939147031e-16M10[t] -1.05726305286780e-16M11[t] -1.29912862524235e-17t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
wisselkoers[t] =  +  8.46828148995742e-15 -2.46149433910034e-17consumptieprijzen[t] -4.29728240716339e-17`Yt-1`[t] +  4.15699978358523e-17`Yt-2`[t] -2.25683481723919e-16`Yt-3`[t] +  1`Yt-4`[t] -8.47467574596834e-17M1[t] -8.20573878030883e-17M2[t] -7.1915385565832e-17M3[t] -2.74872398299474e-16M4[t] -4.20111500162079e-16M5[t] -1.30278565279883e-15M6[t] -2.20954492161706e-16M7[t] -1.98972849676541e-16M8[t] -1.22427412989515e-16M9[t] -1.63618939147031e-16M10[t] -1.05726305286780e-16M11[t] -1.29912862524235e-17t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58343&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]wisselkoers[t] =  +  8.46828148995742e-15 -2.46149433910034e-17consumptieprijzen[t] -4.29728240716339e-17`Yt-1`[t] +  4.15699978358523e-17`Yt-2`[t] -2.25683481723919e-16`Yt-3`[t] +  1`Yt-4`[t] -8.47467574596834e-17M1[t] -8.20573878030883e-17M2[t] -7.1915385565832e-17M3[t] -2.74872398299474e-16M4[t] -4.20111500162079e-16M5[t] -1.30278565279883e-15M6[t] -2.20954492161706e-16M7[t] -1.98972849676541e-16M8[t] -1.22427412989515e-16M9[t] -1.63618939147031e-16M10[t] -1.05726305286780e-16M11[t] -1.29912862524235e-17t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58343&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58343&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
wisselkoers[t] = + 8.46828148995742e-15 -2.46149433910034e-17consumptieprijzen[t] -4.29728240716339e-17`Yt-1`[t] + 4.15699978358523e-17`Yt-2`[t] -2.25683481723919e-16`Yt-3`[t] + 1`Yt-4`[t] -8.47467574596834e-17M1[t] -8.20573878030883e-17M2[t] -7.1915385565832e-17M3[t] -2.74872398299474e-16M4[t] -4.20111500162079e-16M5[t] -1.30278565279883e-15M6[t] -2.20954492161706e-16M7[t] -1.98972849676541e-16M8[t] -1.22427412989515e-16M9[t] -1.63618939147031e-16M10[t] -1.05726305286780e-16M11[t] -1.29912862524235e-17t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.46828148995742e-1500.69090.4937270.246863
consumptieprijzen-2.46149433910034e-170-0.20950.8351440.417572
`Yt-1`-4.29728240716339e-170-0.89970.3737950.186898
`Yt-2`4.15699978358523e-1700.59790.5533870.276693
`Yt-3`-2.25683481723919e-160-3.28920.0021350.001068
`Yt-4`102224666410726978400
M1-8.47467574596834e-170-0.2010.8417490.420875
M2-8.20573878030883e-170-0.19110.8494510.424726
M3-7.1915385565832e-170-0.17490.8620740.431037
M4-2.74872398299474e-160-0.67440.5040340.252017
M5-4.20111500162079e-160-0.97730.3344440.167222
M6-1.30278565279883e-150-3.09670.0036170.001808
M7-2.20954492161706e-160-0.53360.5966480.298324
M8-1.98972849676541e-160-0.4910.6261580.313079
M9-1.22427412989515e-160-0.29820.7671530.383577
M10-1.63618939147031e-160-0.37860.7070540.353527
M11-1.05726305286780e-160-0.24740.8058770.402939
t-1.29912862524235e-170-0.52810.6003980.300199

\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) & 8.46828148995742e-15 & 0 & 0.6909 & 0.493727 & 0.246863 \tabularnewline
consumptieprijzen & -2.46149433910034e-17 & 0 & -0.2095 & 0.835144 & 0.417572 \tabularnewline
`Yt-1` & -4.29728240716339e-17 & 0 & -0.8997 & 0.373795 & 0.186898 \tabularnewline
`Yt-2` & 4.15699978358523e-17 & 0 & 0.5979 & 0.553387 & 0.276693 \tabularnewline
