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R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Mon, 24 Nov 2008 11:04:59 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5.htm/, Retrieved Mon, 24 Nov 2008 18:05:49 +0000

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/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

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Dataseries X:

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7,3 0 7,1 0 6,9 0 6,8 0 7,5 0 7,6 0 7,8 0 8 0 8,1 0 8,2 0 8,3 0 8,2 0 8 0 7,9 0 7,6 0 7,6 0 8,2 0 8,3 0 8,4 0 8,4 0 8,4 0 8,6 0 8,9 0 8,8 0 8,3 0 7,5 0 7,2 0 7,5 0 8,8 0 9,3 0 9,3 0 8,7 0 8,2 0 8,3 0 8,5 0 8,6 0 8,6 0 8,2 0 8,1 0 8 0 8,6 0 8,7 0 8,8 0 8,5 0 8,4 0 8,5 0 8,7 0 8,7 0 8,6 0 8,5 0 8,3 0 8,1 0 8,2 0 8,1 0 8,1 0 7,9 0 7,9 0 7,9 0 8 0 8 0 7,9 0 8 0 7,7 1 7,2 1 7,5 1 7,3 1 7 1 7 1 7 1 7,2 1 7,3 1 7,1 1 6,8 1 6,6 1 6,2 1 6,2 1 6,8 1 6,9 1 6,8 1

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 7 seconds R Server 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 Multiple Linear Regression - Estimated Regression Equation y[t] = + 8.1675925925926 -1.46916666666667x[t] -0.302765652557318M1[t] -0.553018077601411M2[t] -0.607675264550264M3[t] -0.700784832451499M4[t] -0.108180114638448M5[t] -0.0298611111111110M6[t] -0.0372563932980598M7[t] -0.120418871252205M8[t] -0.211147486772487M9[t] -0.101876102292769M10[t] + 0.0573952821869489M11[t] + 0.00739528218694884t + 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) 8.1675925925926 0.211302 38.6536 0 0 x -1.46916666666667 0.174296 -8.4292 0 0 M1 -0.302765652557318 0.244681 -1.2374 0.220394 0.110197 M2 -0.553018077601411 0.24454 -2.2615 0.027083 0.013542 M3 -0.607675264550264 0.245961 -2.4706 0.01612 0.00806 M4 -0.700784832451499 0.245675 -2.8525 0.005811 0.002905 M5 -0.108180114638448 0.245427 -0.4408 0.660833 0.330416 M6 -0.0298611111111110 0.245219 -0.1218 0.903454 0.451727 M7 -0.0372563932980598 0.24505 -0.152 0.87963 0.439815 M8 -0.120418871252205 0.253869 -0.4743 0.63685 0.318425 M9 -0.211147486772487 0.253735 -0.8322 0.408367 0.204183 M10 -0.101876102292769 0.253639 -0.4017 0.689254 0.344627 M11 0.0573952821869489 0.253582 0.2263 0.821649 0.410824 t 0.00739528218694884 0.003113 2.3755 0.020483 0.010242 Multiple Linear Regression - Regression Statistics Multiple R 0.818150968119422 R-squared 0.669371006634748 Adjusted R-squared 0.603245207961698 F-TEST (value) 10.1226906905784 F-TEST (DF numerator) 13 F-TEST (DF denominator) 65 p-value 3.44609896174575e-11 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.439183993768649 Sum Squared Residuals 12.5373677248677 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.3 7.87222222222222 -0.572222222222218 2 7.1 7.62936507936508 -0.529365079365079 3 6.9 7.58210317460317 -0.682103174603174 4 6.8 7.49638888888889 -0.696388888888889 5 7.5 8.09638888888889 -0.596388888888889 6 7.6 8.18210317460317 -0.582103174603175 7 7.8 8.18210317460317 -0.382103174603175 8 8 8.10633597883598 -0.106335978835979 9 8.1 8.02300264550265 0.0769973544973538 10 8.2 8.13966931216931 0.060330687830687 11 8.3 8.30633597883598 -0.00633597883597844 12 8.2 8.25633597883598 -0.0563359788359797 13 8 7.96096560846561 0.0390343915343907 14 7.9 7.71810846560847 0.181891534391534 15 7.6 7.67084656084656 -0.0708465608465614 16 7.6 7.58513227513227 0.0148677248677245 17 8.2 8.18513227513228 0.0148677248677242 18 8.3 8.27084656084656 0.0291534391534395 19 8.4 8.27084656084656 0.129153439153439 