*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, 06 Dec 2010 18:40:08 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye.htm/, Retrieved Mon, 06 Dec 2010 19:38:10 +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/2010/Dec/06/t1291660690pd0woejicpsmiye.htm/}, year = {2010}, } @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 = {2010}, 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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2350.44 10892.76 10540.05 10570 -4.9 -3 1.6 3.38 2440.25 10631.92 10601.61 10297 -4 -1 1.3 3.35 2408.64 11441.08 10323.73 10635 -3.1 -3 1.1 3.22 2472.81 11950.95 10418.4 10872 -1.3 -4 1.9 3.06 2407.6 11037.54 10092.96 10296 0 -6 2.6 3.17 2454.62 11527.72 10364.91 10383 -0.4 0 2.3 3.19 2448.05 11383.89 10152.09 10431 3 -4 2.4 3.35 2497.84 10989.34 10032.8 10574 0.4 -2 2.2 3.24 2645.64 11079.42 10204.59 10653 1.2 -2 2 3.23 2756.76 11028.93 10001.6 10805 0.6 -6 2.9 3.31 2849.27 10973 10411.75 10872 -1.3 -7 2.6 3.25 2921.44 11068.05 10673.38 10625 -3.2 -6 2.3 3.2 2981.85 11394.84 10539.51 10407 -1.8 -6 2.3 3.1 3080.58 11545.71 10723.78 10463 -3.6 -3 2.6 2.93 3106.22 11809.38 10682.06 10556 -4.2 -2 3.1 2.92 3119.31 11395.64 10283.19 10646 -6.9 -5 2.8 2.9 3061.26 11082.38 10377.18 10702 -8 -11 2.5 2.87 3097.31 11402.75 10486.64 11353 -7.5 -11 2.9 2.76 3161.69 11716.87 10545.38 11346 -8.2 -11 3.1 2.67 3257.16 12204.98 10554.27 11451 -7.6 -10 3.1 2.75 3277.01 12986.62 10532.54 11964 -3.7 -14 3.2 2 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 5 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation BEL_20[t] = -694.276827768513 + 0.186289311341488Nikkei[t] + 0.238668063115473DJ_Indust[t] -0.0401626357343952Goudprijs[t] -3.31443430461099Conjunct_Seizoenzuiver[t] + 3.1995090939585Cons_vertrouw[t] + 39.6618902538806Alg_consumptie_index_BE[t] -302.748978637587Gem_rente_kasbon_5j[t] + 14.2294511552077t + 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) -694.276827768513 463.181345 -1.4989 0.138886 0.069443 Nikkei 0.186289311341488 0.014166 13.1507 0 0 DJ_Indust 0.238668063115473 0.035119 6.7961 0 0 Goudprijs -0.0401626357343952 0.019447 -2.0652 0.043021 0.021511 Conjunct_Seizoenzuiver -3.31443430461099 6.070118 -0.546 0.586977 0.293489 Cons_vertrouw 3.1995090939585 7.424182 0.431 0.66797 0.333985 Alg_consumptie_index_BE 39.6618902538806 16.308848 2.4319 0.017873 0.008937 Gem_rente_kasbon_5j -302.748978637587 54.975934 -5.5069 1e-06 0 t 14.2294511552077 4.624991 3.0766 0.003096 0.001548 Multiple Linear Regression - Regression Statistics Multiple R 0.985849235217833 R-squared 0.971898714579585 Adjusted R-squared 0.968330297383342 F-TEST (value) 272.361291051620 F-TEST (DF numerator) 8 F-TEST (DF denominator) 63 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 150.489825361688 Sum Squared Residuals 1426772.81485565 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2350.44 2487.02131874436 -136.581318744363 2 2440.25 2478.91580104686 -38.6658010468649 3 2408.64 2586.03003922044 -177.390039220441 4 2472.81 2779.32284035828 -306.512840358283 5 2407.6 2552.60846810745 -145.008468107445 6 2454.62 2722.13412598806 -267.514125988063 7 2448.05 2588.3076812174 -140.257681217401 8 2497.84 2535.20927140303 -37.3692714030259 9 2645.64 2596.49116635538 49.1488336446237 10 2756.76 2547.42932656193 209.330673438068 11 2849.27 2655.80271375331 193.467286246689 12 2921.44 2772.83767645926 148.602323540742 13 2981.85 2850.38426248588 131.465737514116 14 3080.58 3013.37984090707 67.2001590929269 15 3106.22 3091.08244265726 15.1375573427443 16 3119.31 2922.93124442423 