`Yt-3` & -2.25683481723919e-16 & 0 & -3.2892 & 0.002135 & 0.001068 \tabularnewline
`Yt-4` & 1 & 0 & 22246664107269784 & 0 & 0 \tabularnewline
M1 & -8.47467574596834e-17 & 0 & -0.201 & 0.841749 & 0.420875 \tabularnewline
M2 & -8.20573878030883e-17 & 0 & -0.1911 & 0.849451 & 0.424726 \tabularnewline
M3 & -7.1915385565832e-17 & 0 & -0.1749 & 0.862074 & 0.431037 \tabularnewline
M4 & -2.74872398299474e-16 & 0 & -0.6744 & 0.504034 & 0.252017 \tabularnewline
M5 & -4.20111500162079e-16 & 0 & -0.9773 & 0.334444 & 0.167222 \tabularnewline
M6 & -1.30278565279883e-15 & 0 & -3.0967 & 0.003617 & 0.001808 \tabularnewline
M7 & -2.20954492161706e-16 & 0 & -0.5336 & 0.596648 & 0.298324 \tabularnewline
M8 & -1.98972849676541e-16 & 0 & -0.491 & 0.626158 & 0.313079 \tabularnewline
M9 & -1.22427412989515e-16 & 0 & -0.2982 & 0.767153 & 0.383577 \tabularnewline
M10 & -1.63618939147031e-16 & 0 & -0.3786 & 0.707054 & 0.353527 \tabularnewline
M11 & -1.05726305286780e-16 & 0 & -0.2474 & 0.805877 & 0.402939 \tabularnewline
t & -1.29912862524235e-17 & 0 & -0.5281 & 0.600398 & 0.300199 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58343&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]8.46828148995742e-15[/C][C]0[/C][C]0.6909[/C][C]0.493727[/C][C]0.246863[/C][/ROW]
[ROW][C]consumptieprijzen[/C][C]-2.46149433910034e-17[/C][C]0[/C][C]-0.2095[/C][C]0.835144[/C][C]0.417572[/C][/ROW]
[ROW][C]`Yt-1`[/C][C]-4.29728240716339e-17[/C][C]0[/C][C]-0.8997[/C][C]0.373795[/C][C]0.186898[/C][/ROW]
[ROW][C]`Yt-2`[/C][C]4.15699978358523e-17[/C][C]0[/C][C]0.5979[/C][C]0.553387[/C][C]0.276693[/C][/ROW]
[ROW][C]`Yt-3`[/C][C]-2.25683481723919e-16[/C][C]0[/C][C]-3.2892[/C][C]0.002135[/C][C]0.001068[/C][/ROW]
[ROW][C]`Yt-4`[/C][C]1[/C][C]0[/C][C]22246664107269784[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-8.47467574596834e-17[/C][C]0[/C][C]-0.201[/C][C]0.841749[/C][C]0.420875[/C][/ROW]
[ROW][C]M2[/C][C]-8.20573878030883e-17[/C][C]0[/C][C]-0.1911[/C][C]0.849451[/C][C]0.424726[/C][/ROW]
[ROW][C]M3[/C][C]-7.1915385565832e-17[/C][C]0[/C][C]-0.1749[/C][C]0.862074[/C][C]0.431037[/C][/ROW]
[ROW][C]M4[/C][C]-2.74872398299474e-16[/C][C]0[/C][C]-0.6744[/C][C]0.504034[/C][C]0.252017[/C][/ROW]
[ROW][C]M5[/C][C]-4.20111500162079e-16[/C][C]0[/C][C]-0.9773[/C][C]0.334444[/C][C]0.167222[/C][/ROW]
[ROW][C]M6[/C][C]-1.30278565279883e-15[/C][C]0[/C][C]-3.0967[/C][C]0.003617[/C][C]0.001808[/C][/ROW]
[ROW][C]M7[/C][C]-2.20954492161706e-16[/C][C]0[/C][C]-0.5336[/C][C]0.596648[/C][C]0.298324[/C][/ROW]
[ROW][C]M8[/C][C]-1.98972849676541e-16[/C][C]0[/C][C]-0.491[/C][C]0.626158[/C][C]0.313079[/C][/ROW]
[ROW][C]M9[/C][C]-1.22427412989515e-16[/C][C]0[/C][C]-0.2982[/C][C]0.767153[/C][C]0.383577[/C][/ROW]
[ROW][C]M10[/C][C]-1.63618939147031e-16[/C][C]0[/C][C]-0.3786[/C][C]0.707054[/C][C]0.353527[/C][/ROW]
[ROW][C]M11[/C][C]-1.05726305286780e-16[/C][C]0[/C][C]-0.2474[/C][C]0.805877[/C][C]0.402939[/C][/ROW]
[ROW][C]t[/C][C]-1.29912862524235e-17[/C][C]0[/C][C]-0.5281[/C][C]0.600398[/C][C]0.300199[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58343&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58343&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)8.46828148995742e-1500.69090.4937270.246863
consumptieprijzen-2.46149433910034e-170-0.20950.8351440.417572
