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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: Wed, 18 Nov 2009 12:33:21 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw.htm/, Retrieved Wed, 18 Nov 2009 20:37:41 +0100

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/2009/Nov/18/t1258573049i0dfyr0pa3frygw.htm/},
    year = {2009},
}
@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 = {2009},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

103.8 122.5 80.2 19 103.5 122.4 74.8 18 104.1 121.9 77.8 19 101.9 122.2 73 19 102 123.7 72 22 100.7 122.6 75.8 23 99 115.7 72.6 20 96.5 116.1 71.9 14 101.8 120.5 74.8 14 100.5 122.6 72.9 14 103.3 119.9 72.9 15 102.3 120.7 79.9 11 100.4 120.2 74 17 103 122.1 76 16 99 119.3 69.6 20 104.8 121.7 77.3 24 104.5 113.5 75.2 23 104.8 123.7 75.8 20 103.8 123.4 77.6 21 106.3 126.4 76.7 19 105.2 124.1 77 23 108.2 125.6 77.9 23 106.2 124.8 76.7 23 103.9 123 71.9 23 104.9 126.9 73.4 27 106.2 127.3 72.5 26 107.9 129 73.7 17 106.9 126.2 69.5 24 110.3 125.4 74.7 26 109.8 126.3 72.5 24 108.3 126.3 72.1 27 110.9 128.4 70.7 27 109.8 127.2 71.4 26 109.3 128.5 69.5 24 109 129 73.5 23 107.9 128.9 72.4 23 108.4 128.3 74.5 24 107.2 124.6 72.2 17 109.5 126.2 73 21 109.9 129.1 73.3 19 108 127.3 71.3 22 114.7 129.2 73.6 22 115.6 130.4 71.3 18 107.6 125.9 71.2 16 1 etc...

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
R Framework error message	Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values.

Multiple Linear Regression - Estimated Regression Equation

dzcg[t] = + 86.4486262206775 + 0.820422117853948totid[t] -0.834444510665815ndzcg[t] + 0.270023243827998`indc `[t] + 0.436858127219093M1[t] -0.0399988870685178M2[t] -2.40677166640325M3[t] -2.95706963841343M4[t] -4.03836102042737M5[t] -2.09097820123675M6[t] -3.00419908854042M7[t] -1.78500969314317M8[t] + 0.899750191024355M9[t] -0.347689294709870M10[t] -1.28273233446154M11[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)	86.4486262206775	16.948443	5.1007	6e-06	3e-06
totid	0.820422117853948	0.168203	4.8776	1.3e-05	7e-06
ndzcg	-0.834444510665815	0.205969	-4.0513	0.000194	9.7e-05
`indc `	0.270023243827998	0.088286	3.0585	0.003702	0.001851
M1	0.436858127219093	2.32707	0.1877	0.851915	0.425957
M2	-0.0399988870685178	2.445107	-0.0164	0.987019	0.493509
M3	-2.40677166640325	2.447676	-0.9833	0.330609	0.165305
M4	-2.95706963841343	2.431048	-1.2164	0.230046	0.115023
M5	-4.03836102042737	2.454271	-1.6454	0.106696	0.053348
M6	-2.09097820123675	2.43641	-0.8582	0.39522	0.19761
M7	-3.00419908854042	2.451304	-1.2256	0.226605	0.113303
M8	-1.78500969314317	2.434525	-0.7332	0.467153	0.233576
M9	0.899750191024355	2.437178	0.3692	0.71369	0.356845
M10	-0.347689294709870	2.438123	-0.1426	0.887225	0.443612
M11	-1.28273233446154	2.430166	-0.5278	0.600149	0.300075

Multiple Linear Regression - Regression Statistics
Multiple R	0.806556871701163
R-squared	0.650533987288366
Adjusted R-squared	0.544174766028304
F-TEST (value)	6.11638539264709
F-TEST (DF numerator)	14
F-TEST (DF denominator)	46
p-value	1.30419228838718e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.84022541403812
Sum Squared Residuals	678.377236608714

