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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, 25 Mar 2010 10:28:38 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Mar/25/t1269512956uzbyxi4wnlvttb2.htm/, Retrieved Tue, 18 Jan 2022 12:49:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=74576, Retrieved Tue, 18 Jan 2022 12:49:52 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact181
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [Sleep in Mammals ...] [2010-03-20 10:44:59] [b98453cac15ba1066b407e146608df68]
- RMPD    [Multiple Regression] [] [2010-03-25 10:28:38] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
- RMPD      [Decomposition by Loess] [] [2010-03-25 14:32:43] [b98453cac15ba1066b407e146608df68]
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Dataseries X:
99.2	96.7	101.0
99.0	98.1	100.1
100.0	100.0	100.0
111.6	104.9	90.6
122.2	104.9	86.5
117.6	109.5	89.7
121.1	110.8	90.6
136.0	112.3	82.8
154.2	109.3	70.1
153.6	105.3	65.4
158.5	101.7	61.3
140.6	95.4	62.5
136.2	96.4	63.6
168.0	97.6	52.6
154.3	102.4	59.7
149.0	101.6	59.5
165.5	103.8	61.3




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74576&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74576&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74576&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24







Multiple Linear Regression - Estimated Regression Equation
Cons[t] = + 130.706587487140 + 1.06170962850255Inc[t] -1.38298545741215Price[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Cons[t] =  +  130.706587487140 +  1.06170962850255Inc[t] -1.38298545741215Price[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74576&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Cons[t] =  +  130.706587487140 +  1.06170962850255Inc[t] -1.38298545741215Price[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74576&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74576&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
Cons[t] = + 130.706587487140 + 1.06170962850255Inc[t] -1.38298545741215Price[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)130.70658748714027.0942934.82410.000270.000135
Inc1.061709628502550.2666743.98130.0013650.000683
Price-1.382985457412150.083814-16.500600

\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) & 130.706587487140 & 27.094293 & 4.8241 & 0.00027 & 0.000135 \tabularnewline
Inc & 1.06170962850255 & 0.266674 & 3.9813 & 0.001365 & 0.000683 \tabularnewline
Price & -1.38298545741215 & 0.083814 & -16.5006 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74576&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]130.706587487140[/C][C]27.094293[/C][C]4.8241[/C][C]0.00027[/C][C]0.000135[/C][/ROW]
[ROW][C]Inc[/C][C]1.06170962850255[/C][C]0.266674[/C][C]3.9813[/C][C]0.001365[/C][C]0.000683[/C][/ROW]
[ROW][C]Price[/C][C]-1.38298545741215[/C][C]0.083814[/C][C]-16.5006[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74576&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74576&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)130.70658748714027.0942934.82410.000270.000135
Inc1.061709628502550.2666743.98130.0013650.000683
Price-1.382985457412150.083814-16.500600







Multiple Linear Regression - Regression Statistics
Multiple R0.97533669567652
R-squared0.951281669933193
Adjusted R-squared0.944321908495077
F-TEST (value)136.683085820079
F-TEST (DF numerator)2
F-TEST (DF denominator)14
p-value6.51398490703059e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.56335573538938
Sum Squared Residuals433.312978538859

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.97533669567652 \tabularnewline
R-squared & 0.951281669933193 \tabularnewline
Adjusted R-squared & 0.944321908495077 \tabularnewline
F-TEST (value) & 136.683085820079 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 14 \tabularnewline
p-value & 6.51398490703059e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.56335573538938 \tabularnewline
Sum Squared Residuals & 433.312978538859 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74576&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.97533669567652[/C][/ROW]
[ROW][C]R-squared[/C][C]0.951281669933193[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.944321908495077[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]136.683085820079[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]14[/C][/ROW]
[ROW][C]p-value[/C][C]6.51398490703059e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.56335573538938[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]433.312978538859[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74576&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74576&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 R0.97533669567652
R-squared0.951281669933193
Adjusted R-squared0.944321908495077
F-TEST (value)136.683085820079
F-TEST (DF numerator)2
F-TEST (DF denominator)14
p-value6.51398490703059e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.56335573538938
Sum Squared Residuals433.312978538859







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
199.293.69237736470875.5076226352913
29996.42345775628312.57654224371687
310098.57900459617921.42099540382082
4111.6116.781445075516-5.18144507551595
5122.2122.451685450906-0.251685450905763
6117.6122.909996278299-5.3099962782986
7121.1123.045531883681-1.94553188368099
8136135.4253828942500.574617105750393
9154.2149.8041693178764.39583068212367
10153.6152.0573624537031.54263754629677
11158.5153.9054481664844.59455183351612
12140.6145.557094958023-4.95709495802323
13136.2145.097520583372-8.89752058337242
14168161.5844121691096.41558783089085
15154.3156.861421638295-2.56142163829510
16149156.288651026975-7.2886510269755
17165.5156.1350383863399.36496161366076

