*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, 25 Nov 2009 13:53:06 -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/25/t1259182439hh55jbntzucq0m5.htm/, Retrieved Wed, 25 Nov 2009 21:54:10 +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/25/t1259182439hh55jbntzucq0m5.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 «

2756.76 0 2849.27 0 2921.44 0 2981.85 0 3080.58 0 3106.22 0 3119.31 0 3061.26 0 3097.31 0 3161.69 0 3257.16 0 3277.01 0 3295.32 0 3363.99 0 3494.17 0 3667.03 1 3813.06 1 3917.96 1 3895.51 1 3801.06 1 3570.12 0 3701.61 1 3862.27 1 3970.1 1 4138.52 1 4199.75 1 4290.89 1 4443.91 1 4502.64 1 4356.98 1 4591.27 1 4696.96 1 4621.4 1 4562.84 1 4202.52 1 4296.49 1 4435.23 1 4105.18 1 4116.68 1 3844.49 1 3720.98 1 3674.4 1 3857.62 1 3801.06 1 3504.37 1 3032.6 1 3047.03 0 2962.34 1 2197.82 1 2014.45 1 1862.83 0 1905.41 0 1810.99 0 1670.07 0 1864.44 0 2052.02 0 2029.6 0 2070.83 0 2293.41 0 2443.27 0

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 bel20[t] = + 2661.16616666666 + 1214.45972222222rent[t] -25.1120000000012M1[t] -83.3140000000002M2[t] + 190.251944444445M3[t] -21.3039999999998M4[t] -4.19199999999998M5[t] -44.716M6[t] + 75.7880000000003M7[t] + 92.6300000000002M8[t] + 217.609944444445M9[t] -83.9279999999996M10[t] + 185.527944444445M11[t] + 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) 2661.16616666666 318.175061 8.3638 0 0 rent 1214.45972222222 176.763923 6.8705 0 0 M1 -25.1120000000012 424.233415 -0.0592 0.953049 0.476524 M2 -83.3140000000002 424.233415 -0.1964 0.845154 0.422577 M3 190.251944444445 425.703899 0.4469 0.656992 0.328496 M4 -21.3039999999998 424.233415 -0.0502 0.960162 0.480081 M5 -4.19199999999998 424.233415 -0.0099 0.992158 0.496079 M6 -44.716 424.233415 -0.1054 0.916504 0.458252 M7 75.7880000000003 424.233415 0.1786 0.858983 0.429491 M8 92.6300000000002 424.233415 0.2183 0.828104 0.414052 M9 217.609944444445 425.703899 0.5112 0.61162 0.30581 M10 -83.9279999999996 424.233415 -0.1978 0.844028 0.422014 M11 185.527944444445 425.703899 0.4358 0.664966 0.332483 Multiple Linear Regression - Regression Statistics Multiple R 0.70926712281411 R-squared 0.503059851505006 Adjusted R-squared 0.376181515719051 F-TEST (value) 3.9648995109273 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0.000314353822826074 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 670.771924839074 Sum Squared Residuals 21146943.8321589 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2756.76 2636.05416666667 120.705833333328 2 2849.27 2577.85216666667 271.417833333332 3 2921.44 2851.41811111111 70.0218888888892 4 2981.85 2639.86216666667 341.987833333333 5 3080.58 2656.97416666667 423.605833333334 6 3106.22 2616.45016666667 489.769833333333 7 3119.31 2736.95416666667 382.355833333333 8 3061.26 2753.79616666667 307.463833333334 9 3097.31 2878.77611111111 218.533888888889 10 3161.69 2577.23816666667 584.451833333333 11 3257.16 2846.69411111111 410.465888888889 12 3277.01 2661.16616666667 615.843833333334 13 3295.32 2636.05416666667 659.265833333335 14 3363.99 2577.85216666667 786.137833333334 15 3494.17 2851.41811111111 642.751888888889 16 3667.03 3854.32188888889 -187.291888888889 17 3813.06 3871.43388888889 -58.3738888888891 18 3917.96 3830.90988888889 87.0501111111109 19 3895.51 3951.41388888889 -55.903888888889 20 3801.06 3968.25588888889 -167.195888888889 21 3570.12 2878.77611111111 691.343888888889 22 3701.61 3791.69788888889 -90.0878888888892 23 3862.27 4061.15383333333 -198.883833333334 24 3970.1 3875.62588888889 94.4741111111109 25 4138.52 3850.51388888889 288.006111111113 26 4199.75 3792.31188888889 407.438111111111 27 4290.89 4065.87783333333 225.012166666666 28 4443.91 3854.32188888889 589.588111111111 29 4502.64 3871.43388888889 631.206111111111 30 4356.98 3830.90988888889 526.070111111111 31 4591.27 3951.41388888889 639.856111111111 32 4696.96 3968.25588888889 728.704111111111 33 4621.4 4093.23583333333 528.164166666666 34 4562.84 3791.69788888889 771.142111111111 35 4202.52 4061.15383333333 141.366166666667 36 4296.49 3875.62588888889 420.864111111111 37 4435.23 3850.51388888889 584.716111111112 38 4105.18 3792.31188888889 312.868111111112 39 4116.68 4065.87783333333 50.8021666666665 40 3844.49 3854.32188888889 -9.83188888888929 41 3720.98 3871.43388888889 -150.453888888889 42 3674.4 3830.90988888889 -156.509888888889 43 3857.62 3951.41388888889 -93.7938888888893 44 3801.06 3968.25588888889 -167.195888888889 45 3504.37 4093.23583333333 -588.865833333334 46 3032.6 3791.69788888889 -759.097888888889 47 3047.03 2846.69411111111 200.335888888889 48 2962.34 3875.62588888889 -913.285888888889 49 2197.82 3850.51388888889 -1652.69388888889 50 2014.45 3792.31188888889 -1777.86188888889 51 1862.83 2851.41811111111 -988.588111111111 52 1905.41 2639.86216666667 -734.452166666667 53 1810.99 2656.97416666667 -845.984166666667 54 1670.07 2616.45016666667 -946.380166666666 55 1864.44 2736.95416666667 -872.514166666666 56 2052.02 2753.79616666667 -701.776166666667 57 2029.6 2878.77611111111 -849.176111111111 58 2070.83 2577.23816666667 -506.408166666667 59 2293.41 2846.69411111111 -553.284111111111 60 2443.27 2661.16616666667 -217.896166666666 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.202547663268128 0.405095326536256 0.797452336731872 17 0.0904655951159982 0.180931190231996 0.909534404884002 18 0.0368774110305019 0.0737548220610038 0.963122588969498 19 0.0135864105392059 0.0271728210784118 0.986413589460794 20 0.00466740810562805 0.0093348162112561 0.995332591894372 21 0.00498241248301402 0.00996482496602804 0.995017587516986 22 0.00196038335781960 0.00392076671563920 0.99803961664218 23 0.000752395019537969 0.00150479003907594 0.999247604980462 24 0.000235275011852432 0.000470550023704864 0.999764724988148 25 0.00019290003910683 0.00038580007821366 0.999807099960893 26 0.000155563471853517 0.000311126943707034 0.999844436528146 27 7.26796745806506e-05 0.000145359349161301 0.99992732032542 28 0.000209705732382996 0.000419411464765992 0.999790294267617 29 0.000331138133855544 0.000662276267711089 0.999668861866144 30 0.000245260898310634 0.000490521796621269 0.99975473910169 31 0.000353091695366452 0.000706183390732904 0.999646908304634 32 0.000808830219345184 0.00161766043869037 0.999191169780655 33 0.000659487976122286 0.00131897595224457 0.999340512023878 34 0.00129080561212402 0.00258161122424805 0.998709194387876 35 0.000568811215978735 0.00113762243195747 0.999431188784021 36 0.000361667515302416 0.000723335030604832 0.999638332484698 37 0.00532980131393975 0.0106596026278795 0.99467019868606 38 0.140046281017393 0.280092562034787 0.859953718982607 39 0.141815910789089 0.283631821578178 0.858184089210911 40 0.109670770711905 0.21934154142381 0.890329229288095 41 0.0958840110557552 0.191768022111510 0.904115988944245 42 0.107856894616110 0.215713789232219 0.89214310538389 43 0.147487855031942 0.294975710063883 0.852512144968058 44 0.185401852465652 0.370803704931305 0.814598147534348 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 17 0.586206896551724 NOK 5% type I error level 19 0.655172413793103 NOK 10% type I error level 20 0.689655172413793 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/10r8d61259182382.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/10r8d61259182382.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/17l4p1259182382.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/17l4p1259182382.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/2mgix1259182382.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/2mgix1259182382.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/3amwp1259182382.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/3amwp1259182382.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/4sitv1259182382.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/4sitv1259182382.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/57n8y1259182382.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/57n8y1259182382.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/6ej2t1259182382.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/6ej2t1259182382.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/7lna01259182382.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/7lna01259182382.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/848m51259182382.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/848m51259182382.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/9fo561259182382.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/25/t1259182439hh55jbntzucq0m5/9fo561259182382.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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