Workshop 7: model 4

*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 04:10:35 -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/t1258715526u3seac5f30p9tyk.htm/, Retrieved Fri, 20 Nov 2009 12:12:18 +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/t1258715526u3seac5f30p9tyk.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:

WS7M4MLDG

Dataseries X:

» Textbox « » Textfile « » CSV «

264777 26,4 267366 267413 258863 29,4 264777 267366 254844 34,4 258863 264777 254868 24,4 254844 258863 277267 26,4 254868 254844 285351 25,4 277267 254868 286602 31,4 285351 277267 283042 27,4 286602 285351 276687 27,4 283042 286602 277915 29,4 276687 283042 277128 32,4 277915 276687 277103 26,4 277128 277915 275037 22,4 277103 277128 270150 19,4 275037 277103 267140 21,4 270150 275037 264993 23,4 267140 270150 287259 23,4 264993 267140 291186 25,4 287259 264993 292300 28,4 291186 287259 288186 27,4 292300 291186 281477 21,4 288186 292300 282656 17,4 281477 288186 280190 24,4 282656 281477 280408 26,4 280190 282656 276836 22,4 280408 280190 275216 14,4 276836 280408 274352 18,4 275216 276836 271311 25,4 274352 275216 289802 29,4 271311 274352 290726 26,4 289802 271311 292300 26,4 290726 289802 278506 20,4 292300 290726 269826 26,4 278506 292300 265861 29,4 269826 278506 269034 33,4 265861 269826 264176 32,4 269034 265861 255198 35,4 264176 269034 253353 34,4 255198 264176 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 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 38700.4448705963 -357.374472052318X[t] + 0.855647370845933Y1[t] + 0.0401402690200562Y2[t] -3281.03169319582M1[t] -3516.7130856408M2[t] -5821.00541143949M3[t] -2989.17761744896M4[t] + 20876.6717415511M5[t] + 5718.67789938775M6[t] + 908.13223657077M7[t] -4151.43097655575M8[t] -5064.07466478773M9[t] + 2531.01839913112M10[t] + 3396.50452876132M11[t] -78.2294747601183t + 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) 38700.4448705963 12104.709906 3.1971 0.002385 0.001192 X -357.374472052318 86.779454 -4.1182 0.00014 7e-05 Y1 0.855647370845933 0.150132 5.6993 1e-06 0 Y2 0.0401402690200562 0.14173 0.2832 0.778158 0.389079 M1 -3281.03169319582 2079.842465 -1.5775 0.120855 0.060427 M2 -3516.7130856408 2282.880196 -1.5405 0.129628 0.064814 M3 -5821.00541143949 2148.087604 -2.7099 0.009145 0.004572 M4 -2989.17761744896 2406.723714 -1.242 0.219914 0.109957 M5 20876.6717415511 2148.958423 9.7148 0 0 M6 5718.67789938775 3465.391881 1.6502 0.105043 0.052521 M7 908.13223657077 2075.026862 0.4376 0.663489 0.331745 M8 -4151.43097655575 2155.721403 -1.9258 0.059716 0.029858 M9 -5064.07466478773 2367.82398 -2.1387 0.037271 0.018635 M10 2531.01839913112 2373.771239 1.0662 0.291335 0.145668 M11 3396.50452876132 2125.042774 1.5983 0.116149 0.058075 t -78.2294747601183 32.387464 -2.4154 0.019339 0.009669 Multiple Linear Regression - Regression Statistics Multiple R 0.986040149162972 R-squared 0.972275175761335 Adjusted R-squared 0.96412081569114 F-TEST (value) 119.233780136229 F-TEST (DF numerator) 15 F-TEST (DF denominator) 51 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 3345.57670827937 Sum Squared Residuals 570837059.060053 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 264777 265411.542353513 -634.542353513001 2 258863 261808.350434387 -2945.3504343871 3 254844 252474.734565891 2369.26543410906 4 254868 255125.84127123 -257.841271230105 5 277267 278057.924007074 -790.924007074084 6 285351 282345.683988238 3005.3160117625 7 286602 283128.817250045 3473.18274995475 8 283042 280815.431246054 2226.56875394572 9 276687 276828.668919395 -141.668919394760 10 277915 278050.245165012 -135.245165011543 11 277128 278561.021965501 -1433.02196550102 12 277103 276606.432563794 496.567436205625 13 275037 274623.687708058 413.312291942228 14 270150 273613.129282116 -3463.12928211643 15 267140 266251.380040333 888.619959666526 16 264993 265518.565334512 -525.565334511973 17 287259 287348.288103795 -89.2881037952946 18 291186 290362.979044437 823.02095556329 19 292300 288655.970946015 3644.02905398478 20 288186 284986.374737745 3199.62526225498 21 281477 282664.331383095 -1187.33138309501 22 282656 285705.017582709 -3049.01758270913 23 280190 284730.160118585 -4540.16011858479 24 280408 278477.976131627 1930.02386837271 25 276836 276635.758075322 200.241924678429 26 275216 276133.22115452 -917.221154519716 27 274352 270791.671684042 3560.32831595841 28 271311 270239.342134682 1071.65786531760 29 289802 289960.759283537 -158.759283537221 30 290726 291496.368358993 -770.368358992896 31 292300 288140.445106527 4159.5548934727 32 278506 286530.777821241 -8024.7778212406 33 269826 271656.038775923 -1830.03877592337 34 265861 270120.06489912 -4259.06489911978 35 269034 265736.764305282 3297.23569471762 36 264176 265175.217714843 -999.217714842883 37 255198 256714.463276761 -1516.46327676108 