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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 02:59:32 -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/t1258711278685bytbg1dc9i9f.htm/, Retrieved Fri, 29 Mar 2024 02:23:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58001, Retrieved Fri, 29 Mar 2024 02:23:00 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact149
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 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [workshop 7] [2009-11-20 09:59:32] [a18540c86166a2b66550d1fef0503cc2] [Current]
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Dataseries X:
9	9	8,9	8,9
9	9	9	8,9
9,1	9	9	8,9
9	9	9	8,9
9,1	9	9	9
9,1	9	9	9
9	9	9	9
9	9,1	9	9
9	9	9,1	9
9	9,1	9	9
8,9	9,1	9,1	9
8,9	9	9,1	9,1
8,9	9	9	9
8,9	9	9	9,1
8,8	9	9	9,1
8,8	8,9	9	9
8,7	8,9	8,9	9
8,7	8,9	8,9	9
8,5	8,9	8,9	9
8,5	8,8	8,9	8,9
8,4	8,8	8,8	8,9
8,2	8,7	8,8	8,9
8,2	8,7	8,7	8,9
8,1	8,5	8,7	8,8
8,1	8,5	8,5	8,8
8	8,4	8,5	8,7
7,9	8,2	8,4	8,7
7,8	8,2	8,2	8,5
7,7	8,1	8,2	8,5
7,6	8,1	8,1	8,4
7,5	8	8,1	8,2
7,5	7,9	8	8,2
7,5	7,8	7,9	8,1
7,5	7,7	7,8	8,1
7,5	7,6	7,7	8
7,4	7,5	7,6	7,9
7,4	7,5	7,5	7,8
7,3	7,5	7,5	7,7
7,3	7,5	7,5	7,6
7,3	7,5	7,5	7,5
7,2	7,4	7,5	7,5
7,2	7,4	7,4	7,5
7,3	7,3	7,4	7,5
7,4	7,3	7,3	7,5
7,4	7,3	7,3	7,4
7,5	7,2	7,3	7,4
7,6	7,2	7,2	7,3
7,7	7,3	7,2	7,3
7,9	7,4	7,3	7,3
8	7,4	7,4	7,2
8,2	7,5	7,4	7,2




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=58001&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=58001&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58001&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
Y-1[t] = + 3.06826597807372 + 1.72596113445732`Y-7`[t] + 0.265700076265070`Y-8`[t] -1.32495159043329`Y-11`[t] -0.0604280458310303M1[t] -0.103472111491764M2[t] -0.0640751407679319M3[t] -0.217516798129260M4[t] -0.135389671420886M5[t] -0.174165679050416M6[t] -0.223052423531165M7[t] -0.168679402799261M8[t] -0.160923645373383M9[t] -0.0802778065192143M10[t] -0.084028575548143M11[t] -0.00606277831804904t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y-1[t] =  +  3.06826597807372 +  1.72596113445732`Y-7`[t] +  0.265700076265070`Y-8`[t] -1.32495159043329`Y-11`[t] -0.0604280458310303M1[t] -0.103472111491764M2[t] -0.0640751407679319M3[t] -0.217516798129260M4[t] -0.135389671420886M5[t] -0.174165679050416M6[t] -0.223052423531165M7[t] -0.168679402799261M8[t] -0.160923645373383M9[t] -0.0802778065192143M10[t] -0.084028575548143M11[t] -0.00606277831804904t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58001&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y-1[t] =  +  3.06826597807372 +  1.72596113445732`Y-7`[t] +  0.265700076265070`Y-8`[t] -1.32495159043329`Y-11`[t] -0.0604280458310303M1[t] -0.103472111491764M2[t] -0.0640751407679319M3[t] -0.217516798129260M4[t] -0.135389671420886M5[t] -0.174165679050416M6[t] -0.223052423531165M7[t] -0.168679402799261M8[t] -0.160923645373383M9[t] -0.0802778065192143M10[t] -0.084028575548143M11[t] -0.00606277831804904t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58001&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58001&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-1[t] = + 3.06826597807372 + 1.72596113445732`Y-7`[t] + 0.265700076265070`Y-8`[t] -1.32495159043329`Y-11`[t] -0.0604280458310303M1[t] -0.103472111491764M2[t] -0.0640751407679319M3[t] -0.217516798129260M4[t] -0.135389671420886M5[t] -0.174165679050416M6[t] -0.223052423531165M7[t] -0.168679402799261M8[t] -0.160923645373383M9[t] -0.0802778065192143M10[t] -0.084028575548143M11[t] -0.00606277831804904t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.068265978073721.229862.49480.0174710.008735