20 8.4 8.19507936507937 0.204920634920635 21 8.4 8.11174603174603 0.288253968253969 22 8.6 8.2284126984127 0.371587301587301 23 8.9 8.39507936507936 0.504920634920635 24 8.8 8.34507936507937 0.454920634920635 25 8.3 8.049708994709 0.250291005291005 26 7.5 7.80685185185185 -0.306851851851852 27 7.2 7.75958994708995 -0.559589947089947 28 7.5 7.67387566137566 -0.173875661375661 29 8.8 8.27387566137566 0.52612433862434 30 9.3 8.35958994708995 0.940410052910054 31 9.3 8.35958994708995 0.940410052910054 32 8.7 8.28382275132275 0.416177248677248 33 8.2 8.20048941798942 -0.000489417989418698 34 8.3 8.31715608465609 -0.0171560846560841 35 8.5 8.48382275132275 0.0161772486772487 36 8.6 8.43382275132275 0.166177248677248 37 8.6 8.13845238095238 0.461547619047618 38 8.2 7.89559523809524 0.304404761904761 39 8.1 7.84833333333333 0.251666666666666 40 8 7.76261904761905 0.237380952380953 41 8.6 8.36261904761905 0.237380952380952 42 8.7 8.44833333333333 0.251666666666666 43 8.8 8.44833333333333 0.351666666666667 44 8.5 8.37256613756614 0.127433862433863 45 8.4 8.2892328042328 0.110767195767196 46 8.5 8.40589947089947 0.0941005291005292 47 8.7 8.57256613756614 0.127433862433862 48 8.7 8.52256613756614 0.177433862433862 49 8.6 8.22719576719577 0.372804232804232 50 8.5 7.98433862433862 0.515661375661376 51 8.3 7.93707671957672 0.362923280423281 52 8.1 7.85136243386243 0.248637566137566 53 8.2 8.45136243386243 -0.251362433862434 54 8.1 8.53707671957672 -0.43707671957672 55 8.1 8.53707671957672 -0.43707671957672 56 7.9 8.46130952380952 -0.561309523809523 57 7.9 8.37797619047619 -0.47797619047619 58 7.9 8.49464285714286 -0.594642857142857 59 8 8.66130952380952 -0.661309523809524 60 8 8.61130952380952 -0.611309523809524 61 7.9 8.31593915343915 -0.415939153439153 62 8 8.07308201058201 -0.0730820105820103 63 7.7 6.55665343915344 1.14334656084656 64 7.2 6.47093915343915 0.729060846560847 65 7.5 7.07093915343915 0.429060846560847 66 7.3 7.15665343915344 0.143346560846561 67 7 7.15665343915344 -0.156653439153439 68 7 7.08088624338624 -0.0808862433862434 69 7 6.99755291005291 0.00244708994709005 70 7.2 7.11421957671958 0.0857804232804234 71 7.3 7.28088624338624 0.0191137566137566 72 7.1 7.23088624338624 -0.130886243386244 73 6.8 6.93551587301587 -0.135515873015874 74 6.6 6.69265873015873 -0.0926587301587303 75 6.2 6.64539682539683 -0.445396825396825 76 6.2 6.55968253968254 -0.359682539682539 77 6.8 7.15968253968254 -0.359682539682539 78 6.9 7.24539682539683 -0.345396825396825 79 6.8 7.24539682539683 -0.445396825396826 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00226982600024061 0.00453965200048122 0.99773017399976 18 0.000285987953227789 0.000571975906455579 0.999714012046772 19 0.000182463479866291 0.000364926959732581 0.999817536520134 20 0.00132153926558840 0.00264307853117680 0.998678460734412 21 0.00328227048657668 0.00656454097315337 0.996717729513423 22 0.00194103859528566 0.00388207719057133 0.998058961404714 23 0.000607384015697785 0.00121476803139557 0.999392615984302 24 0.000178251019503547 0.000356502039007094 0.999821748980496 25 0.000164848309474821 0.000329696618949643 0.999835151690525 26 0.0305276716054809 0.0610553432109619 0.969472328394519 27 0.288097457697714 0.576194915395429 0.711902542302286 28 0.464189875925371 0.928379751850742 0.535810124074629 29 0.458575204069678 0.917150408139356 0.541424795930322 30 0.644736720207215 0.71052655958557 0.355263279792785 31 0.715496476776256 0.569007046447488 0.284503523223744 