38 253353 248880.923359254 4472.07664074592 39 246057 243844.603880118 2212.39611988217 40 235372 241710.838073524 -6338.83807352357 41 258556 255348.2534534 3207.74654660028 42 260993 260592.583423846 400.416576153945 43 254663 260149.130814191 -5486.13081419076 44 250643 249692.912104451 950.087895548771 45 243422 243936.125191605 -514.125191604582 46 247105 244398.246290320 2706.75370968044 47 248541 248046.999329421 494.000670578608 48 245039 247735.683921497 -2696.68392149716 49 237080 240008.089198942 -2928.08919894229 50 237085 233458.258629171 3626.74137082918 51 225554 231475.287608440 -5921.28760844024 52 226839 225077.365739896 1761.63426010406 53 247934 249144.260581550 -1210.26058155028 54 248333 253081.622214470 -4748.62221446953 55 246969 251167.881713100 -4198.88171309964 56 245098 243449.504090509 1648.49590949112 57 246263 242589.835729982 3673.16427001772 58 255765 251028.42606284 4736.57393716001 59 264319 262137.054281210 2181.94571878959 60 268347 267077.689668238 1269.31033176171 61 273046 268580.459387404 4465.54061259572 62 273963 274736.117140552 -773.117140551849 63 267430 270539.322221176 -3109.32222117593 64 271993 267704.047446156 4288.95255384398 65 292710 293668.514570643 -958.514570643393 66 295881 294590.762970017 1290.23702998269 67 293299 294890.754170122 -1591.75417012184 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.0268048603408347 0.0536097206816693 0.973195139659165 20 0.00888050386709873 0.0177610077341975 0.991119496132901 21 0.00267390253593636 0.00534780507187272 0.997326097464064 22 0.00123002983133951 0.00246005966267902 0.99876997016866 23 0.000605231439091229 0.00121046287818246 0.999394768560909 24 0.00030069865810754 0.00060139731621508 0.999699301341892 25 8.2610373773177e-05 0.000165220747546354 0.999917389626227 26 0.000278331308391881 0.000556662616783762 0.999721668691608 27 0.000314389322885023 0.000628778645770046 0.999685610677115 28 0.000114358107751812 0.000228716215503624 0.999885641892248 29 3.88131572442882e-05 7.76263144885764e-05 0.999961186842756 30 4.65425799582758e-05 9.30851599165517e-05 0.999953457420042 31 0.000175083915008972 0.000350167830017944 0.999824916084991 32 0.0530987724496575 0.106197544899315 0.946901227550343 33 0.0393941069133485 0.078788213826697 0.960605893086651 34 0.0707997573252944 0.141599514650589 0.929200242674706 35 0.185982650032092 0.371965300064184 0.814017349967908 36 0.133225166264555 0.266450332529111 0.866774833735445 37 0.102169022884274 0.204338045768548 0.897830977115726 38 0.139251271252852 0.278502542505705 0.860748728747148 39 0.380340299677466 0.760680599354933 0.619659700322533 40 0.567532857117105 0.86493428576579 0.432467142882895 41 0.594819939103072 0.810360121793856 0.405180060896928 42 0.761431369922407 0.477137260155185 0.238568630077593 43 0.846337746717805 0.307324506564389 0.153662253282195 44 0.824086546252655 0.351826907494689 0.175913453747345 45 0.778059570073201 0.443880859853599 0.221940429926799 46 0.724107375925224 0.551785248149552 0.275892624074776 47 0.783687312195525 0.432625375608950 0.216312687804475 48 0.955077021348614 0.0898459573027725 0.0449229786513863 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 11 0.366666666666667 NOK 5% type I error level 12 0.4 NOK 10% type I error level 15 0.5 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/10yyks1258715431.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/10yyks1258715431.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/126b71258715431.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/126b71258715431.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/2j9xb1258715431.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/2j9xb1258715431.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/3057f1258715431.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/3057f1258715431.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/45ju81258715431.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/45ju81258715431.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/5aa161258715431.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/5aa161258715431.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/6g8ar1258715431.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/6g8ar1258715431.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/7q1uk1258715431.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/7q1uk1258715431.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/8l5p81258715431.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/8l5p81258715431.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/91coe1258715431.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258715526u3seac5f30p9tyk/91coe1258715431.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') }

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