WS7 Multiple Regression2

*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 10:05:56 -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/t1258736842cbsebbh594ueir0.htm/, Retrieved Fri, 20 Nov 2009 18:07:33 +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/t1258736842cbsebbh594ueir0.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 «

2.05 1.00 2.11 1.00 2.09 1.00 2.05 1.00 2.08 1.00 2.06 1.00 2.06 1.00 2.08 1.00 2.07 1.00 2.06 1.00 2.07 1.00 2.06 1.00 2.09 1.00 2.07 1.00 2.09 1.00 2.28 1.25 2.33 1.25 2.35 1.25 2.52 1.50 2.63 1.50 2.58 1.50 2.70 1.75 2.81 1.75 2.97 2.00 3.04 2.00 3.28 2.25 3.33 2.25 3.50 2.50 3.56 2.50 3.57 2.50 3.69 2.75 3.82 2.75 3.79 2.75 3.96 3.00 4.06 3.00 4.05 3.00 4.03 3.00 3.94 3.00 4.02 3.00 3.88 3.00 4.02 3.00 4.03 3.00 4.09 3.00 3.99 3.00 4.01 3.00 4.01 3.00 4.19 3.25 4.30 3.25 4.27 3.25 3.82 3.25 3.15 2.75 2.49 2.00 1.81 1.00 1.26 1.00 1.06 0.50 0.84 0.25 0.78 0.25 0.70 0.25 0.36 0.25 0.35 0.25

