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Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 18 Nov 2012 09:48:02 -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/Nov/18/t1353250214guculm40gyup5fs.htm/, Retrieved Mon, 29 Apr 2024 18:31:47 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190195, Retrieved Mon, 29 Apr 2024 18:31:47 +0000

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User-defined keywords

Estimated Impact

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]
-   PD    [Multiple Regression] [time in RFC] [2012-11-18 14:48:02] [91c3d91830a25c0bc67fd9a0665302b1] [Current]
- R         [Multiple Regression] [] [2012-11-19 14:25:21] [74be16979710d4c4e7c6647856088456] 

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Dataseries X:

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Histogram

Boxplots

210907	1	1	2	2	3	4	3
120982	NA	NA	NA	NA	NA	NA	NA
176508	2	2	2	4	2	2	1
179321	NA	NA	NA	NA	NA	NA	NA
123185	NA	NA	NA	NA	NA	NA	NA
52746	NA	NA	NA	NA	NA	NA	NA
385534	1	1	2	2	2	3	2
33170	NA	NA	NA	NA	NA	NA	NA
101645	NA	NA	NA	NA	NA	NA	NA
149061	1	1	2	2	3	4	1
165446	2	2	2	3	3	3	2
237213	1	1	1	2	3	4	1
173326	NA	NA	NA	NA	NA	NA	NA
133131	2	1	1	1	1	4	2
258873	NA	NA	NA	NA	NA	NA	NA
180083	NA	NA	NA	NA	NA	NA	NA
324799	1	1	1	1	1	3	1
230964	4	4	4	4	3	2	4
236785	2	1	1	1	2	4	2
135473	NA	NA	NA	NA	NA	NA	NA
202925	NA	NA	NA	NA	NA	NA	NA
215147	1	1	1	1	1	4	1
344297	1	1	1	4	1	4	1
153935	NA	NA	NA	NA	NA	NA	NA
132943	NA	NA	NA	NA	NA	NA	NA
174724	1	1	1	1	1	3	1
174415	1	1	1	3	3	3	2
225548	4	4	4	4	3	3	4
223632	1	1	1	2	2	3	2
124817	1	1	1	1	1	4	2
221698	NA	NA	NA	NA	NA	NA	NA
210767	4	3	3	2	2	2	3
170266	1	1	1	2	1	4	2
260561	NA	NA	NA	NA	NA	NA	NA
84853	NA	NA	NA	NA	NA	NA	NA
294424	1	1	1	1	2	1	2
101011	NA	NA	NA	NA	NA	NA	NA
215641	NA	NA	NA	NA	NA	NA	NA
325107	2	1	2	2	2	3	3
7176	2	1	2	2	3	3	2
167542	NA	NA	NA	NA	NA	NA	NA
106408	3	4	4	2	4	2	4
96560	2	3	3	3	3	2	4
265769	0	0	0	0	0	0	0
269651	NA	NA	NA	NA	NA	NA	NA
149112	2	1	2	2	2	3	3
175824	1	1	1	2	1	1	1
152871	1	1	1	3	1	4	1
111665	1	1	1	1	2	4	1
116408	NA	NA	NA	NA	NA	NA	NA
362301	2	3	3	3	2	3	2
78800	NA	NA	NA	NA	NA	NA	NA
183167	2	3	3	3	2	3	3
277965	NA	NA	NA	NA	NA	NA	NA
150629	NA	NA	NA	NA	NA	NA	NA
168809	NA	NA	NA	NA	NA	NA	NA
24188	NA	NA	NA	NA	NA	NA	NA
329267	2	3	2	2	2	1	1
