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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 10:57:25 -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/t1322150318ljb92rx3ckb43jv.htm/, Retrieved Fri, 29 Mar 2024 14:49:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147019, Retrieved Fri, 29 Mar 2024 14:49:29 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76
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] [489eb911c8db04aca1fc54d886fc3144]
-    D      [Multiple Regression] [] [2011-11-24 15:57:25] [d160b678fd2d7bb562db2147d7efddc2] [Current]
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Dataseries X:
1235	1019	162	30	12	4	8
1298	1093	162	12	13	8	10
1334	1119	146	29	20	3	17
1180	1015	114	17	22	3	9
1163	988	114	32	1	5	23
1066	900	140	9	6	4	7
1090	937	101	18	16	2	16
1082	907	140	9	5	2	19
993	839	115	10	8	1	20
987	830	128	9	1	5	14
1028	909	75	16	8	3	17
804	696	74	11	6	3	14
750	649	55	10	10	1	25
730	637	72	8	4	1	8
709	614	73	5	2	3	12
677	583	56	10	11	2	15
644	576	50	4	3	0	11




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147019&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147019&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147019&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 time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Ongevallen[t] = -1.53267707159933e-13 + 1Droog[t] + 0.999999999999997Regen[t] + 0.99999999999999Mist[t] + 0.999999999999998Sneeuw[t] + 0.999999999999998Wind[t] + 1.00000000000001Andere[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ongevallen[t] =  -1.53267707159933e-13 +  1Droog[t] +  0.999999999999997Regen[t] +  0.99999999999999Mist[t] +  0.999999999999998Sneeuw[t] +  0.999999999999998Wind[t] +  1.00000000000001Andere[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147019&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ongevallen[t] =  -1.53267707159933e-13 +  1Droog[t] +  0.999999999999997Regen[t] +  0.99999999999999Mist[t] +  0.999999999999998Sneeuw[t] +  0.999999999999998Wind[t] +  1.00000000000001Andere[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147019&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147019&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
Ongevallen[t] = -1.53267707159933e-13 + 1Droog[t] + 0.999999999999997Regen[t] + 0.99999999999999Mist[t] + 0.999999999999998Sneeuw[t] + 0.999999999999998Wind[t] + 1.00000000000001Andere[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1.53267707159933e-130-0.84220.4193550.209678
Droog10220730619147576100
Regen0.999999999999997062911623944533800
Mist0.99999999999999022537832630340600
Sneeuw0.999999999999998017398748253585700
Wind0.999999999999998052104050593554.600
Andere1.00000000000001018151891918951400

\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) & -1.53267707159933e-13 & 0 & -0.8422 & 0.419355 & 0.209678 \tabularnewline
Droog & 1 & 0 & 2207306191475761 & 0 & 0 \tabularnewline
Regen & 0.999999999999997 & 0 & 629116239445338 & 0 & 0 \tabularnewline
Mist & 0.99999999999999 & 0 & 225378326303406 & 0 & 0 \tabularnewline
Sneeuw & 0.999999999999998 & 0 & 173987482535857 & 0 & 0 \tabularnewline
Wind & 0.999999999999998 & 0 & 52104050593554.6 & 0 & 0 \tabularnewline
Andere & 1.00000000000001 & 0 & 181518919189514 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147019&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]-1.53267707159933e-13[/C][C]0[/C][C]-0.8422[/C][C]0.419355[/C][C]0.209678[/C][/ROW]
[ROW][C]Droog[/C][C]1[/C][C]0[/C][C]2207306191475761[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Regen[/C][C]0.999999999999997[/C][C]0[/C][C]629116239445338[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Mist[/C][C]0.99999999999999[/C][C]0[/C][C]225378326303406[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Sneeuw[/C][C]0.999999999999998[/C][C]0[/C][C]173987482535857[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Wind[/C][C]0.999999999999998[/C][C]0[/C][C]52104050593554.6[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Andere[/C][C]1.00000000000001[/C][C]0[/C][C]181518919189514[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147019&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147019&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)-1.53267707159933e-130-0.84220.4193550.209678
Droog10220730619147576100
Regen0.999999999999997062911623944533800
Mist0.99999999999999022537832630340600
Sneeuw0.999999999999998017398748253585700
Wind0.999999999999998052104050593554.600
Andere1.00000000000001018151891918951400







Multiple Linear Regression - Regression Statistics
Multiple R1
R-squared1
Adjusted R-squared1
F-TEST (value)1.37889486290288e+31
F-TEST (DF numerator)6
F-TEST (DF denominator)10
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.96275908561008e-14
Sum Squared Residuals9.92565685979061e-26

\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) & 1.37889486290288e+31 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 10 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.96275908561008e-14 \tabularnewline
Sum Squared Residuals & 9.92565685979061e-26 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147019&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]1.37889486290288e+31[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]10[/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]9.96275908561008e-14[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9.92565685979061e-26[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147019&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147019&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)1.37889486290288e+31
F-TEST (DF numerator)6
F-TEST (DF denominator)10
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.96275908561008e-14
Sum Squared Residuals9.92565685979061e-26