`Y-7`1.725961134457320.4498233.8370.0004990.000249
`Y-8`0.2657000762650700.5705090.46570.6442980.322149
`Y-11`-1.324951590433290.230478-5.74872e-061e-06
M1-0.06042804583103030.135006-0.44760.6572030.328602
M2-0.1034721114917640.127033-0.81450.4208490.210425
M3-0.06407514076793190.126967-0.50470.616960.30848
M4-0.2175167981292600.133917-1.62430.1132910.056646
M5-0.1353896714208860.132595-1.02110.3142240.157112
M6-0.1741656790504160.136201-1.27870.2094040.104702
M7-0.2230524235311650.132209-1.68710.1004740.050237
M8-0.1686794027992610.133242-1.2660.2138870.106944
M9-0.1609236453733830.132056-1.21860.2311440.115572
M10-0.08027780651921430.132017-0.60810.5470560.273528
M11-0.0840285755481430.133353-0.63010.5327060.266353
t-0.006062778318049040.006136-0.9880.3299140.164957

\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) & 3.06826597807372 & 1.22986 & 2.4948 & 0.017471 & 0.008735 \tabularnewline
`Y-7` & 1.72596113445732 & 0.449823 & 3.837 & 0.000499 & 0.000249 \tabularnewline
`Y-8` & 0.265700076265070 & 0.570509 & 0.4657 & 0.644298 & 0.322149 \tabularnewline
`Y-11` & -1.32495159043329 & 0.230478 & -5.7487 & 2e-06 & 1e-06 \tabularnewline
M1 & -0.0604280458310303 & 0.135006 & -0.4476 & 0.657203 & 0.328602 \tabularnewline
M2 & -0.103472111491764 & 0.127033 & -0.8145 & 0.420849 & 0.210425 \tabularnewline
M3 & -0.0640751407679319 & 0.126967 & -0.5047 & 0.61696 & 0.30848 \tabularnewline
M4 & -0.217516798129260 & 0.133917 & -1.6243 & 0.113291 & 0.056646 \tabularnewline
M5 & -0.135389671420886 & 0.132595 & -1.0211 & 0.314224 & 0.157112 \tabularnewline
M6 & -0.174165679050416 & 0.136201 & -1.2787 & 0.209404 & 0.104702 \tabularnewline
M7 & -0.223052423531165 & 0.132209 & -1.6871 & 0.100474 & 0.050237 \tabularnewline
M8 & -0.168679402799261 & 0.133242 & -1.266 & 0.213887 & 0.106944 \tabularnewline
M9 & -0.160923645373383 & 0.132056 & -1.2186 & 0.231144 & 0.115572 \tabularnewline
M10 & -0.0802778065192143 & 0.132017 & -0.6081 & 0.547056 & 0.273528 \tabularnewline
M11 & -0.084028575548143 & 0.133353 & -0.6301 & 0.532706 & 0.266353 \tabularnewline
t & -0.00606277831804904 & 0.006136 & -0.988 & 0.329914 & 0.164957 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58001&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]3.06826597807372[/C][C]1.22986[/C][C]2.4948[/C][C]0.017471[/C][C]0.008735[/C][/ROW]
[ROW][C]`Y-7`[/C][C]1.72596113445732[/C][C]0.449823[/C][C]3.837[/C][C]0.000499[/C][C]0.000249[/C][/ROW]
[ROW][C]`Y-8`[/C][C]0.265700076265070[/C][C]0.570509[/C][C]0.4657[/C][C]0.644298[/C][C]0.322149[/C][/ROW]
[ROW][C]`Y-11`[/C][C]-1.32495159043329[/C][C]0.230478[/C][C]-5.7487[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M1[/C][C]-0.0604280458310303[/C][C]0.135006[/C][C]-0.4476[/C][C]0.657203[/C][C]0.328602[/C][/ROW]
[ROW][C]M2[/C][C]-0.103472111491764[/C][C]0.127033[/C][C]-0.8145[/C][C]0.420849[/C][C]0.210425[/C][/ROW]
[ROW][C]M3[/C][C]-0.0640751407679319[/C][C]0.126967[/C][C]-0.5047[/C][C]0.61696[/C][C]0.30848[/C][/ROW]
[ROW][C]M4[/C][C]-0.217516798129260[/C][C]0.133917[/C][C]-1.6243[/C][C]0.113291[/C][C]0.056646[/C][/ROW]
