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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 computationThu, 24 Nov 2011 10:39:50 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322149253x4j86u16l7nwe9r.htm/, Retrieved Thu, 31 Oct 2024 23:31:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146991, Retrieved Thu, 31 Oct 2024 23:31:28 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Multiple Linear R...] [2011-11-23 18:09:50] [489eb911c8db04aca1fc54d886fc3144]
-   P     [Multiple Regression] [Multiple Linear R...] [2011-11-24 15:39:50] [d160b678fd2d7bb562db2147d7efddc2] [Current]
-    D      [Multiple Regression] [] [2011-11-24 15:57:25] [489eb911c8db04aca1fc54d886fc3144]
-    D      [Multiple Regression] [] [2011-11-24 17:14:55] [489eb911c8db04aca1fc54d886fc3144]
-    D      [Multiple Regression] [Multiple Linear R...] [2011-11-24 17:27:18] [489eb911c8db04aca1fc54d886fc3144]
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Dataseries X:
1019	162	30	12	4	8
1093	162	12	13	8	10
1119	146	29	20	3	17
1015	114	17	22	3	9
988	114	32	1	5	23
900	140	9	6	4	7
937	101	18	16	2	16
907	140	9	5	2	19
839	115	10	8	1	20
830	128	9	1	5	14
909	75	16	8	3	17
696	74	11	6	3	14
649	55	10	10	1	25
637	72	8	4	1	8
614	73	5	2	3	12
583	56	10	11	2	15
576	50	4	3	0	11




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.wessa.net

\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 & 5 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146991&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146991&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146991&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 time5 seconds
R Server'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Droog[t] = + 309.477522597241 + 2.7835051479818Regen[t] + 5.0325040141591Mist[t] + 7.81605840346455Sneeuw[t] + 15.5771727299243Wind[t] + 3.93954991138877Andere[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Droog[t] =  +  309.477522597241 +  2.7835051479818Regen[t] +  5.0325040141591Mist[t] +  7.81605840346455Sneeuw[t] +  15.5771727299243Wind[t] +  3.93954991138877Andere[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146991&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Droog[t] =  +  309.477522597241 +  2.7835051479818Regen[t] +  5.0325040141591Mist[t] +  7.81605840346455Sneeuw[t] +  15.5771727299243Wind[t] +  3.93954991138877Andere[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146991&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146991&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
Droog[t] = + 309.477522597241 + 2.7835051479818Regen[t] + 5.0325040141591Mist[t] + 7.81605840346455Sneeuw[t] + 15.5771727299243Wind[t] + 3.93954991138877Andere[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)309.47752259724177.2121374.00810.0020570.001029
Regen2.78350514798180.6440114.32210.001210.000605
Mist5.03250401415912.5332711.98660.0724520.036226
Sneeuw7.816058403464553.012982.59410.0249540.012477
Wind15.577172729924311.8782141.31140.2164330.108217
Andere3.939549911388773.4686961.13570.2802040.140102

\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) & 309.477522597241 & 77.212137 & 4.0081 & 0.002057 & 0.001029 \tabularnewline
Regen & 2.7835051479818 & 0.644011 & 4.3221 & 0.00121 & 0.000605 \tabularnewline
Mist & 5.0325040141591 & 2.533271 & 1.9866 & 0.072452 & 0.036226 \tabularnewline
Sneeuw & 7.81605840346455 & 3.01298 & 2.5941 & 0.024954 & 0.012477 \tabularnewline
Wind & 15.5771727299243 & 11.878214 & 1.3114 & 0.216433 & 0.108217 \tabularnewline
Andere & 3.93954991138877 & 3.468696 & 1.1357 & 0.280204 & 0.140102 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146991&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]309.477522597241[/C][C]77.212137[/C][C]4.0081[/C][C]0.002057[/C][C]0.001029[/C][/ROW]
[ROW][C]Regen[/C][C]2.7835051479818[/C][C]0.644011[/C][C]4.3221[/C][C]0.00121[/C][C]0.000605[/C][/ROW]
[ROW][C]Mist[/C][C]5.0325040141591[/C][C]2.533271[/C][C]1.9866[/C][C]0.072452[/C][C]0.036226[/C][/ROW]
[ROW][C]Sneeuw[/C][C]7.81605840346455[/C][C]3.01298[/C][C]2.5941[/C][C]0.024954[/C][C]0.012477[/C][/ROW]
[ROW][C]Wind[/C][C]15.5771727299243[/C][C]11.878214[/C][C]1.3114[/C][C]0.216433[/C][C]0.108217[/C][/ROW]
[ROW][C]Andere[/C][C]3.93954991138877[/C][C]3.468696[/C][C]1.1357[/C][C]0.280204[/C][C]0.140102[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146991&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146991&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)309.47752259724177.2121374.00810.0020570.001029
Regen2.78350514798180.6440114.32210.001210.000605
Mist5.03250401415912.5332711.98660.0724520.036226
Sneeuw7.816058403464553.012982.59410.0249540.012477
Wind15.577172729924311.8782141.31140.2164330.108217
Andere3.939549911388773.4686961.13570.2802040.140102







Multiple Linear Regression - Regression Statistics
Multiple R0.953642272784482
R-squared0.909433584441553
Adjusted R-squared0.868267031914986
F-TEST (value)22.0915653273285
F-TEST (DF numerator)5
F-TEST (DF denominator)11
p-value2.14280637196307e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation66.3049371089457
Sum Squared Residuals48359.7915352337