`Yt-1`-4.29728240716339e-170-0.89970.3737950.186898
`Yt-2`4.15699978358523e-1700.59790.5533870.276693
`Yt-3`-2.25683481723919e-160-3.28920.0021350.001068
`Yt-4`102224666410726978400
M1-8.47467574596834e-170-0.2010.8417490.420875
M2-8.20573878030883e-170-0.19110.8494510.424726
M3-7.1915385565832e-170-0.17490.8620740.431037
M4-2.74872398299474e-160-0.67440.5040340.252017
M5-4.20111500162079e-160-0.97730.3344440.167222
M6-1.30278565279883e-150-3.09670.0036170.001808
M7-2.20954492161706e-160-0.53360.5966480.298324
M8-1.98972849676541e-160-0.4910.6261580.313079
M9-1.22427412989515e-160-0.29820.7671530.383577
M10-1.63618939147031e-160-0.37860.7070540.353527
M11-1.05726305286780e-160-0.24740.8058770.402939
t-1.29912862524235e-170-0.52810.6003980.300199







Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)4.63354400231108e+32
F-TEST (DF numerator)17
F-TEST (DF denominator)39
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.90015688374994e-16
Sum Squared Residuals1.35766219886161e-29

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 1 \tabularnewline
R-squared & 1 \tabularnewline
Adjusted R-squared & 1 \tabularnewline
F-TEST (value) & 4.63354400231108e+32 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 39 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.90015688374994e-16 \tabularnewline
Sum Squared Residuals & 1.35766219886161e-29 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58343&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]1[/C][/ROW]
[ROW][C]R-squared[/C][C]1[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.63354400231108e+32[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/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]5.90015688374994e-16[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.35766219886161e-29[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58343&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58343&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 R1
R-squared1
Adjusted R-squared1
F-TEST (value)4.63354400231108e+32
F-TEST (DF numerator)17
F-TEST (DF denominator)39
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.90015688374994e-16
Sum Squared Residuals1.35766219886161e-29







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11001004.07763816357643e-16
297.8297.823.5574583317214e-16
394.0594.051.63465453130326e-16
491.1291.12-4.18880846466341e-16
593.1393.13-9.62488669471354e-17
693.8893.88-2.81669334016397e-15
792.5592.554.68571786694248e-16
894.4394.437.33814311449185e-16
996.2596.251.29124121501585e-17
10100.44100.44-1.01829584700990e-16
11101.5101.53.63047501482589e-17
1299.499.46.7355343269277e-17
1399.6999.696.9409120967337e-18
14101.69101.69-1.35191297428201e-16
15103.67103.67-1.14553867389374e-16
16103.05103.054.11428458161613e-16
17100.95100.951.69673394116565e-16
18102.35102.358.42383538803831e-16
19101.65101.65-1.20860021245171e-16
2099.5799.57-3.66619628508846e-16
2195.6895.68-2.68185866743424e-17
2296.5896.582.05121508807187e-17
2396.3396.331.09289807473371e-18
2495.3795.37-1.78863587485951e-16
259696-1.65730977624316e-16
2696.8896.88-2.55204407373543e-17
2794.8594.855.56941512185107e-17
2892.4792.471.57391526335182e-16
2993.9993.991.39688041369639e-16
3093.4593.451.11252863561220e-15
3192.2792.271.52828716138662e-16
