Workshop 7: model 1

*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: Fri, 20 Nov 2009 03:42:03 -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/20/t1258713851yli636uvdcq3pdw.htm/, Retrieved Fri, 20 Nov 2009 11:44:23 +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/20/t1258713851yli636uvdcq3pdw.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:

WS7M1MLDG

Dataseries X:

» Textbox « » Textfile « » CSV «

267413 21,4 267366 26,4 264777 26,4 258863 29,4 254844 34,4 254868 24,4 277267 26,4 285351 25,4 286602 31,4 283042 27,4 276687 27,4 277915 29,4 277128 32,4 277103 26,4 275037 22,4 270150 19,4 267140 21,4 264993 23,4 287259 23,4 291186 25,4 292300 28,4 288186 27,4 281477 21,4 282656 17,4 280190 24,4 280408 26,4 276836 22,4 275216 14,4 274352 18,4 271311 25,4 289802 29,4 290726 26,4 292300 26,4 278506 20,4 269826 26,4 265861 29,4 269034 33,4 264176 32,4 255198 35,4 253353 34,4 246057 36,4 235372 32,4 258556 34,4 260993 31,4 254663 27,4 250643 27,4 243422 30,4 247105 32,4 248541 32,4 245039 27,4 237080 31,4 237085 29,4 225554 27,4 226839 25,4 247934 26,4 248333 23,4 246969 18,4 245098 22,4 246263 17,4 255765 17,4 264319 11,4 268347 9,4 273046 6,4 273963 0 267430 7,8 271993 7,9 292710 12 295881 16,9 293299 12,3