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 99.2 & 93.6923773647087 & 5.5076226352913 \tabularnewline
2 & 99 & 96.4234577562831 & 2.57654224371687 \tabularnewline
3 & 100 & 98.5790045961792 & 1.42099540382082 \tabularnewline
4 & 111.6 & 116.781445075516 & -5.18144507551595 \tabularnewline
5 & 122.2 & 122.451685450906 & -0.251685450905763 \tabularnewline
6 & 117.6 & 122.909996278299 & -5.3099962782986 \tabularnewline
7 & 121.1 & 123.045531883681 & -1.94553188368099 \tabularnewline
8 & 136 & 135.425382894250 & 0.574617105750393 \tabularnewline
9 & 154.2 & 149.804169317876 & 4.39583068212367 \tabularnewline
10 & 153.6 & 152.057362453703 & 1.54263754629677 \tabularnewline
11 & 158.5 & 153.905448166484 & 4.59455183351612 \tabularnewline
12 & 140.6 & 145.557094958023 & -4.95709495802323 \tabularnewline
13 & 136.2 & 145.097520583372 & -8.89752058337242 \tabularnewline
14 & 168 & 161.584412169109 & 6.41558783089085 \tabularnewline
15 & 154.3 & 156.861421638295 & -2.56142163829510 \tabularnewline
16 & 149 & 156.288651026975 & -7.2886510269755 \tabularnewline
17 & 165.5 & 156.135038386339 & 9.36496161366076 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=74576&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]99.2[/C][C]93.6923773647087[/C][C]5.5076226352913[/C][/ROW]
[ROW][C]2[/C][C]99[/C][C]96.4234577562831[/C][C]2.57654224371687[/C][/ROW]
[ROW][C]3[/C][C]100[/C][C]98.5790045961792[/C][C]1.42099540382082[/C][/ROW]
[ROW][C]4[/C][C]111.6[/C][C]116.781445075516[/C][C]-5.18144507551595[/C][/ROW]
[ROW][C]5[/C][C]122.2[/C][C]122.451685450906[/C][C]-0.251685450905763[/C][/ROW]
[ROW][C]6[/C][C]117.6[/C][C]122.909996278299[/C][C]-5.3099962782986[/C][/ROW]
[ROW][C]7[/C][C]121.1[/C][C]123.045531883681[/C][C]-1.94553188368099[/C][/ROW]
[ROW][C]8[/C][C]136[/C][C]135.425382894250[/C][C]0.574617105750393[/C][/ROW]
[ROW][C]9[/C][C]154.2[/C][C]149.804169317876[/C][C]4.39583068212367[/C][/ROW]
[ROW][C]10[/C][C]153.6[/C][C]152.057362453703[/C][C]1.54263754629677[/C][/ROW]
[ROW][C]11[/C][C]158.5[/C][C]153.905448166484[/C][C]4.59455183351612[/C][/ROW]
[ROW][C]12[/C][C]140.6[/C][C]145.557094958023[/C][C]-4.95709495802323[/C][/ROW]
[ROW][C]13[/C][C]136.2[/C][C]145.097520583372[/C][C]-8.89752058337242[/C][/ROW]
[ROW][C]14[/C][C]168[/C][C]161.584412169109[/C][C]6.41558783089085[/C][/ROW]
[ROW][C]15[/C][C]154.3[/C][C]156.861421638295[/C][C]-2.56142163829510[/C][/ROW]
[ROW][C]16[/C][C]149[/C][C]156.288651026975[/C][C]-7.2886510269755[/C][/ROW]
[ROW][C]17[/C][C]165.5[/C][C]156.135038386339[/C][C]9.36496161366076[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=74576&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=74576&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
199.293.69237736470875.5076226352913
29996.42345775628312.57654224371687
310098.57900459617921.42099540382082
4111.6116.781445075516-5.18144507551595
5122.2122.451685450906-0.251685450905763
6117.6122.909996278299-5.3099962782986
7121.1123.045531883681-1.94553188368099
8136135.4253828942500.574617105750393
9154.2149.8041693178764.39583068212367
10153.6152.0573624537031.54263754629677
11158.5153.9054481664844.59455183351612
12140.6145.557094958023-4.95709495802323
13136.2145.097520583372-8.89752058337242
14168161.5844121691096.41558783089085
15154.3156.861421638295-2.56142163829510
16149156.288651026975-7.2886510269755
17165.5156.1350383863399.36496161366076



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