3290.490.42.12554073285373e-17
3390.4390.433.52126995878016e-17
3491.0591.05-9.37796514406058e-17
3589.0889.08-1.98513154521503e-17
3689.6989.69-3.48655124153472e-16
3787.9287.92-6.07306509384967e-17
3885.8885.881.25994483880687e-17
3983.2183.212.78351701157183e-16
4083.8683.86-1.23854471677458e-16
4183.0183.01-1.75557563617057e-16
4282.8582.858.42212237815844e-16
4378.6978.691.01916953661347e-16
4477.5777.57-2.07661662884899e-16
4578.5478.54-1.03610811708113e-16
4678.5678.561.75097085260877e-16
4777.4877.48-1.75463327708429e-17
4881.5981.594.60163368370146e-16
4985.0285.02-1.88243099891565e-16
5091.7191.71-2.07633543394654e-16
5195.9695.96-3.82957438116646e-16
5290.8590.85-2.60846663529970e-17
5392.2992.29-3.755500492201e-17
5495.5795.571.95689279320945e-17
5593.6293.62-6.02457435249086e-16
5692.6392.63-1.80788427383978e-16
5789.5189.518.2304286644496e-17

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100 & 100 & 4.07763816357643e-16 \tabularnewline
2 & 97.82 & 97.82 & 3.5574583317214e-16 \tabularnewline
3 & 94.05 & 94.05 & 1.63465453130326e-16 \tabularnewline
4 & 91.12 & 91.12 & -4.18880846466341e-16 \tabularnewline
5 & 93.13 & 93.13 & -9.62488669471354e-17 \tabularnewline
6 & 93.88 & 93.88 & -2.81669334016397e-15 \tabularnewline
7 & 92.55 & 92.55 & 4.68571786694248e-16 \tabularnewline
8 & 94.43 & 94.43 & 7.33814311449185e-16 \tabularnewline
9 & 96.25 & 96.25 & 1.29124121501585e-17 \tabularnewline
10 & 100.44 & 100.44 & -1.01829584700990e-16 \tabularnewline
11 & 101.5 & 101.5 & 3.63047501482589e-17 \tabularnewline
12 & 99.4 & 99.4 & 6.7355343269277e-17 \tabularnewline
13 & 99.69 & 99.69 & 6.9409120967337e-18 \tabularnewline
14 & 101.69 & 101.69 & -1.35191297428201e-16 \tabularnewline
15 & 103.67 & 103.67 & -1.14553867389374e-16 \tabularnewline
16 & 103.05 & 103.05 & 4.11428458161613e-16 \tabularnewline
17 & 100.95 & 100.95 & 1.69673394116565e-16 \tabularnewline
18 & 102.35 & 102.35 & 8.42383538803831e-16 \tabularnewline
19 & 101.65 & 101.65 & -1.20860021245171e-16 \tabularnewline
20 & 99.57 & 99.57 & -3.66619628508846e-16 \tabularnewline
21 & 95.68 & 95.68 & -2.68185866743424e-17 \tabularnewline
22 & 96.58 & 96.58 & 2.05121508807187e-17 \tabularnewline
23 & 96.33 & 96.33 & 1.09289807473371e-18 \tabularnewline
24 & 95.37 & 95.37 & -1.78863587485951e-16 \tabularnewline
25 & 96 & 96 & -1.65730977624316e-16 \tabularnewline
26 & 96.88 & 96.88 & -2.55204407373543e-17 \tabularnewline
27 & 94.85 & 94.85 & 5.56941512185107e-17 \tabularnewline
28 & 92.47 & 92.47 & 1.57391526335182e-16 \tabularnewline
29 & 93.99 & 93.99 & 1.39688041369639e-16 \tabularnewline
30 & 93.45 & 93.45 & 1.11252863561220e-15 \tabularnewline
31 & 92.27 & 92.27 & 1.52828716138662e-16 \tabularnewline
32 & 90.4 & 90.4 & 2.12554073285373e-17 \tabularnewline
33 & 90.43 & 90.43 & 3.52126995878016e-17 \tabularnewline
34 & 91.05 & 91.05 & -9.37796514406058e-17 \tabularnewline
35 & 89.08 & 89.08 & -1.98513154521503e-17 \tabularnewline
36 & 89.69 & 89.69 & -3.48655124153472e-16 \tabularnewline
37 & 87.92 & 87.92 & -6.07306509384967e-17 \tabularnewline
38 & 85.88 & 85.88 & 1.25994483880687e-17 \tabularnewline
39 & 83.21 & 83.21 & 2.78351701157183e-16 \tabularnewline
40 & 83.86 & 83.86 & -1.23854471677458e-16 \tabularnewline
