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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, 20 Dec 2012 14:37:05 -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/2012/Dec/20/t1356032293q301a6f59hlncb1.htm/, Retrieved Thu, 18 Apr 2024 22:34:55 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203057, Retrieved Thu, 18 Apr 2024 22:34:55 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

140

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

-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [Multiple regression] [2012-12-20 19:37:05] [933e9ab295d38e240eca0a457ef09371] [Current]

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Dataseries X:

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Histogram

Boxplots

35	39	16	6	12	12	72	44
35	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	35	12	8	12	18	84	50
34	36	13	8	13	16	69	46

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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 & 8 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203057&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]8 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=203057&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203057&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	8 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = -25.0502944996332 + 0.835228820194156Connected[t] + 0.316316527346005Separate[t] + 0.30523886686134Software[t] + 0.0923332609152583Happiness[t] -0.252127439604043Depression[t] + 0.183095606876437Belonging[t] -0.298838505205408Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  -25.0502944996332 +  0.835228820194156Connected[t] +  0.316316527346005Separate[t] +  0.30523886686134Software[t] +  0.0923332609152583Happiness[t] -0.252127439604043Depression[t] +  0.183095606876437Belonging[t] -0.298838505205408Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203057&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  -25.0502944996332 +  0.835228820194156Connected[t] +  0.316316527346005Separate[t] +  0.30523886686134Software[t] +  0.0923332609152583Happiness[t] -0.252127439604043Depression[t] +  0.183095606876437Belonging[t] -0.298838505205408Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203057&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203057&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
Learning[t] = -25.0502944996332 + 0.835228820194156Connected[t] + 0.316316527346005Separate[t] + 0.30523886686134Software[t] + 0.0923332609152583Happiness[t] -0.252127439604043Depression[t] + 0.183095606876437Belonging[t] -0.298838505205408Belonging_Final[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)	-25.0502944996332	29.962779	-0.836	0.556698	0.278349
Connected	0.835228820194156	0.784156	1.0651	0.479929	0.239964
Separate	0.316316527346005	0.415828	0.7607	0.586001	0.293001
Software	0.30523886686134	0.545434	0.5596	0.675194	0.337597
Happiness	0.0923332609152583	0.525552	0.1757	0.889283	0.444642
Depression	-0.252127439604043	0.225474	-1.1182	0.464509	0.232254
Belonging	0.183095606876437	0.286651	0.6387	0.638133	0.319066
Belonging_Final	-0.298838505205408	0.482867	-0.6189	0.647192	0.323596

\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) & -25.0502944996332 & 29.962779 & -0.836 & 0.556698 & 0.278349 \tabularnewline
Connected & 0.835228820194156 & 0.784156 & 1.0651 & 0.479929 & 0.239964 \tabularnewline
Separate & 0.316316527346005 & 0.415828 & 0.7607 & 0.586001 & 0.293001 \tabularnewline
Software & 0.30523886686134 & 0.545434 & 0.5596 & 0.675194 & 0.337597 \tabularnewline
Happiness & 0.0923332609152583 & 0.525552 & 0.1757 & 0.889283 & 0.444642 \tabularnewline
Depression & -0.252127439604043 & 0.225474 & -1.1182 & 0.464509 & 0.232254 \tabularnewline
Belonging & 0.183095606876437 & 0.286651 & 0.6387 & 0.638133 & 0.319066 \tabularnewline
Belonging_Final & -0.298838505205408 & 0.482867 & -0.6189 & 0.647192 & 0.323596 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203057&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]-25.0502944996332[/C][C]29.962779[/C][C]-0.836[/C][C]0.556698[/C][C]0.278349[/C][/ROW]
[ROW][C]Connected[/C][C]0.835228820194156[/C][C]0.784156[/C][C]1.0651[/C][C]0.479929[/C][C]0.239964[/C][/ROW]
[ROW][C]Separate[/C][C]0.316316527346005[/C][C]0.415828[/C][C]0.7607[/C][C]0.586001[/C][C]0.293001[/C][/ROW]
[ROW][C]Software[/C][C]0.30523886686134[/C][C]0.545434[/C][C]0.5596[/C][C]0.675194[/C][C]0.337597[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0923332609152583[/C][C]0.525552[/C][C]0.1757[/C][C]0.889283[/C][C]0.444642[/C][/ROW]
[ROW][C]Depression[/C][C]-0.252127439604043[/C][C]0.225474[/C][C]-1.1182[/C][C]0.464509[/C][C]0.232254[/C][/ROW]
[ROW][C]Belonging[/C][C]0.183095606876437[/C][C]0.286651[/C][C]0.6387[/C][C]0.638133[/C][C]0.319066[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.298838505205408[/C][C]0.482867[/C][C]-0.6189[/C][C]0.647192[/C][C]0.323596[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203057&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203057&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)	-25.0502944996332	29.962779	-0.836	0.556698	0.278349
Connected	0.835228820194156	0.784156	1.0651	0.479929	0.239964
Separate	0.316316527346005	0.415828	0.7607	0.586001	0.293001
Software	0.30523886686134	0.545434	0.5596	0.675194	0.337597
Happiness	0.0923332609152583	0.525552	0.1757	0.889283	0.444642
Depression	-0.252127439604043	0.225474	-1.1182	0.464509	0.232254
Belonging	0.183095606876437	0.286651	0.6387	0.638133	0.319066
Belonging_Final	-0.298838505205408	0.482867	-0.6189	0.647192	0.323596

