Multiple regression - Model 4

*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: Wed, 18 Nov 2009 09:09:53 -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/18/t1258560696rfqu0a6wkspj5st.htm/, Retrieved Wed, 18 Nov 2009 17:11:48 +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/18/t1258560696rfqu0a6wkspj5st.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 «

3.27 96.92 3.36 3.45 3.52 3.58 3.21 99.06 3.27 3.36 3.45 3.52 3.19 99.65 3.21 3.27 3.36 3.45 3.16 99.82 3.19 3.21 3.27 3.36 3.12 99.99 3.16 3.19 3.21 3.27 3.06 100.33 3.12 3.16 3.19 3.21 3.01 99.31 3.06 3.12 3.16 3.19 2.98 101.1 3.01 3.06 3.12 3.16 2.97 101.1 2.98 3.01 3.06 3.12 3.02 100.93 2.97 2.98 3.01 3.06 3.07 100.85 3.02 2.97 2.98 3.01 3.18 100.93 3.07 3.02 2.97 2.98 3.29 99.6 3.18 3.07 3.02 2.97 3.43 101.88 3.29 3.18 3.07 3.02 3.61 101.81 3.43 3.29 3.18 3.07 3.74 102.38 3.61 3.43 3.29 3.18 3.87 102.74 3.74 3.61 3.43 3.29 3.88 102.82 3.87 3.74 3.61 3.43 4.09 101.72 3.88 3.87 3.74 3.61 4.19 103.47 4.09 3.88 3.87 3.74 4.2 102.98 4.19 4.09 3.88 3.87 4.29 102.68 4.2 4.19 4.09 3.88 4.37 102.9 4.29 4.2 4.19 4.09 4.47 103.03 4.37 4.29 4.2 4.19 4.61 101.29 4.47 4.37 4.29 4.2 4.65 103.69 4.61 4.47 4.37 4.29 4.69 103.68 4.65 4.61 4.47 4.37 4.82 104.2 4.69 4.65 4.61 4.47 4.86 104.08 4.82 4.69 4.65 4.61 4.87 104.16 4.86 4.82 4.69 4.65 5.01 103.05 4.87 4.86 4.82 4.69 5.03 104.66 5.01 4 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -0.486327079223412 + 0.00611586251224402X[t] + 2.07287164902775y1[t] -1.64300868259871y2[t] + 0.856985292888784y3[t] -0.325886914504195y4[t] + 0.0311898048205206M1[t] + 0.0598635284101705M2[t] + 0.0192550274169363M3[t] + 0.0565525488623294M4[t] + 0.0670503500066354M5[t] -0.0200445648885039M6[t] + 0.127213321639962M7[t] -0.0494587964326408M8[t] + 0.0760095825978439M9[t] + 0.0356136822759580M10[t] -0.0734623990165165M11[t] -0.00057286681626014t + 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) -0.486327079223412 1.706052 -0.2851 0.777146 0.388573 X 0.00611586251224402 0.017734 0.3449 0.7321 0.36605 y1 2.07287164902775 0.156084 13.2805 0 0 y2 -1.64300868259871 0.347094 -4.7336 3e-05 1.5e-05 y3 0.856985292888784 0.353157 2.4266 0.020092 0.010046 y4 -0.325886914504195 0.172331 -1.8911 0.066259 0.033129 M1 0.0311898048205206 0.0807 0.3865 0.701288 0.350644 M2 0.0598635284101705 0.076126 0.7864 0.436524 0.218262 M3 0.0192550274169363 0.076868 0.2505 0.803555 0.401777 M4 0.0565525488623294 0.074954 0.7545 0.455201 0.227601 M5 0.0670503500066354 0.075699 0.8858 0.381321 0.19066 M6 -0.0200445648885039 0.077199 -0.2596 0.796536 0.398268 M7 0.127213321639962 0.074389 1.7101 0.095399 0.047699 M8 -0.0494587964326408 0.081015 -0.6105 0.545174 0.272587 M9 0.0760095825978439 0.077045 0.9866 0.330096 0.165048 M10 0.0356136822759580 0.079233 0.4495 0.655637 0.327818 M11 -0.0734623990165165 0.078248 -0.9388 0.353744 0.176872 t -0.00057286681626014 0.003685 -0.1555 0.87727 0.438635 Multiple Linear Regression - Regression Statistics Multiple R 0.995688523872363 R-squared 0.991395636571125 Adjusted R-squared 0.987546316089786 F-TEST (value) 257.550817443581 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.106935210078802 Sum Squared Residuals 0.434535287874708 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3.27 3.28342111627871 -0.0134211162787115 2 3.21 3.24548649621774 -0.0354864962177405 3 3.19 3.17709537743799 0.0129046225620065 4 3.16 3.22418396261496 -0.0641839626149633 5 3.12 3.18373332248328 -0.0637333224832834 6 3.06 3.06693383755537 -0.0069338375553739 7 3.01 3.12953690537079 -0.119536905370795 8 2.98 2.93367344860296 0.046326551397042 9 2.97 3.04014960448313 -0.0701496044831267 10 3.02 3.00340663493140 0.0165933650686040 11 3.07 3.0039268740376 0.0660731259623981 12 3.18 3.10000557806653 0.079994421933472 13 3.29 3.3144619999821 -0.0244619999821024 14 3.43 3.43034686850983 -0.000346868509831852 15 3.61 3.57618250259506 0.033817497404935 16 3.74 3.81790970173966 -0.0779097017396562 17 3.87 3.88789847848692 -0.0178984784869194 18 3.88 3.96523533610167 -0.0852353361016675 19 4.09 3.96507893826764 0.124921061732363 20 4.19 4.28645246143504 -0.0964524614350379 21 4.2 