ws7

*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 13:48:06 -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/t1258577349ocmby1dkqxt12rz.htm/, Retrieved Wed, 18 Nov 2009 21:49:21 +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/t1258577349ocmby1dkqxt12rz.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 «

243324 612613 260307 241476 213587 216234 244460 611324 243324 260307 209465 213587 233575 594167 244460 243324 204045 209465 237217 595454 233575 244460 200237 204045 235243 590865 237217 233575 203666 200237 230354 589379 235243 237217 241476 203666 227184 584428 230354 235243 260307 241476 221678 573100 227184 230354 243324 260307 217142 567456 221678 227184 244460 243324 219452 569028 217142 221678 233575 244460 256446 620735 219452 217142 237217 233575 265845 628884 256446 219452 235243 237217 248624 628232 265845 256446 230354 235243 241114 612117 248624 265845 227184 230354 229245 595404 241114 248624 221678 227184 231805 597141 229245 241114 217142 221678 219277 593408 231805 229245 219452 217142 219313 590072 219277 231805 256446 219452 212610 579799 219313 219277 265845 256446 214771 574205 212610 219313 248624 265845 211142 572775 214771 212610 241114 248624 211457 572942 211142 214771 229245 241114 240048 619567 211457 211142 231805 229245 240636 625809 240048 211457 219277 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = -60721.7705212927 + 0.378430186715127X[t] + 0.551893801922027`y-1`[t] -0.0290164398423964`y-2`[t] -0.0383645527604562`y-7`[t] -0.191957412457643`y-8`[t] -11360.9430950933M1[t] -7126.03598081202M2[t] -6521.62063369166M3[t] + 180.308736087293M4[t] -5103.57926144473M5[t] -3767.20866081333M6[t] + 4660.79447437359M7[t] + 6372.59597424071M8[t] + 3973.56866582381M9[t] + 8115.21546804956M10[t] + 16405.0150215552M11[t] -228.053191701309t + 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) -60721.7705212927 24267.167805 -2.5022 0.016224 0.008112 X 0.378430186715127 0.097173 3.8944 0.000339 0.000169 `y-1` 0.551893801922027 0.163032 3.3852 0.001528 0.000764 `y-2` -0.0290164398423964 0.139036 -0.2087 0.83567 0.417835 `y-7` -0.0383645527604562 0.140089 -0.2739 0.785505 0.392753 `y-8` -0.191957412457643 0.1305 -1.4709 0.148588 0.074294 M1 -11360.9430950933 5068.498012 -2.2415 0.03021 0.015105 M2 -7126.03598081202 7279.085802 -0.979 0.333069 0.166534 M3 -6521.62063369166 6404.196497 -1.0183 0.314214 0.157107 M4 180.308736087293 6158.454416 0.0293 0.976778 0.488389 M5 -5103.57926144473 4648.766936 -1.0978 0.278386 0.139193 M6 -3767.20866081333 6356.222523 -0.5927 0.556499 0.27825 M7 4660.79447437359 5447.447376 0.8556 0.396966 0.198483 M8 6372.59597424071 5922.105156 1.0761 0.287897 0.143948 M9 3973.56866582381 5838.584514 0.6806 0.499792 0.249896 M10 8115.21546804956 5703.190974 1.4229 0.161973 0.080986 M11 16405.0150215552 5129.510413 3.1982 0.002596 0.001298 t -228.053191701309 85.255419 -2.6749 0.010528 0.005264 Multiple Linear Regression - Regression Statistics Multiple R 0.992695728709955 R-squared 0.985444809798989 Adjusted R-squared 0.979690432277659 F-TEST (value) 171.251331033843 F-TEST (DF numerator) 17 F-TEST (DF denominator) 43 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 4142.55600204583 Sum Squared Residuals 737913119.893694 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 243324 246473.642379748 -3149.64237974800 2 244460 240739.728732192 3720.27126780802 3 233575 236742.286061079 -3167.28606107859 4 237217 238849.376572309 -1632.37657230861 5 235243 234526.082205747 716.91779425283 6 230354 231768.150411128 -1414.15041112837 7 227184 227473.209496784 -289.209496783845 8 221678 220099.253837678 1578.74616232171 9 217142 215605.99880849 1536.00119151008 10 219452 217970.39344103 1481.60655896987 11 256446 248955.855454326 7490.14454567365 12 265845 255132.968895857 10712.0311041426 13 248624 247977.540026697 646.459973302723 14 241114 237169.198231141 3944.80176885884 15 229245 228395.566559072 849.433440928427 16 231805 230425.201023990 1379.79897600954 17 219277 226039.920911191 -6762.92091119055 18 219313 217034.707693166 2278.29230683417 19 212610 214268.569515888 -1658.56951588809 20 214771 208791.458856850 5979.54114314977 21 211142 210604.179283111 537.82071688942 22 211457 214412.392645413 -2955.39264541276 23 240048 242577.722943996 -2529.72294399631 24 240636 244066.091609509 -3430.09160950943 25 230580 232145.372095769 -1565.37209576874 26 208795 