Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 24 Nov 2011 10:09:16 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322147398w0loeo4egjwdxtq.htm/, Retrieved Sat, 20 Apr 2024 10:22:32 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146933, Retrieved Sat, 20 Apr 2024 10:22:32 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

workhop 7

IsPrivate?

No (this computation is public)

User-defined keywords

workshop 7

Estimated Impact

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Multiple Regression] [] [2010-11-17 09:55:05] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [workshop 7.2] [2011-11-24 15:09:16] [c96824ac395d6a5bf5e3e103e13cc1ae] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

8	17	2	6	0
3	16	0	6	-2
-3	15	0	0	-3
4	8	3	6	1
-5	5	-2	2	-2
-1	6	0	2	-1
5	5	1	2	1
0	12	-1	3	-3
-6	8	-1	-1	-4
-13	17	-1	-4	-9
-15	22	-1	4	-9
-8	24	1	5	-7
-20	36	-2	3	-14

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146933&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146933&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146933&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
d[t] = -0.430332294448803 -0.437119161948204a[t] + 0.658167216343528b[t] -0.185719923382024c[t] + 2.08835414762538V5[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
d[t] =  -0.430332294448803 -0.437119161948204a[t] +  0.658167216343528b[t] -0.185719923382024c[t] +  2.08835414762538V5[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146933&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]d[t] =  -0.430332294448803 -0.437119161948204a[t] +  0.658167216343528b[t] -0.185719923382024c[t] +  2.08835414762538V5[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146933&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146933&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
d[t] = -0.430332294448803 -0.437119161948204a[t] + 0.658167216343528b[t] -0.185719923382024c[t] + 2.08835414762538V5[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-0.430332294448803	1.343833	-0.3202	0.757	0.3785
a	-0.437119161948204	0.395321	-1.1057	0.300982	0.150491
b	0.658167216343528	0.243087	2.7075	0.02676	0.01338
c	-0.185719923382024	0.684027	-0.2715	0.792874	0.396437
V5	2.08835414762538	1.090757	1.9146	0.091879	0.045939

\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) & -0.430332294448803 & 1.343833 & -0.3202 & 0.757 & 0.3785 \tabularnewline
a & -0.437119161948204 & 0.395321 & -1.1057 & 0.300982 & 0.150491 \tabularnewline
b & 0.658167216343528 & 0.243087 & 2.7075 & 0.02676 & 0.01338 \tabularnewline
c & -0.185719923382024 & 0.684027 & -0.2715 & 0.792874 & 0.396437 \tabularnewline
V5 & 2.08835414762538 & 1.090757 & 1.9146 & 0.091879 & 0.045939 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146933&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]-0.430332294448803[/C][C]1.343833[/C][C]-0.3202[/C][C]0.757[/C][C]0.3785[/C][/ROW]
[ROW][C]a[/C][C]-0.437119161948204[/C][C]0.395321[/C][C]-1.1057[/C][C]0.300982[/C][C]0.150491[/C][/ROW]
[ROW][C]b[/C][C]0.658167216343528[/C][C]0.243087[/C][C]2.7075[/C][C]0.02676[/C][C]0.01338[/C][/ROW]
[ROW][C]c[/C][C]-0.185719923382024[/C][C]0.684027[/C][C]-0.2715[/C][C]0.792874[/C][C]0.396437[/C][/ROW]
[ROW][C]V5[/C][C]2.08835414762538[/C][C]1.090757[/C][C]1.9146[/C][C]0.091879[/C][C]0.045939[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146933&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146933&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-0.430332294448803	1.343833	-0.3202	0.757	0.3785
a	-0.437119161948204	0.395321	-1.1057	0.300982	0.150491
b	0.658167216343528	0.243087	2.7075	0.02676	0.01338
c	-0.185719923382024	0.684027	-0.2715	0.792874	0.396437
V5	2.08835414762538	1.090757	1.9146	0.091879	0.045939

Multiple Linear Regression - Regression Statistics
Multiple R	0.81095169443103
R-squared	0.657642650700558
Adjusted R-squared	0.486463976050837
F-TEST (value)	3.84184917920574
F-TEST (DF numerator)	4
F-TEST (DF denominator)	8
p-value	0.0498762172047272
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.14063575707533
Sum Squared Residuals	36.6585715557556

