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

Title produced by software: Multiple Regression

Date of computation: Thu, 19 Nov 2009 17:54:39 -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/20/t12586785627pced0ym3hmgrwt.htm/, Retrieved Fri, 20 Nov 2009 01:56:14 +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/20/t12586785627pced0ym3hmgrwt.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 «

6802,96 0 6349.71 6303.79 6158.17 6091.43 7132,68 0 6802.96 6349.71 6303.79 6158.17 7073,29 0 7132.68 6802.96 6349.71 6303.79 7264,5 0 7073.29 7132.68 6802.96 6349.71 7105,33 0 7264.5 7073.29 7132.68 6802.96 7218,71 0 7105.33 7264.5 7073.29 7132.68 7225,72 0 7218.71 7105.33 7264.5 7073.29 7354,25 0 7225.72 7218.71 7105.33 7264.5 7745,46 0 7354.25 7225.72 7218.71 7105.33 8070,26 0 7745.46 7354.25 7225.72 7218.71 8366,33 0 8070.26 7745.46 7354.25 7225.72 8667,51 0 8366.33 8070.26 7745.46 7354.25 8854,34 0 8667.51 8366.33 8070.26 7745.46 9218,1 0 8854.34 8667.51 8366.33 8070.26 9332,9 0 9218.1 8854.34 8667.51 8366.33 9358,31 0 9332.9 9218.1 8854.34 8667.51 9248,66 0 9358.31 9332.9 9218.1 8854.34 9401,2 0 9248.66 9358.31 9332.9 9218.1 9652,04 0 9401.2 9248.66 9358.31 9332.9 9957,38 0 9652.04 9401.2 9248.66 9358.31 10110,63 0 9957.38 9652.04 9401.2 9248.66 10169,26 0 10110.63 9957.38 9652.04 9401.2 10343,78 0 10169.26 10110.63 9957.38 9652.04 10750,21 0 10343.78 10169.26 10110.63 9957.38 11 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

Multiple Linear Regression - Estimated Regression Equation

Y[t] = -8.2638347760359 -293.374157093177X[t] + 1.02469638380372Y1[t] -0.211002410605720Y2[t] + 0.316813468663976Y3[t] -0.0490383619885906Y4[t] -99.3752633320272M1[t] -2.92769282470823M2[t] -229.805644167399M3[t] + 73.407137286512M4[t] -312.343953515152M5[t] -434.547944602932M6[t] -401.501954448354M7[t] -270.918868937814M8[t] + 2.58702742390048M9[t] -299.756615294635M10[t] -158.836682680342M11[t] -20.2906549503601t + 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)	-8.2638347760359	458.159706	-0.018	0.985695	0.492847
X	-293.374157093177	402.161209	-0.7295	0.469749	0.234874
Y1	1.02469638380372	0.151917	6.7451	0	0
Y2	-0.211002410605720	0.218097	-0.9675	0.338846	0.169423
Y3	0.316813468663976	0.251223	1.2611	0.214238	0.107119
Y4	-0.0490383619885906	0.196424	-0.2497	0.804071	0.402036
M1	-99.3752633320272	319.651277	-0.3109	0.757425	0.378712
M2	-2.92769282470823	323.522995	-0.009	0.992823	0.496411
M3	-229.805644167399	316.481369	-0.7261	0.471788	0.235894
M4	73.407137286512	316.18102	0.2322	0.817535	0.408768
M5	-312.343953515152	313.758726	-0.9955	0.325199	0.162599
M6	-434.547944602932	318.304515	-1.3652	0.17946	0.08973
M7	-401.501954448354	308.731621	-1.3005	0.200525	0.100262
M8	-270.918868937814	309.463729	-0.8754	0.38631	0.193155
M9	2.58702742390048	306.975332	0.0084	0.993316	0.496658
M10	-299.756615294635	296.032723	-1.0126	0.317059	0.15853
M11	-158.836682680342	293.554017	-0.5411	0.59131	0.295655
t	-20.2906549503601	16.467423	-1.2322	0.224739	0.11237

Multiple Linear Regression - Regression Statistics
Multiple R	0.987348952648184
R-squared	0.974857954295467
Adjusted R-squared	0.964681411986489
F-TEST (value)	95.7946151744927
F-TEST (DF numerator)	17
F-TEST (DF denominator)	42
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	449.915088892889
Sum Squared Residuals	8501790.66296685

