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: Fri, 20 Nov 2009 12:22:54 -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/t1258745027mhvinry1psiuokx.htm/, Retrieved Fri, 20 Nov 2009 20:23:58 +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/t1258745027mhvinry1psiuokx.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 «

95.1 8.9 96.9 98.6 111.7 109.8 97 8.8 95.1 96.9 98.6 111.7 112.7 8.3 97 95.1 96.9 98.6 102.9 7.5 112.7 97 95.1 96.9 97.4 7.2 102.9 112.7 97 95.1 111.4 7.4 97.4 102.9 112.7 97 87.4 8.8 111.4 97.4 102.9 112.7 96.8 9.3 87.4 111.4 97.4 102.9 114.1 9.3 96.8 87.4 111.4 97.4 110.3 8.7 114.1 96.8 87.4 111.4 103.9 8.2 110.3 114.1 96.8 87.4 101.6 8.3 103.9 110.3 114.1 96.8 94.6 8.5 101.6 103.9 110.3 114.1 95.9 8.6 94.6 101.6 103.9 110.3 104.7 8.5 95.9 94.6 101.6 103.9 102.8 8.2 104.7 95.9 94.6 101.6 98.1 8.1 102.8 104.7 95.9 94.6 113.9 7.9 98.1 102.8 104.7 95.9 80.9 8.6 113.9 98.1 102.8 104.7 95.7 8.7 80.9 113.9 98.1 102.8 113.2 8.7 95.7 80.9 113.9 98.1 105.9 8.5 113.2 95.7 80.9 113.9 108.8 8.4 105.9 113.2 95.7 80.9 102.3 8.5 108.8 105.9 113.2 95.7 99 8.7 102.3 108.8 105.9 113.2 100.7 8.7 99 102.3 108.8 105.9 115.5 8.6 100.7 99 102.3 108.8 100.7 8.5 115.5 100.7 99 102.3 109.9 8.3 100.7 115.5 100.7 99 114.6 8 109.9 100.7 115.5 100.7 85.4 8.2 114.6 109.9 100.7 115.5 100.5 8.1 85.4 114.6 109 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 8 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation X[t] = + 18.3307933952183 + 0.00373894172101956Y[t] -0.00994243414329733Y1[t] -0.0286088377747364Y2[t] -0.0351917944930868Y3[t] -0.0205447551967472Y4[t] + 0.246350043357412M1[t] -0.176443499281986M2[t] -0.7592473898286M3[t] -1.01309436201666M4[t] -0.960018480853686M5[t] -0.882068729586974M6[t] + 0.0324099427149764M7[t] -0.0479806478054456M8[t] -0.549765441448620M9[t] -1.09462221361560M10[t] -0.872758620940506M11[t] -0.0116552255662847t + 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) 18.3307933952183 4.676352 3.9199 0.000338 0.000169 Y 0.00373894172101956 0.021192 0.1764 0.860844 0.430422 Y1 -0.00994243414329733 0.022279 -0.4463 0.65781 0.328905 Y2 -0.0286088377747364 0.020082 -1.4246 0.16202 0.08101 Y3 -0.0351917944930868 0.022778 -1.545 0.130231 0.065116 Y4 -0.0205447551967472 0.021909 -0.9377 0.354017 0.177009 M1 0.246350043357412 0.561956 0.4384 0.663469 0.331735 M2 -0.176443499281986 0.621947 -0.2837 0.778107 0.389054 M3 -0.7592473898286 0.677966 -1.1199 0.26944 0.13472 M4 -1.01309436201666 0.553894 -1.829 0.074856 0.037428 M5 -0.960018480853686 0.433549 -2.2143 0.032568 0.016284 M6 -0.882068729586974 0.488682 -1.805 0.078607 0.039304 M7 0.0324099427149764 0.487602 0.0665 0.947336 0.473668 M8 -0.0479806478054456 0.641393 -0.0748 0.940741 0.470371 M9 -0.549765441448620 0.771893 -0.7122 0.480456 0.240228 M10 -1.09462221361560 0.933406 -1.1727 0.247845 0.123923 M11 -0.872758620940506 0.693778 -1.258 0.215694 0.107847 t -0.0116552255662847 0.01016 -1.1472 0.25811 0.129055 Multiple Linear Regression - Regression Statistics Multiple R 0.83781925068927 R-squared 0.701941096825531 Adjusted R-squared 0.575266062976381 F-TEST (value) 5.54127419978774 F-TEST (DF numerator) 17 F-TEST (DF denominator) 40 p-value 4.24438353996415e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.471101869906799 Sum Squared Residuals 8.87747887318731 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 8.9 8.9500707321233 -0.0500707321232941 2 8.8 9.01123483184811 -0.211234831848113 3 8.3 8.83704472759312 -0.537044727593118 4 7.5 8.42271920707304 -0.92271920707304 5 7.2 8.06196893456235 -0.861968934562356 6 7.4 7.92411243392233 -0.524112433922327 7 8.8 8.77768273855173 0.0223172614482707 8 9.3 8.95377113587553 0.346228864124469 9 9.3 8.71847906476528 0.581520935234717 10 8.7 8.2638083978102 0.436191602189792 11 8.2 8.155207150633 0.0447928493670056 12 8.3 8.37811739853015 -0.0781173985301458 13 8.5 8.57090833873204 -0.0709083387320373 14 8.6 8.58001511515205 0.01998488484795 15 8.5 8.41822294681427 0.0817770531857263 16 8.2 8.31452734862594 -0.114527348625942 17 8.1 8.20357178412464 -0.103571784124643 18 7.9 8.09363184796774 -0.193631847967741 19 8.6 8.73651185979242 -0.136511859792415 20 8.7 8.7803195400561 -0.0803195400561012 21 8.7 8.66978461864393 0.0302153813560743 22 8.5 8.32529703593668 0.174702964063322 23 8.4 8.2754118052196 0.124588194780404 24 8.5 8.37230475560633 0.127695244393673 25 8.7 8.47368814195925 0.226311858040754 26 8.7 8.31228356179427 0.387716438205733 27 8.6 8.01983468389998 0.580165316100019 28 8.5 7.75288692974273 0.747113070257266 29 8.3 7.56041471693852 0.739585283061476 30 8 7.42045803134335 0.579541968656647 31 8.2 8.12094981341 0.0790501865899989 32 8.1 8.22151742432917 -0.121517424329170 33 8.1 8.09237839726122 0.00762160273877814 34 8 7.89909239157944 0.100907608420555 35 7.9 7.73834280516965 0.161657194830348 36 7.9 7.73238100976156 0.167618990238445 37 8 7.83977989263602 0.160220107363980 38 8 7.7665448367377 0.233455163262297 39 7.9 7.57263746534287 0.327362534657132 40 8 7.22862468691157 0.771375313088429 41 7.7 6.96451479503463 0.735485204965369 42 7.2 6.93115551379277 0.268844486207234 43 7.5 7.74111518431416 -0.241115184314158 44 7.3 7.86068793990322 -0.560687939903218 45 7 7.62483455741265 -0.624834557412654 46 7 7.39502623528864 -0.395026235288645 47 7 7.33103823897776 -0.331038238977757 48 7.2 7.41719683610197 -0.217196836101970 49 7.3 7.5655528945494 -0.265552894549402 50 7.1 7.52992165446787 -0.429921654467867 51 6.8 7.25226017634976 -0.452260176349760 52 6.4 6.88124182764671 -0.481241827646712 53 6.1 6.60952976933985 -0.509529769339847 54 6.5 6.63064217297381 -0.130642172973813 55 7.7 7.4237404039317 0.276259596068304 56 7.9 7.48370395983598 0.41629604016402 57 7.5 7.49452336191692 0.00547663808308477 58 6.9 7.21677593938503 -0.316775939385026 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.79271118526718 0.414577629465639 0.207288814732819 22 0.743217970052408 0.513564059895184 0.256782029947592 23 0.638302609545252 0.723394780909496 0.361697390454748 24 0.520061256725813 0.959877486548373 0.479938743274187 25 0.392804188405288 0.785608376810576 0.607195811594712 26 0.283880023102818 0.567760046205637 0.716119976897182 27 0.186291962436433 0.372583924872867 0.813708037563567 28 0.233132772889901 0.466265545779802 0.766867227110099 29 0.168280118171714 0.336560236343429 0.831719881828286 30 0.102982680797510 0.205965361595021 0.89701731920249 31 0.10055854740214 0.20111709480428 0.89944145259786 32 0.274539984765206 0.549079969530411 0.725460015234795 33 0.438212942165467 0.876425884330935 0.561787057834533 34 0.371821268925547 0.743642537851095 0.628178731074453 35 0.268352323723245 0.536704647446489 0.731647676276755 36 0.166577128761237 0.333154257522474 0.833422871238763 37 0.0967359004152693 0.193471800830539 0.90326409958473 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 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/10i2e51258744965.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/10i2e51258744965.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/13y421258744965.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/13y421258744965.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/2v7o01258744965.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/2v7o01258744965.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/3032d1258744965.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/3032d1258744965.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/4mis01258744965.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/4mis01258744965.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/58wa01258744965.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/58wa01258744965.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/60ms01258744965.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/60ms01258744965.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/73x7j1258744965.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/73x7j1258744965.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/8d8vu1258744965.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/8d8vu1258744965.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/9gy891258744965.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258745027mhvinry1psiuokx/9gy891258744965.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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