41 & 83.01 & 83.01 & -1.75557563617057e-16 \tabularnewline
42 & 82.85 & 82.85 & 8.42212237815844e-16 \tabularnewline
43 & 78.69 & 78.69 & 1.01916953661347e-16 \tabularnewline
44 & 77.57 & 77.57 & -2.07661662884899e-16 \tabularnewline
45 & 78.54 & 78.54 & -1.03610811708113e-16 \tabularnewline
46 & 78.56 & 78.56 & 1.75097085260877e-16 \tabularnewline
47 & 77.48 & 77.48 & -1.75463327708429e-17 \tabularnewline
48 & 81.59 & 81.59 & 4.60163368370146e-16 \tabularnewline
49 & 85.02 & 85.02 & -1.88243099891565e-16 \tabularnewline
50 & 91.71 & 91.71 & -2.07633543394654e-16 \tabularnewline
51 & 95.96 & 95.96 & -3.82957438116646e-16 \tabularnewline
52 & 90.85 & 90.85 & -2.60846663529970e-17 \tabularnewline
53 & 92.29 & 92.29 & -3.755500492201e-17 \tabularnewline
54 & 95.57 & 95.57 & 1.95689279320945e-17 \tabularnewline
55 & 93.62 & 93.62 & -6.02457435249086e-16 \tabularnewline
56 & 92.63 & 92.63 & -1.80788427383978e-16 \tabularnewline
57 & 89.51 & 89.51 & 8.2304286644496e-17 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58343&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]100[/C][C]100[/C][C]4.07763816357643e-16[/C][/ROW]
[ROW][C]2[/C][C]97.82[/C][C]97.82[/C][C]3.5574583317214e-16[/C][/ROW]
[ROW][C]3[/C][C]94.05[/C][C]94.05[/C][C]1.63465453130326e-16[/C][/ROW]
[ROW][C]4[/C][C]91.12[/C][C]91.12[/C][C]-4.18880846466341e-16[/C][/ROW]
[ROW][C]5[/C][C]93.13[/C][C]93.13[/C][C]-9.62488669471354e-17[/C][/ROW]
[ROW][C]6[/C][C]93.88[/C][C]93.88[/C][C]-2.81669334016397e-15[/C][/ROW]
[ROW][C]7[/C][C]92.55[/C][C]92.55[/C][C]4.68571786694248e-16[/C][/ROW]
[ROW][C]8[/C][C]94.43[/C][C]94.43[/C][C]7.33814311449185e-16[/C][/ROW]
[ROW][C]9[/C][C]96.25[/C][C]96.25[/C][C]1.29124121501585e-17[/C][/ROW]
[ROW][C]10[/C][C]100.44[/C][C]100.44[/C][C]-1.01829584700990e-16[/C][/ROW]
[ROW][C]11[/C][C]101.5[/C][C]101.5[/C][C]3.63047501482589e-17[/C][/ROW]
[ROW][C]12[/C][C]99.4[/C][C]99.4[/C][C]6.7355343269277e-17[/C][/ROW]
[ROW][C]13[/C][C]99.69[/C][C]99.69[/C][C]6.9409120967337e-18[/C][/ROW]
[ROW][C]14[/C][C]101.69[/C][C]101.69[/C][C]-1.35191297428201e-16[/C][/ROW]
[ROW][C]15[/C][C]103.67[/C][C]103.67[/C][C]-1.14553867389374e-16[/C][/ROW]
[ROW][C]16[/C][C]103.05[/C][C]103.05[/C][C]4.11428458161613e-16[/C][/ROW]
[ROW][C]17[/C][C]100.95[/C][C]100.95[/C][C]1.69673394116565e-16[/C][/ROW]
[ROW][C]18[/C][C]102.35[/C][C]102.35[/C][C]8.42383538803831e-16[/C][/ROW]
[ROW][C]19[/C][C]101.65[/C][C]101.65[/C][C]-1.20860021245171e-16[/C][/ROW]
[ROW][C]20[/C][C]99.57[/C][C]99.57[/C][C]-3.66619628508846e-16[/C][/ROW]
[ROW][C]21[/C][C]95.68[/C][C]95.68[/C][C]-2.68185866743424e-17[/C][/ROW]
[ROW][C]22[/C][C]96.58[/C][C]96.58[/C][C]2.05121508807187e-17[/C][/ROW]
[ROW][C]23[/C][C]96.33[/C][C]96.33[/C][C]1.09289807473371e-18[/C][/ROW]
[ROW][C]24[/C][C]95.37[/C][C]95.37[/C][C]-1.78863587485951e-16[/C][/ROW]
[ROW][C]25[/C][C]96[/C][C]96[/C][C]-1.65730977624316e-16[/C][/ROW]
[ROW][C]26[/C][C]96.88[/C][C]96.88[/C][C]-2.55204407373543e-17[/C][/ROW]
[ROW][C]27[/C][C]94.85[/C][C]94.85[/C][C]5.56941512185107e-17[/C][/ROW]
[ROW][C]28[/C][C]92.47[/C][C]92.47[/C][C]1.57391526335182e-16[/C][/ROW]
[ROW][C]29[/C][C]93.99[/C][C]93.99[/C][C]1.39688041369639e-16[/C][/ROW]
[ROW][C]30[/C][C]93.45[/C][C]93.45[/C][C]1.11252863561220e-15[/C][/ROW]
[ROW][C]31[/C][C]92.27[/C][C]92.27[/C][C]1.52828716138662e-16[/C][/ROW]