[ROW][C]M5[/C][C]-0.135389671420886[/C][C]0.132595[/C][C]-1.0211[/C][C]0.314224[/C][C]0.157112[/C][/ROW]
[ROW][C]M6[/C][C]-0.174165679050416[/C][C]0.136201[/C][C]-1.2787[/C][C]0.209404[/C][C]0.104702[/C][/ROW]
[ROW][C]M7[/C][C]-0.223052423531165[/C][C]0.132209[/C][C]-1.6871[/C][C]0.100474[/C][C]0.050237[/C][/ROW]
[ROW][C]M8[/C][C]-0.168679402799261[/C][C]0.133242[/C][C]-1.266[/C][C]0.213887[/C][C]0.106944[/C][/ROW]
[ROW][C]M9[/C][C]-0.160923645373383[/C][C]0.132056[/C][C]-1.2186[/C][C]0.231144[/C][C]0.115572[/C][/ROW]
[ROW][C]M10[/C][C]-0.0802778065192143[/C][C]0.132017[/C][C]-0.6081[/C][C]0.547056[/C][C]0.273528[/C][/ROW]
[ROW][C]M11[/C][C]-0.084028575548143[/C][C]0.133353[/C][C]-0.6301[/C][C]0.532706[/C][C]0.266353[/C][/ROW]
[ROW][C]t[/C][C]-0.00606277831804904[/C][C]0.006136[/C][C]-0.988[/C][C]0.329914[/C][C]0.164957[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58001&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58001&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)3.068265978073721.229862.49480.0174710.008735
`Y-7`1.725961134457320.4498233.8370.0004990.000249
`Y-8`0.2657000762650700.5705090.46570.6442980.322149
`Y-11`-1.324951590433290.230478-5.74872e-061e-06
M1-0.06042804583103030.135006-0.44760.6572030.328602
M2-0.1034721114917640.127033-0.81450.4208490.210425
M3-0.06407514076793190.126967-0.50470.616960.30848
M4-0.2175167981292600.133917-1.62430.1132910.056646
M5-0.1353896714208860.132595-1.02110.3142240.157112
M6-0.1741656790504160.136201-1.27870.2094040.104702
M7-0.2230524235311650.132209-1.68710.1004740.050237
M8-0.1686794027992610.133242-1.2660.2138870.106944
M9-0.1609236453733830.132056-1.21860.2311440.115572
M10-0.08027780651921430.132017-0.60810.5470560.273528
M11-0.0840285755481430.133353-0.63010.5327060.266353
t-0.006062778318049040.006136-0.9880.3299140.164957







Multiple Linear Regression - Regression Statistics
Multiple R0.972818282888942
R-squared0.94637541152299
Adjusted R-squared0.923393445032841
F-TEST (value)41.1790440965398
F-TEST (DF numerator)15
F-TEST (DF denominator)35
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.185945402320779
Sum Squared Residuals1.21014924254827

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.972818282888942 \tabularnewline
R-squared & 0.94637541152299 \tabularnewline
Adjusted R-squared & 0.923393445032841 \tabularnewline
F-TEST (value) & 41.1790440965398 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 35 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.185945402320779 \tabularnewline
Sum Squared Residuals & 1.21014924254827 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58001&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.972818282888942[/C][/ROW]
[ROW][C]R-squared[/C][C]0.94637541152299[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.923393445032841[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]41.1790440965398[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]35[/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]0.185945402320779[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.21014924254827[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58001&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58001&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 R0.972818282888942
R-squared0.94637541152299