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.953642272784482 \tabularnewline
R-squared & 0.909433584441553 \tabularnewline
Adjusted R-squared & 0.868267031914986 \tabularnewline
F-TEST (value) & 22.0915653273285 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 11 \tabularnewline
p-value & 2.14280637196307e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 66.3049371089457 \tabularnewline
Sum Squared Residuals & 48359.7915352337 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146991&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.953642272784482[/C][/ROW]
[ROW][C]R-squared[/C][C]0.909433584441553[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.868267031914986[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]22.0915653273285[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]11[/C][/ROW]
[ROW][C]p-value[/C][C]2.14280637196307e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]66.3049371089457[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]48359.7915352337[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146991&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146991&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 R0.953642272784482
R-squared0.909433584441553
Adjusted R-squared0.868267031914986
F-TEST (value)22.0915653273285
F-TEST (DF numerator)5
F-TEST (DF denominator)11
p-value2.14280637196307e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation66.3049371089457
Sum Squared Residuals48359.7915352337







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
110191098.99826804745-79.9982680474476
210931086.417044938526.58295506147671
311191131.83692536587-12.8369253658708
41015966.49042997636348.509570023637
5988964.14880793528623.8511920647145
6900881.24267016233118.7573298376691
7937900.44069329576836.5593067042316
8907889.54686523568317.4531347643171
9839836.8022929421552.19770705784486
10830851.914338478872-21.9143384788723
11909774.99280683352134.00719316648
12696719.596015073647-23.5960150736475
13649705.12185042712-56.1218504271202
14637628.5077310000968.49226899990385
15614647.474152404075-33.4741524040753
16583691.903087594603-108.903087594603
17576535.56602028863840.4339797113624

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1019 & 1098.99826804745 & -79.9982680474476 \tabularnewline
2 & 1093 & 1086.41704493852 & 6.58295506147671 \tabularnewline
3 & 1119 & 1131.83692536587 & -12.8369253658708 \tabularnewline
4 & 1015 & 966.490429976363 & 48.509570023637 \tabularnewline
5 & 988 & 964.148807935286 & 23.8511920647145 \tabularnewline
6 & 900 & 881.242670162331 & 18.7573298376691 \tabularnewline
7 & 937 & 900.440693295768 & 36.5593067042316 \tabularnewline
8 & 907 & 889.546865235683 & 17.4531347643171 \tabularnewline
9 & 839 & 836.802292942155 & 2.19770705784486 \tabularnewline
10 & 830 & 851.914338478872 & -21.9143384788723 \tabularnewline
11 & 909 & 774.99280683352 & 134.00719316648 \tabularnewline
12 & 696 & 719.596015073647 & -23.5960150736475 \tabularnewline
13 & 649 & 705.12185042712 & -56.1218504271202 \tabularnewline
14 & 637 & 628.507731000096 & 8.49226899990385 \tabularnewline
15 & 614 & 647.474152404075 & -33.4741524040753 \tabularnewline
16 & 583 & 691.903087594603 & -108.903087594603 \tabularnewline
17 & 576 & 535.566020288638 & 40.4339797113624 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146991&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]1019[/C][C]1098.99826804745[/C][C]-79.9982680474476[/C][/ROW]
[ROW][C]2[/C][C]1093[/C][C]1086.41704493852[/C][C]6.58295506147671[/C][/ROW]
[ROW][C]3[/C][C]1119[/C][C]1131.83692536587[/C][C]-12.8369253658708[/C][/ROW]
[ROW][C]4[/C][C]1015[/C][C]966.490429976363[/C][C]48.509570023637[/C][/ROW]
[ROW][C]5[/C][C]988[/C][C]964.148807935286[/C][C]23.8511920647145[/C][/ROW]
[ROW][C]6[/C][C]900[/C][C]881.242670162331[/C][C]18.7573298376691[/C][/ROW]
[ROW][C]7[/C][C]937[/C][C]900.440693295768[/C][C]36.5593067042316[/C][/ROW]
[ROW][C]8[/C][C]907[/C][C]889.546865235683[/C][C]17.4531347643171[/C][/ROW]
[ROW][C]9[/C][C]839[/C][C]836.802292942155[/C][C]2.19770705784486[/C][/ROW]
[ROW][C]10[/C][C]830[/C][C]851.914338478872[/C][C]-21.9143384788723[/C][/ROW]
[ROW][C]11[/C][C]909[/C][C]774.99280683352[/C][C]134.00719316648[/C][/ROW]
[ROW][C]12[/C][C]696[/C][C]719.596015073647[/C][C]-23.5960150736475[/C][/ROW]
[ROW][C]13[/C][C]649[/C][C]705.12185042712[/C][C]-56.1218504271202[/C][/ROW]
[ROW][C]14[/C][C]637[/C][C]628.507731000096[/C][C]8.49226899990385[/C][/ROW]
[ROW][C]15[/C][C]614[/C][C]647.474152404075[/C][C]-33.4741524040753[/C][/ROW]
[ROW][C]16[/C][C]583[/C][C]691.903087594603[/C][C]-108.903087594603[/C][/ROW]
[ROW][C]17[/C][C]576[/C][C]535.566020288638[/C][C]40.4339797113624[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146991&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146991&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
110191098.99826804745-79.9982680474476
210931086.417044938526.58295506147671
311191131.83692536587-12.8369253658708
41015966.49042997636348.509570023637
5988964.14880793528623.8511920647145
6900881.24267016233118.7573298376691
7937900.44069329576836.5593067042316
8907889.54686523568317.4531347643171
9839836.8022929421552.19770705784486
10830851.914338478872-21.9143384788723
11909774.99280683352134.00719316648
12696719.596015073647-23.5960150736475
13649705.12185042712-56.1218504271202
14637628.5077310000968.49226899990385
15614647.474152404075-33.4741524040753
16583691.903087594603-108.903087594603
17576535.56602028863840.4339797113624



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')
}