W7

*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 14:32:41 -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/t1258580070r581tvdbx7dp0to.htm/, Retrieved Wed, 18 Nov 2009 22:34:43 +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/t1258580070r581tvdbx7dp0to.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:

W7

Dataseries X:

» Textbox « » Textfile « » CSV «

16224.2 14931.4 17318.8 16913 17665.9 13132.1 15469.6 13333.7 16224.2 17318.8 16913 17665.9 16557.5 14711.2 15469.6 16224.2 17318.8 16913 19414.8 17197.3 16557.5 15469.6 16224.2 17318.8 17335 14985.2 19414.8 16557.5 15469.6 16224.2 16525.2 14734.4 17335 19414.8 16557.5 15469.6 18160.4 15937.8 16525.2 17335 19414.8 16557.5 15553.8 13028.1 18160.4 16525.2 17335 19414.8 15262.2 13836.8 15553.8 18160.4 16525.2 17335 18581 16677.5 15262.2 15553.8 18160.4 16525.2 17564.1 15130 18581 15262.2 15553.8 18160.4 18948.6 17504 17564.1 18581 15262.2 15553.8 17187.8 16979.9 18948.6 17564.1 18581 15262.2 17564.8 16012.5 17187.8 18948.6 17564.1 18581 17668.4 16247.7 17564.8 17187.8 18948.6 17564.1 20811.7 19268.2 17668.4 17564.8 17187.8 18948.6 17257.8 15533 20811.7 17668.4 17564.8 17187.8 18984.2 16803.3 17257.8 20811.7 17668.4 17564.8 20532.6 17396.1 18984.2 17257.8 20811.7 17668.4 17082.3 15155.4 20532.6 18984.2 17257.8 20811.7 16894.9 15692.4 17082.3 20532.6 18984.2 17257.8 20274.9 18063.7 16894.9 170 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 U[t] = + 245.686435485724 + 0.955192505216772I[t] + 0.0222593304802574m1[t] -0.040221227634457m2[t] + 0.146917738867787m3[t] + 0.0103105939618765m4[t] -1215.36363530938M1[t] + 92.2307226034118M2[t] -169.102266848386M3[t] + 356.20737689281M4[t] + 218.124658416461M5[t] + 385.431534181864M6[t] + 413.280276552429M7[t] + 327.775499528374M8[t] -366.035010095673M9[t] + 172.48562872356M10[t] + 503.086478783288M11[t] -6.29157578223689t + 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) 245.686435485724 658.163983 0.3733 0.711007 0.355503 I 0.955192505216772 0.05498 17.3735 0 0 m1 0.0222593304802574 0.057913 0.3844 0.702853 0.351427 m2 -0.040221227634457 0.055617 -0.7232 0.473997 0.236998 m3 0.146917738867787 0.055363 2.6537 0.011559 0.00578 m4 0.0103105939618765 0.059472 0.1734 0.863282 0.431641 M1 -1215.36363530938 356.369728 -3.4104 0.001551 0.000775 M2 92.2307226034118 396.81429 0.2324 0.817453 0.408726 M3 -169.102266848386 388.479472 -0.4353 0.665812 0.332906 M4 356.20737689281 329.980814 1.0795 0.287178 0.143589 M5 218.124658416461 290.38887 0.7511 0.457191 0.228596 M6 385.431534181864 281.945315 1.367 0.179645 0.089822 M7 413.280276552429 347.241813 1.1902 0.241358 0.120679 M8 327.775499528374 312.021397 1.0505 0.300128 0.150064 M9 -366.035010095673 327.985785 -1.116 0.271426 0.135713 M10 172.48562872356 421.821791 0.4089 0.684904 0.342452 M11 503.086478783288 376.661278 1.3356 0.18961 0.094805 t -6.29157578223689 4.060336 -1.5495 0.129546 0.064773 Multiple Linear Regression - Regression Statistics Multiple R 0.99064315244172 R-squared 0.98137385547967 Adjusted R-squared 0.97304110661531 F-TEST (value) 117.773122825926 F-TEST (DF numerator) 17 F-TEST (DF denominator) 38 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 355.133026604445 Sum Squared Residuals 4792539.73023888 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 16224.2 15722.4697005589 501.730299441139 2 15469.6 15393.1063850977 76.4936149023166 3 16557.5 16520.3451330267 37.1548669733197 4 19414.8 19312.0020342725 102.797965727460 5 17335 16952.3412087613 382.658791238656 6 16525.2 16864.626593094 -339.426593094002 7 18160.4 18532.2938743095 -371.893874309476 8 15553.8 15454.0444432437 99.7555567563075 9 15262.2 15262.1976562915 0.00234370853682009 10 18581 18838.0822676880 -257.082267687954 11 17564.1 17403.7380212323 160.361978767734 12 18948.6 18936.1484437379 12.4515562621255 13 17187.8 17770.1798726487 -582.379872648667 14 17564.8 17937.367057049 -372.567057049 15 17668.4 18166.5398407144 -498.139840714376 16 20811.7 21313.4417972420 -501.741797241979 17 17257.8 17704.2663855185 -446.466385518454 18 18984.2 18892.2356771316 91.9643228683616 19 20532.6 20124.2763959612 408.323604038752 20 17082.3 17367.4869544814 -285.186954481378 21 