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*The author of this computation has been verified*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Fri, 05 Dec 2008 08:10:56 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym.htm/, Retrieved Fri, 05 Dec 2008 15:20:47 +0000

BibTeX entries for LaTeX users:

@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym.htm/},
    year = {2008},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2008},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

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

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc] [reply] 
test

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

95.2 0 95.00 0 94.00 0 92.2 0 91.00 0 91.2 0 103.4 1 105.00 0 104.6 0 103.8 0 101.8 0 102.4 0 103.8 0 103.4 0 102.00 0 101.8 0 100.2 0 101.4 0 113.8 0 116.00 0 115.6 0 113.00 0 109.4 0 111.00 0 112.4 0 112.2 0 111.00 0 108.8 0 107.4 0 108.6 0 118.8 0 122.2 1 122.6 0 122.2 0 118.8 0 119.00 0 118.2 0 117.8 0 116.8 0 114.6 0 113.4 0 113.8 0 124.2 0 125.8 0 125.6 0 122.4 0 119.00 0 119.4 0 118.6 0 118.00 0 116.00 0 114.8 0 114.6 0 114.6 0 124.00 0 125.2 0 124.00 0 117.6 1 113.2 0 111.4 0 112.2 0 109.8 0 106.4 0 105.2 0 102.2 0 99.8 0 111.00 0 113.00 0 108.4 0 105.4 0 102.00 0 102.8 0

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits!

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' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

Werkloosheid[t] = + 110.623188405797 + 3.7768115942029Dumivariabele[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STAT H0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	110.623188405797	1.079065	102.5176	0	0
Dumivariabele	3.7768115942029	5.286318	0.7145	0.477325	0.238662

Multiple Linear Regression - Regression Statistics
Multiple R	0.0850834970138966
R-squared	0.00723920146411376
Adjusted R-squared	-0.00694309565782736
F-TEST (value)	0.510439275236597
F-TEST (DF numerator)	1
F-TEST (DF denominator)	70
p-value	0.477324574359064
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	8.96338814969837
Sum Squared Residuals	5623.96289855072

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	95.2	110.623188405797	-15.4231884057970
2	95	110.623188405797	-15.6231884057971
3	94	110.623188405797	-16.6231884057971
4	92.2	110.623188405797	-18.4231884057971
5	91	110.623188405797	-19.6231884057971
6	91.2	110.623188405797	-19.4231884057971
7	103.4	114.4	-11.0000000000000
8	105	110.623188405797	-5.6231884057971
9	104.6	110.623188405797	-6.02318840579711
10	103.8	110.623188405797	-6.8231884057971
11	101.8	110.623188405797	-8.8231884057971
12	102.4	110.623188405797	-8.2231884057971
13	103.8	110.623188405797	-6.8231884057971
14	103.4	110.623188405797	-7.2231884057971
15	102	110.623188405797	-8.6231884057971
16	101.8	110.623188405797	-8.8231884057971
17	100.2	110.623188405797	-10.4231884057971
18	101.4	110.623188405797	-9.2231884057971
19	113.8	110.623188405797	3.17681159420289
20	116	110.623188405797	5.3768115942029
21	115.6	110.623188405797	4.97681159420289
22	113	110.623188405797	2.37681159420290
23	109.4	110.623188405797	-1.22318840579710
24	111	110.623188405797	0.376811594202896
25	112.4	110.623188405797	1.77681159420290
26	112.2	110.623188405797	1.5768115942029
27	111	110.623188405797	0.376811594202896
28	108.8	110.623188405797	-1.82318840579711
29	107.4	110.623188405797	-3.2231884057971
30	108.6	110.623188405797	-2.02318840579711
31	118.8	110.623188405797	8.1768115942029
32	122.2	114.4	7.79999999999998
33	122.6	110.623188405797	11.9768115942029
34	122.2	110.623188405797	11.5768115942029
35	118.8	110.623188405797	8.1768115942029
36	119	110.623188405797	8.3768115942029
37	118.2	110.623188405797	7.5768115942029
38	117.8	110.623188405797	7.1768115942029
39	116.8	110.623188405797	6.1768115942029
40	114.6	110.623188405797	3.97681159420289
41	113.4	110.623188405797	2.7768115942029
42	113.8	110.623188405797	3.17681159420289
43	124.2	110.623188405797	13.5768115942029
44	125.8	110.623188405797	15.1768115942029
45	125.6	110.623188405797	14.9768115942029
46	122.4	110.623188405797	11.7768115942029
47	119	110.623188405797	8.3768115942029
48	119.4	110.623188405797	8.7768115942029
49	118.6	110.623188405797	7.97681159420289
50	118	110.623188405797	7.3768115942029
51	116	110.623188405797	5.3768115942029
52	114.8	110.623188405797	4.17681159420289
53	114.6	110.623188405797	3.97681159420289
54	114.6	110.623188405797	3.97681159420289
55	124	110.623188405797	13.3768115942029
56	125.2	110.623188405797	14.5768115942029
57	124	110.623188405797	13.3768115942029
58	117.6	114.4	3.19999999999997
59	113.2	110.623188405797	2.5768115942029
60	111.4	110.623188405797	0.776811594202902
61	112.2	110.623188405797	1.5768115942029
62	109.8	110.623188405797	-0.823188405797107
63	106.4	110.623188405797	-4.2231884057971
64	105.2	110.623188405797	-5.4231884057971
65	102.2	110.623188405797	-8.4231884057971
66	99.8	110.623188405797	-10.8231884057971
67	111	110.623188405797	0.376811594202896
68	113	110.623188405797	2.37681159420290
69	108.4	110.623188405797	-2.22318840579710
70	105.4	110.623188405797	-5.2231884057971
71	102	110.623188405797	-8.6231884057971
72	102.8	110.623188405797	-7.8231884057971