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] = + 282428.042215116 -644.709018428348X[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) 282428.042215116 6765.860871 41.7431 0 0 X -644.709018428348 265.31138 -2.43 0.017784 0.008892 Multiple Linear Regression - Regression Statistics Multiple R 0.284596384591662 R-squared 0.0809951021226453 Adjusted R-squared 0.0672786111095505 F-TEST (value) 5.90494333028185 F-TEST (DF numerator) 1 F-TEST (DF denominator) 67 p-value 0.0177840213312728 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 16805.5367906118 Sum Squared Residuals 18922546476.9807 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 267413 268631.269220751 -1218.26922075110 2 267366 265407.724128608 1958.27587139188 3 264777 265407.724128608 -630.724128608104 4 258863 263473.597073323 -4610.59707332306 5 254844 260250.051981181 -5406.05198118132 6 254868 266697.142165465 -11829.1421654648 7 277267 265407.724128608 11859.2758713919 8 285351 266052.433147036 19298.5668529636 9 286602 262184.179036466 24417.8209635336 10 283042 264763.01511018 18278.9848898202 11 276687 264763.01511018 11923.9848898202 12 277915 263473.597073323 14441.4029266769 13 277128 261539.470018038 15588.5299819620 14 277103 265407.724128608 11695.2758713919 15 275037 267986.560202322 7050.4397976785 16 270150 269920.687257607 229.312742393460 17 267140 268631.26922075 -1491.26922074984 18 264993 267341.851183893 -2348.85118389315 19 287259 267341.851183893 19917.1488161069 20 291186 266052.433147036 25133.5668529636 21 292300 264118.306091751 28181.6939082486 22 288186 264763.01511018 23422.9848898202 23 281477 268631.26922075 12845.7307792502 24 282656 271210.105294463 11445.8947055368 25 280190 266697.142165465 13492.8578345352 26 280408 265407.724128608 15000.2758713919 27 276836 267986.560202322 8849.4397976785 28 275216 273144.232349748 2071.76765025172 29 274352 270565.396276035 3786.60372396511 30 271311 266052.433147036 5258.56685296355 31 289802 263473.597073323 26328.4029266769 32 290726 265407.724128608 25318.2758713919 33 292300 265407.724128608 26892.2758713919 34 278506 269275.978239178 9230.02176082181 35 269826 265407.724128608 4418.2758713919 36 265861 263473.597073323 2387.40292667694 37 269034 260894.760999610 8139.23900039033 38 264176 261539.470018038 2636.52998196198 39 255198 259605.342962753 -4407.34296275297 40 253353 260250.051981181 -6897.05198118132 41 246057 258960.633944325 -12903.6339443246 42 235372 261539.470018038 -26167.470018038 43 258556 260250.051981181 -1694.05198118132 44 260993 262184.179036466 -1191.17903646636 45 254663 264763.01511018 -10100.0151101798 46 250643 264763.01511018 -14120.0151101798 47 243422 262828.888054895 -19406.8880548947 48 247105 261539.470018038 -14434.4700180380 49 248541 261539.470018038 -12998.4700180380 50 245039 264763.01511018 -19724.0151101798 51 237080 262184.179036466 -25104.1790364664 52 237085 263473.597073323 -26388.5970733231 53 225554 264763.01511018 -39209.0151101798 54 226839 266052.433147036 -39213.4331470364 55 247934 265407.724128608 -17473.7241286081 56 248333 267341.851183893 -19008.8511838931 57 246969 270565.396276035 -23596.3962760349 58 245098 267986.560202322 -22888.5602023215 59 246263 271210.105294463 -24947.1052944632 60 255765 271210.105294463 -15445.1052944632 61 264319 275078.359405033 -10759.3594050333 62 268347 276367.77744189 -8020.77744189002 63 273046 278301.904497175 -5255.90449717506 64 273963 282428.042215116 -8465.04221511649 65 267430 277399.311871375 -9969.31187137538 66 271993 277334.840969533 -5341.84096953254 67 292710 274691.533993976 18018.4660060237 68 295881 271532.459803677 24348.5401963226 69 293299 274498.121288448 18800.8787115522 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00435589360917865 0.0087117872183573 0.995644106390821 6 0.0160833048610161 0.0321666097220322 0.983916695138984 7 0.0410256545129135 0.082051309025827 0.958974345487086 8 0.100984023125877 0.201968046251754 0.899015976874123 9 0.204742024620827 0.409484049241654 0.795257975379173 10 0.192818019911055 0.38563603982211 0.807181980088945 11 0.136949239691239 0.273898479382478 0.863050760308761 12 0.0997284358929584 0.199456871785917 0.900271564107042 13 0.0697769396736587 0.139553879347317 0.930223060326341 14 0.0462085281919695 0.092417056383939 0.95379147180803 15 0.0276136564315919 0.0552273128631839 0.972386343568408 16 0.0155247814957799 0.0310495629915598 0.98447521850422 17 0.00884976797253382 0.0176995359450676 0.991150232027466 18 0.00523634458372783 0.0104726891674557 0.994763655416272 19 0.00727149617410129 0.0145429923482026 0.992728503825899 20 0.0137732017412447 0.0275464034824894 0.986226798258755 21 0.0265301089043092 0.0530602178086184 0.97346989109569 22 0.0326362759632232 0.0652725519264463 0.967363724036777 23 0.0254614874622582 0.0509229749245163 0.974538512537742 24 0.0193323712493979 0.0386647424987958 0.980667628750602 25 0.0147017037544001 0.0294034075088002 0.9852982962456 26 0.0118994379837654 0.0237988759675309 0.988100562016235 27 0.00804539299193946 0.0160907859838789 0.99195460700806 28 0.00485856014432846 0.00971712028865693 0.995141439855671 29 0.00295404441871627 0.00590808883743254 0.997045955581284 30 0.00192520160728056 0.00385040321456111 0.99807479839272 31 0.00454891069987411 0.00909782139974822 0.995451089300126 32 0.0111115772042875 0.0222231544085750 0.988888422795712 33 0.0346686006201798 0.0693372012403596 0.96533139937982 34 0.0311818079007783 0.0623636158015566 0.968818192099222 35 0.0286316488445855 0.057263297689171 0.971368351155415 36 0.0293589029190543 0.0587178058381086 0.970641097080946 37 0.0369983634917421 0.0739967269834843 0.963001636508258 38 0.0442430919352338 0.0884861838704676 0.955756908064766 39 0.0595095481592743 0.119019096318549 0.940490451840726 40 0.0734906680921346 0.146981336184269 0.926509331907865 41 0.0945943553602336 0.189188710720467 0.905405644639766 42 0.182068079680161 0.364136159360322 0.817931920319839 43 0.197920256354751 0.395840512709503 0.802079743645248 44 0.223545667561808 0.447091335123616 0.776454332438192 45 0.224099173276113 0.448198346552225 0.775900826723887 46 0.228324524006658 0.456649048013316 0.771675475993342 47 0.240412883673471 0.480825767346943 0.759587116326529 48 0.239646496691101 0.479292993382202 0.760353503308899 49 0.249946652621883 0.499893305243767 0.750053347378117 50 0.252056958166155 0.50411391633231 0.747943041833845 51 0.256389591632373 0.512779183264746 0.743610408367627 52 0.258855872907869 0.517711745815738 0.741144127092131 53 0.379019811580528 0.758039623161055 0.620980188419472 54 0.546949291650606 0.906101416698789 0.453050708349394 55 0.481532455939909 0.963064911879818 0.518467544060091 56 0.431471298501172 0.862942597002345 0.568528701498828 57 0.454852828348487 0.909705656696974 0.545147171651513 58 0.49894980607098 0.99789961214196 0.50105019392902 59 0.702361521487503 0.595276957024993 0.297638478512497 60 0.897642506340449 0.204714987319102 0.102357493659551 61 0.939003633384758 0.121992733230484 0.0609963666152418 62 0.94096184901468 0.118076301970641 0.0590381509853206 63 0.878528000565274 0.242943998869453 0.121471999434726 64 0.914490897068185 0.171018205863630 0.0855091029318149 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.0833333333333333 NOK 5% type I error level 16 0.266666666666667 NOK 10% type I error level 28 0.466666666666667 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/10tbfo1258713718.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/10tbfo1258713718.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/1qc8g1258713718.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/1qc8g1258713718.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/237s31258713718.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/237s31258713718.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/3vnm21258713718.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/3vnm21258713718.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/4l9r31258713718.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/4l9r31258713718.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/531541258713718.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/531541258713718.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/625g41258713718.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/625g41258713718.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/7b5nx1258713718.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/7b5nx1258713718.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/8yclj1258713718.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/8yclj1258713718.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/91h0g1258713718.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258713851yli636uvdcq3pdw/91h0g1258713718.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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