Home » date » 2009 » Nov » 20 »

Multiple Linear Regression Model 2

*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, 20 Nov 2009 12:12:38 -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/t1258744390zsuxkbqytzv40sy.htm/, Retrieved Fri, 20 Nov 2009 20:13:22 +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/t1258744390zsuxkbqytzv40sy.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 «
9.3 104.1 8.7 90.2 8.2 99.2 8.3 116.5 8.5 98.4 8.6 90.6 8.5 130.5 8.2 107.4 8.1 106 7.9 196.5 8.6 107.8 8.7 90.5 8.7 123.8 8.5 114.7 8.4 115.3 8.5 197 8.7 88.4 8.7 93.8 8.6 111.3 8.5 105.9 8.3 123.6 8 171 8.2 97 8.1 99.2 8.1 126.6 8 103.4 7.9 121.3 7.9 129.6 8 110.8 8 98.9 7.9 122.8 8 120.9 7.7 133.1 7.2 203.1 7.5 110.2 7.3 119.5 7 135.1 7 113.9 7 137.4 7.2 157.1 7.3 126.4 7.1 112.2 6.8 128.8 6.4 136.8 6.1 156.5 6.5 215.2 7.7 146.7 7.9 130.8 7.5 133.1 6.9 153.4 6.6 159.9 6.9 174.6 7.7 145 8 112.9 8 137.8 7.7 150.6 7.3 162.1 7.4 226.4 8.1 112.3 8.3 126.3
 
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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 252.997616013072 -17.3371732026144X[t] + 12.3202303921566M1[t] -2.30092156862746M2[t] + 5.73164379084967M3[t] + 36.4988480392157M4[t] + 0.193256535947739M5[t] -11.2332565359477M6[t] + 11.2462826797386M7[t] + 5.85884803921567M8[t] + 13.2911830065359M9[t] + 77.7374656862745M10[t] + 0.846513071895421M11[t] + e[t]


Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STAT
H0: parameter = 0
2-tail p-value1-tail p-value
(Intercept)252.99761601307227.8606259.080800
X-17.33717320261443.333326-5.20124e-062e-06
M112.320230392156610.4326191.18090.2435710.121785
M2-2.3009215686274610.461335-0.21990.8268660.413433
M35.7316437908496710.5333110.54410.5889140.294457
M436.498848039215710.4785273.48320.0010820.000541
M50.19325653594773910.4309140.01850.9852970.492648
M6-11.233256535947710.430914-1.07690.2870130.143506
M711.246282679738610.4360261.07760.2866940.143347
M85.8588480392156710.4785270.55910.5787280.289364
M913.291183006535910.5964131.25430.2159320.107966
M1077.737465686274510.6601837.292300
M110.84651307189542110.4315540.08110.9356680.467834


Multiple Linear Regression - Regression Statistics
Multiple R0.885262235683724
R-squared0.783689225927746
Adjusted R-squared0.728460943185894
F-TEST (value)14.18999807745
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value8.0138118363493e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation16.4923870073344
Sum Squared Residuals12783.9449723856


Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolation
Forecast
Residuals
Prediction Error
1104.1104.0821356209160.0178643790841724
290.299.8632875816993-9.6632875816993
399.2116.564439542484-17.3644395424836
4116.5145.597926470588-29.0979264705882
598.4105.824900326797-7.42490032679736
690.692.6646699346405-2.06466993464051
7130.5116.87792647058813.6220735294117
8107.4116.691643790850-9.29164379084967
9106125.857696078431-19.8576960784313
10196.5193.7714133986932.72858660130724
11107.8104.7444395424843.05556045751636
1290.5102.164209150327-11.6642091503267
13123.8114.4844395424839.31556045751657
14114.7103.33072222222211.3692777777778
15115.3113.0970049019612.20299509803925
16197142.13049183006554.8695081699347
1788.4102.357465686274-13.9574656862745
1893.890.9309526143792.86904738562093
19111.3115.144209150327-3.84420915032678
20105.9111.490491830065-5.59049183006532
21123.6122.3902614379081.20973856209155
22171192.037696078431-21.0376960784314
2397111.679308823529-14.6793088235294
2499.2112.566513071895-13.3665130718954
25126.6124.8867434640521.71325653594792
26103.4111.999308823529-8.59930882352942
27121.3121.765591503268-0.465591503267971
28129.6152.532795751634-22.932795751634
29110.8114.493486928105-3.69348692810458
3098.9103.066973856209-4.16697385620915
31122.8127.280230392157-4.48023039215686
32120.9120.1590784313730.740921568627468
33133.1132.7925653594770.307434640522879
34203.1205.907434640523-2.80743464052288
35110.2123.815330065359-13.6153300653595
36119.5126.436251633987-6.93625163398695
37135.1143.957633986928-8.85763398692795
38113.9129.336482026144-15.4364820261438
39137.4137.3690473856210.0309526143790445
40157.1164.668816993464-7.5688169934641
41126.4126.629508169935-0.229508169934665
42112.2118.670429738562-6.47042973856214
43128.8146.351120915033-17.5511209150327
44136.8147.898555555556-11.0985555555556
45156.5160.532042483660-4.03204248366019
46215.2218.043455882353-2.843455882353
47146.7120.34789542483726.3521045751634
48130.8116.03394771241814.7660522875817
49133.1135.289047385621-2.18904738562073
50153.4131.07019934640522.3298006535947
51159.9144.30391666666715.5960833333333
52174.6169.8699689542484.73003104575157
53145119.69463888888925.3053611111111
54112.9103.0669738562099.83302614379085
55137.8125.54651307189512.2534869281046
56150.6125.36023039215725.2397696078431
57162.1139.72743464052322.3725653594771
58226.4202.4423.96
59112.3113.413026143791-1.11302614379084
60126.3109.09907843137317.2009215686275


Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.9971112999026970.005777400194605910.00288870009730295
170.9941387670303920.01172246593921670.00586123296960834
180.9860347336804140.02793053263917140.0139652663195857
190.9758759135367220.0482481729265560.024124086463278
200.95633645326050.08732709347900170.0436635467395008
210.9340487888469490.1319024223061020.065951211153051
220.944736871798160.1105262564036800.0552631282018399
230.9284266042081340.1431467915837320.0715733957918659
240.9158040048619580.1683919902760830.0841959951380417
250.8733327774547050.253334445090590.126667222545295
260.8385778430764290.3228443138471430.161422156923571
270.8048826899700420.3902346200599160.195117310029958
280.8727848352038340.2544303295923320.127215164796166
290.8704525160002650.2590949679994710.129547483999735
300.8252055723302240.3495888553395510.174794427669776
310.7632801015714420.4734397968571150.236719898428558
320.752422223023680.4951555539526390.247577776976319
330.7964662310852010.4070675378295970.203533768914799
340.782546554393610.4349068912127810.217453445606391
350.7542241496420980.4915517007158050.245775850357902
360.681675225659870.636649548680260.31832477434013
370.5814806950945370.8370386098109260.418519304905463
380.7648441375753930.4703117248492140.235155862424607
390.7974201327101720.4051597345796560.202579867289828
400.7860291006482420.4279417987035160.213970899351758
410.7626353934211140.4747292131577720.237364606578886
420.6406514876470360.7186970247059290.359348512352964
430.4982882622742470.9965765245484940.501711737725753
440.3636110943566320.7272221887132630.636388905643368


Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0344827586206897NOK
5% type I error level40.137931034482759NOK
10% type I error level50.172413793103448NOK
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/10o9bo1258744353.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/10o9bo1258744353.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/1lwh71258744353.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/1lwh71258744353.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/2cuvf1258744353.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/2cuvf1258744353.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/334d61258744353.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/334d61258744353.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/400p61258744353.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/400p61258744353.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/5q1ao1258744353.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/5q1ao1258744353.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/6n0dj1258744353.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/6n0dj1258744353.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/7we211258744353.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/7we211258744353.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/879lj1258744353.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/879lj1258744353.ps (open in new window)


http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/9xdos1258744353.png (open in new window)
http://www.freestatistics.org/blog/date/2009/Nov/20/t1258744390zsuxkbqytzv40sy/9xdos1258744353.ps (open in new window)


 
Parameters (Session):
par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
 
Parameters (R input):
par1 = 2 ; 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')
}
 





Copyright

Creative Commons License

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

Software written by Ed van Stee & Patrick Wessa


Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.


Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

  • personalize online software applications according to your needs
  • enforce strict security rules with respect to the data that you upload (e.g. statistical data)
  • manage user sessions of online applications
  • alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.


FreeStatistics.org is powered by