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 0.697386631716908 + 1.07821756225426X[t] + 0.188267365661863M1[t] + 0.0823564875491473M2[t] + 0.0821782437745737M3[t] + 0.040089121887287M4[t] + 0.175732634338139M5[t] + 0.069732634338139M6[t] + 0.099732634338139M7[t] + 0.141643512450852M8[t] + 0.115643512450852M9[t] + 0.047821756225426M10[t] + 0.00591087811271298M11[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) 0.697386631716908 0.134558 5.1828 5e-06 2e-06 X 1.07821756225426 0.034679 31.0911 0 0 M1 0.188267365661863 0.165999 1.1341 0.262485 0.131242 M2 0.0823564875491473 0.166063 0.4959 0.62225 0.311125 M3 0.0821782437745737 0.165954 0.4952 0.622775 0.311388 M4 0.040089121887287 0.165927 0.2416 0.810135 0.405068 M5 0.175732634338139 0.165999 1.0586 0.295176 0.147588 M6 0.069732634338139 0.165999 0.4201 0.676343 0.338171 M7 0.099732634338139 0.165999 0.6008 0.550858 0.275429 M8 0.141643512450852 0.166063 0.853 0.398011 0.199006 M9 0.115643512450852 0.166063 0.6964 0.489617 0.244809 M10 0.047821756225426 0.165954 0.2882 0.774489 0.387245 M11 0.00591087811271298 0.165927 0.0356 0.971733 0.485867 Multiple Linear Regression - Regression Statistics Multiple R 0.97698956501841 R-squared 0.954508610154864 Adjusted R-squared 0.94289378721568 F-TEST (value) 82.1802118855151 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.262338833659341 Sum Squared Residuals 3.23461819134993 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2.05 1.96387155963302 0.0861284403669798 2 2.11 1.85796068152032 0.252039318479683 3 2.09 1.85778243774574 0.232217562254259 4 2.05 1.81569331585845 0.234306684141546 5 2.08 1.95133682830931 0.128663171690695 6 2.06 1.84533682830931 0.214663171690695 7 2.06 1.87533682830931 0.184663171690695 8 2.08 1.91724770642202 0.162752293577982 9 2.07 1.89124770642202 0.178752293577981 10 2.06 1.82342595019659 0.236574049803407 11 2.07 1.78151507208388 0.288484927916120 12 2.06 1.77560419397117 0.284395806028833 13 2.09 1.96387155963303 0.126128440366970 14 2.07 1.85796068152031 0.212039318479686 15 2.09 1.85778243774574 0.232217562254259 16 2.28 2.08524770642202 0.194752293577981 17 2.33 2.22089121887287 0.109108781127130 18 2.35 2.11489121887287 0.23510878112713 19 2.52 2.41444560943644 0.105554390563565 20 2.63 2.45635648754915 0.173643512450852 21 2.58 2.43035648754915 0.149643512450852 22 2.7 2.63208912188729 0.0679108781127131 23 2.81 2.59017824377457 0.219821756225426 24 2.97 2.85382175622543 0.116178243774574 25 3.04 3.04208912188729 -0.00208912188728887 26 3.28 3.20573263433814 0.0742673656618617 27 3.33 3.20555439056356 0.124445609436436 28 3.5 3.43301965923984 0.0669803407601573 29 3.56 3.56866317169069 -0.00866317169069452 30 3.57 3.46266317169069 0.107336828309305 31 3.69 3.76221756225426 -0.0722175622542595 32 3.82 3.80412844036697 0.0158715596330275 33 3.79 3.77812844036697 0.0118715596330278 34 3.96 3.97986107470511 -0.0198610747051113 35 4.06 3.9379501965924 0.122049803407601 36 4.05 3.93203931847969 0.117960681520315 37 4.03 4.12030668414155 -0.090306684141548 38 3.94 4.01439580602883 -0.0743958060288326 39 4.02 4.01421756225426 0.00578243774574043 40 3.88 3.97212844036697 -0.0921284403669724 41 4.02 4.10777195281782 -0.0877719528178247 42 4.03 4.00177195281782 0.028228047182176 43 4.09 4.03177195281782 0.0582280471821756 44 3.99 4.07368283093054 -0.0836828309305369 45 4.01 4.04768283093054 -0.0376828309305373 46 4.01 3.97986107470511 0.0301389252948886 47 4.19 4.20750458715596 -0.0175045871559628 48 4.3 4.20159370904325 0.0984062909567495 49 4.27 4.38986107470511 -0.119861074705114 50 3.82 4.2839501965924 -0.463950196592398 51 3.15 3.74466317169069 -0.594663171690695 52 2.49 2.89391087811271 -0.403910878112713 53 1.81 1.95133682830931 -0.141336828309305 54 1.26 1.84533682830931 -0.585336828309306 55 1.06 1.33622804718218 -0.276228047182176 56 0.84 1.10858453473132 -0.268584534731324 57 0.78 1.08258453473132 -0.302584534731324 58 0.7 1.01476277850590 -0.314762778505898 59 0.36 0.972851900393185 -0.612851900393185 60 0.35 0.966941022280472 -0.616941022280472 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.00130293052395701 0.00260586104791403 0.998697069476043 17 0.000122735999409401 0.000245471998818801 0.99987726400059 18 3.20495567024431e-05 6.40991134048863e-05 0.999967950443298 19 5.80184528839881e-06 1.16036905767976e-05 0.999994198154712 20 2.36944212831141e-06 4.73888425662281e-06 0.999997630557872 21 3.0694626968905e-07 6.138925393781e-07 0.99999969305373 22 7.4878157055439e-07 1.49756314110878e-06 0.99999925121843 23 2.05774205941695e-07 4.11548411883389e-07 0.999999794225794 24 6.08229157996809e-08 1.21645831599362e-07 0.999999939177084 25 1.23874614883201e-08 2.47749229766401e-08 0.999999987612538 26 4.73477909853461e-09 9.46955819706923e-09 0.99999999526522 27 7.00362497182087e-09 1.40072499436417e-08 0.999999992996375 28 2.41042358537337e-09 4.82084717074674e-09 0.999999997589576 29 4.8166292083947e-10 9.6332584167894e-10 0.999999999518337 30 2.65902668303813e-10 5.31805336607625e-10 0.999999999734097 31 1.49883220209994e-10 2.99766440419987e-10 0.999999999850117 32 2.44924477020674e-11 4.89848954041348e-11 0.999999999975508 33 3.7148352069936e-12 7.4296704139872e-12 0.999999999996285 34 5.81288040798854e-13 1.16257608159771e-12 0.999999999999419 35 3.71915688163386e-13 7.43831376326773e-13 0.999999999999628 36 7.03787142243884e-13 1.40757428448777e-12 0.999999999999296 37 1.03731517877432e-13 2.07463035754864e-13 0.999999999999896 38 4.42167437494018e-12 8.84334874988037e-12 0.999999999995578 39 1.08885410194382e-09 2.17770820388763e-09 0.999999998911146 40 8.2585411691057e-09 1.65170823382114e-08 0.999999991741459 41 1.95898810897004e-08 3.91797621794007e-08 0.99999998041012 42 3.84583717314523e-07 7.69167434629045e-07 0.999999615416283 43 1.54454215402667e-06 3.08908430805333e-06 0.999998455457846 44 3.91727109508774e-05 7.83454219017548e-05 0.99996082728905 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 29 1 NOK 5% type I error level 29 1 NOK 10% type I error level 29 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/10exnv1258736750.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/10exnv1258736750.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/1k8mz1258736750.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/1k8mz1258736750.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/2egh01258736750.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/2egh01258736750.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/3yb2n1258736750.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/3yb2n1258736750.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/4ay061258736750.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/4ay061258736750.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/5omrt1258736750.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/5omrt1258736750.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/6pw5c1258736750.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/6pw5c1258736750.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/79cpt1258736750.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/79cpt1258736750.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/8y6jl1258736750.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/8y6jl1258736750.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/9crku1258736750.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258736842cbsebbh594ueir0/9crku1258736750.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') }

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