Adjusted R-squared0.923393445032841
F-TEST (value)41.1790440965398
F-TEST (DF numerator)15
F-TEST (DF denominator)35
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.185945402320779
Sum Squared Residuals1.21014924254827







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
199.10808688794345-0.108086887943447
299.08555005159114-0.0855500515911418
39.19.11888424399692-0.0188842439969227
498.959379808317540.0406201916824546
59.18.902948997664540.197051002335458
69.18.858110211716960.241889788283037
798.803160688918170.196839311081836
899.02406704477775-0.0240670447777507
998.879733918066360.120266081933645
1099.1003430844217-0.100343084421699
118.99.11709954470123-0.217099544701229
128.98.889974069442260.0100259305577380
138.98.92940839671-0.0294083967100043
148.98.74780639368790.152193606312107
158.88.781140586093680.0188594139063243
168.88.58153519601190.218464803988105
178.78.631029536775710.0689704632242854
188.78.586190750828140.113809249171864
198.58.53124122802934-0.0312412280293368
208.58.53945051604079-0.039450516040788
218.48.51457348752211-0.114573487522110
228.28.4165604346125-0.216560434612496
238.28.38017687963901-0.180176879639011
248.18.24544560902097-0.145445609020969
258.18.12581476961888-0.0258147696188757
2688.03660697123769-0.0366069712376917
277.97.69817892912550.201821070874500
287.87.750524796279770.0494752037202332
297.77.653993031224360.0460069687756409
307.67.7150793966936-0.115079396693603
317.57.75252407853573-0.252524078535731
327.57.60166819987735-0.101668199877347
337.57.53669021695626-0.0366902169562639
347.57.412107156420150.0878928435798552
357.57.335622647044260.164377352955745
367.47.346917482245440.0530825177545618
377.47.386351809513180.0136481904868184
387.37.46974012457773-0.169740124577728
397.37.63556947602684-0.335569476026841
407.37.6085601993908-0.308560199390792
417.27.51202843433538-0.312028434335385
427.27.4406196407613-0.240619640761299
437.37.213074004516770.0869259954832325
447.47.234814239304120.165185760695885
457.47.369002377455270.0309976225447282
467.57.270989324545660.22901067545434
477.67.36710092861550.232899071384494
487.77.617662839291330.082337160708669
497.97.75033813621450.149661863785508
5087.860296458905550.139703541094455
518.28.066226764757060.133773235242939

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9 & 9.10808688794345 & -0.108086887943447 \tabularnewline
2 & 9 & 9.08555005159114 & -0.0855500515911418 \tabularnewline
3 & 9.1 & 9.11888424399692 & -0.0188842439969227 \tabularnewline
4 & 9 & 8.95937980831754 & 0.0406201916824546 \tabularnewline
5 & 9.1 & 8.90294899766454 & 0.197051002335458 \tabularnewline
6 & 9.1 & 8.85811021171696 & 0.241889788283037 \tabularnewline
7 & 9 & 8.80316068891817 & 0.196839311081836 \tabularnewline
8 & 9 & 9.02406704477775 & -0.0240670447777507 \tabularnewline
9 & 9 & 8.87973391806636 & 0.120266081933645 \tabularnewline
10 & 9 & 9.1003430844217 & -0.100343084421699 \tabularnewline
11 & 8.9 & 9.11709954470123 & -0.217099544701229 \tabularnewline
12 & 8.9 & 8.88997406944226 & 0.0100259305577380 \tabularnewline
13 & 8.9 & 8.92940839671 & -0.0294083967100043 \tabularnewline
14 & 8.9 & 8.7478063936879 & 0.152193606312107 \tabularnewline
15 & 8.8 & 8.78114058609368 & 0.0188594139063243 \tabularnewline
16 & 8.8 & 8.5815351960119 & 0.218464803988105 \tabularnewline
