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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 computationThu, 24 Nov 2011 10:14:53 -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/t1322147735urkcjg60fsgk68k.htm/, Retrieved Fri, 29 Mar 2024 14:26:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146949, Retrieved Fri, 29 Mar 2024 14:26:39 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7] [2011-11-24 15:14:53] [20d72825ced8e4e20cafbe8bf70803f4] [Current]
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Dataseries X:
58,2	3,7	0,57	53100	9429
59,2	1,9	0,57	53600	10357
60	3,5	0,58	55600	11422
61,1	3,7	0,57	59900	15683
61,8	0,8	0,58	60400	16375
61,4	1,4	0,59	63700	11702
61,1	0,8	0,58	63800	17399
61,4	3,2	0,57	65100	18856
61,9	1,7	0,56	66200	20349
62,1	2,7	0,56	68100	27524
62,4	2,9	0,56	70100	44141
63	1	0,57	69200	52648




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 3 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146949&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146949&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146949&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'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 39.3430786860412 -0.125978269097324X1[t] + 16.7449154519559X2[t] + 0.000196453738417245X3[t] + 1.15191714505031e-05X4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  39.3430786860412 -0.125978269097324X1[t] +  16.7449154519559X2[t] +  0.000196453738417245X3[t] +  1.15191714505031e-05X4[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146949&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  39.3430786860412 -0.125978269097324X1[t] +  16.7449154519559X2[t] +  0.000196453738417245X3[t] +  1.15191714505031e-05X4[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146949&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146949&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[t] = + 39.3430786860412 -0.125978269097324X1[t] + 16.7449154519559X2[t] + 0.000196453738417245X3[t] + 1.15191714505031e-05X4[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)39.343078686041215.2772742.57530.0367250.018362
X1-0.1259782690973240.191047-0.65940.5307190.26536
X216.744915451955924.8181830.67470.5215170.260759
X30.0001964537384172454.9e-053.97520.0053560.002678
X41.15191714505031e-052.2e-050.53050.6121480.306074

\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) & 39.3430786860412 & 15.277274 & 2.5753 & 0.036725 & 0.018362 \tabularnewline
X1 & -0.125978269097324 & 0.191047 & -0.6594 & 0.530719 & 0.26536 \tabularnewline
X2 & 16.7449154519559 & 24.818183 & 0.6747 & 0.521517 & 0.260759 \tabularnewline
X3 & 0.000196453738417245 & 4.9e-05 & 3.9752 & 0.005356 & 0.002678 \tabularnewline
X4 & 1.15191714505031e-05 & 2.2e-05 & 0.5305 & 0.612148 & 0.306074 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146949&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]39.3430786860412[/C][C]15.277274[/C][C]2.5753[/C][C]0.036725[/C][C]0.018362[/C][/ROW]
[ROW][C]X1[/C][C]-0.125978269097324[/C][C]0.191047[/C][C]-0.6594[/C][C]0.530719[/C][C]0.26536[/C][/ROW]
[ROW][C]X2[/C][C]16.7449154519559[/C][C]24.818183[/C][C]0.6747[/C][C]0.521517[/C][C]0.260759[/C][/ROW]
[ROW][C]X3[/C][C]0.000196453738417245[/C][C]4.9e-05[/C][C]3.9752[/C][C]0.005356[/C][C]0.002678[/C][/ROW]
[ROW][C]X4[/C][C]1.15191714505031e-05[/C][C]2.2e-05[/C][C]0.5305[/C][C]0.612148[/C][C]0.306074[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146949&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146949&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)39.343078686041215.2772742.57530.0367250.018362
X1-0.1259782690973240.191047-0.65940.5307190.26536
X216.744915451955924.8181830.67470.5215170.260759
X30.0001964537384172454.9e-053.97520.0053560.002678
X41.15191714505031e-052.2e-050.53050.6121480.306074







Multiple Linear Regression - Regression Statistics
Multiple R0.94108754452892
R-squared0.885645766467472
Adjusted R-squared0.820300490163171
F-TEST (value)13.5533249923556
F-TEST (DF numerator)4
F-TEST (DF denominator)7
p-value0.00207320056693405
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.583293298399467
Sum Squared Residuals2.38161750370411

