*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: Tue, 17 Nov 2009 09:40:29 -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/17/t1258476116djwhw76ob2osvag.htm/, Retrieved Tue, 17 Nov 2009 17:42:08 +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/17/t1258476116djwhw76ob2osvag.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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20604.6 2.05 18714.9 2.03 18492.6 2.04 18183.6 2.03 19435.1 2.01 22686.8 2.01 20396.7 2.01 19233.6 2.01 22751 2.01 19864 2.01 17165.4 2.02 22309.7 2.02 21786.3 2.03 21927.6 2.05 20957.9 2.08 19726 2.07 21315.7 2.06 24771.5 2.05 22592.4 2.05 21942.1 2.05 23973.7 2.05 20815.7 2.05 19931.4 2.06 24436.8 2.06 22838.7 2.07 24465.3 2.07 23007.3 2.3 22720.8 2.31 23045.7 2.31 27198.5 2.53 22401.9 2.58 25122.7 2.59 26100.5 2.73 22904.9 2.82 22040.4 3 25981.5 3.04 26157.1 3.23 25975.4 3.32 22589.8 3.49 25370.4 3.57 25091.1 3.56 28760.9 3.72 24325.9 3.82 25821.7 3.82 27645.7 3.98 26296.9 4.06 24141.5 4.08 27268.1 4.19 29060.3 4.16 28226.4 4.17 23268.5 4.21 26938.2 4.21 27217.5 4.17 27540.5 4.19 29167.6 4.25 26671.5 4.25 30184 4.2 28422.3 4.33 23774.3 4.41 29601 4.56 28523.6 5.18 23622 3.42 21320.3 2.71 20423.6 2.29 21174.9 2 23050.2 1.64 21202.9 1.3 20476.4 1.08 23173.3 1 22468 1 19842.7 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 17180.2125921855 + 2309.26909950376X[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) 17180.2125921855 683.817605 25.124 0 0 X 2309.26909950376 229.974521 10.0414 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.770526630666746 R-squared 0.593711288566648 Adjusted R-squared 0.587823046371962 F-TEST (value) 100.829970802226 F-TEST (DF numerator) 1 F-TEST (DF denominator) 69 p-value 3.99680288865056e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2011.6954426771 Sum Squared Residuals 279237380.232059 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 20604.6 21914.2142461682 -1309.61424616825 2 18714.9 21868.0288641782 -3153.12886417815 3 18492.6 21891.1215551732 -3398.52155517318 4 18183.6 21868.0288641781 -3684.42886417814 5 19435.1 21821.8434821881 -2386.74348218807 6 22686.8 21821.8434821881 864.956517811935 7 20396.7 21821.8434821881 -1425.14348218806 8 19233.6 21821.8434821881 -2588.24348218807 9 22751 21821.8434821881 929.156517811936 10 19864 21821.8434821881 -1957.84348218806 11 17165.4 21844.9361731831 -4679.5361731831 12 22309.7 21844.9361731831 464.763826816898 13 21786.3 21868.0288641781 -81.7288641781405 14 21927.6 21914.2142461682 13.3857538317833 15 20957.9 21983.4923191533 -1025.59231915333 16 19726 21960.3996281583 -2234.39962815829 17 21315.7 21937.3069371633 -621.606937163252 18 24771.5 21914.2142461682 2857.28575383178 19 22592.4 21914.2142461682 678.185753831786 20 21942.1 21914.2142461682 27.8857538317833 21 23973.7 21914.2142461682 2059.48575383179 22 20815.7 21914.2142461682 -1098.51424616821 23 19931.4 21937.3069371633 -2005.90693716325 24 24436.8 21937.3069371633 2499.49306283675 25 22838.7 21960.3996281583 878.30037184171 26 24465.3 21960.3996281583 2504.90037184171 27 23007.3 22491.5315210442 515.768478955844 28 22720.8 22514.6242120392 206.175787960806 29 23045.7 22514.6242120392 531.075787960807 30 27198.5 23022.6634139300 4175.83658606998 31 22401.9 23138.1268689052 -736.226868905208 32 25122.7 23161.2195599002 1961.48044009975 33 26100.5 23484.5172338308 2615.98276616923 34 22904.9 23692.3514527861 -787.45145278611 35 22040.4 24108.0198906968 -2067.61989069679 36 25981.5 24200.3906546769 1781.10934532306 37 26157.1 24639.1517835827 1517.94821641734 38 25975.4 24846.986002538 1128.41399746201 39 22589.8 25239.5617494536 -2649.76174945363 40 25370.4 25424.3032774139 -53.903277413932 41 25091.1 25401.2105864189 -310.110586418898 42 28760.9 25770.6936423395 2990.20635766050 43 24325.9 26001.6205522899 -1675.72055228987 44 25821.7 26001.6205522899 -179.920552289873 45 27645.7 26371.1036082105 1274.59639178952 46 26296.9 26555.8451361708 -258.945136170775 47 24141.5 26602.0305181609 -2460.53051816085 48 27268.1 26856.0501191063 412.049880893732 49 29060.3 26786.7720461212 2273.52795387885 50 28226.4 26809.8647371162 1416.53526288381 51 23268.5 26902.2355010963 -3633.73550109634 52 26938.2 26902.2355010963 35.9644989036596 53 27217.5 26809.8647371162 407.635262883809 54 27540.5 26856.0501191063 684.449880893733 55 29167.6 26994.6062650765 2172.99373492351 56 26671.5 26994.6062650765 -323.106265076492 57 30184 26879.1428101013 3304.85718989870 58 28422.3 27179.3477930368 1242.95220696321 59 23774.3 27364.0893209971 -3589.78932099709 60 29601 27710.4796859227 1890.52031407734 61 28523.6 29142.226527615 -618.626527614992 62 23622 25077.9129124884 -1455.91291248837 63 21320.3 23438.3318518407 -2118.0318518407 64 20423.6 22468.4388300491 -2044.83883004912 65 21174.9 21798.7507911930 -623.850791193026 66 23050.2 20967.4139153717 2082.78608462833 67 21202.9 20182.2624215404 1020.63757845961 68 20476.4 19674.2232196496 802.176780350436 69 23173.3 19489.4816916893 3683.81830831073 70 22468 19489.4816916893 2978.51830831073 71 19842.7 19489.4816916893 