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	80.2	74.9562892573062	5.24371074269383
2	74.8	74.046726814901	0.753273185099047
3	77.8	72.8594528054395	4.94054719456051
4	73	70.2538928209509	2.74610717904912
5	72	68.8130466162076	3.18695338379240
6	75.8	70.8817928877485	4.9182071122515
7	72.6	73.5214517922032	-0.921451792203235
8	71.9	70.7356686257313	1.16433137426871
9	74.8	74.0971098875952	0.702890112404835
10	72.9	70.0307881762526	2.8692118237474
11	72.9	73.9159504891177	-1.01595048911766
12	79.9	72.6306121218806	7.26938787811938
13	74	73.5460299434781	0.453970056521884
14	76	73.3468026215177	2.65319737848227
15	69.6	71.1148789759435	-1.51487897594348
16	77.3	74.4004554372002	2.89954456279978
17	75.2	79.6454591634618	-4.44545916346178
18	75.8	72.5175648777333	3.28243512226671
19	77.6	71.3042784696034	6.29572153039659
20	76.7	71.5311431399821	5.16885686001792
21	77	76.3127540443536	0.687245955646354
22	77.9	76.2749141461826	1.62508585381746
23	76.7	74.3665824792556	2.33341752074438
24	71.9	75.2643440618515	-3.36434406185155
25	73.4	74.3473836906399	-0.9473836906399
26	72.5	74.3332743814681	-1.83327438146811
27	73.7	69.5124543399012	4.18754566009878
28	69.5	72.3683415866974	-2.86834158669735
29	74.7	75.2840875015755	-0.584087501575478
30	72.5	75.5302127145839	-3.03021271458391
31	72.1	74.1964283819833	-2.09642838198331
32	70.7	75.7963818114026	-5.0963818114026
33	71.4	78.3099875349018	-6.90998753490176
34	69.5	75.027512638719	-5.52751263871902
35	73.5	73.1590974644503	0.340902535549746
36	72.4	73.622809920339	-1.22280992033903
37	74.5	75.2405690567126	-0.740569056712586
38	72.2	74.9764874836678	-2.77648748366777
39	73	74.2416673336438	-1.24166733364381
40	73.3	71.0596026401883	2.24039735981165
41	71.3	70.7315790849344	0.568420915065627
42	73.6	76.5903455234814	-2.99034552348141
43	71.3	74.3340781541353	-3.0340781541353
44	71.2	72.2048444170411	-1.00484441704112
45	81.4	72.8980607361489	8.50193926385113
46	76.1	75.5906224798161	0.50937752018388
47	71.1	71.1320881325193	-0.0320881325193157
48	75.7	73.2164182220861	2.48358177791392
49	70	72.8450284871207	-2.84502848712074
50	68.5	67.2967086984454	1.20329130155456
51	56.7	63.071546545072	-6.37154654507201
52	57.9	62.9177075149632	-5.01770751496319
53	58.8	57.5258276338208	1.27417236617924
54	59.3	61.4800839964529	-2.18008399645289
55	61.3	61.5437632020747	-0.243763202074747
56	62.9	63.1319620058429	-0.231962005842906
57	61.4	64.3820877970006	-2.98208779700056
58	64.5	63.9761625590297	0.523837440970271
59	63.8	65.4262814346571	-1.62628143465715
60	61.6	66.7658156738427	-5.16581567384272
61	64.7	65.8646995647425	-1.16469956474248

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
18	0.230096815355985	0.46019363071197	0.769903184644015
19	0.157575342339981	0.315150684679962	0.842424657660019
20	0.270647713129717	0.541295426259434	0.729352286870283
21	0.184665272659414	0.369330545318827	0.815334727340586
22	0.128853553723435	0.257707107446869	0.871146446276565
23	0.100726002065253	0.201452004130506	0.899273997934747
24	0.277037468023952	0.554074936047905	0.722962531976048
25	0.230754896273267	0.461509792546535	0.769245103726732
26	0.167479154376447	0.334958308752894	0.832520845623553
27	0.309145885572048	0.618291771144097	0.690854114427952
28	0.434669578491932	0.869339156983864	0.565330421508068
29	0.358841775729175	0.71768355145835	0.641158224270825
30	0.441266473269945	0.88253294653989	0.558733526730055
31	0.3942774109774	0.7885548219548	0.6057225890226
32	0.399634997313609	0.799269994627217	0.600365002686391
33	0.486343992348948	0.972687984697896	0.513656007651052
34	0.733185329410597	0.533629341178806	0.266814670589403
35	0.64216584490751	0.71566831018498	0.35783415509249
36	0.568922348132738	0.862155303734524	0.431077651867262
37	0.48439982387301	0.96879964774602	0.51560017612699
38	0.419566280061255	0.83913256012251	0.580433719938745
39	0.479594851460572	0.959189702921145	0.520405148539428
40	0.505214738519704	0.989570522960593	0.494785261480296
41	0.440849078523938	0.881698157047875	0.559150921476062
42	0.316864428818821	0.633728857637642	0.683135571181179
43	0.514080667111528	0.971838665776944	0.485919332888472

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/10w4jq1258572796.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/10w4jq1258572796.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/1oxgu1258572796.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/1oxgu1258572796.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/2eipe1258572796.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/2eipe1258572796.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/3j0tp1258572796.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/3j0tp1258572796.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/4ef081258572796.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/4ef081258572796.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/5bd2m1258572796.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/5bd2m1258572796.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/6csi71258572796.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/6csi71258572796.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/739y01258572796.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/739y01258572796.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/8e05q1258572796.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/8e05q1258572796.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/99pj81258572796.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258573049i0dfyr0pa3frygw/99pj81258572796.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; par2 = Include Monthly 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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