4.23681109661865 -0.0368110966186523 22 4.29 4.22714346131884 0.0628565386811591 23 4.37 4.30623064179231 0.0637693582076916 24 4.47 4.37385534808596 0.0961446519140386 25 4.61 4.54354696282874 0.0664530371712579 26 4.65 4.75145605336126 -0.101456053361259 27 4.69 4.62273475345247 0.0672652465475257 28 4.82 4.76722342479915 0.052776575200853 29 4.86 4.96882266638035 -0.108822666380346 30 4.87 4.77221182602859 0.0977881739714121 31 5.01 4.96548921903391 0.0445107809660947 32 5.03 5.06377682965766 -0.0337768296576594 33 5.13 4.99441976313489 0.135580236865108 34 5.18 5.24759383173793 -0.0675938317379329 35 5.21 5.05430741190892 0.155692588091077 36 5.26 5.18873747820352 0.0712625217964756 37 5.25 5.27558957973346 -0.0255895797334554 38 5.2 5.22600542422968 -0.0260054242296767 39 5.16 5.13551474938877 0.0244852506112321 40 5.19 5.14795626328532 0.0420437367146778 41 5.39 5.25194620965026 0.138053790349738 42 5.58 5.51622548678541 0.063774513214586 43 5.76 5.7606612388975 -0.000661238897494783 44 5.89 5.81301519417158 0.076984805828418 45 5.98 6.00861953576333 -0.0286195357633292 46 6.02 6.03185607201183 -0.0118560720118302 47 5.62 5.90553507226117 -0.285535072261167 48 4.87 5.11740159564399 -0.247401595643986 49 4.24 4.24298034117699 -0.0029803411769885 50 4.02 3.85670515768149 0.163294842318508 51 3.74 3.8784726171257 -0.138472617125699 52 3.45 3.40272664756091 0.0472733524390887 53 3.34 3.28759932299919 0.0524006770008114 54 3.21 3.27939351352896 -0.0693935135289567 55 3.12 3.16923369843017 -0.0492336984301682 56 3.04 3.03308206613276 0.00691793386723723 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.120150597810117 0.240301195620234 0.879849402189883 22 0.123633348076580 0.247266696153161 0.87636665192342 23 0.0947346416182835 0.189469283236567 0.905265358381717 24 0.0581129194746928 0.116225838949386 0.941887080525307 25 0.0319915498326611 0.0639830996653221 0.968008450167339 26 0.111270841917143 0.222541683834285 0.888729158082857 27 0.105971435500202 0.211942871000404 0.894028564499798 28 0.0674255050598925 0.134851010119785 0.932574494940107 29 0.0752398489489327 0.150479697897865 0.924760151051067 30 0.0656168457153556 0.131233691430711 0.934383154284644 31 0.0395009757110614 0.0790019514221228 0.960499024288939 32 0.0212771123613291 0.0425542247226582 0.97872288763867 33 0.0269337973425585 0.0538675946851171 0.973066202657441 34 0.204749690022567 0.409499380045134 0.795250309977433 35 0.343199701523980 0.686399403047961 0.65680029847602 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 1 0.0666666666666667 NOK 10% type I error level 4 0.266666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/10ciyi1258560588.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/10ciyi1258560588.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/1vxv31258560588.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/1vxv31258560588.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/2pmm61258560588.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/2pmm61258560588.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/30o4w1258560588.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/30o4w1258560588.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/4kcj11258560588.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/4kcj11258560588.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/5ivjs1258560588.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/5ivjs1258560588.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/6c8nd1258560588.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/6c8nd1258560588.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/74rij1258560588.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/74rij1258560588.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/8ae3o1258560588.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/8ae3o1258560588.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/90c3g1258560588.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258560696rfqu0a6wkspj5st/90c3g1258560588.ps (open in new window)

Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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