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	6802.96	6700.75768514592	102.202314854076
2	7132.68	7274.53056299462	-141.85056299462
3	7073.29	7276.99511397056	-203.70511397056
4	7264.5	7570.83267050452	-306.332670504523
5	7105.33	7455.48765278204	-350.157652782038
6	7218.71	7074.56183178311	144.148168216890
7	7225.72	7300.57278834085	-74.8527883408454
8	7354.25	7334.32106223393	19.9289377660697
9	7745.46	7761.48715011208	-16.0271501120761
10	8070.26	7809.26507784895	260.994922151053
11	8366.33	8220.5457641291	145.784235870896
12	8667.51	8711.57776365675	-44.0677636567545
13	8854.34	8921.77513556884	-67.4351355688379
14	9218.1	9203.69767417906	14.4023258209366
15	9332.9	9370.75013674324	-37.8501367432404
16	9358.31	9738.97405771229	-380.664057712288
17	9248.66	9440.82900052606	-192.169000526059
18	9401.2	9199.146816396	202.053183603996
19	9652.04	9393.76637859102	258.273621408980
20	9957.38	9692.9226807336	264.457319266406
21	10110.63	10259.7946542013	-149.164654201287
22	10169.26	10101.7567800379	67.503219962108
23	10343.78	10334.5629290596	9.21707094044965
24	10750.21	10673.1461889803	77.0638110196967
25	11337.5	10944.1831259614	393.316874038635
26	11786.96	11588.7914384078	198.168561592226
27	12083.04	11798.4675861894	284.572413810645
28	12007.74	12456.0753950873	-448.335395087311
29	11745.93	12023.9956599162	-278.065659916154
30	11051.51	11700.8750847756	-649.365084775614
31	11445.9	11018.9279658516	426.972034148441
32	11924.88	11600.6223516197	324.257648380264
33	12247.63	12054.2665508692	193.363449130801
34	12690.91	12120.2883596795	570.621640320473
35	12910.7	12759.4510979690	151.248902031035
36	13202.12	13108.4451477079	93.6748522921336
37	13654.67	13365.6299728241	289.040027175923
38	13862.82	13891.9176215480	-29.0976215480415
39	13523.93	13844.0980660807	-320.168066080698
40	14211.17	13864.9238591026	346.246140897404
41	14510.35	14278.3534758705	231.996524129522
42	14289.23	14179.8459458306	109.384054169398
43	14111.82	14137.2382141421	-25.4182141420642
44	13086.59	14173.4792419466	-1086.88924194661
45	13351.54	13328.8538561257	22.6861438742812
46	13747.69	13448.6763518982	299.013648101837
47	12855.61	13603.2272366380	-747.61723663798
48	12926.93	12878.2888377270	48.6411622730297
49	12121.95	12839.0740804998	-717.124080499796
50	11731.65	11773.2727028705	-41.6227028705009
51	11639.51	11362.3590970161	277.150902983853
52	12163.78	11374.6940175933	789.085982406717
53	12029.53	11441.1342109053	588.39578909473
54	11234.18	11040.4003212147	193.779678785330
55	9852.13	10437.1046530745	-584.974653074511
56	9709.04	9230.79466346613	478.245336533875
57	9332.75	9383.60778869172	-50.8577886917192
58	7108.6	8306.73343053547	-1198.13343053547
59	6691.49	6250.1229722044	441.3670277956
60	6143.05	6318.3620619281	-175.312061928106

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.0265552878003875	0.0531105756007750	0.973444712199612
22	0.00908618661348883	0.0181723732269777	0.990913813386511
23	0.00293207907877454	0.00586415815754908	0.997067920921225
24	0.000658798584190608	0.00131759716838122	0.99934120141581
25	0.000162236350742073	0.000324472701484146	0.999837763649258
26	3.60728114571536e-05	7.21456229143072e-05	0.999963927188543
27	5.74045473760715e-05	0.000114809094752143	0.999942595452624
28	2.44794183670405e-05	4.89588367340811e-05	0.999975520581633
29	1.13724702369786e-05	2.27449404739573e-05	0.999988627529763
30	0.000648321526902901	0.00129664305380580	0.999351678473097
31	0.000296090357475758	0.000592180714951517	0.999703909642524
32	0.000111666502299438	0.000223333004598877	0.9998883334977
33	6.97681770834204e-05	0.000139536354166841	0.999930231822917
34	2.29928147778418e-05	4.59856295556837e-05	0.999977007185222
35	5.75027431962601e-06	1.15005486392520e-05	0.99999424972568
36	1.3024290361657e-06	2.6048580723314e-06	0.999998697570964
37	7.3079956699905e-06	1.4615991339981e-05	0.99999269200433
38	6.57844337229648e-06	1.31568867445930e-05	0.999993421556628
39	5.19287966714735e-05	0.000103857593342947	0.999948071203329

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	17	0.894736842105263	NOK
5% type I error level	18	0.947368421052632	NOK
10% type I error level	19	1	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/101foc1258678474.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/101foc1258678474.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/1veja1258678474.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/1veja1258678474.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/2eqim1258678474.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/2eqim1258678474.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/3taz01258678474.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/3taz01258678474.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/45kh21258678474.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/45kh21258678474.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/5zqk51258678474.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/5zqk51258678474.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/6eyxb1258678474.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/6eyxb1258678474.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/7v1q81258678474.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/7v1q81258678474.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/85ckc1258678474.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/85ckc1258678474.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/991ve1258678474.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/20/t12586785627pced0ym3hmgrwt/991ve1258678474.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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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