218615.67825068 -9820.6782506802 27 197922 200183.420211584 -2261.42021158414 28 194596 197808.536395873 -3212.53639587323 29 194581 192710.597264076 1870.40273592381 30 185686 188313.838783320 -2627.83878332019 31 178106 179592.506830755 -1486.50683075478 32 172608 175246.437042425 -2638.43704242495 33 167302 166939.664946398 362.335053602390 34 168053 167999.920451945 53.0795480549326 35 202300 200301.845323677 1998.15467632254 36 202388 206679.250813842 -4291.25081384246 37 182516 185810.309322201 -3294.30932220083 38 173476 175325.479526402 -1849.47952640161 39 166444 166437.220366518 6.77963348178002 40 171297 172221.066100082 -924.066100081668 41 169701 171589.361405334 -1888.36140533391 42 164182 166675.523690858 -2493.52369085797 43 161914 159645.171835107 2268.82816489274 44 159612 159882.569457968 -270.569457968325 45 151001 152308.339088969 -1307.33908896884 46 158114 156653.276984647 1460.72301535298 47 186530 189278.040927803 -2748.04092780292 48 187069 189222.853259885 -2153.85325988510 49 174330 171704.354303574 2625.64569642589 50 169362 165356.915259585 4005.08474041496 51 166827 162254.506801747 4572.49319825252 52 178037 173647.819907746 4389.18009225397 53 186412 180348.038213652 6063.96178634783 54 189226 184968.779421528 4257.22057847236 55 191563 190397.542321466 1165.45767853398 56 188906 193555.280805078 -4649.2808050782 57 186005 187133.817873033 -1128.81787303305 58 195309 195349.016476965 -40.0164769650219 59 223532 227742.535350197 -4210.53535019696 60 226899 227735.835420906 -836.835420905644 61 214126 209388.781872011 4737.21812798895 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.788220284371297 0.423559431257406 0.211779715628703 22 0.856116193680583 0.287767612638834 0.143883806319417 23 0.926517390552235 0.14696521889553 0.073482609447765 24 0.929702891367888 0.140594217264224 0.070297108632112 25 0.943525016974647 0.112949966050707 0.0564749830253533 26 0.907898154994715 0.184203690010570 0.0921018450052852 27 0.921974347810604 0.156051304378791 0.0780256521893956 28 0.900337412504788 0.199325174990424 0.099662587495212 29 0.944884277231654 0.110231445536692 0.0551157227683462 30 0.940345854575238 0.119308290849523 0.0596541454247616 31 0.953588223285336 0.0928235534293282 0.0464117767146641 32 0.917112215529137 0.165775568941725 0.0828877844708627 33 0.989853034764216 0.0202939304715677 0.0101469652357839 34 0.990636513214298 0.0187269735714036 0.00936348678570181 35 0.995943199427566 0.0081136011448686 0.0040568005724343 36 0.989553135989108 0.0208937280217840 0.0104468640108920 37 0.972477619407303 0.0550447611853933 0.0275223805926967 38 0.936810056828585 0.126379886342829 0.0631899431714147 39 0.923362967051894 0.153274065896211 0.0766370329481056 40 0.99711469798935 0.00577060402130167 0.00288530201065083 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.1 NOK 5% type I error level 5 0.25 NOK 10% type I error level 7 0.35 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/10cp011258577282.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/10cp011258577282.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/1hpwa1258577282.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/1hpwa1258577282.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/2s38y1258577282.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/2s38y1258577282.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/3z1a01258577282.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/3z1a01258577282.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/4bpwu1258577282.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/4bpwu1258577282.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/5prpf1258577282.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/5prpf1258577282.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/6d1we1258577282.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/6d1we1258577282.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/7r0941258577282.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/7r0941258577282.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/8y0du1258577282.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/8y0du1258577282.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/9cbwb1258577282.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/18/t1258577349ocmby1dkqxt12rz/9cbwb1258577282.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