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WS 7 Multiple Regression(model twee)

*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: Sat, 21 Nov 2009 00:14:01 -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/21/t1258787747m6ta4cb3acishbe.htm/, Retrieved Sat, 21 Nov 2009 08:15:59 +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/21/t1258787747m6ta4cb3acishbe.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 «
83.4 108.8 113.6 128.4 112.9 121.1 104 119.5 109.9 128.7 99 108.7 106.3 105.5 128.9 119.8 111.1 111.3 102.9 110.6 130 120.1 87 97.5 87.5 107.7 117.6 127.3 103.4 117.2 110.8 119.8 112.6 116.2 102.5 111 112.4 112.4 135.6 130.6 105.1 109.1 127.7 118.8 137 123.9 91 101.6 90.5 112.8 122.4 128 123.3 129.6 124.3 125.8 120 119.5 118.1 115.7 119 113.6 142.7 129.7 123.6 112 129.6 116.8 151.6 127 110.4 112.1 99.2 114.2 130.5 121.1 136.2 131.6 129.7 125 128 120.4 121.6 117.7 135.8 117.5 143.8 120.6 147.5 127.5 136.2 112.3 156.6 124.5 123.3 115.2 104.5 104.7 139.8 130.9 136.5 129.2 112.1 113.5 118.5 125.6 94.4 107.6 102.3 107 111.4 121.6 99.2 110.7 87.8 106.3 115.8 118.6 79.7 104.6
 
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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
inv[t] = -100.311791928551 + 1.86997920836677cons[t] -11.6927284767817M1[t] -12.6573646232001M2[t] -12.3593937314866M3[t] -9.25209810548548M4[t] -10.1752698288643M5[t] -2.26767649769861M6[t] + 7.53010395816615M7[t] + 0.0541796552228235M8[t] + 4.20976466973519M9[t] + 5.91894055144064M10[t] + 8.84094555694431M11[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)-100.31179192855130.421899-3.29740.0018640.000932
cons1.869979208366770.2831126.605100
M1-11.69272847678176.628718-1.7640.084240.04212
M2-12.65736462320018.839514-1.43190.1587870.079394
M3-12.35939373148668.57873-1.44070.1562980.078149
M4-9.252098105485487.738864-1.19550.2378750.118937
M5-10.17526982886437.950085-1.27990.2068640.103432
M6-2.267676497698616.769009-0.3350.7391090.369555
M77.530103958166156.7078481.12260.2673170.133659
M80.05417965522282358.3496410.00650.994850.497425
M94.209764669735196.9295810.60750.5464390.27322
M105.918940551440646.8303880.86660.3905860.195293
M118.840945556944318.070411.09550.278890.139445


Multiple Linear Regression - Regression Statistics
Multiple R0.854903900048446
R-squared0.730860678318043
Adjusted R-squared0.662144255760947
F-TEST (value)10.6358953379853
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value9.79218484076227e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.3671854805278
Sum Squared Residuals5051.49113502033


