*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: Mon, 30 Nov 2009 11:43:43 -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/30/t1259606725nihbwi18a3fj0kj.htm/, Retrieved Mon, 30 Nov 2009 19:45:37 +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/30/t1259606725nihbwi18a3fj0kj.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 «

267413 294912 267366 293488 264777 290555 258863 284736 254844 281818 254868 287854 277267 316263 285351 325412 286602 326011 283042 328282 276687 317480 277915 317539 277128 313737 277103 312276 275037 309391 270150 302950 267140 300316 264993 304035 287259 333476 291186 337698 292300 335932 288186 323931 281477 313927 282656 314485 280190 313218 280408 309664 276836 302963 275216 298989 274352 298423 271311 301631 289802 329765 290726 335083 292300 327616 278506 309119 269826 295916 265861 291413 269034 291542 264176 284678 255198 276475 253353 272566 246057 264981 235372 263290 258556 296806 260993 303598 254663 286994 250643 276427 243422 266424 247105 267153 248541 268381 245039 262522 237080 255542 237085 253158 225554 243803 226839 250741 247934 280445 248333 285257 246969 270976 245098 261076 246263 255603 255765 260376 264319 263903 268347 264291 273046 263276 273963 262572 267430 256167 271993 264221 292710 293860

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -19851.0732190765 + 0.927997599540739X[t] + 3488.77411053802M1[t] + 5238.89859350010M2[t] + 5813.70309850114M3[t] + 6726.8417983221M4[t] + 5285.69400486605M5[t] -899.207493524473M6[t] -7657.43328196939M7[t] -13975.7005108038M8[t] -7847.9064228879M9[t] -4738.03673981474M10[t] -1569.55783649424M11[t] + 455.91333933419t + 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) -19851.0732190765 24988.342126 -0.7944 0.430498 0.215249 X 0.927997599540739 0.076411 12.1448 0 0 M1 3488.77411053802 4731.157147 0.7374 0.464129 0.232064 M2 5238.89859350010 4742.258034 1.1047 0.274268 0.137134 M3 5813.70309850114 4778.274174 1.2167 0.229111 0.114555 M4 6726.8417983221 4817.976952 1.3962 0.168476 0.084238 M5 5285.69400486605 4888.685793 1.0812 0.284499 0.14225 M6 -899.207493524473 4797.847171 -0.1874 0.852048 0.426024 M7 -7657.43328196939 4937.908632 -1.5507 0.126914 0.063457 M8 -13975.7005108038 5248.174096 -2.663 0.010235 0.005117 M9 -7847.9064228879 5087.925466 -1.5425 0.128913 0.064456 M10 -4738.03673981474 4961.929918 -0.9549 0.343975 0.171987 M11 -1569.55783649424 4926.611899 -0.3186 0.751291 0.375646 t 455.91333933419 85.376267 5.34 2e-06 1e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.911439378287059 R-squared 0.830721740292301 Adjusted R-squared 0.789200657722488 F-TEST (value) 20.0072273861245 F-TEST (DF numerator) 13 F-TEST (DF denominator) 53 p-value 6.66133814775094e-16 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7787.83778390367 Sum Squared Residuals 3214472119.46507 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 267413 257771.242306554 9641.75769344596 2 267366 258655.811547104 8710.18845289556 3 264777 256964.712431987 7812.28756801327 4 258863 252933.746439414 5929.25356058568 5 254844 249240.614989833 5603.38501016744 6 254868 249113.020341604 5754.97965839585 7 277267 269174.191697846 8092.80830215374 8 285351 271802.087846544 13548.9121534557 9 286602 278941.665835919 7660.33416408076 10 283042 284614.931406884 -1572.93140688361 11 276687 278215.093579299 -1528.09357929924 12 277915 280295.316613501 -2380.31661350057 13 277128 280711.757189919 -3583.75718991889 14 277103 281561.990519286 -4458.99051928617 15 275037 279915.435288946 -4878.43528894634 16 270150 275307.254789460 -5157.2547894596 17 267140 271877.674658147 -4737.67465814742 18 264993 269599.909571783 -4606.90957178310 19 287259 290618.774450751 -3359.77445075127 20 291186 288674.426426512 2511.57357348796 21 292300 293619.290092973 -1319.29009297319 22 288186 286048.173923292 2137.82607670787 23 281477 280388.878180141 1088.12181985873 24 282656 282932.172016513 -276.172016513435 25 280190 285701.086507767 -5511.08650776752 26 280408 284609.020861296 -4201.02086129601 27 276836 279421.226791109 -2585.22679110875 28 275216 277102.416369689 -1886.41636968901 29 274352 275591.935274227 -1239.93527422708 30 271311 272839.963414497 -1528.96341449744 31 289802 292645.935430866 -2843.93543086586 32 290726 291718.672775723 -992.672775723291 33 292300 291373.022127203 926.977872797318 34 278506 277773.633550905 732.366449095013 35 269826 269145.673486823 680.326513176694 36 265861 266992.37147192 -1131.37147191979 37 269034 271056.770612133 -2022.77061213275 38 264176 266893.032911181 -2717.03291118140 39 255198 260311.386446484 -5113.38644648395 40 253353 258052.895869034 -4699.89586903435 41 246057 250028.799622396 -3971.79962239598 42 235372 242730.567522516 -7358.56752251627 43 258556 267531.022619613 -8975.02261961294 44 260993 267971.628426193 -6978.62842619341 45 254663 259146.863710669 -4483.86371066908 46 250643 252906.496098729 -2263.49609872944 47 243422 247248.128353178 -3826.12835317812 48 247105 249950.109779072 -2845.10977907175 49 248541 255034.37828118 -6493.37828117998 50 245039 251803.278167767 -6764.27816776707 51 237080 246356.572767308 -9276.57276730794 52 237085 245513.278529158 -8428.27852915797 53 225554 235846.626531332 -10292.6265313325 54 226839 236556.08571789 -9717.08571788981 55 247934 257819.013965537 -9885.01396553719 56 248333 256422.184525027 -8089.184525027 57 246969 249753.158233236 -2784.15823323581 58 245098 244131.76502019 966.23497981016 59 246263 242677.226400558 3585.77359944193 60 255765 249132.030118994 6632.96988100556 61 264319 256349.765102447 7969.23489755317 62 268347 258915.865993365 9431.1340066351 63 273046 259004.666274166 14041.3337258337 64 273963 259720.408003245 14242.5919967552 65 267430 252791.348924064 14638.6510759355 66 271993 254536.453431709 17456.5465682908 67 292710 275739.061835386 16970.9381646135 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00726201907065687 0.0145240381413137 0.992737980929343 18 0.0055463371653911 0.0110926743307822 0.994453662834609 19 0.00175074949827093 0.00350149899654186 0.99824925050173 20 0.00706026674206305 0.0141205334841261 0.992939733257937 21 0.00250555708055028 0.00501111416110055 0.99749444291945 22 0.0175471017057880 0.0350942034115760 0.982452898294212 23 0.00842422176546652 0.0168484435309330 0.991575778234533 24 0.0034080255791513 0.0068160511583026 0.996591974420849 25 0.00435305364689601 0.00870610729379202 0.995646946353104 26 0.00225632652978714 0.00451265305957428 0.997743673470213 27 0.00104744799551491 0.00209489599102982 0.998952552004485 28 0.00148080876257197 0.00296161752514393 0.998519191237428 29 0.00742524834270035 0.0148504966854007 0.9925747516573 30 0.00600324064779367 0.0120064812955873 0.993996759352206 31 0.0041811848539413 0.0083623697078826 0.995818815146059 32 0.0387550303304036 0.0775100606608073 0.961244969669596 33 0.113675971696636 0.227351943393272 0.886324028303364 34 0.552578638305095 0.894842723389811 0.447421361694905 35 0.798966972629651 0.402066054740698 0.201033027370349 36 0.932690654049104 0.134618691901791 0.0673093459508957 37 0.94201608151558 0.115967836968841 0.0579839184844206 38 0.936026293682779 0.127947412634443 0.0639737063172214 39 0.954289155680746 0.091421688638508 0.045710844319254 40 0.94320897396948 0.11358205206104 0.05679102603052 41 0.949441181493093 0.101117637013814 0.050558818506907 42 0.999722883958528 0.000554232082943912 0.000277116041471956 43 0.999951528647924 9.69427041527627e-05 4.84713520763814e-05 44 0.99996365616824 7.26876635207699e-05 3.63438317603850e-05 45 0.99991809203713 0.000163815925739454 8.1907962869727e-05 46 0.999651688370477 0.000696623259046901 0.000348311629523451 47 0.998504597696979 0.00299080460604252 0.00149540230302126 48 0.997196532503452 0.00560693499309545 0.00280346749654773 49 0.996474419669294 0.00705116066141138 0.00352558033070569 50 0.987433637580712 0.0251327248385755 0.0125663624192877 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 16 0.470588235294118 NOK 5% type I error level 24 0.705882352941177 NOK 10% type I error level 26 0.764705882352941 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/10thbi1259606617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/10thbi1259606617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/1kau81259606617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/1kau81259606617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/2sxbi1259606617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/2sxbi1259606617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/3yt691259606617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/3yt691259606617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/4bds71259606617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/4bds71259606617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/57a7w1259606617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/57a7w1259606617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/60elt1259606617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/60elt1259606617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/759mm1259606617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/759mm1259606617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/84a2q1259606617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/84a2q1259606617.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/9xzj81259606617.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259606725nihbwi18a3fj0kj/9xzj81259606617.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