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112351235-1.84225352530096e-13
212981298-6.18746252936679e-14
3133413341.38710037373704e-13
4118011804.10478339124642e-14
5116311637.32869581125771e-14
6106610668.14098770653336e-14
710901090-1.32596558765014e-14
810821082-9.88729310336947e-15
9993993-2.2076649681241e-14
109879873.84812604146348e-14
1110281028-9.87558365782493e-14
128048041.24344421526314e-14
13750750-8.1776159800559e-14
147307306.83674575033772e-14
157097091.6490299484451e-14
166776774.9708366237747e-14
17644644-4.80809593932363e-14

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1235 & 1235 & -1.84225352530096e-13 \tabularnewline
2 & 1298 & 1298 & -6.18746252936679e-14 \tabularnewline
3 & 1334 & 1334 & 1.38710037373704e-13 \tabularnewline
4 & 1180 & 1180 & 4.10478339124642e-14 \tabularnewline
5 & 1163 & 1163 & 7.32869581125771e-14 \tabularnewline
6 & 1066 & 1066 & 8.14098770653336e-14 \tabularnewline
7 & 1090 & 1090 & -1.32596558765014e-14 \tabularnewline
8 & 1082 & 1082 & -9.88729310336947e-15 \tabularnewline
9 & 993 & 993 & -2.2076649681241e-14 \tabularnewline
10 & 987 & 987 & 3.84812604146348e-14 \tabularnewline
11 & 1028 & 1028 & -9.87558365782493e-14 \tabularnewline
12 & 804 & 804 & 1.24344421526314e-14 \tabularnewline
13 & 750 & 750 & -8.1776159800559e-14 \tabularnewline
14 & 730 & 730 & 6.83674575033772e-14 \tabularnewline
15 & 709 & 709 & 1.6490299484451e-14 \tabularnewline
16 & 677 & 677 & 4.9708366237747e-14 \tabularnewline
17 & 644 & 644 & -4.80809593932363e-14 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147019&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]1235[/C][C]1235[/C][C]-1.84225352530096e-13[/C][/ROW]
[ROW][C]2[/C][C]1298[/C][C]1298[/C][C]-6.18746252936679e-14[/C][/ROW]
[ROW][C]3[/C][C]1334[/C][C]1334[/C][C]1.38710037373704e-13[/C][/ROW]
[ROW][C]4[/C][C]1180[/C][C]1180[/C][C]4.10478339124642e-14[/C][/ROW]
[ROW][C]5[/C][C]1163[/C][C]1163[/C][C]7.32869581125771e-14[/C][/ROW]
[ROW][C]6[/C][C]1066[/C][C]1066[/C][C]8.14098770653336e-14[/C][/ROW]
[ROW][C]7[/C][C]1090[/C][C]1090[/C][C]-1.32596558765014e-14[/C][/ROW]
[ROW][C]8[/C][C]1082[/C][C]1082[/C][C]-9.88729310336947e-15[/C][/ROW]
[ROW][C]9[/C][C]993[/C][C]993[/C][C]-2.2076649681241e-14[/C][/ROW]
[ROW][C]10[/C][C]987[/C][C]987[/C][C]3.84812604146348e-14[/C][/ROW]
[ROW][C]11[/C][C]1028[/C][C]1028[/C][C]-9.87558365782493e-14[/C][/ROW]
[ROW][C]12[/C][C]804[/C][C]804[/C][C]1.24344421526314e-14[/C][/ROW]
[ROW][C]13[/C][C]750[/C][C]750[/C][C]-8.1776159800559e-14[/C][/ROW]
[ROW][C]14[/C][C]730[/C][C]730[/C][C]6.83674575033772e-14[/C][/ROW]
[ROW][C]15[/C][C]709[/C][C]709[/C][C]1.6490299484451e-14[/C][/ROW]
[ROW][C]16[/C][C]677[/C][C]677[/C][C]4.9708366237747e-14[/C][/ROW]
[ROW][C]17[/C][C]644[/C][C]644[/C][C]-4.80809593932363e-14[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147019&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147019&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
112351235-1.84225352530096e-13
212981298-6.18746252936679e-14
3133413341.38710037373704e-13
4118011804.10478339124642e-14
5116311637.32869581125771e-14
6106610668.14098770653336e-14
710901090-1.32596558765014e-14
810821082-9.88729310336947e-15
9993993-2.2076649681241e-14
109879873.84812604146348e-14
1110281028-9.87558365782493e-14
128048041.24344421526314e-14
13750750-8.1776159800559e-14
147307306.83674575033772e-14
157097091.6490299484451e-14
166776774.9708366237747e-14
17644644-4.80809593932363e-14



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')
}