32 0.659098527506565 0.68180294498687 0.340901472493435 33 0.803845653836836 0.392308692326329 0.196154346163164 34 0.897689337048119 0.204621325903762 0.102310662951881 35 0.943600269083403 0.112799461833194 0.056399730916597 36 0.948534822138475 0.10293035572305 0.051465177861525 37 0.931207224010168 0.137585551979664 0.0687927759898319 38 0.957622986237893 0.0847540275242147 0.0423770137621074 39 0.980741492662916 0.0385170146741688 0.0192585073370844 40 0.991109695281965 0.0177806094360696 0.00889030471803481 41 0.992417405613048 0.0151651887739044 0.0075825943869522 42 0.991684833222786 0.0166303335544270 0.00831516677721352 43 0.987980678138926 0.024038643722148 0.012019321861074 44 0.985559004429536 0.0288819911409288 0.0144409955704644 45 0.983125869885018 0.0337482602299641 0.0168741301149820 46 0.980761480723211 0.0384770385535772 0.0192385192767886 47 0.974568520890986 0.0508629582180272 0.0254314791090136 48 0.962960679777725 0.0740786404445497 0.0370393202222748 49 0.941838208660905 0.11632358267819 0.058161791339095 50 0.912165666395816 0.175668667208368 0.087834333604184 51 0.875318049386056 0.249363901227887 0.124681950613944 52 0.831673392578003 0.336653214843994 0.168326607421997 53 0.834558635952681 0.330882728094638 0.165441364047319 54 0.873935732798844 0.252128534402313 0.126064267201156 55 0.878077991155955 0.24384401768809 0.121922008844045 56 0.863615700393736 0.272768599212527 0.136384299606264 57 0.823513895008181 0.352972209983638 0.176486104991819 58 0.800452838269024 0.399094323461952 0.199547161730976 59 0.786890472408343 0.426219055183315 0.213109527591657 60 0.732611056799325 0.53477788640135 0.267388943200675 61 0.622993375864221 0.754013248271558 0.377006624135779 62 0.449861316246051 0.899722632492102 0.550138683753949 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.195652173913043 NOK 5% type I error level 17 0.369565217391304 NOK 10% type I error level 21 0.456521739130435 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/10fmyu1227549885.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/10fmyu1227549885.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/1ako41227549885.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/1ako41227549885.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/2b3pb1227549885.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/2b3pb1227549885.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/38hyh1227549885.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/38hyh1227549885.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/4fuqz1227549885.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/4fuqz1227549885.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/5lgdy1227549885.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/5lgdy1227549885.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/6fpb21227549885.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/6fpb21227549885.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/7otj41227549885.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/7otj41227549885.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/8pj951227549885.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/8pj951227549885.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/9kfme1227549885.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227549948r8xy1x1qbyrf4e5/9kfme1227549885.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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