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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: Tue, 17 Nov 2009 10:43:04 -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/17/t12584801544gmtmunhav86vwj.htm/, Retrieved Tue, 17 Nov 2009 18:49:26 +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/17/t12584801544gmtmunhav86vwj.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 «

15836.8 89.1 17570.4 82.6 18252.1 102.7 16196.7 91.8 16643 94.1 17729 103.1 16446.1 93.2 15993.8 91 16373.5 94.3 17842.2 99.4 22321.5 115.7 22786.7 116.8 18274.1 99.8 22392.9 96 23899.3 115.9 21343.5 109.1 22952.3 117.3 21374.4 109.8 21164.1 112.8 20906.5 110.7 17877.4 100 20664.3 113.3 22160 122.4 19813.6 112.5 17735.4 104.2 19640.2 92.5 20844.4 117.2 19823.1 109.3 18594.6 106.1 21350.6 118.8 18574.1 105.3 18924.2 106 17343.4 102 19961.2 112.9 19932.1 116.5 19464.6 114.8 16165.4 100.5 17574.9 85.4 19795.4 114.6 19439.5 109.9 17170 100.7 21072.4 115.5 17751.8 100.7 17515.5 99 18040.3 102.3 19090.1 108.8 17746.5 105.9 19202.1 113.2 15141.6 95.7 16258.1 80.9 18586.5 113.9 17209.4 98.1 17838.7 102.8 19123.5 104.7 16583.6 95.9 15991.2 94.6 16704.4 101.6 17420.4 103.9 17872 110.3 17823.2 114.1

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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation

uitvoer[t] = -8033.86813731782 + 255.273412361827indproc[t] + 600.65077261152M1[t] + 5343.71594368815M2[t] + 489.803888705864M3[t] + 1407.01190144279M4[t] + 1138.02594128105M5[t] + 1087.38340364584M6[t] + 1344.43658319079M7[t] + 1480.38463826929M8[t] + 974.791939749771M9[t] + 794.13568831353M10[t] + 182.325658722537M11[t] -36.6871507608812t + 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)	-8033.86813731782	1983.559289	-4.0502	0.000195	9.7e-05
indproc	255.273412361827	17.075623	14.9496	0	0
M1	600.65077261152	564.907159	1.0633	0.293208	0.146604
M2	5343.71594368815	669.637828	7.98	0	0
M3	489.803888705864	491.151655	0.9973	0.323858	0.161929
M4	1407.01190144279	521.930621	2.6958	0.009776	0.004888
M5	1138.02594128105	518.30722	2.1957	0.033198	0.016599
M6	1087.38340364584	493.384844	2.2039	0.032573	0.016287
M7	1344.43658319079	534.116608	2.5171	0.01538	0.00769
M8	1480.38463826929	543.434556	2.7241	0.009084	0.004542
M9	974.791939749771	544.989062	1.7886	0.080259	0.040129
M10	794.13568831353	500.75916	1.5859	0.119622	0.059811
M11	182.325658722537	487.853199	0.3737	0.710322	0.355161
t	-36.6871507608812	5.873454	-6.2463	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.94446092820677
R-squared	0.892006444909194
Adjusted R-squared	0.86148652716614
F-TEST (value)	29.2270265083597
F-TEST (DF numerator)	13
F-TEST (DF denominator)	46
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	771.302682407173
Sum Squared Residuals	27365760.082871

