Workshop 7: Multiple Regression Analysis

*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 05:20:08 -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/t1258719703cbzbpi2qb0gxtfb.htm/, Retrieved Fri, 20 Nov 2009 13:21:55 +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/t1258719703cbzbpi2qb0gxtfb.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:

ETSHWW7(4)

Dataseries X:

» Textbox « » Textfile « » CSV «

1.43 0.51 1.43 0.51 1.43 0.51 1.43 0.51 1.43 0.52 1.43 0.52 1.44 0.52 1.48 0.53 1.48 0.53 1.48 0.52 1.48 0.52 1.48 0.52 1.48 0.52 1.48 0.52 1.48 0.52 1.48 0.52 1.48 0.52 1.48 0.52 1.48 0.52 1.48 0.53 1.48 0.53 1.48 0.53 1.48 0.54 1.48 0.54 1.48 0.54 1.48 0.54 1.48 0.54 1.48 0.54 1.48 0.54 1.48 0.54 1.48 0.54 1.48 0.54 1.48 0.53 1.48 0.53 1.48 0.53 1.48 0.53 1.48 0.53 1.57 0.54 1.58 0.55 1.58 0.55 1.58 0.55 1.58 0.55 1.59 0.55 1.6 0.55 1.6 0.55 1.61 0.55 1.61 0.56 1.61 0.56 1.62 0.56 1.63 0.56 1.63 0.56 1.64 0.55 1.64 0.56 1.64 0.55 1.64 0.55 1.64 0.56 1.65 0.55 1.65 0.55 1.65 0.55 1.65 0.55

