model 3

*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 15:11:20 -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/t1259619145qc3rghg6nz8svg3.htm/, Retrieved Mon, 30 Nov 2009 23:12:36 +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/t1259619145qc3rghg6nz8svg3.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 10001.60 2849.27 10411.75 2921.44 10673.38 2981.85 10539.51 3080.58 10723.78 3106.22 10682.06 3119.31 10283.19 3061.26 10377.18 3097.31 10486.64 3161.69 10545.38 3257.16 10554.27 3277.01 10532.54 3295.32 10324.31 3363.99 10695.25 3494.17 10827.81 3667.03 10872.48 3813.06 10971.19 3917.96 11145.65 3895.51 11234.68 3801.06 11333.88 3570.12 10997.97 3701.61 11036.89 3862.27 11257.35 3970.10 11533.59 4138.52 11963.12 4199.75 12185.15 4290.89 12377.62 4443.91 12512.89 4502.64 12631.48 4356.98 12268.53 4591.27 12754.80 4696.96 13407.75 4621.40 13480.21 4562.84 13673.28 4202.52 13239.71 4296.49 13557.69 4435.23 13901.28 4105.18 13200.58 4116.68 13406.97 3844.49 12538.12 3720.98 12419.57 3674.40 12193.88 3857.62 12656.63 3801.06 12812.48 3504.37 12056.67 3032.60 11322.38 3047.03 11530.75 2962.34 11114.08 2197.82 9181.73 2014.45 8614.55 1862.83 8595.56 1905.41 8396.20 1810.99 7690.50 1670.07 7235.47 1864.44 7992.12 2052.02 8398.37 2029.60 8593.01 2070.83 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 R Framework error message The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'. Multiple Linear Regression - Estimated Regression Equation Dow [t] = + 4315.15037055132 + 1.91786649769111Bel20[t] -12.0114399264399M1[t] + 33.9335808282130M2[t] + 117.190298734077M3[t] -160.062610981528M4[t] -290.143787633976M5[t] -407.336810825499M6[t] -372.008040407226M7[t] -135.387393105297M8[t] -65.006563773501M9[t] -36.6220102938663M10[t] + 39.5111389175097M11[t] + 12.7266451439576t + 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) 4315.15037055132 468.495622 9.2107 0 0 Bel20 1.91786649769111 0.091256 21.0163 0 0 M1 -12.0114399264399 355.211874 -0.0338 0.973171 0.486586 M2 33.9335808282130 354.910027 0.0956 0.924244 0.462122 M3 117.190298734077 354.166217 0.3309 0.74223 0.371115 M4 -160.062610981528 353.523534 -0.4528 0.652847 0.326424 M5 -290.143787633976 353.026624 -0.8219 0.415388 0.207694 M6 -407.336810825499 352.772292 -1.1547 0.254187 0.127094 M7 -372.008040407226 352.230648 -1.0561 0.296416 0.148208 M8 -135.387393105297 352.00662 -0.3846 0.702294 0.351147 M9 -65.006563773501 351.894068 -0.1847 0.85425 0.427125 M10 -36.6220102938663 351.862776 -0.1041 0.917558 0.458779 M11 39.5111389175097 351.685546 0.1123 0.911036 0.455518 t 12.7266451439576 4.519599 2.8159 0.00714 0.00357 Multiple Linear Regression - Regression Statistics Multiple R 0.954439759066023 R-squared 0.910955253686008 Adjusted R-squared 0.885790434075532 F-TEST (value) 36.1995542899415 F-TEST (DF numerator) 13 F-TEST (DF denominator) 46 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 555.918609004132 Sum Squared Residuals 14216092.9925061 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10001.6 9602.96322194372 398.636778056275 2 10411.75 9839.05671754377 572.693282456226 3 10673.38 10073.4525057320 599.927494268031 4 10539.51 9924.78455628584 614.72544371416 5 10723.78 9996.7809840944 726.999015905608 6 10682.06 9941.48870304763 740.571296952373 7 10283.19 10014.6489910646 268.541008935366 8 10377.18 10152.6641333196 224.515866680448 9 10486.64 10304.9106950371 181.729304962929 10 10545.38 10469.4941387820 75.885861217983 11 10554.27 10741.4526476719 -187.182647671919 12 10532.54 10752.7378038775 -220.197803877536 13 10324.31 10788.5691446678 -464.259144667778 14 10695.25 10978.9407029628 -283.690702962836 15 10827.81 11324.5919266821 -496.781926682086 16 10872.48 11391.5880649013 -519.108064901324 17 10971.19 11554.2995780507 -583.109578050664 18 11145.65 11651.0173956109 -505.367395610897 19 11234.68 11656.0167083000 -421.336708299961 20 11333.88 11724.2215100389 -390.341510038924 21 10997.97 11364.4168955379 -366.446895537893 22 11036.89 11657.7083599429 -620.818359942889 23 11257.35 12054.6925858173 -797.342585817275 24 11533.59 12234.7116364898 -701.121636489755 25 11963.12 12558.4339172484 -595.313917248409 26 12185.15 12734.5365488006 -549.386548800646 27 12377.62 13005.3142644500 -627.694264450035 28 12512.89 13034.2599313551 -521.36993135508 29 12631.48 13029.541699256 -398.061699255989 30 12268.53 12645.7188871547 -377.188887154735 31 12754.8 13143.1112444610 -388.311244461017 32 13407.75 13595.1578470479 -187.407847047875 33 13480.21 13533.3513289581 -53.1413289580891 34 