*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: Sun, 22 Nov 2009 10:14:11 -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/22/t12589100922kzxcfpv8szfn6h.htm/, Retrieved Sun, 22 Nov 2009 18:15:04 +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/22/t12589100922kzxcfpv8szfn6h.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 0 262813 269645 267366 0 267413 267037 264777 0 267366 258113 258863 0 264777 262813 254844 0 258863 267413 254868 0 254844 267366 277267 0 254868 264777 285351 0 277267 258863 286602 0 285351 254844 283042 0 286602 254868 276687 0 283042 277267 277915 0 276687 285351 277128 0 277915 286602 277103 0 277128 283042 275037 0 277103 276687 270150 0 275037 277915 267140 0 270150 277128 264993 0 267140 277103 287259 0 264993 275037 291186 0 287259 270150 292300 0 291186 267140 288186 0 292300 264993 281477 0 288186 287259 282656 0 281477 291186 280190 0 282656 292300 280408 0 280190 288186 276836 0 280408 281477 275216 0 276836 282656 274352 0 275216 280190 271311 0 274352 280408 289802 0 271311 276836 290726 0 289802 275216 292300 0 290726 274352 278506 0 292300 271311 269826 0 278506 289802 265861 0 269826 290726 269034 0 265861 292300 264176 0 269034 278506 255198 0 264176 269826 253353 0 255198 265861 246057 0 253353 269034 235372 0 246057 264176 258556 0 235372 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] = + 20183.8908363885 + 4420.45606204759x[t] + 1.07278534683268y1[t] -0.128942160563676y4[t] -431.278490014834M1[t] -4299.96610657626M2[t] -7684.23161222854M3[t] -5830.93324768869M4[t] -8608.0669432388M5[t] -4351.97049942333M6[t] + 18422.3835583305M7[t] -2290.05787685714M8[t] -6937.72173064447M9[t] -11912.1094256655M10[t] -8764.12912603719M11[t] -67.3332347975218t + 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) 20183.8908363885 9546.436481 2.1143 0.039305 0.019652 x 4420.45606204759 1933.254625 2.2865 0.026325 0.013163 y1 1.07278534683268 0.073851 14.5264 0 0 y4 -0.128942160563676 0.078557 -1.6414 0.106752 0.053376 M1 -431.278490014834 2018.035954 -0.2137 0.831607 0.415804 M2 -4299.96610657626 2159.582509 -1.9911 0.051735 0.025867 M3 -7684.23161222854 2360.116241 -3.2559 0.001992 0.000996 M4 -5830.93324768869 2160.531412 -2.6988 0.009363 0.004681 M5 -8608.0669432388 2059.70848 -4.1793 0.000112 5.6e-05 M6 -4351.97049942333 2020.839733 -2.1535 0.035933 0.017967 M7 18422.3835583305 2046.670279 9.0011 0 0 M8 -2290.05787685714 2684.606209 -0.853 0.397553 0.198777 M9 -6937.72173064447 3251.784405 -2.1335 0.03762 0.01881 M10 -11912.1094256655 3395.39403 -3.5083 0.000939 0.00047 M11 -8764.12912603719 2182.651035 -4.0154 0.000192 9.6e-05 t -67.3332347975218 33.724977 -1.9965 0.051123 0.025561 Multiple Linear Regression - Regression Statistics Multiple R 0.985621546213983 R-squared 0.971449832361242 Adjusted R-squared 0.963214207080831 F-TEST (value) 117.957021997093 F-TEST (DF numerator) 15 F-TEST (DF denominator) 52 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3303.31748786203 Sum Squared Residuals 567419134.131984 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 267413 266858.605583522 554.394416478448 2 267366 268193.678482343 -827.678482342807 3 264777 265842.338671462 -1065.33867146211 4 258863 264244.834383605 -5381.83438360534 5 254844 254462.780973496 381.219026503680 6 254868 254346.08015514 521.919844859791 7 277267 277412.67908012 -145.679080119890 8 285351 281424.787331414 3926.21266858648 9 286602 285900.405529929 701.594470070513 10 283042 282197.644457145 844.35554285488 11 276687 278571.000232786 -1884.00023278576 12 277915 279407.876818907 -1492.87681890696 13 277128 280065.33885714 -2937.33885713999 14 277103 275744.070029430 1358.92997056959 15 275037 273085.079085692 1951.92091430805 16 270150 272496.328715706 -2346.32871570576 17 267140 264510.637275750 2629.36272424957 18 264993 265473.540144816 -480.54014481609 19 287259 286143.685331847 1115.31466815279 20 291186 289880.689533113 1305.31046688680 21 292300 289766.636404837 2533.36359516304 22 288186 286196.837170120 1989.16282987976 23 281477 281993.019170971 -516.019170970558 24 282656 282986.142305776 -330.142305776204 25 280190 283608.702938012 -3418.70293801165 26 280408 277557.661469922 2850.33853007773 27 276836 275205.002890304 1630.99710969631 28 275216 273006.955953855 2209.04404614490 29 274352 268742.548129589 5609.45187041144 30 271311 271976.31540794 -665.315407940186 31 289802 291881.577388712 -2079.57738871178 32 290726 291147.562867123 -421.562867122867 33 292300 287535.225465738 4764.77453426157 34 278506 284574.181782109 -6068.18178210868 35 269826 270472.558281747 -646.558281746506 36 265861 269738.434806118 -3877.43480611765 37 269034 264783.274220386 4250.72577961351 38 264176 266029.829437343 -1853.82943734299 39 255198 258485.857435673 -3287.85743567272 