196.378755575766 17 3061.26 2880.61973501399 180.640264986011 18 3097.31 3002.01934976858 95.2906502314214 19 3161.69 3126.56639002130 35.1236099786953 20 3257.16 3206.62112948458 50.538870515424 21 3277.01 3327.9963970342 -50.9863970341988 22 3295.32 3328.49878197328 -33.1787819732749 23 3363.99 3549.65334232637 -185.663342326373 24 3494.17 3785.4780615772 -291.308061577200 25 3667.03 3840.49091679821 -173.460916798215 26 3813.06 3895.43834951639 -82.378349516389 27 3917.96 3910.96674876031 6.99325123969077 28 3895.51 4002.90986145241 -107.399861452414 29 3801.06 3789.08095459939 11.9790454006065 30 3570.12 3541.38474395526 28.7352560447420 31 3701.61 3508.93067997176 192.679320028236 32 3862.27 3698.22777675659 164.042223243408 33 3970.1 3848.72042868652 121.379571313481 34 4138.52 4086.98952228785 51.5304777121477 35 4199.75 4070.15856361785 129.591436382153 36 4290.89 4237.82689590292 53.0631040970839 37 4443.91 4355.20440441228 88.7055955877244 38 4502.64 4375.44658313298 127.193416867024 39 4356.98 4202.99735998728 153.982640012719 40 4591.27 4397.1229519667 194.147048033301 41 4696.96 4590.58289480372 106.377105196278 42 4621.4 4595.05713181937 26.3428681806313 43 4562.84 4602.57140316969 -39.73140316969 44 4202.52 4238.09720034287 -35.5772003428662 45 4296.49 4290.90014027307 5.58985972693068 46 4435.23 4520.57511193465 -85.345111934655 47 4105.18 4125.96061717701 -20.7806171770052 48 4116.68 4225.78714297501 -109.107142975014 49 3844.49 3607.19117713873 237.298822861266 50 3720.98 3591.33284186625 129.647158133755 51 3674.4 3507.91951496874 166.480485031262 52 3857.62 3850.82185036344 6.79814963655667 53 3801.06 3973.36812494501 -172.308124945013 54 3504.37 3688.75518496188 -184.385184961879 55 3032.6 3187.4458297138 -154.845829713798 56 3047.03 3253.06100858942 -206.031008589422 57 2962.34 3126.91581941074 -164.575819410737 58 2197.82 2038.50447139775 159.315528602246 59 2014.45 1891.44290525407 123.007094745931 60 1862.83 1890.41832365926 -27.5883236592584 61 1905.41 1825.03887268300 80.3711273169951 62 1810.99 1497.77012922094 313.219870779062 63 1670.07 1492.50093920378 177.569060796216 64 1864.44 1908.70209595658 -44.2620959565847 65 2052.02 2061.05625069554 -9.03625069554037 66 2029.6 2166.05577177178 -136.455771771780 67 2070.83 2159.25080121525 -88.4208012152534 68 2293.41 2519.36423985320 -225.954239853204 69 2443.27 2528.26721163569 -84.9972116356874 70 2513.17 2513.01143088941 0.158569110587452 71 2466.92 2550.56186804724 -83.641868047237 72 2502.66 2684.46354536869 -181.803545368686 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.158795515063539 0.317591030127077 0.841204484936461 13 0.111718727605642 0.223437455211285 0.888281272394358 14 0.0600640817215143 0.120128163443029 0.939935918278486 15 0.0289275151511681 0.0578550303023362 0.971072484848832 16 0.0144575113630586 0.0289150227261172 0.985542488636941 17 0.0559859491349409 0.111971898269882 0.94401405086506 18 0.0786883005489053 0.157376601097811 0.921311699451095 19 0.054723175301944 0.109446350603888 0.945276824698056 20 0.0326035835140038 0.0652071670280075 0.967396416485996 21 0.0175940215637836 0.0351880431275671 0.982405978436216 22 0.00924010477361167 0.0184802095472233 0.990759895226388 23 0.007362607015966 0.014725214031932 0.992637392984034 24 0.00788623950635404 0.0157724790127081 0.992113760493646 25 0.0132137254807296 0.0264274509614592 0.98678627451927 26 0.0287516774673101 0.0575033549346203 0.97124832253269 27 0.0301946073766427 0.0603892147532855 0.969805392623357 28 0.0432135064307333 0.0864270128614665 