65029	NA	NA	NA	NA	NA	NA	NA
101097	NA	NA	NA	NA	NA	NA	NA
218946	3	3	3	4	4	2	3
244052	1	1	1	2	2	4	1
341570	2	2	2	2	2	3	2
103597	2	1	2	2	3	4	2
233328	NA	NA	NA	NA	NA	NA	NA
256462	2	2	2	3	3	2	2
206161	NA	NA	NA	NA	NA	NA	NA
311473	NA	NA	NA	NA	NA	NA	NA
235800	NA	NA	NA	NA	NA	NA	NA
177939	NA	NA	NA	NA	NA	NA	NA
207176	NA	NA	NA	NA	NA	NA	NA
196553	2	1	1	2	2	4	1
174184	NA	NA	NA	NA	NA	NA	NA
143246	1	1	2	1	1	4	1
187559	NA	NA	NA	NA	NA	NA	NA
187681	2	2	2	3	3	2	2
119016	NA	NA	NA	NA	NA	NA	NA
182192	NA	NA	NA	NA	NA	NA	NA
73566	1	1	1	3	3	4	1
194979	NA	NA	NA	NA	NA	NA	NA
167488	1	1	2	1	2	3	1
143756	3	1	2	3	4	3	4
275541	NA	NA	NA	NA	NA	NA	NA
243199	1	2	2	3	2	3	3
182999	NA	NA	NA	NA	NA	NA	NA
135649	NA	NA	NA	NA	NA	NA	NA
152299	1	1	2	2	2	3	1
120221	NA	NA	NA	NA	NA	NA	NA
346485	1	1	2	3	4	3	3
145790	NA	NA	NA	NA	NA	NA	NA
193339	1	1	1	3	1	2	2
80953	NA	NA	NA	NA	NA	NA	NA
122774	1	1	2	1	3	3	1
130585	1	1	1	2	2	4	3
112611	1	1	1	2	1	4	3
286468	2	2	2	2	2	3	1
241066	NA	NA	NA	NA	NA	NA	NA
148446	2	2	2	4	3	3	3
204713	NA	NA	NA	NA	NA	NA	NA
182079	2	1	1	3	4	4	2
140344	1	1	1	1	2	4	1
220516	1	1	1	2	2	3	2
243060	1	1	2	3	1	3	1
162765	1	1	2	3	2	3	2
182613	NA	NA	NA	NA	NA	NA	NA
232138	2	1	2	2	3	3	2
265318	3	3	3	4	2	2	1
85574	1	2	1	2	4	3	3
310839	NA	NA	NA	NA	NA	NA	NA
225060	2	2	2	3	1	3	3
232317	3	3	3	2	3	1	4
144966	NA	NA	NA	NA	NA	NA	NA
43287	NA	NA	NA	NA	NA	NA	NA
155754	NA	NA	NA	NA	NA	NA	NA
164709	3	2	2	2	2	3	3
201940	NA	NA	NA	NA	NA	NA	NA
235454	NA	NA	NA	NA	NA	NA	NA
220801	2	1	2	2	1	4	3
99466	1	2	2	1	2	3	2
92661	2	1	3	3	2	2	3
133328	1	1	2	3	3	4	3
61361	2	2	2	3	3	3	1
125930	NA	NA	NA	NA	NA	NA	NA
100750	1	1	1	2	4	3	1
224549	NA	NA	NA	NA	NA	NA	NA
82316	NA	NA	NA	NA	NA	NA	NA
102010	2	1	2	1	2	4	3
101523	2	1	1	2	3	4	3
243511	1	1	1	1	1	4	1
22938	4	2	2	3	3	2	3
41566	NA	NA	NA	NA	NA	NA	NA
152474	1	1	1	1	2	4	1
61857	NA	NA	NA	NA	NA	NA	NA
99923	3	1	2	2	2	3	4
132487	3	3	3	2	3	2	3
317394	1	1	2	2	2	4	3
21054	NA	NA	NA	NA	NA	NA	NA
209641	NA	NA	NA	NA	NA	NA	NA
22648	2	1	1	2	2	3	4
31414	NA	NA	NA	NA	NA	NA	NA
46698	2	2	3	2	3	2	3
131698	1	1	1	2	3	4	1
91735	NA	NA	NA	NA	NA	NA	NA
244749	2	2	2	3	2	3	3
184510	NA	NA	NA	NA	NA	NA	NA
79863	NA	NA	NA	NA	NA	NA	NA
128423	3	1	1	2	1	3	1
97839	4	4	4	4	4	3	4
38214	NA	NA	NA	NA	NA	NA	NA
151101	NA	NA	NA	NA	NA	NA	NA
272458	1	1	2	1	1	4	2
172494	3	1	1	2	2	1	2
108043	1	2	2	2	3	4	1