353.218308310736 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.182835237969053 0.365670475938107 0.817164762030947 6 0.508763690389712 0.982472619220576 0.491236309610288 7 0.366186879159676 0.732373758319352 0.633813120840324 8 0.298295134079161 0.596590268158322 0.701704865920839 9 0.373820538546144 0.747641077092288 0.626179461453856 10 0.296688498716948 0.593376997433895 0.703311501283052 11 0.51335674279766 0.97328651440468 0.48664325720234 12 0.561036810119407 0.877926379761186 0.438963189880593 13 0.566223284201135 0.867553431597729 0.433776715798865 14 0.587033051442326 0.825933897115348 0.412966948557674 15 0.524977231265708 0.950045537468585 0.475022768734292 16 0.490593968811617 0.981187937623234 0.509406031188383 17 0.437810406222332 0.875620812444665 0.562189593777668 18 0.69320799935147 0.61358400129706 0.30679200064853 19 0.664895740901496 0.670208518197009 0.335104259098504 20 0.608613934029035 0.78277213194193 0.391386065970965 21 0.659501387859245 0.68099722428151 0.340498612140755 22 0.612411555868174 0.775176888263651 0.387588444131826 23 0.622297842573823 0.755404314852353 0.377702157426177 24 0.68450580045856 0.630988399082879 0.315494199541440 25 0.628976964801018 0.742046070397964 0.371023035198982 26 0.647483452548825 0.70503309490235 0.352516547451175 27 0.654198145685464 0.691603708629072 0.345801854314536 28 0.60008152109925 0.799836957801501 0.399918478900750 29 0.531736268662067 0.936527462675867 0.468263731337933 30 0.583743657943195 0.83251268411361 0.416256342056805 31 0.64466188888916 0.71067622222168 0.35533811111084 32 0.59338773773919 0.81322452452162 0.40661226226081 33 0.56491127264277 0.87017745471446 0.43508872735723 34 0.608203130537054 0.783593738925893 0.391796869462946 35 0.714121532804139 0.571756934391722 0.285878467195861 36 0.670371738767956 0.659256522464089 0.329628261232044 37 0.618690534104452 0.762618931791096 0.381309465895548 38 0.560882455431045 0.87823508913791 0.439117544568955 39 0.691761276145792 0.616477447708416 0.308238723854208 40 0.63312831162634 0.733743376747321 0.366871688373660 41 0.574185776963227 0.851628446073546 0.425814223036773 42 0.621328222525732 0.757343554948536 0.378671777474268 43 0.630554200083307 0.738891599833386 0.369445799916693 44 0.56583843771457 0.868323124570861 0.434161562285430 45 0.512430584417427 0.975138831165146 0.487569415582573 46 0.446360712267344 0.892721424534688 0.553639287732656 47 0.503956594614914 0.992086810770172 0.496043405385086 48 0.430510540258989 0.861021080517978 0.569489459741011 49 0.437443791950532 0.874887583901064 0.562556208049468 50 0.394948060201499 0.789896120402998 0.605051939798501 51 0.588654342036121 0.822691315927757 0.411345657963879 52 0.509436441269237 0.981127117461526 0.490563558730763 53 0.429530255194495 0.85906051038899 0.570469744805505 54 0.355715327159032 0.711430654318063 0.644284672840968 55 0.363963382250505 0.727926764501009 0.636036617749495 56 0.288351698346913 0.576703396693827 0.711648301653087 57 0.464167036820595 0.92833407364119 0.535832963179405 58 0.471617690170387 0.943235380340775 0.528382309829613 59 0.581179517944047 0.837640964111906 0.418820482055953 60 0.72289704500161 0.55420590999678 0.27710295499839 61 0.881872678434495 0.23625464313101 0.118127321565505 62 0.898659919991181 0.202680160017638 0.101340080008819 63 0.832819174397088 0.334361651205825 0.167180825602912 64 0.768245542333173 0.463508915333654 0.231754457666827 65 0.69150283668788 0.61699432662424 0.30849716331212 66 0.602510256645126 0.794979486709748 0.397489743354874 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/10jfmv1258476022.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/10jfmv1258476022.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/1kesp1258476022.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/1kesp1258476022.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/2cdqr1258476022.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/2cdqr1258476022.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/356s61258476022.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/356s61258476022.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/4it871258476022.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/4it871258476022.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/5gldf1258476022.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/5gldf1258476022.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/6j7e41258476022.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/6j7e41258476022.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/7p8k01258476022.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/7p8k01258476022.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/81rg81258476022.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/81rg81258476022.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/9l0631258476022.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/17/t1258476116djwhw76ob2osvag/9l0631258476022.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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