16894.9 17258.2392920515 -363.339292051508 22 20274.9 20435.4078821629 -160.507882162867 23 20078.6 19878.6299914645 199.970008535452 24 19900.9 19725.2839046163 175.616095383740 25 17012.2 16435.7116472807 576.48835271934 26 19642.9 19107.8661515711 535.033848428855 27 19024 18889.3298607250 134.670139274971 28 21691 21291.0096059605 399.990394039488 29 18835.9 18780.4864124065 55.4135875935025 30 19873.4 19496.7244573484 376.67554265163 31 21468.2 21102.6875240813 365.512475918725 32 19406.8 19221.3609426103 185.439057389692 33 18385.3 18033.3150870397 351.984912960312 34 20739.3 20550.9990697454 188.300930254611 35 22268.3 22350.4325722104 -82.1325722103985 36 21569 21326.4360731576 242.563926842426 37 17514.8 18074.6740560229 -559.874056022916 38 21124.7 20997.6583072569 127.041692743072 39 21251 21181.4413691876 69.5586308124035 40 21393 21312.6651211254 80.3348788746184 41 22145.2 22228.3309071250 -83.1309071249703 42 20310.5 20453.6886167968 -143.188616796834 43 23466.9 23629.1727185054 -162.272718505362 44 21264.6 21401.412158723 -136.812158722990 45 18388.1 18376.7479646173 11.3520353826587 46 22635.4 22406.1107804038 229.289219596211 47 22014.3 22292.4994150928 -278.199415092787 48 18422.7 18853.3315784883 -430.631578488291 49 16120.2 16056.1647234889 64.0352765111041 50 16037.7 16403.7020990252 -366.002099025243 51 16410.7 16153.9437963463 256.756203653681 52 17749.8 17831.1814413996 -81.3814413995873 53 16349.8 16258.2750861887 91.5249138112657 54 15662.3 15648.3246556292 13.9753443708437 55 17782.3 18021.9694871426 -239.669487142638 56 16398.9 16262.0955009416 136.804499058368 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.989733914314784 0.020532171370433 0.0102660856852165 22 0.996958436948551 0.00608312610289779 0.00304156305144889 23 0.991433590931518 0.017132818136965 0.0085664090684825 24 0.990099782177534 0.0198004356449321 0.00990021782246603 25 0.991705194767118 0.016589610465764 0.008294805232882 26 0.993804516096433 0.0123909678071339 0.00619548390356695 27 0.98739142464141 0.0252171507171811 0.0126085753585905 28 0.977946470895631 0.0441070582087378 0.0220535291043689 29 0.953642519099257 0.092714961801486 0.046357480900743 30 0.909180158858979 0.181639682282042 0.0908198411410212 31 0.924882740111916 0.150234519776168 0.0751172598880841 32 0.869900804055884 0.260198391888232 0.130099195944116 33 0.801148727687835 0.397702544624330 0.198851272312165 34 0.67054481334746 0.65891037330508 0.32945518665254 35 0.556050777268745 0.88789844546251 0.443949222731255 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0666666666666667 NOK 5% type I error level 8 0.533333333333333 NOK 10% type I error level 9 0.6 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/1000r51258579956.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/1000r51258579956.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/1ur1y1258579956.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/1ur1y1258579956.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/2r8ax1258579956.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/2r8ax1258579956.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/3drb21258579956.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/3drb21258579956.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/4jwvw1258579956.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/4jwvw1258579956.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/5c4yj1258579956.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/5c4yj1258579956.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/6v59e1258579956.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/6v59e1258579956.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/7gdku1258579956.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/7gdku1258579956.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/80wfx1258579956.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/80wfx1258579956.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/9er4v1258579956.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258580070r581tvdbx7dp0to/9er4v1258579956.ps (open in new window)

Parameters (Session):

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