17 & 8.7 & 8.63102953677571 & 0.0689704632242854 \tabularnewline
18 & 8.7 & 8.58619075082814 & 0.113809249171864 \tabularnewline
19 & 8.5 & 8.53124122802934 & -0.0312412280293368 \tabularnewline
20 & 8.5 & 8.53945051604079 & -0.039450516040788 \tabularnewline
21 & 8.4 & 8.51457348752211 & -0.114573487522110 \tabularnewline
22 & 8.2 & 8.4165604346125 & -0.216560434612496 \tabularnewline
23 & 8.2 & 8.38017687963901 & -0.180176879639011 \tabularnewline
24 & 8.1 & 8.24544560902097 & -0.145445609020969 \tabularnewline
25 & 8.1 & 8.12581476961888 & -0.0258147696188757 \tabularnewline
26 & 8 & 8.03660697123769 & -0.0366069712376917 \tabularnewline
27 & 7.9 & 7.6981789291255 & 0.201821070874500 \tabularnewline
28 & 7.8 & 7.75052479627977 & 0.0494752037202332 \tabularnewline
29 & 7.7 & 7.65399303122436 & 0.0460069687756409 \tabularnewline
30 & 7.6 & 7.7150793966936 & -0.115079396693603 \tabularnewline
31 & 7.5 & 7.75252407853573 & -0.252524078535731 \tabularnewline
32 & 7.5 & 7.60166819987735 & -0.101668199877347 \tabularnewline
33 & 7.5 & 7.53669021695626 & -0.0366902169562639 \tabularnewline
34 & 7.5 & 7.41210715642015 & 0.0878928435798552 \tabularnewline
35 & 7.5 & 7.33562264704426 & 0.164377352955745 \tabularnewline
36 & 7.4 & 7.34691748224544 & 0.0530825177545618 \tabularnewline
37 & 7.4 & 7.38635180951318 & 0.0136481904868184 \tabularnewline
38 & 7.3 & 7.46974012457773 & -0.169740124577728 \tabularnewline
39 & 7.3 & 7.63556947602684 & -0.335569476026841 \tabularnewline
40 & 7.3 & 7.6085601993908 & -0.308560199390792 \tabularnewline
41 & 7.2 & 7.51202843433538 & -0.312028434335385 \tabularnewline
42 & 7.2 & 7.4406196407613 & -0.240619640761299 \tabularnewline
43 & 7.3 & 7.21307400451677 & 0.0869259954832325 \tabularnewline
44 & 7.4 & 7.23481423930412 & 0.165185760695885 \tabularnewline
45 & 7.4 & 7.36900237745527 & 0.0309976225447282 \tabularnewline
46 & 7.5 & 7.27098932454566 & 0.22901067545434 \tabularnewline
47 & 7.6 & 7.3671009286155 & 0.232899071384494 \tabularnewline
48 & 7.7 & 7.61766283929133 & 0.082337160708669 \tabularnewline
49 & 7.9 & 7.7503381362145 & 0.149661863785508 \tabularnewline
50 & 8 & 7.86029645890555 & 0.139703541094455 \tabularnewline
51 & 8.2 & 8.06622676475706 & 0.133773235242939 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58001&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]9[/C][C]9.10808688794345[/C][C]-0.108086887943447[/C][/ROW]
[ROW][C]2[/C][C]9[/C][C]9.08555005159114[/C][C]-0.0855500515911418[/C][/ROW]
[ROW][C]3[/C][C]9.1[/C][C]9.11888424399692[/C][C]-0.0188842439969227[/C][/ROW]
[ROW][C]4[/C][C]9[/C][C]8.95937980831754[/C][C]0.0406201916824546[/C][/ROW]
[ROW][C]5[/C][C]9.1[/C][C]8.90294899766454[/C][C]0.197051002335458[/C][/ROW]
[ROW][C]6[/C][C]9.1[/C][C]8.85811021171696[/C][C]0.241889788283037[/C][/ROW]
[ROW][C]7[/C][C]9[/C][C]8.80316068891817[/C][C]0.196839311081836[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]9.02406704477775[/C][C]-0.0240670447777507[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]8.87973391806636[/C][C]0.120266081933645[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]9.1003430844217[/C][C]-0.100343084421699[/C][/ROW]
[ROW][C]11[/C][C]8.9[/C][C]9.11709954470123[/C][C]-0.217099544701229[/C][/ROW]
[ROW][C]12[/C][C]8.9[/C][C]8.88997406944226[/C][C]0.0100259305577380[/C][/ROW]