328107	2	3	3	2	4	1	3
250579	NA	NA	NA	NA	NA	NA	NA
351067	2	2	3	3	3	2	4
158015	NA	NA	NA	NA	NA	NA	NA
98866	NA	NA	NA	NA	NA	NA	NA
85439	NA	NA	NA	NA	NA	NA	NA
229242	3	2	3	4	4	3	2
351619	NA	NA	NA	NA	NA	NA	NA
84207	NA	NA	NA	NA	NA	NA	NA
120445	2	1	1	1	2	4	2
324598	3	1	2	3	1	3	3
131069	1	1	1	2	2	4	2
204271	3	2	3	3	3	2	4
165543	NA	NA	NA	NA	NA	NA	NA
141722	NA	NA	NA	NA	NA	NA	NA
116048	2	1	2	3	3	3	3
250047	1	1	1	2	1	3	1
299775	NA	NA	NA	NA	NA	NA	NA
195838	3	2	2	3	4	3	1
173260	1	1	1	1	1	3	3
254488	2	3	4	3	3	2	1
104389	NA	NA	NA	NA	NA	NA	NA
136084	NA	NA	NA	NA	NA	NA	NA
199476	NA	NA	NA	NA	NA	NA	NA
92499	1	1	2	2	2	3	3
224330	1	1	1	1	2	4	1
135781	1	1	2	4	1	4	1
74408	1	1	2	2	2	3	1
81240	3	4	3	3	4	1	4
14688	NA	NA	NA	NA	NA	NA	NA
181633	1	1	2	1	2	4	1
271856	1	1	1	1	2	4	2
7199	NA	NA	NA	NA	NA	NA	NA
46660	NA	NA	NA	NA	NA	NA	NA
17547	NA	NA	NA	NA	NA	NA	NA
133368	NA	NA	NA	NA	NA	NA	NA
95227	2	2	2	2	2	4	1
152601	NA	NA	NA	NA	NA	NA	NA
98146	1	1	2	3	1	3	2
79619	NA	NA	NA	NA	NA	NA	NA
59194	1	1	1	1	1	4	4
139942	3	1	2	3	3	2	2
118612	1	1	2	2	1	4	1
72880	2	2	2	2	2	4	2
65475	1	1	2	2	4	3	2
99643	NA	NA	NA	NA	NA	NA	NA
71965	NA	NA	NA	NA	NA	NA	NA
77272	NA	NA	NA	NA	NA	NA	NA
49289	NA	NA	NA	NA	NA	NA	NA
135131	2	3	3	3	3	2	3
108446	2	2	2	2	2	3	2
89746	NA	NA	NA	NA	NA	NA	NA
44296	NA	NA	NA	NA	NA	NA	NA
77648	NA	NA	NA	NA	NA	NA	NA
181528	2	1	1	2	2	4	1
134019	1	1	1	1	1	3	1
124064	NA	NA	NA	NA	NA	NA	NA
92630	NA	NA	NA	NA	NA	NA	NA
121848	2	3	3	1	1	3	1
52915	NA	NA	NA	NA	NA	NA	NA
81872	2	1	2	2	1	4	2
58981	1	1	2	1	2	4	1
53515	1	1	3	2	2	4	4
60812	NA	NA	NA	NA	NA	NA	NA
56375	1	1	2	1	1	4	1
65490	NA	NA	NA	NA	NA	NA	NA
80949	NA	NA	NA	NA	NA	NA	NA
76302	1	1	2	3	3	3	4
104011	1	2	2	3	3	3	1
98104	1	1	1	2	4	3	3
67989	NA	NA	NA	NA	NA	NA	NA
30989	NA	NA	NA	NA	NA	NA	NA
135458	NA	NA	NA	NA	NA	NA	NA
73504	NA	NA	NA	NA	NA	NA	NA
63123	1	1	1	1	1	4	2
61254	NA	NA	NA	NA	NA	NA	NA
74914	1	1	1	1	3	4	3
31774	1	1	2	2	2	4	2
81437	0	1	2	2	2	4	3
87186	NA	NA	NA	NA	NA	NA	NA
50090	NA	NA	NA	NA	NA	NA	NA
65745	1	1	2	2	3	3	2
56653	1	1	2	1	3	3	2
158399	2	1	2	2	3	3	1
46455	NA	NA	NA	NA	NA	NA	NA
73624	1	1	2	1	1	3	1
38395	NA	NA	NA	NA	NA	NA	NA
91899	NA	NA	NA	NA	NA	NA	NA
139526	NA	NA	NA	NA	NA	NA	NA
52164	NA	NA	NA	NA	NA	NA	NA
51567	3	2	3	2	3	3	1
70551	NA	NA	NA	NA	NA	NA	NA
84856	NA	NA	NA	NA	NA	NA	NA
102538	1	1	2	1	1	3	2
86678	1	1	2	2	3	3	1