[ROW][C]32[/C][C]90.4[/C][C]90.4[/C][C]2.12554073285373e-17[/C][/ROW]
[ROW][C]33[/C][C]90.43[/C][C]90.43[/C][C]3.52126995878016e-17[/C][/ROW]
[ROW][C]34[/C][C]91.05[/C][C]91.05[/C][C]-9.37796514406058e-17[/C][/ROW]
[ROW][C]35[/C][C]89.08[/C][C]89.08[/C][C]-1.98513154521503e-17[/C][/ROW]
[ROW][C]36[/C][C]89.69[/C][C]89.69[/C][C]-3.48655124153472e-16[/C][/ROW]
[ROW][C]37[/C][C]87.92[/C][C]87.92[/C][C]-6.07306509384967e-17[/C][/ROW]
[ROW][C]38[/C][C]85.88[/C][C]85.88[/C][C]1.25994483880687e-17[/C][/ROW]
[ROW][C]39[/C][C]83.21[/C][C]83.21[/C][C]2.78351701157183e-16[/C][/ROW]
[ROW][C]40[/C][C]83.86[/C][C]83.86[/C][C]-1.23854471677458e-16[/C][/ROW]
[ROW][C]41[/C][C]83.01[/C][C]83.01[/C][C]-1.75557563617057e-16[/C][/ROW]
[ROW][C]42[/C][C]82.85[/C][C]82.85[/C][C]8.42212237815844e-16[/C][/ROW]
[ROW][C]43[/C][C]78.69[/C][C]78.69[/C][C]1.01916953661347e-16[/C][/ROW]
[ROW][C]44[/C][C]77.57[/C][C]77.57[/C][C]-2.07661662884899e-16[/C][/ROW]
[ROW][C]45[/C][C]78.54[/C][C]78.54[/C][C]-1.03610811708113e-16[/C][/ROW]
[ROW][C]46[/C][C]78.56[/C][C]78.56[/C][C]1.75097085260877e-16[/C][/ROW]
[ROW][C]47[/C][C]77.48[/C][C]77.48[/C][C]-1.75463327708429e-17[/C][/ROW]
[ROW][C]48[/C][C]81.59[/C][C]81.59[/C][C]4.60163368370146e-16[/C][/ROW]
[ROW][C]49[/C][C]85.02[/C][C]85.02[/C][C]-1.88243099891565e-16[/C][/ROW]
[ROW][C]50[/C][C]91.71[/C][C]91.71[/C][C]-2.07633543394654e-16[/C][/ROW]
[ROW][C]51[/C][C]95.96[/C][C]95.96[/C][C]-3.82957438116646e-16[/C][/ROW]
[ROW][C]52[/C][C]90.85[/C][C]90.85[/C][C]-2.60846663529970e-17[/C][/ROW]
[ROW][C]53[/C][C]92.29[/C][C]92.29[/C][C]-3.755500492201e-17[/C][/ROW]
[ROW][C]54[/C][C]95.57[/C][C]95.57[/C][C]1.95689279320945e-17[/C][/ROW]
[ROW][C]55[/C][C]93.62[/C][C]93.62[/C][C]-6.02457435249086e-16[/C][/ROW]
[ROW][C]56[/C][C]92.63[/C][C]92.63[/C][C]-1.80788427383978e-16[/C][/ROW]
[ROW][C]57[/C][C]89.51[/C][C]89.51[/C][C]8.2304286644496e-17[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58343&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58343&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
11001004.07763816357643e-16
297.8297.823.5574583317214e-16
394.0594.051.63465453130326e-16
491.1291.12-4.18880846466341e-16
593.1393.13-9.62488669471354e-17
693.8893.88-2.81669334016397e-15
792.5592.554.68571786694248e-16
894.4394.437.33814311449185e-16
996.2596.251.29124121501585e-17
10100.44100.44-1.01829584700990e-16
11101.5101.53.63047501482589e-17
1299.499.46.7355343269277e-17
1399.6999.696.9409120967337e-18
14101.69101.69-1.35191297428201e-16
15103.67103.67-1.14553867389374e-16
16103.05103.054.11428458161613e-16
17100.95100.951.69673394116565e-16
18102.35102.358.42383538803831e-16
19101.65101.65-1.20860021245171e-16
2099.5799.57-3.66619628508846e-16
2195.6895.68-2.68185866743424e-17
2296.5896.582.05121508807187e-17
2396.3396.331.09289807473371e-18
2495.3795.37-1.78863587485951e-16
259696-1.65730977624316e-16
2696.8896.88-2.55204407373543e-17
2794.8594.855.56941512185107e-17
2892.4792.471.57391526335182e-16
2993.9993.991.39688041369639e-16
3093.4593.451.11252863561220e-15
3192.2792.271.52828716138662e-16
3290.490.42.12554073285373e-17
3390.4390.433.52126995878016e-17
3491.0591.05-9.37796514406058e-17
3589.0889.08-1.98513154521503e-17
3689.6989.69-3.48655124153472e-16
