*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, 15 Dec 2009 07:51:18 -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/Dec/15/t12608887833q7xrzivhhuvzfe.htm/, Retrieved Tue, 15 Dec 2009 15:53:15 +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/Dec/15/t12608887833q7xrzivhhuvzfe.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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2350.44 27 2440.25 29 2408.64 27 2472.81 26 2407.6 24 2454.62 30 2448.05 26 2497.84 28 2645.64 28 2756.76 24 2849.27 23 2921.44 24 2981.85 24 3080.58 27 3106.22 28 3119.31 25 3061.26 19 3097.31 19 3161.69 19 3257.16 20 3277.01 16 3295.32 22 3363.99 21 3494.17 25 3667.03 29 3813.06 28 3917.96 25 3895.51 26 3801.06 24 3570.12 28 3701.61 28 3862.27 28 3970.1 28 4138.52 32 4199.75 31 4290.89 22 4443.91 29 4502.64 31 4356.98 29 4591.27 32 4696.96 32 4621.4 31 4562.84 29 4202.52 28 4296.49 28 4435.23 29 4105.18 22 4116.68 26 3844.49 24 3720.98 27 3674.4 27 3857.62 23 3801.06 21 3504.37 19 3032.6 17 3047.03 19 2962.34 21 2197.82 13 2014.45 8 1862.83 5 1905.41 10 1810.99 6 1670.07 6 1864.44 8 2052.02 11 2029.6 12 2070.83 13 2293.41 19 2443.27 19 2513.17 18 2466.92 20