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
183.491.4492174649721-8.04921746497212
2113.6127.136173802542-13.5361738025421
3112.9113.783296473178-0.883296473178192
4104113.898625365793-9.89862536579254
5109.9130.179262359388-20.279262359388
699100.687271523218-1.68727152321832
7106.3104.5011185123091.79888148769056
8128.9123.7658968890115.13410311098914
9111.1112.026658632406-0.92665863240571
10102.9112.426849068254-9.52684906825442
11130133.113656553242-3.11365655324238
128782.01118088720914.98881911279087
1387.589.3922403357684-1.89224033576843
14117.6125.079196673339-7.4791966733387
15103.4106.490377560548-3.09037756054782
16110.8114.459619128303-3.65961912830257
17112.6106.8045222548035.79547774519658
18102.5104.988223702462-2.48822370246188
19112.4117.40397505004-5.00397505004013
20135.6143.961672339372-8.36167233937196
21105.1107.912704373999-2.81270437399882
22127.7127.760678576862-0.060678576861918
23137140.219577545036-3.21957754503612
249189.67809564151291.32190435848713
2590.598.929134298439-8.42913429843894
26122.4126.388182119195-3.98818211919543
27123.3129.678119744296-6.37811974429573
28124.3125.679494378503-1.37949437850318
29120112.9754536424147.02454635758626
30118.1113.7771259817864.32287401821429
31119119.647950100080-0.647950100080233
32142.7142.2786910518420.421308948158136
33123.6113.33564407826210.2643559217375
34129.6124.0207201601285.5792798398716
35151.6146.0165130909735.58348690902691
36110.4109.3128773293641.08712267063608
3799.2101.547105190152-2.34710519015241
38130.5113.48532558146517.0146744185353
39136.2133.4180781610292.78192183897073
40129.7124.1835110118105.51648898819022
41128114.65843492994413.3415650700562
42121.6117.5170843985194.08291560148076
43135.8126.9408690127118.85913098728938
44143.8125.26188025570418.5381197442957
45147.5142.3203218079475.17967819205265
46136.2115.60581372247820.5941862775221
47156.6141.34156507005615.2584349299438
48123.3115.1098128753018.19018712469907
49104.583.782302710668120.7176972893319
50139.8131.8111218234597.98887817654094
51136.5128.9301280609497.56987193905099
52112.1102.6787501155929.42124988440806
53118.5124.382326813451-5.88232681345101
5494.498.6302943940149-4.23029439401486
55102.3107.306087324860-5.00608732485958
56111.4127.131859464071-15.7318594640710
5799.2110.904671107386-11.7046711073857
5887.8104.385938472277-16.5859384722773
59115.8130.308687740692-14.5086877406922
6079.795.2880332666132-15.5880332666132


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.0824658523128890.1649317046257780.917534147687111
170.0540968771353420.1081937542706840.945903122864658
180.02187090799096720.04374181598193440.978129092009033
190.01047698144415940.02095396288831880.98952301855584
200.004360566708918730.008721133417837460.995639433291081
210.002009550684472860.004019101368945720.997990449315527
220.019894535033650.03978907006730.98010546496635
230.009886639431520240.01977327886304050.99011336056848
240.004330402167981570.008660804335963150.995669597832018
250.002438768311074110.004877536622148220.997561231688926
260.002014497470330230.004028994940660460.99798550252967
270.001220288783889700.002440577567779390.99877971121611
280.001471714404934460.002943428809868920.998528285595066
290.001855492848167270.003710985696334540.998144507151833
300.001718475092782190.003436950185564380.998281524907218
310.000821941383385420.001643882766770840.999178058616615
320.0004071203889300160.0008142407778600320.99959287961107
330.0008099178223108550.001619835644621710.99919008217769
340.0006971274139575230.001394254827915050.999302872586042
350.0005414466762773880.001082893352554780.999458553323723
360.00022975356622540.00045950713245080.999770246433775
370.0004505984510938520.0009011969021877040.999549401548906
380.002989527495142590.005979054990285180.997010472504857
390.002108781844234650.004217563688469300.997891218155765
400.00259392752367510.00518785504735020.997406072476325
410.008429481617532110.01685896323506420.991570518382468
420.004687455489307550.00937491097861510.995312544510693
430.002748048682240800.005496097364481610.99725195131776
440.04179315658130140.08358631316260290.958206843418699


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.724137931034483NOK
5% type I error level260.896551724137931NOK
10% type I error level270.93103448275862NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/103q801258787636.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/103q801258787636.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/1v1te1258787635.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/1v1te1258787635.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/25o8m1258787635.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/25o8m1258787635.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/3vxux1258787635.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/3vxux1258787635.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/4iifa1258787635.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/4iifa1258787635.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/53lgx1258787635.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/53lgx1258787635.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/6gcdf1258787635.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/6gcdf1258787635.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/7mmz41258787635.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/7mmz41258787635.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/8nd1x1258787635.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/8nd1x1258787635.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/93vl01258787635.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/21/t1258787747m6ta4cb3acishbe/93vl01258787635.ps (open in new window)


 
Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 1 ; 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')
}
 





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Software written by Ed van Stee & Patrick Wessa


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