13673.28 13462.1522654769 211.127734523110 35 13239.71 12859.9664033842 379.743596615834 36 13557.69 13013.4038243986 544.286175601355 37 13901.28 13280.2038275058 621.076172494174 38 13200.58 12705.8836558415 494.696344158511 39 13406.97 12823.9224836148 583.047516385241 40 12538.12 12037.3721370366 500.747862963433 41 12419.57 11683.1419143982 736.42808560175 42 12193.88 11489.3413148882 704.538685111767 43 12656.63 11888.7882301574 767.841769842572 44 12812.48 12029.6609934939 782.819006506095 45 12056.67 11543.7566567697 512.913343230316 46 11322.38 10680.0759777775 642.304022222455 47 11530.75 10796.6105856946 734.13941430544 48 11114.08 10607.4019782315 506.678021768451 49 9181.73 9141.86988863426 39.8601113657374 50 8614.55 8848.86237485125 -234.312374851255 51 8595.56 8654.05881952115 -58.4988195211513 52 8396.2 8471.19531042119 -74.9953104211893 53 7690.5 8172.7558242007 -482.255824200705 54 7235.47 7798.02369929851 -562.553699298509 55 7992.12 8218.85482601696 -226.73482601696 56 8398.37 8827.95551609974 -429.585516099744 57 8593.01 8868.06442369726 -275.054423697263 58 8679.75 8988.24925802066 -308.49925802066 59 9374.63 9503.98777743208 -129.357777432080 60 9634.97 9764.61475700252 -129.644757002518 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00329953395434436 0.00659906790868872 0.996700466045656 18 0.00105575254545439 0.00211150509090878 0.998944247454546 19 0.0179772612681773 0.0359545225363546 0.982022738731823 20 0.0235340486588837 0.0470680973177673 0.976465951341116 21 0.0207264871124536 0.0414529742249072 0.979273512887546 22 0.0104803397418398 0.0209606794836795 0.98951966025816 23 0.00600819552201116 0.0120163910440223 0.993991804477989 24 0.00680046239140797 0.0136009247828159 0.993199537608592 25 0.0275038666077415 0.055007733215483 0.972496133392258 26 0.0230752728171869 0.0461505456343737 0.976924727182813 27 0.0203312700953431 0.0406625401906861 0.979668729904657 28 0.0218357303186767 0.0436714606373533 0.978164269681323 29 0.0282733880118983 0.0565467760237965 0.971726611988102 30 0.0377427899514072 0.0754855799028143 0.962257210048593 31 0.0898752285194427 0.179750457038885 0.910124771480557 32 0.170643424533418 0.341286849066836 0.829356575466582 33 0.29618699254754 0.59237398509508 0.70381300745246 34 0.67206797775608 0.65586404448784 0.32793202224392 35 0.969116332688274 0.0617673346234511 0.0308836673117256 36 0.998180850746 0.00363829850800184 0.00181914925400092 37 0.99828482133908 0.00343035732183853 0.00171517866091926 38 0.996271271924832 0.0074574561503354 0.0037287280751677 39 0.996280071091943 0.00743985781611394 0.00371992890805697 40 0.99952709814567 0.000945803708660037 0.000472901854330018 41 0.998059920302892 0.00388015939421543 0.00194007969710771 42 0.99399939936836 0.0120012012632780 0.00600060063163902 43 0.97810855371872 0.0437828925625585 0.0218914462812793 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 8 0.296296296296296 NOK 5% type I error level 19 0.703703703703704 NOK 10% type I error level 23 0.851851851851852 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/10zeeu1259619073.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/10zeeu1259619073.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/146o01259619073.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/146o01259619073.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/2rwzi1259619073.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/2rwzi1259619073.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/3z6ek1259619073.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/3z6ek1259619073.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/4yctd1259619073.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/4yctd1259619073.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/5gsw21259619073.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/5gsw21259619073.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/6g6yq1259619073.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/6g6yq1259619073.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/7bkbr1259619073.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/7bkbr1259619073.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/8eua71259619073.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/8eua71259619073.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/9ksuo1259619073.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259619145qc3rghg6nz8svg3/9ksuo1259619073.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; 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