*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 08:25:57 -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/t1258903603extotp7en1p5pv6.htm/, Retrieved Sun, 22 Nov 2009 16:26:55 +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/t1258903603extotp7en1p5pv6.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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274412 244752 272433 244576 268361 241572 268586 240541 264768 236089 269974 236997 304744 264579 309365 270349 308347 269645 298427 267037 289231 258113 291975 262813 294912 267413 293488 267366 290555 264777 284736 258863 281818 254844 287854 254868 316263 277267 325412 285351 326011 286602 328282 283042 317480 276687 317539 277915 313737 277128 312276 277103 309391 275037 302950 270150 300316 267140 304035 264993 333476 287259 337698 291186 335932 292300 323931 288186 313927 281477 314485 282656 313218 280190 309664 280408 302963 276836 298989 275216 298423 274352 301631 271311 329765 289802 335083 290726 327616 292300 309119 278506 295916 269826 291413 265861 291542 269034 284678 264176 276475 255198 272566 253353 264981 246057 263290 235372 296806 258556 303598 260993 286994 254663 276427 250643 266424 243422 267153 247105 268381 248541 262522 245039 255542 237080 253158 237085 243803 225554 250741 226839 280445 247934 285257 248333 2709 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] = + 41433.5106011802 + 0.998158246327505X[t] -2917.07031357424M1[t] -4883.42917985957M2[t] -5844.67238720495M3[t] -6705.09432199351M4[t] -5706.65455866484M5[t] + 462.027526960597M6[t] + 9147.79114581125M7[t] + 14952.8874585376M8[t] + 9314.87896653174M9[t] + 4907.54056574619M10[t] + 1613.12804336646M11[t] -376.360237931731t + 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) 41433.5106011802 17590.265434 2.3555 0.021527 0.010763 X 0.998158246327505 0.063612 15.6914 0 0 M1 -2917.07031357424 4748.529565 -0.6143 0.541155 0.270577 M2 -4883.42917985957 4747.570711 -1.0286 0.307473 0.153736 M3 -5844.67238720495 4756.019422 -1.2289 0.22354 0.11177 M4 -6705.09432199351 4765.249537 -1.4071 0.164167 0.082084 M5 -5706.65455866484 4808.228098 -1.1869 0.239606 0.119803 M6 462.027526960597 4821.111165 0.0958 0.923947 0.461974 M7 9147.79114581125 4775.871507 1.9154 0.05984 0.02992 M8 14952.8874585376 4956.181622 3.017 0.003642 0.001821 M9 9314.87896653174 4950.51654 1.8816 0.06437 0.032185 M10 4907.54056574619 4926.99516 0.9961 0.322919 0.161459 M11 1613.12804336646 4925.530201 0.3275 0.74434 0.37217 t -376.360237931731 42.686818 -8.8168 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.947209342671728 R-squared 0.897205538844606 Adjusted R-squared 0.876646646613528 F-TEST (value) 43.6407530503179 F-TEST (DF numerator) 13 F-TEST (DF denominator) 65 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 8525.28419309094 Sum Squared Residuals 4724230587.2428 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 274412 282441.307154824 -8029.30715482375 2 272433 279922.912199253 -7489.912199253 3 268361 275586.841382008 -7225.84138200802 4 268586 273320.958057324 -4734.95805732407 5 264768 269499.237070071 -4731.23707007095 6 269974 276197.88660543 -6223.88660543005 7 304744 312038.490736554 -7294.49073655419 8 309365 323226.599892659 -13861.5998926585 9 308347 316509.527757306 -8162.52775730634 10 298427 309122.632412167 -10695.6324121669 11 289231 296544.295461629 -7313.29546162884 12 291975 299246.15093807 -7271.15093806991 13 294912 300544.248319670 -5632.24831967046 14 293488 298154.615777876 -4666.61577787602 15 290555 294232.780632857 -3677.78063285699 16 284736 287092.890591356 -2356.89059135583 17 281818 283703.372124763 -1885.37212476252 18 287854 289519.649770368 -1665.64977036810 19 316263 320186.799710777 -3923.79971077679 20 325412 333684.647048883 -8272.64704888297 21 326011 328918.974285101 -2907.97428510106 22 328282 320581.832289458 7700.16771054213 23 317480 310567.763873735 6912.23612626488 24 317539 309804.013918927 7734.9860810729 25 313737 305725.032827561 8011.96717243862 26 312276 303357.359767186 8918.64023281386 27 309391 299957.561384996 9433.4386150036 28 302950 293842.779862474 9107.2201375264 29 300316 291460.403066425 8855.59693357526 30 304035 295109.679159253 8925.3208407467 31 333476 325644.074052900 7831.92594709957 32 337698 334992.577561023 2705.42243897683 33 335932 330090.157117494 5841.8428825056 34 323931 321200.035453386 2730.96454661423 35 313927 310832.619018463 3094.38098153691 36 314485 310019.959309585 4465.04069041498 37 313218 304265.070522635 8952.92947736457 38 309664 302139.949916118 7524.05008388223 39 302963 297236.925214959 5726.07478504119 40 298989 294383.126683188 4605.87331681205 41 298423 294142.797483758 4280.20251624208 42 301631 296899.720104370 4731.2798956303 43 329765 323666.067618130 6098.9323818695 44 335083 330017.101912532 5065.89808746825 45 327616 325573.834262314 2042.16573768637 46 309119 307021.540773755 2097.45922624525 47 295916 294686.754435321 1229.24556467944 48 291413 288739.568707334 2673.43129266618 49 291542 288613.294271425 2928.70572857498 50 284678 281421.522406549 3256.47759345106 51 276475 271122.454225743 5352.5457742565 52 272566 268044.070088549 4521.92991145105 53 264981 261383.587048740 3597.41295125958 54 263290 256510.588034425 6779.41196557525 55 296806 287961.292198201 8844.70780179947 56 303598 295822.539919295 7775.46008070471 57 286994 283489.829490105 3504.17050989542 58 276427 274693.534701151 1733.46529884927 59 266424 263815.061244108 