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	Interpolation Forecast	Residuals Prediction Error
1	15836.8	15274.9565259715	561.843474028527
2	17570.4	18322.0573659354	-751.657365935433
3	18252.1	18562.453748665	-310.353748664989
4	16196.7	16660.4944158971	-463.79441589712
5	16643	16941.9501534067	-298.950153406699
6	17729	19152.0811762670	-1423.08117626705
7	16446.1	16845.2404226690	-399.140422669041
8	15993.8	16382.8998197906	-389.099819790635
9	16373.5	16683.0222313043	-309.522231304264
10	17842.2	17767.5732321525	74.6267678475399
11	22321.5	21280.0326732984	1041.46732670164
12	22786.7	21341.8206174130	1444.87938258705
13	18274.1	17566.1362291125	707.963770887464
14	22392.9	21302.4752824533	1090.42471754666
15	23899.3	21491.8169827105	2407.48301728947
16	21343.5	20636.4786406261	707.021359373852
17	22952.3	22424.0475110705	528.252488929496
18	21374.4	20422.1672299607	952.232770039286
19	21164.1	21408.3534958303	-244.253495830269
20	20906.5	20971.5402341880	-65.040234188046
21	17877.4	17697.8348726361	179.565127363899
22	20664.3	20875.6278548513	-211.327854851277
23	22160	22550.1187269920	-390.118726992027
24	19813.6	19803.8991351265	9.70086487347598
25	17735.4	18249.093434374	-513.693434373999
26	19640.2	19968.7725300564	-328.572530056371
27	20844.4	21383.4266096503	-539.026609650327
28	19823.1	20247.2875139679	-424.187513967941
29	18594.6	19124.7394834875	-530.139483487471
30	21350.6	22279.3821320866	-928.782132086583
31	18574.1	19053.557093986	-479.457093985995
32	18924.2	19331.5093869569	-407.309386956885
33	17343.4	17768.1358882292	-424.735888229181
34	19961.2	20333.2726807760	-372.072680775973
35	19932.1	20603.7597849267	-671.659784926675
36	19464.6	19950.7821744282	-486.18217442815
37	16165.4	16864.3359995047	-698.935999504667
38	17574.9	17716.0854931568	-141.185493156827
39	19795.4	20279.469928379	-484.069928379002
40	19439.5	19960.2057522545	-520.705752254463
41	17170	17306.0172476030	-136.017247603034
42	21072.4	20996.734062162	75.6659378380214
43	17751.8	17439.0535879910	312.746412008982
44	17515.5	17104.3496912935	411.150308706476
45	18040.3	17404.4721028072	635.827897192845
46	19090.1	18846.4058809619	243.694119038091
47	17746.5	17457.6158047607	288.884195239263
48	19202.1	19102.0989055187	100.001094481345
49	15141.6	15198.7778110373	-57.1778110373246
50	16258.1	16127.1093283980	130.990671601967
51	18586.5	19660.5327305952	-1074.03273059515
52	17209.4	16507.7336772543	701.666322745671
53	17838.7	17401.8456044323	436.854395567707
54	19123.5	17799.5353995237	1323.96460047632
55	16583.6	15773.4953995237	810.104600476323
56	15991.2	15540.9008677709	450.299132229090
57	16704.4	16785.5349050233	-81.1349050232993
58	17420.4	17155.3203512584	265.079648741617
59	17872	18140.5730100222	-268.573010022198
60	17823.2	18891.5991675137	-1068.39916751372

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.989387894912499	0.0212242101750023	0.0106121050875012
18	0.981985460119762	0.036029079760477	0.0180145398802385
19	0.982593851436694	0.0348122971266124	0.0174061485633062
20	0.978553994295268	0.0428920114094638	0.0214460057047319
21	0.989980493844117	0.0200390123117659	0.0100195061558830
22	0.993742819385807	0.0125143612283855	0.00625718061419276
23	0.99979479153886	0.000410416922279049	0.000205208461139525
24	0.999794342636775	0.000411314726450598	0.000205657363225299
25	0.999819670186837	0.000360659626325472	0.000180329813162736
26	0.99974980252476	0.000500394950479134	0.000250197475239567
27	0.999793502705728	0.000412994588543346	0.000206497294271673
28	0.999465852474575	0.00106829505084922	0.00053414752542461
29	0.998874959513717	0.00225008097256604	0.00112504048628302
30	0.999098481074178	0.00180303785164402	0.000901518925822008
31	0.998636024097467	0.00272795180506660	0.00136397590253330
32	0.996886652920194	0.00622669415961114	0.00311334707980557
33	0.99856802035174	0.00286395929651835	0.00143197964825917
34	0.996696082892573	0.00660783421485377	0.00330391710742689
35	0.992238806524734	0.0155223869505328	0.00776119347526639
36	0.98493348843278	0.0301330231344416	0.0150665115672208
37	0.972791905523192	0.0544161889536159	0.0272080944768080
38	0.958184582688477	0.0836308346230461	0.0418154173115231
39	0.929407983803977	0.141184032392045	0.0705920161960226
40	0.869461715126432	0.261076569747136	0.130538284873568
41	0.968539230958926	0.0629215380821469	0.0314607690410735
42	0.938401665868179	0.123196668263643	0.0615983341318215
43	0.919199858371093	0.161600283257814	0.0808001416289071

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	12	0.444444444444444	NOK
5% type I error level	20	0.740740740740741	NOK
10% type I error level	23	0.851851851851852	NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/10rxio1258479779.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/10rxio1258479779.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/1u3od1258479779.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/1u3od1258479779.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/294qr1258479779.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/294qr1258479779.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/3o6sx1258479779.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/3o6sx1258479779.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/44ovi1258479779.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/44ovi1258479779.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/58vk01258479779.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/58vk01258479779.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/6wyxq1258479779.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/6wyxq1258479779.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/7yued1258479779.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/7yued1258479779.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/8czua1258479779.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/8czua1258479779.ps (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/930321258479779.png (open in new window)

http://www.freestatistics.org/blog/date/2009/Nov/17/t12584801544gmtmunhav86vwj/930321258479779.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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