85709	NA	NA	NA	NA	NA	NA	NA
34662	NA	NA	NA	NA	NA	NA	NA
150580	1	1	1	2	2	4	3
99611	2	1	2	2	1	3	3
19349	NA	NA	NA	NA	NA	NA	NA
99373	3	1	3	2	4	3	2
86230	3	3	2	2	1	4	3
30837	1	1	2	3	3	4	4
31706	2	1	1	2	1	4	3
89806	NA	NA	NA	NA	NA	NA	NA
62088	NA	NA	NA	NA	NA	NA	NA
40151	NA	NA	NA	NA	NA	NA	NA
27634	NA	NA	NA	NA	NA	NA	NA
76990	NA	NA	NA	NA	NA	NA	NA
37460	NA	NA	NA	NA	NA	NA	NA
54157	1	1	1	1	2	3	3
49862	NA	NA	NA	NA	NA	NA	NA
84337	NA	NA	NA	NA	NA	NA	NA
64175	2	2	2	2	3	3	2
59382	2	2	2	2	3	4	2
119308	2	1	1	1	3	4	1
76702	NA	NA	NA	NA	NA	NA	NA
103425	NA	NA	NA	NA	NA	NA	NA
70344	NA	NA	NA	NA	NA	NA	NA
43410	NA	NA	NA	NA	NA	NA	NA
104838	NA	NA	NA	NA	NA	NA	NA
62215	NA	NA	NA	NA	NA	NA	NA
69304	NA	NA	NA	NA	NA	NA	NA
53117	NA	NA	NA	NA	NA	NA	NA
19764	NA	NA	NA	NA	NA	NA	NA
86680	NA	NA	NA	NA	NA	NA	NA
84105	3	2	3	2	1	3	2
77945	NA	NA	NA	NA	NA	NA	NA
89113	NA	NA	NA	NA	NA	NA	NA
91005	NA	NA	NA	NA	NA	NA	NA
40248	NA	NA	NA	NA	NA	NA	NA
64187	1	2	2	2	3	3	2
50857	NA	NA	NA	NA	NA	NA	NA
56613	NA	NA	NA	NA	NA	NA	NA
62792	NA	NA	NA	NA	NA	NA	NA
72535	NA	NA	NA	NA	NA	NA	NA

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	'George Udny Yule' @ yule.wessa.net
R Engine error message	Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 + : missing value where TRUE/FALSE needed Execution halted

\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 & 'George Udny Yule' @ yule.wessa.net \tabularnewline
R Engine error message & Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 +  : 
  missing value where TRUE/FALSE needed
Execution halted
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190195&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[ROW][C]R Engine error message[/C][C]Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 +  : 
  missing value where TRUE/FALSE needed
Execution halted
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190195&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190195&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	'George Udny Yule' @ yule.wessa.net
R Engine error message	Error in if (gqarr[mypoint - kp3 + 1, 2] < 0.01) numsignificant1 <- numsignificant1 + : missing value where TRUE/FALSE needed Execution halted

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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code