[ROW][C]13[/C][C]8.9[/C][C]8.92940839671[/C][C]-0.0294083967100043[/C][/ROW]
[ROW][C]14[/C][C]8.9[/C][C]8.7478063936879[/C][C]0.152193606312107[/C][/ROW]
[ROW][C]15[/C][C]8.8[/C][C]8.78114058609368[/C][C]0.0188594139063243[/C][/ROW]
[ROW][C]16[/C][C]8.8[/C][C]8.5815351960119[/C][C]0.218464803988105[/C][/ROW]
[ROW][C]17[/C][C]8.7[/C][C]8.63102953677571[/C][C]0.0689704632242854[/C][/ROW]
[ROW][C]18[/C][C]8.7[/C][C]8.58619075082814[/C][C]0.113809249171864[/C][/ROW]
[ROW][C]19[/C][C]8.5[/C][C]8.53124122802934[/C][C]-0.0312412280293368[/C][/ROW]
[ROW][C]20[/C][C]8.5[/C][C]8.53945051604079[/C][C]-0.039450516040788[/C][/ROW]
[ROW][C]21[/C][C]8.4[/C][C]8.51457348752211[/C][C]-0.114573487522110[/C][/ROW]
[ROW][C]22[/C][C]8.2[/C][C]8.4165604346125[/C][C]-0.216560434612496[/C][/ROW]
[ROW][C]23[/C][C]8.2[/C][C]8.38017687963901[/C][C]-0.180176879639011[/C][/ROW]
[ROW][C]24[/C][C]8.1[/C][C]8.24544560902097[/C][C]-0.145445609020969[/C][/ROW]
[ROW][C]25[/C][C]8.1[/C][C]8.12581476961888[/C][C]-0.0258147696188757[/C][/ROW]
[ROW][C]26[/C][C]8[/C][C]8.03660697123769[/C][C]-0.0366069712376917[/C][/ROW]
[ROW][C]27[/C][C]7.9[/C][C]7.6981789291255[/C][C]0.201821070874500[/C][/ROW]
[ROW][C]28[/C][C]7.8[/C][C]7.75052479627977[/C][C]0.0494752037202332[/C][/ROW]
[ROW][C]29[/C][C]7.7[/C][C]7.65399303122436[/C][C]0.0460069687756409[/C][/ROW]
[ROW][C]30[/C][C]7.6[/C][C]7.7150793966936[/C][C]-0.115079396693603[/C][/ROW]
[ROW][C]31[/C][C]7.5[/C][C]7.75252407853573[/C][C]-0.252524078535731[/C][/ROW]
[ROW][C]32[/C][C]7.5[/C][C]7.60166819987735[/C][C]-0.101668199877347[/C][/ROW]
[ROW][C]33[/C][C]7.5[/C][C]7.53669021695626[/C][C]-0.0366902169562639[/C][/ROW]
[ROW][C]34[/C][C]7.5[/C][C]7.41210715642015[/C][C]0.0878928435798552[/C][/ROW]
[ROW][C]35[/C][C]7.5[/C][C]7.33562264704426[/C][C]0.164377352955745[/C][/ROW]
[ROW][C]36[/C][C]7.4[/C][C]7.34691748224544[/C][C]0.0530825177545618[/C][/ROW]
[ROW][C]37[/C][C]7.4[/C][C]7.38635180951318[/C][C]0.0136481904868184[/C][/ROW]
[ROW][C]38[/C][C]7.3[/C][C]7.46974012457773[/C][C]-0.169740124577728[/C][/ROW]
[ROW][C]39[/C][C]7.3[/C][C]7.63556947602684[/C][C]-0.335569476026841[/C][/ROW]
[ROW][C]40[/C][C]7.3[/C][C]7.6085601993908[/C][C]-0.308560199390792[/C][/ROW]
[ROW][C]41[/C][C]7.2[/C][C]7.51202843433538[/C][C]-0.312028434335385[/C][/ROW]
[ROW][C]42[/C][C]7.2[/C][C]7.4406196407613[/C][C]-0.240619640761299[/C][/ROW]
[ROW][C]43[/C][C]7.3[/C][C]7.21307400451677[/C][C]0.0869259954832325[/C][/ROW]
[ROW][C]44[/C][C]7.4[/C][C]7.23481423930412[/C][C]0.165185760695885[/C][/ROW]
[ROW][C]45[/C][C]7.4[/C][C]7.36900237745527[/C][C]0.0309976225447282[/C][/ROW]
[ROW][C]46[/C][C]7.5[/C][C]7.27098932454566[/C][C]0.22901067545434[/C][/ROW]
[ROW][C]47[/C][C]7.6[/C][C]7.3671009286155[/C][C]0.232899071384494[/C][/ROW]
[ROW][C]48[/C][C]7.7[/C][C]7.61766283929133[/C][C]0.082337160708669[/C][/ROW]
[ROW][C]49[/C][C]7.9[/C][C]7.7503381362145[/C][C]0.149661863785508[/C][/ROW]
[ROW][C]50[/C][C]8[/C][C]7.86029645890555[/C][C]0.139703541094455[/C][/ROW]
[ROW][C]51[/C][C]8.2[/C][C]8.06622676475706[/C][C]0.133773235242939[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58001&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58001&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
199.10808688794345-0.108086887943447
299.08555005159114-0.0855500515911418
39.19.11888424399692-0.0188842439969227
498.959379808317540.0406201916824546
