Model 2

*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 15:07:30 -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/t1259618906n8lq2jhryjf1bxb.htm/, Retrieved Mon, 30 Nov 2009 23:08:38 +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/t1259618906n8lq2jhryjf1bxb.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:

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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 4 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] = + 5080.60794199417 + 1.82721379285696Bel20[t] -154.281007233768M1[t] -100.885510061914M2[t] -2.12146594401014M3[t] -263.807037356975M4[t] -379.610319780344M5[t] -487.750308038608M6[t] -428.770878933044M7[t] -177.896813632340M8[t] -105.478380888990M9[t] -69.6836007931012M10[t] + 21.5842920134465M11[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) 5080.60794199417 408.727323 12.4303 0 0 Bel20 1.82721379285696 0.091467 19.9767 0 0 M1 -154.281007233768 376.627695 -0.4096 0.683933 0.341966 M2 -100.885510061914 376.697779 -0.2678 0.790013 0.395007 M3 -2.12146594401014 376.651466 -0.0056 0.99553 0.497765 M4 -263.807037356975 376.625732 -0.7004 0.4871 0.24355 M5 -379.610319780344 376.620886 -1.0079 0.318646 0.159323 M6 -487.750308038608 376.642898 -1.295 0.201648 0.100824 M7 -428.770878933044 376.684482 -1.1383 0.260774 0.130387 M8 -177.896813632340 376.71598 -0.4722 0.638948 0.319474 M9 -105.478380888990 376.62779 -0.2801 0.78066 0.39033 M10 -69.6836007931012 376.698919 -0.185 0.854037 0.427018 M11 21.5842920134465 376.657238 0.0573 0.954545 0.477273 Multiple Linear Regression - Regression Statistics Multiple R 0.946364785215958 R-squared 0.895606306696847 Adjusted R-squared 0.868952597768382 F-TEST (value) 33.6015640112431 F-TEST (DF numerator) 12 F-TEST (DF denominator) 47 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 595.489598070232 Sum Squared Residuals 16666569.4862628 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10001.6 9963.51683035672 38.0831696432784 2 10411.75 10185.9478755058 225.802124494193 3 10673.38 10416.5819390542 256.798060945796 4 10539.51 10265.2783528677 274.231647132273 5 10723.78 10329.8758882131 393.904111786874 6 10682.06 10268.5856616037 413.474338396285 7 10283.19 10351.4833192578 -68.2933192577764 8 10377.18 10496.2876238831 -119.107623883133 9 10486.64 10634.5771138590 -147.937113858977 10 10545.38 10788.007917939 -242.627917938998 11 10554.27 11053.7199115496 -499.449911549598 12 10532.54 11068.4058133244 -535.865813324363 13 10324.31 10947.5810906378 -623.271090637807 14 10695.25 11126.4513589651 -431.201358965147 15 10827.81 11463.0820946372 -635.272094637172 16 10872.48 11517.2486994575 -644.768699457461 17 10971.19 11668.273447205 -697.083447204993 18 11145.65 11751.8081858174 -606.158185817426 19 11234.68 11769.7666652733 -535.08666527335 20 11333.88 11848.0603878387 -514.180387838715 21 10997.97 11498.5020672597 -500.532067259678 22 11036.89 11774.5571889783 -737.667188978329 23 11257.35 12159.3852497453 -902.035249745275 24 11533.59 12334.8294210156 -801.239421015595 25 11963.12 12488.2877607748 -525.167760774797 26 12185.15 12653.5635584833 -468.413558483282 27 12377.62 12918.8598676822 -541.23986768217 28 12512.89 12936.7745508522 -423.884550852178 29 12631.48 12928.2835344833 -296.803534483299 30 12268.53 12553.9915851575 -285.461585157487 31 12754.8 13041.0689337915 -286.268933791512 32 13407.75 13485.0612248593 -77.311224859266 33 13480.21 13419.4153834143 60.7946165856559 34 13673.28 13348.2085238005 325.071476199471 35 13239.71 12781.0947427649 458.615257235142 36 13557.69 12931.2137308662 626.476269133822 37 13901.28 13030.4403652534 870.839634746616 38 13200.58 12480.7639500928 719.8160499072 39 13406.97 12600.5409528286 806.42904717144 40 12538.12 11841.5060591379 696.613940862145 41 12419.57 11500.0236011587 919.546398841275 42 12193.88 11306.7719944292 887.108005570816 43 12656.63 11700.533534662 956.09646533800 44 12812.48 11848.0603878387 964.419612161286 45 12056.67 11378.3627603793 678.307239620669 46 11322.38 10552.1328894191 770.247110580907 47 11530.75 10669.7674772566 