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.94108754452892 \tabularnewline
R-squared & 0.885645766467472 \tabularnewline
Adjusted R-squared & 0.820300490163171 \tabularnewline
F-TEST (value) & 13.5533249923556 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 7 \tabularnewline
p-value & 0.00207320056693405 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.583293298399467 \tabularnewline
Sum Squared Residuals & 2.38161750370411 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146949&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.94108754452892[/C][/ROW]
[ROW][C]R-squared[/C][C]0.885645766467472[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.820300490163171[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.5533249923556[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]7[/C][/ROW]
[ROW][C]p-value[/C][C]0.00207320056693405[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.583293298399467[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2.38161750370411[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146949&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146949&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 R0.94108754452892
R-squared0.885645766467472
Adjusted R-squared0.820300490163171
F-TEST (value)13.5533249923556
F-TEST (DF numerator)4
F-TEST (DF denominator)7
p-value0.00207320056693405
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.583293298399467
Sum Squared Residuals2.38161750370411







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
158.258.9618686755585-0.761868675558479
259.259.2975462202483-0.0975462202483345
36059.66860553864150.331394461358544
461.160.36979499504720.730205004952821
561.861.00877926580140.791220734198649
661.461.6951097074512-0.295109707451223
761.161.6885176079853-0.588517607985296
861.461.490893900378-0.0908939003779655
961.961.7457093847390.154290615261036
1062.162.07564327379180.0243567262082361
1162.462.6347691687998-0.234769168799803
126362.96276226155820.0372377384418165

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 58.2 & 58.9618686755585 & -0.761868675558479 \tabularnewline
2 & 59.2 & 59.2975462202483 & -0.0975462202483345 \tabularnewline
3 & 60 & 59.6686055386415 & 0.331394461358544 \tabularnewline
4 & 61.1 & 60.3697949950472 & 0.730205004952821 \tabularnewline
5 & 61.8 & 61.0087792658014 & 0.791220734198649 \tabularnewline
6 & 61.4 & 61.6951097074512 & -0.295109707451223 \tabularnewline
7 & 61.1 & 61.6885176079853 & -0.588517607985296 \tabularnewline
8 & 61.4 & 61.490893900378 & -0.0908939003779655 \tabularnewline
9 & 61.9 & 61.745709384739 & 0.154290615261036 \tabularnewline
10 & 62.1 & 62.0756432737918 & 0.0243567262082361 \tabularnewline
11 & 62.4 & 62.6347691687998 & -0.234769168799803 \tabularnewline
12 & 63 & 62.9627622615582 & 0.0372377384418165 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146949&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]58.2[/C][C]58.9618686755585[/C][C]-0.761868675558479[/C][/ROW]
[ROW][C]2[/C][C]59.2[/C][C]59.2975462202483[/C][C]-0.0975462202483345[/C][/ROW]
[ROW][C]3[/C][C]60[/C][C]59.6686055386415[/C][C]0.331394461358544[/C][/ROW]
[ROW][C]4[/C][C]61.1[/C][C]60.3697949950472[/C][C]0.730205004952821[/C][/ROW]
[ROW][C]5[/C][C]61.8[/C][C]61.0087792658014[/C][C]0.791220734198649[/C][/ROW]
[ROW][C]6[/C][C]61.4[/C][C]61.6951097074512[/C][C]-0.295109707451223[/C][/ROW]
[ROW][C]7[/C][C]61.1[/C][C]61.6885176079853[/C][C]-0.588517607985296[/C][/ROW]
[ROW][C]8[/C][C]61.4[/C][C]61.490893900378[/C][C]-0.0908939003779655[/C][/ROW]
[ROW][C]9[/C][C]61.9[/C][C]61.745709384739[/C][C]0.154290615261036[/C][/ROW]
[ROW][C]10[/C][C]62.1[/C][C]62.0756432737918[/C][C]0.0243567262082361[/C][/ROW]
[ROW][C]11[/C][C]62.4[/C][C]62.6347691687998[/C][C]-0.234769168799803[/C][/ROW]
[ROW][C]12[/C][C]63[/C][C]62.9627622615582[/C][C]0.0372377384418165[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146949&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146949&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
158.258.9618686755585-0.761868675558479
259.259.2975462202483-0.0975462202483345
36059.66860553864150.331394461358544
461.160.36979499504720.730205004952821
561.861.00877926580140.791220734198649
661.461.6951097074512-0.295109707451223
761.161.6885176079853-0.588517607985296
861.461.490893900378-0.0908939003779655
961.961.7457093847390.154290615261036
1062.162.07564327379180.0243567262082361
1162.462.6347691687998-0.234769168799803
126362.96276226155820.0372377384418165



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')
}