40 253353 251151.611388186 2201.38861181379 41 246057 245918.722017464 138.277982536256 42 235372 242906.844352009 -7534.84435200876 43 258556 255308.796461599 3247.20353840141 44 260993 259638.375558822 1354.62444117772 45 254663 258478.518363941 -3815.51836394125 46 250643 248023.813174295 2619.18682570528 47 243422 243802.46809435 -380.468094349849 48 247105 244438.448950817 2666.55104918297 49 248541 248707.109534758 -166.109534757523 50 245039 246829.955926916 -1790.95592691628 51 237080 240552.554243289 -3472.55424328873 52 237085 233325.326820234 3759.67317976628 53 225554 230301.062874051 -4747.06287405083 54 226839 222571.093695035 4267.9063049649 55 247934 247682.894344598 251.105655402272 56 248333 253953.337917293 -5620.33791729274 57 246969 251153.214235554 -4184.21423555387 58 245098 244482.523416331 615.476583668764 59 246263 242835.954220147 3427.04577985268 60 255765 252731.097118382 3033.90288161785 61 264319 262601.968866183 1717.03113381720 62 268347 268083.804654045 263.19534595475 63 273046 268803.167673581 4242.8323264192 64 273963 274404.942738414 -441.942738413858 65 267430 271441.24872965 -4011.24872965012 66 271993 268102.126245060 3890.87375494034 67 292710 295098.367393125 -2388.3673931248 68 295881 296425.246792235 -544.246792235398 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.0170914526508557 0.0341829053017114 0.982908547349144 20 0.0706844224037705 0.141368844807541 0.92931557759623 21 0.0429904510758182 0.0859809021516364 0.957009548924182 22 0.0207952243827509 0.0415904487655018 0.97920477561725 23 0.00843768952783331 0.0168753790556666 0.991562310472167 24 0.00294948500473889 0.00589897000947777 0.997050514995261 25 0.00333756549116453 0.00667513098232906 0.996662434508836 26 0.00137273597756269 0.00274547195512539 0.998627264022437 27 0.000540342691815725 0.00108068538363145 0.999459657308184 28 0.00085613879615001 0.00171227759230002 0.99914386120385 29 0.00219149455709473 0.00438298911418946 0.997808505442905 30 0.000964058387817218 0.00192811677563444 0.999035941612183 31 0.000816063436217151 0.00163212687243430 0.999183936563783 32 0.00267520203552088 0.00535040407104176 0.997324797964479 33 0.00709851440769193 0.0141970288153839 0.992901485592308 34 0.0938021356735913 0.187604271347183 0.906197864326409 35 0.0593867115559281 0.118773423111856 0.940613288444072 36 0.0591410939852005 0.118282187970401 0.9408589060148 37 0.0850138359867299 0.170027671973460 0.91498616401327 38 0.057368782566079 0.114737565132158 0.94263121743392 39 0.0428196121240415 0.085639224248083 0.957180387875958 40 0.0514371927909442 0.102874385581888 0.948562807209056 41 0.0716367205460333 0.143273441092067 0.928363279453967 42 0.375774902245328 0.751549804490656 0.624225097754672 43 0.490208769455847 0.980417538911694 0.509791230544153 44 0.669212421538788 0.661575156922424 0.330787578461212 45 0.621742369356011 0.756515261287978 0.378257630643989 46 0.687528830931524 0.624942338136952 0.312471169068476 47 0.559921334964295 0.88015733007141 0.440078665035705 48 0.545984299359196 0.908031401281608 0.454015700640804 49 0.509296013761324 0.981407972477351 0.490703986238676 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.290322580645161 NOK 5% type I error level 13 0.419354838709677 NOK 10% type I error level 15 0.483870967741935 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/10101w1258910047.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/10101w1258910047.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/1upow1258910047.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/1upow1258910047.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/27zxl1258910047.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/27zxl1258910047.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/3swzp1258910047.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/3swzp1258910047.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/4urkb1258910047.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/4urkb1258910047.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/5ve5d1258910047.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/5ve5d1258910047.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/6dm2g1258910047.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/6dm2g1258910047.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/7oucf1258910047.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/7oucf1258910047.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/81cvl1258910047.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/81cvl1258910047.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/9b8031258910047.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589100922kzxcfpv8szfn6h/9b8031258910047.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