2608.93875589164 60 267153 265501.789784034 1651.21021596563 61 268381 263641.714474255 4739.2855257453 62 262522 257803.445191399 4718.55480860128 63 255542 248521.500263601 7020.49973639899 64 253158 247289.708882112 5868.29111788766 65 243803 236402.025669107 7400.97433089318 66 250741 243476.980863331 7264.01913666862 67 280445 272842.532450529 7602.46754947099 68 285257 278669.533665608 6587.46633439169 69 270976 271293.67708768 -317.677087679987 70 261076 264642.424370084 -3566.42437008395 71 255603 262134.505966744 -6531.50596674403 72 260376 269629.51734205 -9253.51734204978 73 263903 274874.332429629 -10971.3324296293 74 264291 276552.194741619 -12261.1947416194 75 263276 279904.936895835 -16628.9368958352 76 262572 279583.465834997 -17011.4658349973 77 256167 273684.577537137 -17517.5775371366 78 264221 284031.495462823 -19810.4954628227 79 293860 313019.743232909 -19159.7432329086 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.000303159900603116 0.000606319801206231 0.999696840099397 18 0.000413543223750872 0.000827086447501745 0.99958645677625 19 0.000100428906878789 0.000200857813757577 0.999899571093121 20 0.000668325464221363 0.00133665092844273 0.999331674535779 21 0.0019124263939987 0.0038248527879974 0.998087573606001 22 0.894132999346514 0.211734001306973 0.105867000653486 23 0.96568565178384 0.06862869643232 0.03431434821616 24 0.978852331226528 0.0422953375469436 0.0211476687734718 25 0.980942837929733 0.0381143241405343 0.0190571620702671 26 0.97799595626149 0.0440080874770188 0.0220040437385094 27 0.969722554123945 0.0605548917521098 0.0302774458760549 28 0.970119703976043 0.0597605920479131 0.0298802960239566 29 0.964740775460913 0.0705184490781736 0.0352592245390868 30 0.965076668719823 0.0698466625603543 0.0349233312801772 31 0.982906236989868 0.0341875260202629 0.0170937630101315 32 0.99990794946334 0.000184101073319207 9.20505366596034e-05 33 0.999892919714215 0.000214160571569930 0.000107080285784965 34 0.999990656898267 1.8686203466313e-05 9.3431017331565e-06 35 0.999997220666625 5.55866675083073e-06 2.77933337541536e-06 36 0.999997209305513 5.58138897490804e-06 2.79069448745402e-06 37 0.999995399942145 9.2001157093108e-06 4.6000578546554e-06 38 0.99999311208741 1.37758251820586e-05 6.88791259102932e-06 39 0.99999007794924 1.98441015197832e-05 9.9220507598916e-06 40 0.999991557230387 1.68855392259453e-05 8.44276961297265e-06 41 0.999993073397532 1.38532049357139e-05 6.92660246785696e-06 42 0.999989698260467 2.06034790649440e-05 1.03017395324720e-05 43 0.999973452046162 5.30959076763942e-05 2.65479538381971e-05 44 0.999950483052518 9.90338949647559e-05 4.95169474823779e-05 45 0.999993687001995 1.26259960098093e-05 6.31299800490467e-06 46 0.999999380975051 1.23804989712533e-06 6.19024948562663e-07 47 0.9999998318634 3.362732016508e-07 1.681366008254e-07 48 0.999999917811849 1.64376302575744e-07 8.21881512878721e-08 49 0.999999896509543 2.06980913317275e-07 1.03490456658638e-07 50 0.999999636146695 7.27706609056076e-07 3.63853304528038e-07 51 0.999998879428577 2.24114284495526e-06 1.12057142247763e-06 52 0.999995689378765 8.62124246968321e-06 4.31062123484161e-06 53 0.999989574436664 2.08511266725569e-05 1.04255633362785e-05 54 0.99999993287721 1.34245578164038e-07 6.71227890820189e-08 55 0.99999996676083 6.64783390017644e-08 3.32391695008822e-08 56 0.99999992922233 1.41555340513487e-07 7.07776702567435e-08 57 0.999999595896842 8.0820631691593e-07 4.04103158457965e-07 58 0.999996530745052 6.93850989492345e-06 3.46925494746172e-06 59 0.999970752024965 5.84959500705752e-05 2.92479750352876e-05 60 0.999878866915076 0.000242266169848753 0.000121133084924376 61 0.999803474702866 0.000393050594268165 0.000196525297134083 62 0.998200024592745 0.00359995081451051 0.00179997540725526 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 36 0.782608695652174 NOK 5% type I error level 40 0.869565217391304 NOK 10% type I error level 45 0.978260869565217 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/109djl1258903552.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/109djl1258903552.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/155na1258903552.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/155na1258903552.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/20rr31258903552.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/20rr31258903552.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/3ngq81258903552.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/3ngq81258903552.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/4mw781258903552.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/4mw781258903552.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/5uthq1258903552.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/5uthq1258903552.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/601uc1258903552.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/601uc1258903552.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/7uedv1258903552.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/7uedv1258903552.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/8u8y41258903552.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/8u8y41258903552.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/9fuga1258903552.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258903603extotp7en1p5pv6/9fuga1258903552.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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