0.956786493569267 29 0.181641068739809 0.363282137479617 0.818358931260191 30 0.739078903821189 0.521842192357622 0.260921096178811 31 0.747750425250416 0.504499149499167 0.252249574749584 32 0.761480134199222 0.477039731601557 0.238519865800778 33 0.780823816187647 0.438352367624706 0.219176183812353 34 0.874241187916358 0.251517624167285 0.125758812083643 35 0.955211303750926 0.0895773924981485 0.0447886962490742 36 0.95819890207012 0.0836021958597602 0.0418010979298801 37 0.967636398039393 0.0647272039212136 0.0323636019606068 38 0.962236834596462 0.075526330807076 0.037763165403538 39 0.954909164841078 0.0901816703178436 0.0450908351589218 40 0.940530169764407 0.118939660471187 0.0594698302355935 41 0.927301926326635 0.145396147346730 0.0726980736733652 42 0.93503381303516 0.129932373929680 0.0649661869648401 43 0.96160454147808 0.0767909170438408 0.0383954585219204 44 0.992791251763655 0.01441749647269 0.007208748236345 45 0.99587561619595 0.00824876760810101 0.00412438380405051 46 0.995005548277862 0.00998890344427602 0.00499445172213801 47 0.992590369988253 0.0148192600234936 0.00740963001174679 48 0.992152064895689 0.0156958702086226 0.00784793510431132 49 0.989609577059617 0.0207808458807661 0.0103904229403831 50 0.98701080429181 0.0259783914163800 0.0129891957081900 51 0.98896712948339 0.0220657410332195 0.0110328705166097 52 0.98194703914462 0.0361059217107615 0.0180529608553807 53 0.977982756537893 0.0440344869242131 0.0220172434621065 54 0.978152858285082 0.0436942834298364 0.0218471417149182 55 0.958349420244093 0.0833011595118133 0.0416505797559067 56 0.934122144317846 0.131755711364308 0.065877855682154 57 0.907942735703283 0.184114528593433 0.0920572642967166 58 0.830516147594944 0.338967704810112 0.169483852405056 59 0.986618706175916 0.0267625876481688 0.0133812938240844 60 0.954555267938476 0.0908894641230486 0.0454447320615243 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 2 0.0408163265306122 NOK 5% type I error level 18 0.36734693877551 NOK 10% type I error level 31 0.63265306122449 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/10161a1291660799.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/10161a1291660799.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/1u5my1291660799.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/1u5my1291660799.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/25w3j1291660799.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/25w3j1291660799.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/35w3j1291660799.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/35w3j1291660799.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/45w3j1291660799.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/45w3j1291660799.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/55w3j1291660799.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/55w3j1291660799.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/6fo241291660799.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/6fo241291660799.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/7qx271291660799.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/7qx271291660799.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/8qx271291660799.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/8qx271291660799.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/9qx271291660799.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/06/t1291660690pd0woejicpsmiye/9qx271291660799.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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