3787.9287.92-6.07306509384967e-17
3885.8885.881.25994483880687e-17
3983.2183.212.78351701157183e-16
4083.8683.86-1.23854471677458e-16
4183.0183.01-1.75557563617057e-16
4282.8582.858.42212237815844e-16
4378.6978.691.01916953661347e-16
4477.5777.57-2.07661662884899e-16
4578.5478.54-1.03610811708113e-16
4678.5678.561.75097085260877e-16
4777.4877.48-1.75463327708429e-17
4881.5981.594.60163368370146e-16
4985.0285.02-1.88243099891565e-16
5091.7191.71-2.07633543394654e-16
5195.9695.96-3.82957438116646e-16
5290.8590.85-2.60846663529970e-17
5392.2992.29-3.755500492201e-17
5495.5795.571.95689279320945e-17
5593.6293.62-6.02457435249086e-16
5692.6392.63-1.80788427383978e-16
5789.5189.518.2304286644496e-17







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.08733134367438440.1746626873487690.912668656325616
220.1303675232158530.2607350464317050.869632476784147
238.19721403014708e-081.63944280602942e-070.99999991802786
243.58082185052652e-057.16164370105305e-050.999964191781495
250.01051488680329060.02102977360658110.98948511319671
260.0002513593554455900.0005027187108911810.999748640644554
270.0001276107263907050.0002552214527814100.99987238927361
280.998820773262390.002358453475218420.00117922673760921
290.004662618608296870.009325237216593750.995337381391703
300.1874876103827070.3749752207654130.812512389617293
310.3433355449504710.6866710899009410.656664455049529
320.996083947789510.007832104420982230.00391605221049111
330.5952987766538180.8094024466923640.404701223346182
340.0003738973934488650.000747794786897730.999626102606551
350.6778688903866110.6442622192267780.322131109613389
360.886958895161190.226082209677620.11304110483881

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.0873313436743844 & 0.174662687348769 & 0.912668656325616 \tabularnewline
22 & 0.130367523215853 & 0.260735046431705 & 0.869632476784147 \tabularnewline
23 & 8.19721403014708e-08 & 1.63944280602942e-07 & 0.99999991802786 \tabularnewline
24 & 3.58082185052652e-05 & 7.16164370105305e-05 & 0.999964191781495 \tabularnewline
25 & 0.0105148868032906 & 0.0210297736065811 & 0.98948511319671 \tabularnewline
26 & 0.000251359355445590 & 0.000502718710891181 & 0.999748640644554 \tabularnewline
27 & 0.000127610726390705 & 0.000255221452781410 & 0.99987238927361 \tabularnewline
28 & 0.99882077326239 & 0.00235845347521842 & 0.00117922673760921 \tabularnewline
29 & 0.00466261860829687 & 0.00932523721659375 & 0.995337381391703 \tabularnewline
30 & 0.187487610382707 & 0.374975220765413 & 0.812512389617293 \tabularnewline
31 & 0.343335544950471 & 0.686671089900941 & 0.656664455049529 \tabularnewline
32 & 0.99608394778951 & 0.00783210442098223 & 0.00391605221049111 \tabularnewline
33 & 0.595298776653818 & 0.809402446692364 & 0.404701223346182 \tabularnewline
34 & 0.000373897393448865 & 0.00074779478689773 & 0.999626102606551 \tabularnewline
35 & 0.677868890386611 & 0.644262219226778 & 0.322131109613389 \tabularnewline
36 & 0.88695889516119 & 0.22608220967762 & 0.11304110483881 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58343&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]21[/C][C]0.0873313436743844[/C][C]0.174662687348769[/C][C]0.912668656325616[/C][/ROW]
[ROW][C]22[/C][C]0.130367523215853[/C][C]0.260735046431705[/C][C]0.869632476784147[/C][/ROW]