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.0251084122848634	0.0502168245697267	0.974891587715137
6	0.0114493152424813	0.0228986304849627	0.988550684757519
7	0.00303265014108208	0.00606530028216417	0.996967349858918
8	0.142805550808769	0.285611101617538	0.85719444919123
9	0.23904468734128	0.47808937468256	0.76095531265872
10	0.264101509506086	0.528203019012172	0.735898490493914
11	0.23549082256237	0.47098164512474	0.76450917743763
12	0.213868144969157	0.427736289938314	0.786131855030843
13	0.207642274843172	0.415284549686343	0.792357725156828
14	0.192619466586843	0.385238933173686	0.807380533413157
15	0.169324329667185	0.338648659334369	0.830675670332815
16	0.150379468600635	0.300758937201269	0.849620531399365
17	0.13829677322951	0.27659354645902	0.86170322677049
18	0.130801247704550	0.261602495409101	0.86919875229545
19	0.343952907817689	0.687905815635378	0.656047092182311
20	0.59466534805828	0.810669303883441	0.405334651941720
21	0.731949046635141	0.536101906729718	0.268050953364859
22	0.766972692849995	0.466054614300009	0.233027307150005
23	0.753526493027518	0.492947013944963	0.246473506972482
24	0.747577299685475	0.50484540062905	0.252422700314525
25	0.748853446595286	0.502293106809428	0.251146553404714
26	0.741659378866076	0.516681242267848	0.258340621133924
27	0.720918621290165	0.55816275741967	0.279081378709835
28	0.690539801256443	0.618920397487114	0.309460198743557
29	0.661764280350065	0.67647143929987	0.338235719649935
30	0.630969419941004	0.738061160117992	0.369030580058996
31	0.701962906585235	0.59607418682953	0.298037093414765
32	0.742196640093895	0.51560671981221	0.257803359906105
33	0.838772505660812	0.322454988678376	0.161227494339188
34	0.891539154003392	0.216921691993216	0.108460845996608
35	0.897429721679396	0.205140556641208	0.102570278320604
36	0.901151939960952	0.197696120078095	0.0988480600390477
37	0.896912107334848	0.206175785330303	0.103087892665152
38	0.88798450419071	0.224030991618579	0.112015495809290
39	0.870952382844825	0.25809523431035	0.129047617155175
40	0.840713160188773	0.318573679622453	0.159286839811227
41	0.801787361205253	0.396425277589495	0.198212638794748
42	0.757866534912262	0.484266930175476	0.242133465087738
43	0.816063426759506	0.367873146480988	0.183936573240494
44	0.884495455040811	0.231009089918377	0.115504544959189
45	0.931953443991037	0.136093112017926	0.0680465560089629
46	0.945000455422112	0.109999089155775	0.0549995445778877
47	0.939742412370608	0.120515175258785	0.0602575876293923
48	0.93673281156763	0.126534376864739	0.0632671884323697
49	0.930121003120903	0.139757993758193	0.0698789968790967
50	0.92047998879437	0.159040022411259	0.0795200112056297
51	0.899317045758072	0.201365908483856	0.100682954241928
52	0.868157236270186	0.263685527459628	0.131842763729814
53	0.829818282421813	0.340363435156374	0.170181717578187
54	0.785083186395295	0.42983362720941	0.214916813604705
55	0.874904619107808	0.250190761784383	0.125095380892191
56	0.966200527752513	0.0675989444949744	0.0337994722474872
57	0.997673264205005	0.00465347158999027	0.00232673579499513
58	0.994900700354035	0.0101985992919306	0.0050992996459653
59	0.994195477257092	0.0116090454858155	0.00580452274290775
60	0.991410329777978	0.0171793404440445	0.00858967022202224
61	0.99026792272828	0.0194641545434392	0.00973207727171961
62	0.983893105820854	0.0322137883582912	0.0161068941791456
63	0.965196506965646	0.0696069860687085	0.0348034930343543
64	0.928693250442907	0.142613499114185	0.0713067495570927
65	0.884791392645976	0.230417214708048	0.115208607354024
66	0.882593192067932	0.234813615864135	0.117406807932068
67	0.809563681409045	0.380872637181910	0.190436318590955

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.0317460317460317	NOK
5% type I error level	8	0.126984126984127	NOK
10% type I error level	11	0.174603174603175	NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/10q3811228489851.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/10q3811228489851.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/19r9z1228489851.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/19r9z1228489851.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/2rtez1228489851.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/2rtez1228489851.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/33lcu1228489851.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/33lcu1228489851.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/458ux1228489851.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/458ux1228489851.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/5pecg1228489851.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/5pecg1228489851.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/6jypd1228489851.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/6jypd1228489851.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/7kvcd1228489851.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/7kvcd1228489851.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/88dle1228489851.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/88dle1228489851.ps (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/9ws0b1228489851.png (open in new window)

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/05/t1228490437zidds7yjd9aslym/9ws0b1228489851.ps (open in new window)

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('http://www.xycoon.com/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<br />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<br />Forecast', 1, TRUE)

a<-table.element(a, 'Residuals<br />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')

}

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

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