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.81095169443103 \tabularnewline
R-squared & 0.657642650700558 \tabularnewline
Adjusted R-squared & 0.486463976050837 \tabularnewline
F-TEST (value) & 3.84184917920574 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 8 \tabularnewline
p-value & 0.0498762172047272 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.14063575707533 \tabularnewline
Sum Squared Residuals & 36.6585715557556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146933&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.81095169443103[/C][/ROW]
[ROW][C]R-squared[/C][C]0.657642650700558[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.486463976050837[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.84184917920574[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]8[/C][/ROW]
[ROW][C]p-value[/C][C]0.0498762172047272[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.14063575707533[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]36.6585715557556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146933&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146933&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 R	0.81095169443103
R-squared	0.657642650700558
Adjusted R-squared	0.486463976050837
F-TEST (value)	3.84184917920574
F-TEST (DF numerator)	4
F-TEST (DF denominator)	8
p-value	0.0498762172047272
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.14063575707533
Sum Squared Residuals	36.6585715557556

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6	6.89011724104149	-0.890117241041492
2	6	4.61227738595227	1.38772261404773
3	0	4.48847099367258	-4.48847099367258
4	6	4.61772316598591	1.38227683401409
5	2	1.24083114852314	0.75916885147686
6	2	1.86743601793519	0.132563982064812
7	2	2.57754220177117	-0.577542201771173
8	3	1.38833178217941	1.61166821782059
9	-1	-0.709976259130856	-0.290023740869144
10	-4	-2.16840791652858	-1.83159208347142
11	4	1.99666648908547	2.00333351091453
12	5	4.05843523662181	0.941564763378192
13	3	3.14055251289099	-0.140552512890994

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 6 & 6.89011724104149 & -0.890117241041492 \tabularnewline
2 & 6 & 4.61227738595227 & 1.38772261404773 \tabularnewline
3 & 0 & 4.48847099367258 & -4.48847099367258 \tabularnewline
4 & 6 & 4.61772316598591 & 1.38227683401409 \tabularnewline
5 & 2 & 1.24083114852314 & 0.75916885147686 \tabularnewline
6 & 2 & 1.86743601793519 & 0.132563982064812 \tabularnewline
7 & 2 & 2.57754220177117 & -0.577542201771173 \tabularnewline
8 & 3 & 1.38833178217941 & 1.61166821782059 \tabularnewline
9 & -1 & -0.709976259130856 & -0.290023740869144 \tabularnewline
10 & -4 & -2.16840791652858 & -1.83159208347142 \tabularnewline
11 & 4 & 1.99666648908547 & 2.00333351091453 \tabularnewline
12 & 5 & 4.05843523662181 & 0.941564763378192 \tabularnewline
13 & 3 & 3.14055251289099 & -0.140552512890994 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146933&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]6[/C][C]6.89011724104149[/C][C]-0.890117241041492[/C][/ROW]
[ROW][C]2[/C][C]6[/C][C]4.61227738595227[/C][C]1.38772261404773[/C][/ROW]
[ROW][C]3[/C][C]0[/C][C]4.48847099367258[/C][C]-4.48847099367258[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]4.61772316598591[/C][C]1.38227683401409[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]1.24083114852314[/C][C]0.75916885147686[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.86743601793519[/C][C]0.132563982064812[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]2.57754220177117[/C][C]-0.577542201771173[/C][/ROW]
[ROW][C]8[/C][C]3[/C][C]1.38833178217941[/C][C]1.61166821782059[/C][/ROW]
[ROW][C]9[/C][C]-1[/C][C]-0.709976259130856[/C][C]-0.290023740869144[/C][/ROW]
[ROW][C]10[/C][C]-4[/C][C]-2.16840791652858[/C][C]-1.83159208347142[/C][/ROW]
[ROW][C]11[/C][C]4[/C][C]1.99666648908547[/C][C]2.00333351091453[/C][/ROW]
[ROW][C]12[/C][C]5[/C][C]4.05843523662181[/C][C]0.941564763378192[/C][/ROW]
[ROW][C]13[/C][C]3[/C][C]3.14055251289099[/C][C]-0.140552512890994[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146933&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146933&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6	6.89011724104149	-0.890117241041492
2	6	4.61227738595227	1.38772261404773
3	0	4.48847099367258	-4.48847099367258
4	6	4.61772316598591	1.38227683401409
5	2	1.24083114852314	0.75916885147686
6	2	1.86743601793519	0.132563982064812
7	2	2.57754220177117	-0.577542201771173
8	3	1.38833178217941	1.61166821782059
9	-1	-0.709976259130856	-0.290023740869144
10	-4	-2.16840791652858	-1.83159208347142
11	4	1.99666648908547	2.00333351091453
12	5	4.05843523662181	0.941564763378192
13	3	3.14055251289099	-0.140552512890994

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code