Multiple Linear Regression - Regression Statistics
Multiple R	0.982367301396426
R-squared	0.965045514852896
Adjusted R-squared	0.720364118823172
F-TEST (value)	3.9440902762205
F-TEST (DF numerator)	7
F-TEST (DF denominator)	1
p-value	0.36995359696414
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.182446364908
Sum Squared Residuals	1.39817940588414

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.982367301396426 \tabularnewline
R-squared & 0.965045514852896 \tabularnewline
Adjusted R-squared & 0.720364118823172 \tabularnewline
F-TEST (value) & 3.9440902762205 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 1 \tabularnewline
p-value & 0.36995359696414 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.182446364908 \tabularnewline
Sum Squared Residuals & 1.39817940588414 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203057&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.982367301396426[/C][/ROW]
[ROW][C]R-squared[/C][C]0.965045514852896[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.720364118823172[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.9440902762205[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]1[/C][/ROW]
[ROW][C]p-value[/C][C]0.36995359696414[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.182446364908[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1.39817940588414[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203057&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203057&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	0.982367301396426
R-squared	0.965045514852896
Adjusted R-squared	0.720364118823172
F-TEST (value)	3.9440902762205
F-TEST (DF numerator)	7
F-TEST (DF denominator)	1
p-value	0.36995359696414
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.182446364908
Sum Squared Residuals	1.39817940588414

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	16	16.4669512966246	-0.466951296624549
2	14	13.2035062182192	0.796493781780773
3	10	10.4627118993026	-0.462711899302641
4	17	17.1625678027355	-0.162567802735492
5	13	12.7585951085486	0.241404891451398
6	15	14.6200348558324	0.379965144167645
7	16	15.8976944451218	0.102305554878165
8	12	12.1978280740413	-0.197828074041278
9	13	13.230110299574	-0.23011029957402

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 16 & 16.4669512966246 & -0.466951296624549 \tabularnewline
2 & 14 & 13.2035062182192 & 0.796493781780773 \tabularnewline
3 & 10 & 10.4627118993026 & -0.462711899302641 \tabularnewline
4 & 17 & 17.1625678027355 & -0.162567802735492 \tabularnewline
5 & 13 & 12.7585951085486 & 0.241404891451398 \tabularnewline
6 & 15 & 14.6200348558324 & 0.379965144167645 \tabularnewline
7 & 16 & 15.8976944451218 & 0.102305554878165 \tabularnewline
8 & 12 & 12.1978280740413 & -0.197828074041278 \tabularnewline
9 & 13 & 13.230110299574 & -0.23011029957402 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203057&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]16[/C][C]16.4669512966246[/C][C]-0.466951296624549[/C][/ROW]
[ROW][C]2[/C][C]14[/C][C]13.2035062182192[/C][C]0.796493781780773[/C][/ROW]
[ROW][C]3[/C][C]10[/C][C]10.4627118993026[/C][C]-0.462711899302641[/C][/ROW]
[ROW][C]4[/C][C]17[/C][C]17.1625678027355[/C][C]-0.162567802735492[/C][/ROW]
[ROW][C]5[/C][C]13[/C][C]12.7585951085486[/C][C]0.241404891451398[/C][/ROW]
[ROW][C]6[/C][C]15[/C][C]14.6200348558324[/C][C]0.379965144167645[/C][/ROW]
[ROW][C]7[/C][C]16[/C][C]15.8976944451218[/C][C]0.102305554878165[/C][/ROW]
[ROW][C]8[/C][C]12[/C][C]12.1978280740413[/C][C]-0.197828074041278[/C][/ROW]
[ROW][C]9[/C][C]13[/C][C]13.230110299574[/C][C]-0.23011029957402[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203057&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203057&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	16	16.4669512966246	-0.466951296624549
2	14	13.2035062182192	0.796493781780773
3	10	10.4627118993026	-0.462711899302641
4	17	17.1625678027355	-0.162567802735492
5	13	12.7585951085486	0.241404891451398
6	15	14.6200348558324	0.379965144167645
7	16	15.8976944451218	0.102305554878165
8	12	12.1978280740413	-0.197828074041278
9	13	13.230110299574	-0.23011029957402

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Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; 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