[ROW][C]23[/C][C]8.19721403014708e-08[/C][C]1.63944280602942e-07[/C][C]0.99999991802786[/C][/ROW]
[ROW][C]24[/C][C]3.58082185052652e-05[/C][C]7.16164370105305e-05[/C][C]0.999964191781495[/C][/ROW]
[ROW][C]25[/C][C]0.0105148868032906[/C][C]0.0210297736065811[/C][C]0.98948511319671[/C][/ROW]
[ROW][C]26[/C][C]0.000251359355445590[/C][C]0.000502718710891181[/C][C]0.999748640644554[/C][/ROW]
[ROW][C]27[/C][C]0.000127610726390705[/C][C]0.000255221452781410[/C][C]0.99987238927361[/C][/ROW]
[ROW][C]28[/C][C]0.99882077326239[/C][C]0.00235845347521842[/C][C]0.00117922673760921[/C][/ROW]
[ROW][C]29[/C][C]0.00466261860829687[/C][C]0.00932523721659375[/C][C]0.995337381391703[/C][/ROW]
[ROW][C]30[/C][C]0.187487610382707[/C][C]0.374975220765413[/C][C]0.812512389617293[/C][/ROW]
[ROW][C]31[/C][C]0.343335544950471[/C][C]0.686671089900941[/C][C]0.656664455049529[/C][/ROW]
[ROW][C]32[/C][C]0.99608394778951[/C][C]0.00783210442098223[/C][C]0.00391605221049111[/C][/ROW]
[ROW][C]33[/C][C]0.595298776653818[/C][C]0.809402446692364[/C][C]0.404701223346182[/C][/ROW]
[ROW][C]34[/C][C]0.000373897393448865[/C][C]0.00074779478689773[/C][C]0.999626102606551[/C][/ROW]
[ROW][C]35[/C][C]0.677868890386611[/C][C]0.644262219226778[/C][C]0.322131109613389[/C][/ROW]
[ROW][C]36[/C][C]0.88695889516119[/C][C]0.22608220967762[/C][C]0.11304110483881[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58343&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58343&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
210.08733134367438440.1746626873487690.912668656325616
220.1303675232158530.2607350464317050.869632476784147
238.19721403014708e-081.63944280602942e-070.99999991802786
243.58082185052652e-057.16164370105305e-050.999964191781495
250.01051488680329060.02102977360658110.98948511319671
260.0002513593554455900.0005027187108911810.999748640644554
270.0001276107263907050.0002552214527814100.99987238927361
280.998820773262390.002358453475218420.00117922673760921
290.004662618608296870.009325237216593750.995337381391703
300.1874876103827070.3749752207654130.812512389617293
310.3433355449504710.6866710899009410.656664455049529
320.996083947789510.007832104420982230.00391605221049111
330.5952987766538180.8094024466923640.404701223346182
340.0003738973934488650.000747794786897730.999626102606551
350.6778688903866110.6442622192267780.322131109613389
360.886958895161190.226082209677620.11304110483881







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.5NOK
5% type I error level90.5625NOK
10% type I error level90.5625NOK

\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 & 8 & 0.5 & NOK \tabularnewline
5% type I error level & 9 & 0.5625 & NOK \tabularnewline
10% type I error level & 9 & 0.5625 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58343&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]8[/C][C]0.5[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.5625[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.5625[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58343&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58343&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 level80.5NOK
5% type I error level90.5625NOK
10% type I error level90.5625NOK



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')
}