59.18.902948997664540.197051002335458
69.18.858110211716960.241889788283037
798.803160688918170.196839311081836
899.02406704477775-0.0240670447777507
998.879733918066360.120266081933645
1099.1003430844217-0.100343084421699
118.99.11709954470123-0.217099544701229
128.98.889974069442260.0100259305577380
138.98.92940839671-0.0294083967100043
148.98.74780639368790.152193606312107
158.88.781140586093680.0188594139063243
168.88.58153519601190.218464803988105
178.78.631029536775710.0689704632242854
188.78.586190750828140.113809249171864
198.58.53124122802934-0.0312412280293368
208.58.53945051604079-0.039450516040788
218.48.51457348752211-0.114573487522110
228.28.4165604346125-0.216560434612496
238.28.38017687963901-0.180176879639011
248.18.24544560902097-0.145445609020969
258.18.12581476961888-0.0258147696188757
2688.03660697123769-0.0366069712376917
277.97.69817892912550.201821070874500
287.87.750524796279770.0494752037202332
297.77.653993031224360.0460069687756409
307.67.7150793966936-0.115079396693603
317.57.75252407853573-0.252524078535731
327.57.60166819987735-0.101668199877347
337.57.53669021695626-0.0366902169562639
347.57.412107156420150.0878928435798552
357.57.335622647044260.164377352955745
367.47.346917482245440.0530825177545618
377.47.386351809513180.0136481904868184
387.37.46974012457773-0.169740124577728
397.37.63556947602684-0.335569476026841
407.37.6085601993908-0.308560199390792
417.27.51202843433538-0.312028434335385
427.27.4406196407613-0.240619640761299
437.37.213074004516770.0869259954832325
447.47.234814239304120.165185760695885
457.47.369002377455270.0309976225447282
467.57.270989324545660.22901067545434
477.67.36710092861550.232899071384494
487.77.617662839291330.082337160708669
497.97.75033813621450.149661863785508
5087.860296458905550.139703541094455
518.28.066226764757060.133773235242939







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.2192500117064820.4385000234129640.780749988293518
200.302082029998690.604164059997380.69791797000131
210.7474850173755720.5050299652488550.252514982624428
220.8595955833829110.2808088332341770.140404416617089
230.9091513105891830.1816973788216340.0908486894108172
240.8903220446459730.2193559107080530.109677955354027
250.889761689256780.2204766214864420.110238310743221
260.889665472940020.2206690541199590.110334527059979
270.9579360284449370.08412794311012520.0420639715550626
280.9374424460722610.1251151078554770.0625575539277385
290.8825629023202410.2348741953595180.117437097679759
300.8012559123973050.3974881752053900.198744087602695
310.7208132500148370.5583734999703250.279186749985163
320.6420290501727150.715941899654570.357970949827285

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.219250011706482 & 0.438500023412964 & 0.780749988293518 \tabularnewline
20 & 0.30208202999869 & 0.60416405999738 & 0.69791797000131 \tabularnewline
21 & 0.747485017375572 & 0.505029965248855 & 0.252514982624428 \tabularnewline
22 & 0.859595583382911 & 0.280808833234177 & 0.140404416617089 \tabularnewline
23 & 0.909151310589183 & 0.181697378821634 & 0.0908486894108172 \tabularnewline
24 & 0.890322044645973 & 0.219355910708053 & 0.109677955354027 \tabularnewline
25 & 0.88976168925678 & 0.220476621486442 & 0.110238310743221 \tabularnewline
26 & 0.88966547294002 & 0.220669054119959 & 0.110334527059979 \tabularnewline