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 Broodprijs[t] = + 0.865076923076913 + 1.03846153846156Bakmeelprijs[t] + 0.00118803418803283M1[t] + 0.0159401709401710M2[t] + 0.0126923076923077M3[t] + 0.0135982905982907M4[t] + 0.00627350427350428M5[t] + 0.00517948717948723M6[t] + 0.00600854700854706M7[t] + 0.00660683760683758M8[t] + 0.00958974358974363M9[t] + 0.0104957264957266M10[t] + 0.00317094017094018M11[t] + 0.00317094017094017t + 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) 0.865076923076913 0.345279 2.5054 0.015832 0.007916 Bakmeelprijs 1.03846153846156 0.67425 1.5402 0.130369 0.065185 M1 0.00118803418803283 0.020227 0.0587 0.953418 0.476709 M2 0.0159401709401710 0.020233 0.7878 0.43483 0.217415 M3 0.0126923076923077 0.020274 0.626 0.53438 0.26719 M4 0.0135982905982907 0.020141 0.6752 0.502952 0.251476 M5 0.00627350427350428 0.020259 0.3097 0.758215 0.379107 M6 0.00517948717948723 0.020105 0.2576 0.797852 0.398926 M7 0.00600854700854706 0.020083 0.2992 0.766147 0.383074 M8 0.00660683760683758 0.02037 0.3243 0.747152 0.373576 M9 0.00958974358974363 0.020062 0.478 0.634902 0.317451 M10 0.0104957264957266 0.020119 0.5217 0.60439 0.302195 M11 0.00317094017094018 0.020055 0.1581 0.875063 0.437532 t 0.00317094017094017 0.000586 5.4138 2e-06 1e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.927993068953007 R-squared 0.861171136024821 Adjusted R-squared 0.821936891857923 F-TEST (value) 21.9494768998605 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 1.88737914186277e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.031696899257175 Sum Squared Residuals 0.046215897435897 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.43 1.39905128205129 0.0309487179487123 2 1.43 1.41697435897436 0.0130256410256412 3 1.43 1.41689743589744 0.0131025641025644 4 1.43 1.42097435897436 0.00902564102564134 5 1.43 1.42720512820513 0.00279487179487202 6 1.43 1.42928205128205 0.000717948717948887 7 1.44 1.43328205128205 0.00671794871794893 8 1.48 1.44743589743590 0.0325641025641027 9 1.48 1.45358974358974 0.0264102564102565 10 1.48 1.44728205128205 0.0327179487179489 11 1.48 1.44312820512820 0.0368717948717951 12 1.48 1.44312820512820 0.0368717948717951 13 1.48 1.44748717948718 0.0325128205128222 14 1.48 1.46541025641026 0.0145897435897438 15 1.48 1.46533333333333 0.0146666666666669 16 1.48 1.46941025641026 0.0105897435897438 17 1.48 1.46525641025641 0.01474358974359 18 1.48 1.46733333333333 0.0126666666666669 19 1.48 1.47133333333333 0.0086666666666669 20 1.48 1.48548717948718 -0.00548717948717933 21 1.48 1.49164102564103 -0.0116410256410255 22 1.48 1.49571794871795 -0.0157179487179486 23 1.48 1.50194871794872 -0.0219487179487180 24 1.48 1.50194871794872 -0.0219487179487180 25 1.48 1.50630769230769 -0.0263076923076910 26 1.48 1.52423076923077 -0.0442307692307693 27 1.48 1.52415384615385 -0.0441538461538462 28 1.48 1.52823076923077 -0.0482307692307693 29 1.48 1.52407692307692 -0.0440769230769231 30 1.48 1.52615384615385 -0.0461538461538462 31 1.48 1.53015384615385 -0.0501538461538462 32 1.48 1.53392307692308 -0.0539230769230769 33 1.48 1.52969230769231 -0.0496923076923076 34 1.48 1.53376923076923 -0.0537692307692307 35 1.48 1.52961538461538 -0.0496153846153845 36 1.48 1.52961538461538 -0.0496153846153845 37 1.48 1.53397435897436 -0.0539743589743575 38 1.57 1.56228205128205 0.0077179487179488 39 1.58 1.57258974358974 0.0074102564102563 40 1.58 1.57666666666667 0.00333333333333320 41 1.58 1.57251282051282 0.0074871794871794 42 1.58 1.57458974358974 0.00541025641025631 43 1.59 1.57858974358974 0.0114102564102563 44 1.6 1.58235897435897 0.0176410256410256 45 1.6 1.58851282051282 0.0114871794871794 46 1.61 1.59258974358974 0.0174102564102563 47 1.61 1.59882051282051 0.011179487179487 48 1.61 1.59882051282051 0.011179487179487 49 1.62 1.60317948717949 0.016820512820514 50 1.63 1.62110256410256 0.00889743589743551 51 1.63 1.62102564102564 0.00897435897435856 52 1.64 1.61471794871795 0.025282051282051 53 1.64 1.62094871794872 0.0190512820512817 54 1.64 1.61264102564103 0.0273589743589741 55 1.64 1.61664102564103 0.0233589743589741 56 1.64 1.63079487179487 0.0092051282051279 57 1.65 1.62656410256410 0.0234358974358972 58 1.65 1.63064102564103 0.0193589743589741 59 1.65 1.62648717948718 0.0235128205128203 60 1.65 1.62648717948718 0.0235128205128203 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 4.14511201567853e-43 8.29022403135707e-43 1 18 2.56484413468655e-53 5.12968826937309e-53 1 19 0.000110056797172912 0.000220113594345823 0.999889943202827 20 0.147155832153032 0.294311664306065 0.852844167846968 21 0.287686363842397 0.575372727684794 0.712313636157603 22 0.626980500200978 0.746038999598045 0.373019499799022 23 0.740719765604403 0.518560468791195 0.259280234395597 24 0.775755332204643 0.448489335590713 0.224244667795357 25 0.783082021935597 0.433835956128807 0.216917978064404 26 0.70155946451634 0.596881070967319 0.298440535483659 27 0.606055347430421 0.787889305139158 0.393944652569579 28 0.536086635275976 0.927826729448048 0.463913364724024 29 0.443624271580076 0.887248543160152 0.556375728419924 30 0.390931135803982 0.781862271607964 0.609068864196018 31 0.398643734175648 0.797287468351295 0.601356265824353 32 0.556034085479068 0.887931829041865 0.443965914520932 33 0.630128449468049 0.739743101063902 0.369871550531951 34 0.69774075369005 0.6045184926199 0.302259246309950 35 0.698099469167313 0.603801061665374 0.301900530832687 36 0.76135282523991 0.477294349520179 0.238647174760090 37 0.998480581227661 0.00303883754467755 0.00151941877233878 38 0.999337300528689 0.00132539894262231 0.000662699471311155 39 0.999196106023488 0.00160778795302498 0.000803893976512492 40 0.999135261763755 0.00172947647249082 0.00086473823624541 41 0.998394765457259 0.00321046908548266 0.00160523454274133 42 0.99913693920046 0.00172612159907782 0.000863060799538912 43 0.997334744313351 0.0053305113732976 0.0026652556866488 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 10 0.370370370370370 NOK 5% type I error level 10 0.370370370370370 NOK 10% type I error level 10 0.370370370370370 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/10j45m1258719603.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/10j45m1258719603.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/1vy0u1258719603.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/1vy0u1258719603.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/2g84m1258719603.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/2g84m1258719603.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/3vvff1258719603.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/3vvff1258719603.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/4uncy1258719603.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/4uncy1258719603.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/5t1dc1258719603.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/5t1dc1258719603.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/6rls51258719603.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/6rls51258719603.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/7f8ri1258719603.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/7f8ri1258719603.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/88u491258719603.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/88u491258719603.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/9u6zw1258719603.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258719703cbzbpi2qb0gxtfb/9u6zw1258719603.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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