*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, 12 Dec 2009 10:47:59 -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/Dec/12/t126064012954q6rsnpq15m9f3.htm/, Retrieved Sat, 12 Dec 2009 18:49:01 +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/Dec/12/t126064012954q6rsnpq15m9f3.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 «

19 80.2 18 74.8 19 77.8 19 73 22 72 23 75.8 20 72.6 14 71.9 14 74.8 14 72.9 15 72.9 11 79.9 17 74 16 76 20 69.6 24 77.3 23 75.2 20 75.8 21 77.6 19 76.7 23 77 23 77.9 23 76.7 23 71.9 27 73.4 26 72.5 17 73.7 24 69.5 26 74.7 24 72.5 27 72.1 27 70.7 26 71.4 24 69.5 23 73.5 23 72.4 24 74.5 17 72.2 21 73 19 73.3 22 71.3 22 73.6 18 71.3 16 71.2 14 81.4 12 76.1 14 71.1 16 75.7 8 70 3 68.5 0 56.7 5 57.9 1 58.8 1 59.3 3 61.3 6 62.9 7 61.4 8 64.5 14 63.8 14 61.6 13 64.7

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 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation dzcg [t] = + 62.7286165220799 + 0.513169574290042indcvtr[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) 62.7286165220799 1.465188 42.8127 0 0 indcvtr 0.513169574290042 0.078524 6.5352 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.648007328327549 R-squared 0.419913497566208 Adjusted R-squared 0.410081522948686 F-TEST (value) 42.7089688390637 F-TEST (DF numerator) 1 F-TEST (DF denominator) 59 p-value 1.64733496843539e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4.36871320336193 Sum Squared Residuals 1126.0536481405 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 80.2 72.4788384335906 7.72116156640942 2 74.8 71.9656688593007 2.83433114069932 3 77.8 72.4788384335907 5.32116156640926 4 73 72.4788384335907 0.521161566409267 5 72 74.0183471564609 -2.01834715646086 6 75.8 74.5315167307509 1.26848326924910 7 72.6 72.9920080078808 -0.39200800788078 8 71.9 69.9129905621405 1.98700943785948 9 74.8 69.9129905621405 4.88700943785947 10 72.9 69.9129905621405 2.98700943785948 11 72.9 70.4261601364306 2.47383986356944 12 79.9 68.3734818392704 11.5265181607296 13 74 71.4524992850106 2.54750071498935 14 76 70.9393297107206 5.0606702892794 15 69.6 72.9920080078808 -3.39200800788078 16 77.3 75.044686305041 2.25531369495905 17 75.2 74.5315167307509 0.668483269249101 18 75.8 72.9920080078808 2.80799199211922 19 77.6 73.5051775821708 4.09482241782918 20 76.7 72.4788384335907 4.22116156640927 21 77 74.5315167307509 2.4684832692491 22 77.9 74.5315167307509 3.36848326924910 23 76.7 74.5315167307509 2.1684832692491 24 71.9 74.5315167307509 -2.63151673075090 25 73.4 76.584195027911 -3.18419502791106 26 72.5 76.071025453621 -3.57102545362103 27 73.7 71.4524992850106 2.24750071498935 28 69.5 75.044686305041 -5.54468630504094 29 74.7 76.071025453621 -1.37102545362102 30 72.5 75.044686305041 -2.54468630504094 31 72.1 76.584195027911 -4.48419502791107 32 70.7 76.584195027911 -5.88419502791107 33 71.4 76.071025453621 -4.67102545362102 34 69.5 75.044686305041 -5.54468630504094 35 73.5 74.5315167307509 -1.03151673075090 36 72.4 74.5315167307509 -2.13151673075090 37 74.5 75.044686305041 -0.544686305040943 38 72.2 71.4524992850106 0.747500714989355 39 73 73.5051775821708 -0.505177582170817 40 73.3 72.4788384335907 0.821161566409265 41 71.3 74.0183471564609 -2.71834715646086 42 73.6 74.0183471564609 -0.418347156460865 43 71.3 71.9656688593007 -0.665668859300693 44 71.2 70.9393297107206 0.260670289279397 45 81.4 69.9129905621405 11.4870094378595 46 76.1 68.8866514135604 7.21334858643956 47 71.1 69.9129905621405 1.18700943785947 48 75.7 70.9393297107206 4.7606702892794 49 70 66.8339731164003 3.16602688359973 50 68.5 64.26812524495 4.23187475504994 51 56.7 62.7286165220799 -6.02861652207993 52 57.9 65.2944643935301 -7.39446439353014 53 58.8 63.24178609637 -4.44178609636998 54 59.3 63.24178609637 -3.94178609636998 55 61.3 64.26812524495 -2.96812524495006 56 62.9 65.8076339678202 -2.90763396782019 57 61.4 66.3208035421102 -4.92080354211023 58 64.5 66.8339731164003 -2.33397311640027 59 63.8 69.9129905621405 -6.11299056214053 60 61.6 69.9129905621405 -8.31299056214052 61 64.7 69.3998209878505 -4.69982098785048 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.399948384598097 0.799896769196194 0.600051615401903 6 0.298814347741707 0.597628695483413 0.701185652258293 7 0.231777397409364 0.463554794818729 0.768222602590636 8 0.210781339681757 0.421562679363515 0.789218660318243 9 0.137090627723079 0.274181255446158 0.86290937227692 10 0.0862333271020925 0.172466654204185 0.913766672897907 11 