27 & 0.957936028444937 & 0.0841279431101252 & 0.0420639715550626 \tabularnewline
28 & 0.937442446072261 & 0.125115107855477 & 0.0625575539277385 \tabularnewline
29 & 0.882562902320241 & 0.234874195359518 & 0.117437097679759 \tabularnewline
30 & 0.801255912397305 & 0.397488175205390 & 0.198744087602695 \tabularnewline
31 & 0.720813250014837 & 0.558373499970325 & 0.279186749985163 \tabularnewline
32 & 0.642029050172715 & 0.71594189965457 & 0.357970949827285 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58001&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]19[/C][C]0.219250011706482[/C][C]0.438500023412964[/C][C]0.780749988293518[/C][/ROW]
[ROW][C]20[/C][C]0.30208202999869[/C][C]0.60416405999738[/C][C]0.69791797000131[/C][/ROW]
[ROW][C]21[/C][C]0.747485017375572[/C][C]0.505029965248855[/C][C]0.252514982624428[/C][/ROW]
[ROW][C]22[/C][C]0.859595583382911[/C][C]0.280808833234177[/C][C]0.140404416617089[/C][/ROW]
[ROW][C]23[/C][C]0.909151310589183[/C][C]0.181697378821634[/C][C]0.0908486894108172[/C][/ROW]
[ROW][C]24[/C][C]0.890322044645973[/C][C]0.219355910708053[/C][C]0.109677955354027[/C][/ROW]
[ROW][C]25[/C][C]0.88976168925678[/C][C]0.220476621486442[/C][C]0.110238310743221[/C][/ROW]
[ROW][C]26[/C][C]0.88966547294002[/C][C]0.220669054119959[/C][C]0.110334527059979[/C][/ROW]
[ROW][C]27[/C][C]0.957936028444937[/C][C]0.0841279431101252[/C][C]0.0420639715550626[/C][/ROW]
[ROW][C]28[/C][C]0.937442446072261[/C][C]0.125115107855477[/C][C]0.0625575539277385[/C][/ROW]
[ROW][C]29[/C][C]0.882562902320241[/C][C]0.234874195359518[/C][C]0.117437097679759[/C][/ROW]
[ROW][C]30[/C][C]0.801255912397305[/C][C]0.397488175205390[/C][C]0.198744087602695[/C][/ROW]
[ROW][C]31[/C][C]0.720813250014837[/C][C]0.558373499970325[/C][C]0.279186749985163[/C][/ROW]
[ROW][C]32[/C][C]0.642029050172715[/C][C]0.71594189965457[/C][C]0.357970949827285[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58001&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58001&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
190.2192500117064820.4385000234129640.780749988293518
200.302082029998690.604164059997380.69791797000131
210.7474850173755720.5050299652488550.252514982624428
220.8595955833829110.2808088332341770.140404416617089
230.9091513105891830.1816973788216340.0908486894108172
240.8903220446459730.2193559107080530.109677955354027
250.889761689256780.2204766214864420.110238310743221
260.889665472940020.2206690541199590.110334527059979
270.9579360284449370.08412794311012520.0420639715550626
280.9374424460722610.1251151078554770.0625575539277385
290.8825629023202410.2348741953595180.117437097679759
300.8012559123973050.3974881752053900.198744087602695
310.7208132500148370.5583734999703250.279186749985163
320.6420290501727150.715941899654570.357970949827285







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 level10.0714285714285714OK

\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 & 1 & 0.0714285714285714 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58001&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]1[/C][C]0.0714285714285714[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58001&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58001&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 level10.0714285714285714OK



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')
}