860.982522743434 48 11114.08 10493.4364491261 620.643550873937 49 9181.73 8942.2139529773 239.516047022709 50 8614.55 8660.55325695296 -46.0032569529636 51 8595.56 8482.2751457979 113.284854202105 52 8396.2 8298.39233768478 97.8076623152214 53 7690.5 8010.06352893986 -319.563528939856 54 7235.47 7644.43257299219 -408.962572992188 55 7992.12 8058.56754701536 -66.4475470153609 56 8398.37 8652.19037558017 -253.820375580172 57 8593.01 8683.64267508767 -90.6326750876692 58 8679.75 8794.77347986305 -115.023479863052 59 9374.63 9292.7426186837 81.8873813162975 60 9634.97 9544.9845856678 89.9854143321996 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.000915297056985617 0.00183059411397123 0.999084702943014 17 0.000121279413678726 0.000242558827357451 0.999878720586321 18 1.97790705066177e-05 3.95581410132355e-05 0.999980220929493 19 0.000982521155222042 0.00196504231044408 0.999017478844778 20 0.00127346744925903 0.00254693489851806 0.998726532550741 21 0.000446022142584769 0.000892044285169539 0.999553977857415 22 0.000163117202090055 0.00032623440418011 0.99983688279791 23 0.000122305315913707 0.000244610631827415 0.999877694684086 24 0.000242107932808926 0.000484215865617852 0.999757892067191 25 0.00324902764204664 0.00649805528409329 0.996750972357953 26 0.00433259445653402 0.00866518891306804 0.995667405543466 27 0.00648096452236246 0.0129619290447249 0.993519035477638 28 0.0114435020521876 0.0228870041043751 0.988556497947812 29 0.0178006092837947 0.0356012185675894 0.982199390716205 30 0.0210866116318919 0.0421732232637838 0.978913388368108 31 0.0765743655196869 0.153148731039374 0.923425634480313 32 0.252744951453329 0.505489902906658 0.747255048546671 33 0.540496408433165 0.91900718313367 0.459503591566835 34 0.852960780583375 0.29407843883325 0.147039219416625 35 0.969920984139128 0.0601580317217447 0.0300790158608724 36 0.990246209632509 0.0195075807349824 0.00975379036749119 37 0.995404907217923 0.00919018556415319 0.00459509278207659 38 0.994522350856226 0.0109552982875484 0.0054776491437742 39 0.997566880638166 0.0048662387236673 0.00243311936183365 40 0.999494603774267 0.0010107924514659 0.00050539622573295 41 0.998407272111265 0.00318545577747081 0.00159272788873541 42 0.994704976677784 0.0105900466444314 0.00529502332221568 43 0.992194551708255 0.0156108965834900 0.00780544829174499 44 0.970538515341088 0.0589229693178248 0.0294614846589124 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 15 0.517241379310345 NOK 5% type I error level 23 0.793103448275862 NOK 10% type I error level 25 0.862068965517241 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/10kppn1259618846.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/10kppn1259618846.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/152o71259618846.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/152o71259618846.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/2p9bj1259618846.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/2p9bj1259618846.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/3fyah1259618846.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/3fyah1259618846.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/4bfvo1259618846.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/4bfvo1259618846.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/5rgwi1259618846.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/5rgwi1259618846.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/66r711259618846.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/66r711259618846.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/7c6251259618846.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/7c6251259618846.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/8k9321259618846.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/8k9321259618846.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/9v0b31259618846.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/30/t1259618906n8lq2jhryjf1bxb/9v0b31259618846.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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