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] = -30.9802710985411 + 115.500191970367X[t] + 17.2956251455641t + 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) -30.9802710985411 338.976556 -0.0914 0.927449 0.463724 X 115.500191970367 10.741034 10.7532 0 0 t 17.2956251455641 3.585723 4.8235 8e-06 4e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.795247840667979 R-squared 0.632419128087083 Adjusted R-squared 0.621607925971997 F-TEST (value) 58.4966520239793 F-TEST (DF numerator) 2 F-TEST (DF denominator) 68 p-value 1.66533453693773e-15 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 521.185047863245 Sum Squared Residuals 18471102.0799025 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 2350.44 3104.82053724692 -754.380537246918 2 2440.25 3353.11654633322 -912.866546333219 3 2408.64 3139.41178753805 -730.77178753805 4 2472.81 3041.20722071325 -568.397220713247 5 2407.6 2827.50246191808 -419.902461918078 6 2454.62 3537.79923888584 -1083.17923888584 7 2448.05 3093.09409614994 -645.04409614994 8 2497.84 3341.39010523624 -843.550105236237 9 2645.64 3358.6857303818 -713.045730381801 10 2756.76 2913.9805876459 -157.220587645898 11 2849.27 2815.77602082110 33.493979178904 12 2921.44 2948.57183793703 -27.1318379370266 13 2981.85 2965.86746308259 15.9825369174092 14 3080.58 3329.66366413925 -249.083664139255 15 3106.22 3462.45948125519 -356.239481255186 16 3119.31 3133.25453048965 -13.9445304896497 17 3061.26 2457.54900381301 603.710996186987 18 3097.31 2474.84462895858 622.465371041422 19 3161.69 2492.14025410414 669.549745895858 20 3257.16 2624.93607122007 632.223928779927 21 3277.01 2180.23092848417 1096.77907151583 22 3295.32 2890.52770545193 404.792294548066 23 3363.99 2792.32313862713 571.666861372868 24 3494.17 3271.61953165416 222.550468345838 25 3667.03 3750.91592468119 -83.8859246811926 26 3813.06 3652.71135785639 160.348642143610 27 3917.96 3323.50640709085 594.453592909146 28 3895.51 3456.30222420679 439.207775793215 29 3801.06 3242.59746541162 558.462534588384 30 3570.12 3721.89385843865 -151.773858438647 31 3701.61 3739.18948358421 -37.5794835842104 32 3862.27 3756.48510872977 105.784891270225 33 3970.1 3773.78073387534 196.319266124661 34 4138.52 4253.07712690237 -114.557126902369 35 4199.75 4154.87256007757 44.8774399224332 36 4290.89 3132.66645748983 1158.22354251017 37 4443.91 3958.46342642796 485.446573572038 38 4502.64 4206.75943551426 295.880564485741 39 4356.98 3993.05467671909 363.92532328091 40 4591.27 4356.85087777575 234.419122224247 41 4696.96 4374.14650292132 322.813497078682 42 4621.4 4275.94193609651 345.458063903484 43 4562.84 4062.23717730135 500.602822698654 44 4202.52 3964.03261047654 238.487389523457 45 4296.49 3981.32823562211 315.161764377892 46 4435.23 4114.12405273804 321.105947261961 47 4105.18 3322.91833409104 782.261665908965 48 4116.68 3802.21472711807 314.465272881934 49 3844.49 3588.5099683229 255.980031677102 50 3720.98 3952.30616937956 -231.326169379561 51 3674.4 3969.60179452513 -295.201794525125 52 3857.62 3524.89665178922 332.723348210777 53 3801.06 3311.19189299405 489.868107005946 54 3504.37 3097.48713419888 406.882865801115 55 3032.6 2883.78237540372 148.817624596285 56 3047.03 3132.07838449001 -85.0483844900124 57 2962.34 3380.37439357631 -418.03439357631 58 2197.82 2473.66848295894 -275.848482958941 59 2014.45 1913.46314825267 100.986851747328 60 1862.83 1584.25819748714 278.571802512864 61 1905.41 2179.05478248453 -273.644782484533 62 1810.99 1734.34963974863 76.6403602513692 63 1670.07 1751.64526489419 -81.575264894195 64 1864.44 1999.94127398049 -135.501273980492 65 2052.02 2363.73747503716 -311.717475037156 66 2029.6 2496.53329215309 -466.933292153087 67 2070.83 2629.32910926902 -558.499109269018 68 2293.41 3339.62588623678 -1046.21588623678 69 2443.27 3356.92151138235 -913.651511382345 70 2513.17 3258.71694455754 -745.546944557543 71 2466.92 3507.01295364384 -1040.09295364384 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.00102668144419874 0.00205336288839748 0.998973318555801 7 9.12010137824352e-05 0.000182402027564870 0.999908798986218 8 9.19373795798606e-06 1.83874759159721e-05 0.999990806262042 9 2.98002280332412e-05 5.96004560664823e-05 0.999970199771967 10 7.1261832432127e-05 0.000142523664864254 0.999928738167568 11 4.57827162826245e-05 9.15654325652489e-05 0.999954217283717 12 2.67812251226739e-05 5.35624502453479e-05 0.999973218774877 13 1.24133821541939e-05 2.48267643083878e-05 0.999987586617846 14 1.47910185554862e-05 2.95820371109723e-05 0.999985208981444 15 1.07837206629824e-05 2.15674413259648e-05 0.999989216279337 16 4.60184083977071e-06 9.20368167954142e-06 0.99999539815916 17 1.92709806946177e-06 3.85419613892355e-06 0.99999807290193 18 6.68576701161044e-07 1.33715340232209e-06 0.9999993314233 19 1.87044193950222e-07 3.74088387900444e-07 0.999999812955806 20 4.60602315516615e-08 9.2120463103323e-08 0.999999953939768 21 1.18953009013329e-08 2.37906018026658e-08 0.9999999881047 22 4.8473220705328e-09 9.6946441410656e-09 0.999999995152678 23 1.29785872319355e-09 2.59571744638709e-09 0.99999999870214 24 4.02779256391995e-10 8.0555851278399e-10 0.99999999959722 25 3.69342650349873e-10 7.38685300699745e-10 0.999999999630657 26 5.70709356733038e-10 1.14141871346608e-09 0.99999999942929 27 1.28448963018446e-09 2.56897926036891e-09 0.99999999871551 28 5.03902086140383e-10 1.00780417228077e-09 0.999999999496098 29 1.69088989558892e-10 3.38177979117785e-10 0.999999999830911 30 1.2353993465258e-07 2.4707986930516e-07 0.999999876460065 31 1.41738596497632e-06 2.83477192995265e-06 0.999998582614035 32 3.53074540526981e-06 7.06149081053963e-06 0.999996469254595 33 6.78814541456882e-06 1.35762908291376e-05 0.999993211854585 34 6.97300541571676e-05 0.000139460108314335 0.999930269945843 35 0.000659962238123255 0.00131992447624651 0.999340037761877 36 0.000626782898489448 0.00125356579697890 0.99937321710151 37 0.000969739659123436 0.00193947931824687 0.999030260340876 38 0.00163033196053022 0.00326066392106044 0.99836966803947 39 0.00185918981677770 0.00371837963355541 0.998140810183222 40 0.00235482604570854 0.00470965209141707 0.997645173954292 41 0.00221101430769277 0.00442202861538554 0.997788985692307 42 0.00139095149524300 0.00278190299048601 0.998609048504757 43 0.000881015551712103 0.00176203110342421 0.999118984448288 44 0.0144904202961532 0.0289808405923064 0.985509579703847 45 0.0349249667234579 0.0698499334469157 0.965075033276542 46 0.0401378699316444 0.0802757398632889 0.959862130068356 47 0.182076585365441 0.364153170730881 0.81792341463456 48 0.331786742044383 0.663573484088766 0.668213257955617 49 0.594081979422888 0.811836041154224 0.405918020577112 50 0.875712956374629 0.248574087250742 0.124287043625371 51 0.980369604037913 0.0392607919241736 0.0196303959620868 52 0.982272958549966 0.0354540829000683 0.0177270414500342 53 0.995353054318079 0.00929389136384191 0.00464694568192096 54 0.999740104787878 0.000519790424243005 0.000259895212121502 55 0.999941753008288 0.000116493983423989 5.82469917119945e-05 56 0.999987390346677 2.52193066461695e-05 1.26096533230848e-05 57 0.999995877554644 8.2448907114044e-06 4.1224453557022e-06 58 0.999990888002567 1.82239948653831e-05 9.11199743269157e-06 59 0.99998525356801 2.94928639815058e-05 1.47464319907529e-05 60 0.99999002657879 1.99468424198326e-05 9.9734212099163e-06 61 0.99995965927586 8.06814482811266e-05 4.03407241405633e-05 62 0.99991464514875 0.000170709702498171 8.53548512490857e-05 63 0.99965746433913 0.000685071321738711 0.000342535660869356 64 0.997862956800195 0.00427408639961012 0.00213704319980506 65 0.99295094297214 0.0140981140557201 0.00704905702786004 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 50 0.833333333333333 NOK 5% type I error level 54 0.9 NOK 10% type I error level 56 0.933333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/10c9wg1260888673.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/10c9wg1260888673.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/11oxb1260888673.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/11oxb1260888673.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/2lw3n1260888673.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/2lw3n1260888673.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/3f39b1260888673.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/3f39b1260888673.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/4y25f1260888673.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/4y25f1260888673.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/53x7n1260888673.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/53x7n1260888673.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/6grpk1260888673.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/6grpk1260888673.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/7ixce1260888673.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/7ixce1260888673.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/8fze11260888673.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/8fze11260888673.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/95fvl1260888673.png (open in new window) http://www.freestatistics.org/blog/date/2009/Dec/15/t12608887833q7xrzivhhuvzfe/95fvl1260888673.ps (open in new window)

Parameters (Session):

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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