0.0515629819181611 0.103125963836322 0.94843701808184 12 0.154668102745004 0.309336205490008 0.845331897254996 13 0.106812791893071 0.213625583786142 0.89318720810693 14 0.0810622387539523 0.162124477507905 0.918937761246048 15 0.108244303408682 0.216488606817363 0.891755696591318 16 0.103063373753574 0.206126747507147 0.896936626246426 17 0.0690894958556869 0.138178991711374 0.930910504144313 18 0.0493192809943355 0.098638561988671 0.950680719005665 19 0.0467888063975454 0.0935776127950909 0.953211193602455 20 0.0395304697139033 0.0790609394278066 0.960469530286097 21 0.0306738317619044 0.0613476635238088 0.969326168238096 22 0.0282817813391016 0.0565635626782031 0.971718218660898 23 0.0205970680249387 0.0411941360498774 0.979402931975061 24 0.0198313557412161 0.0396627114824322 0.980168644258784 25 0.0137681321080492 0.0275362642160984 0.98623186789195 26 0.0103787599184673 0.0207575198369345 0.989621240081533 27 0.00763408958319647 0.0152681791663929 0.992365910416804 28 0.0128809463790121 0.0257618927580243 0.987119053620988 29 0.00769398341736228 0.0153879668347246 0.992306016582638 30 0.0050715640137392 0.0101431280274784 0.99492843598626 31 0.00366372967450665 0.0073274593490133 0.996336270325493 32 0.00393715435266324 0.00787430870532648 0.996062845647337 33 0.00341747880527386 0.00683495761054772 0.996582521194726 34 0.00557186227622221 0.0111437245524444 0.994428137723778 35 0.0032907778746211 0.0065815557492422 0.996709222125379 36 0.00221155295216500 0.00442310590432999 0.997788447047835 37 0.00129341770249072 0.00258683540498145 0.99870658229751 38 0.000885139282786457 0.00177027856557291 0.999114860717214 39 0.0004876795588048 0.0009753591176096 0.999512320441195 40 0.000252949608898364 0.000505899217796727 0.999747050391102 41 0.000224335030708594 0.000448670061417187 0.999775664969291 42 0.000124839454191488 0.000249678908382976 0.999875160545808 43 9.64540403455025e-05 0.000192908080691005 0.999903545959654 44 7.0019452803131e-05 0.000140038905606262 0.999929980547197 45 0.00280126224510495 0.00560252449020991 0.997198737754895 46 0.0124203103164082 0.0248406206328163 0.987579689683592 47 0.0138332616561852 0.0276665233123704 0.986166738343815 48 0.069137518149662 0.138275036299324 0.930862481850338 49 0.292984230866881 0.585968461733761 0.707015769133119 50 0.923582187971904 0.152835624056191 0.0764178120280957 51 0.976694261158813 0.0466114776823745 0.0233057388411873 52 0.993057878557554 0.0138842428848925 0.00694212144244623 53 0.990176015642288 0.0196479687154248 0.00982398435771238 54 0.986351303442901 0.0272973931141981 0.0136486965570991 55 0.966338986772979 0.0673220264540422 0.0336610132270211 56 0.904447133939626 0.191105732120749 0.0955528660603744 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 14 0.269230769230769 NOK 5% type I error level 29 0.557692307692308 NOK 10% type I error level 35 0.673076923076923 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/10kwqn1260640075.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/10kwqn1260640075.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/1izax1260640075.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/1izax1260640075.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/2k54c1260640075.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/2k54c1260640075.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/3e63c1260640075.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/3e63c1260640075.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/4eow81260640075.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/4eow81260640075.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/527eg1260640075.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/527eg1260640075.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/6n5ar1260640075.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/6n5ar1260640075.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/7h8ph1260640075.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/7h8ph1260640075.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/8c4tn1260640075.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/8c4tn1260640075.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/9mdbu1260640075.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/12/t126064012954q6rsnpq15m9f3/9mdbu1260640075.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal 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

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