ws7beurscompetitief

*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: Wed, 01 Dec 2010 19:10:51 +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/01/t12912305939xoyypzvtm31ues.htm/, Retrieved Wed, 01 Dec 2010 20:10:03 +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/01/t12912305939xoyypzvtm31ues.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:

» Textbox « » Textfile « » CSV «

162556 807 213118 6282154 29790 444 81767 4321023 87550 412 153198 4111912 54660 315 126942 1491348 42634 168 157214 1629616 45187 267 234817 1926517 37704 228 60448 983660 16275 129 47818 1443586 18014 393 -1710 1405225 24811 280 95350 929118 21950 265 114337 856956 37597 234 37884 992426 12988 73 82340 857217 22330 67 79801 711969 27664 236 74996 657954 3369 26 87161 688779 11819 70 106113 574339 6984 40 80570 741409 4519 42 102129 597793 5336 80 112477 697458 2365 83 191778 550608 3689 25 101792 377305 4891 49 210568 370837 7489 149 136996 430866 3160 90 108094 530670 4150 136 134759 518365 7285 97 188873 491303 1134 63 146216 527021 4658 114 156608 233773 9327 85 87419 387699 5565 43 94355 493408 3122 25 94670 414462 7561 77 82425 364304 2053 44 100423 397144 4036 85 100269 424898 3045 49 27330 202055 1765 20 77623 247060 666 13 117869 339836 4677 66 90131 426280 5692 68 64239 357312 2949 40 82903 378509 6533 29 126910 364839 3055 30 60247 376641 1414 22 70184 33 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 11 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Wealth[t] = + 119279.402431916 + 21.8351832830495Costs[t] + 2901.79845530046Orders[t] + 0.586265162789953Dividends[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) 119279.402431916 92573.146804 1.2885 0.201156 0.100578 Costs 21.8351832830495 3.964506 5.5077 0 0 Orders 2901.79845530046 677.504293 4.2831 4.9e-05 2.5e-05 Dividends 0.586265162789953 1.009186 0.5809 0.562863 0.281431 Multiple Linear Regression - Regression Statistics Multiple R 0.917211289346806 R-squared 0.84127654930523 Adjusted R-squared 0.835539557111444 F-TEST (value) 146.640699671220 F-TEST (DF numerator) 3 F-TEST (DF denominator) 83 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 364153.911839319 Sum Squared Residuals 11006469935153.9 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6282154 6135414.46858224 146739.531417759 2 4321023 2106085.17015321 2214937.82984679 3 4111912 3316305.31285578 795606.68714422 4 1491348 2301278.70639793 -809930.706397927 5 1629616 1629871.83831478 -255.83831478335 6 1926517 2018391.04373914 -91874.043739143 7 983660 1639601.75730484 -655941.757304844 8 1443586 877013.038651594 566572.961348406 9 1405225 1652022.67359748 -246797.673597477 10 929118 1529436.08562381 -600318.085623806 11 856956 1434570.06606739 -577614.066067387 12 992426 1641447.69629217 -649021.696292168 13 857217 662979.12365322 194237.876346780 14 711969 848064.087903342 -136095.087903342 15 657954 1452119.8903737 -794165.8903737 16 688779 319388.352604256 369390.647395744 17 574339 642685.68074444 -68346.6807444396 18 741409 435083.644858738 306325.355141262 19 597793 399702.805621211 198090.194378789 20 697458 533877.16356943 163580.836430570 21 550608 524201.643075797 26406.3569242028 22 377305 332051.458396311 45253.5416036887 23 370837 491712.090977388 -120875.090977388 24 430866 795487.042120014 -364621.042120014 25 530670 512812.18909001 17857.8109099897 26 518365 683544.510049844 -165179.510049844 27 491303 670552.822904702 -179249.822904702 28 527021 412575.150001318 114445.849998682 29 233773 643606.524682821 -409833.524682821 30 387699 620839.739879392 -233140.739879392 31 493408 420886.580415051 72521.4195849485 32 414462 315495.528985432 98966.4710145677 33 364304 556136.61033615 -191832.610336150 34 397144 350660.672188092 46483.3278119083 35 424898 512843.292470628 -87945.2924706279 36 202055 343978.286737573 -141923.286737573 37 247060 261362.130763752 -14302.1307637515 38 339836 240647.502890221 99188.4971097786 39 426280 465761.918083989 -39481.9180839893 40 357312 478548.648431928 -121236.648431928 41 378509 348346.436936422 30162.5630635779 42 364839 420483.721833464 -55644.721833464 43 376641 308360.558283252 68280.4417167484 44 330546 255140.351796007 75405.6482039926 45 317892 218520.56849472 99371.4315052798 46 307528 281392.307266749 26135.6927332505 47 125390 363340.464280717 -237950.464280717 48 510834 267750.624316068 243083.375683932 49 249898 262271.334326955 -12373.3343269545 50 158492 318239.902048171 -159747.902048171 51 289513 252737.064030003 36775.9359699965 52 378049 200747.09755918 177301.90244082 53 214215 245238.655461937 -31023.6554619375 54 480382 291117.580298461 189264.419701539 55 353058 263295.510364999 89762.4896350008 56 217193 302403.520462879 -85210.5204628786 57 316176 156442.377758688 159733.622241312 58 330068 204794.290290955 125273.709709045 59 297413 216098.513042918 81314.4869570822 60 314806 170964.421182253 143841.578817747 61 333210 200522.860252435 132687.139747565 62 352108 274274.468360512 77833.5316394877 63 409642 552832.085248321 -143190.085248321 64 269587 231647.374547745 37939.6254522548 65 300962 188318.141432361 112643.858567639 66 325479 216164.947380128 109314.052619872 67 316155 170153.511418226 146001.488581774 68 318574 164969.201902695 153604.798097305 69 343613 247977.688708166 95635.3112918343 70 306948 169369.142052431 137578.857947569 71 291841 271879.062880941 19961.9371190595 72 319210 182509.360546136 136700.639453864 73 340968 188540.25993277 152427.74006723 74 313164 167185.401118912 145978.598881088 75 316647 216507.477049943 100139.522950057 76 322031 198558.205577967 123472.794422033 77 308336 275139.121336206 33196.8786637945 78 283910 269204.58418656 14705.41581344 79 438493 915335.076113629 -476842.076113629 80 230621 296986.893442749 -66365.893442749 81 278990 254195.734987805 24794.2650121954 82 286963 379296.597642448 -92333.5976424476 83 269753 221827.251734012 47925.7482659881 84 448243 328516.137865112 119726.862134888 85 290476 238613.830228558 51862.169771442 86 -83265 559790.463486561 -643055.463486561 87 215362 508084.871142766 -292722.871142766 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.999999999999822 3.55120587379173e-13 1.77560293689587e-13 8 1 3.95895057102111e-17 1.97947528551056e-17 9 1 4.59634540170336e-26 2.29817270085168e-26 10 1 1.58986651732619e-27 7.94933258663095e-28 11 1 3.78544010479121e-28 1.89272005239561e-28 12 1 6.50071673623598e-28 3.25035836811799e-28 13 1 1.46096810066471e-29 7.30484050332357e-30 14 1 3.50071810706007e-29 1.75035905353004e-29 15 1 1.14229450721315e-29 5.71147253606574e-30 16 1 3.52431337140946e-31 1.76215668570473e-31 17 1 8.22570258216168e-31 4.11285129108084e-31 18 1 6.67811223860382e-34 3.33905611930191e-34 19 1 2.0050685749502e-34 1.0025342874751e-34 20 1 1.74976778705464e-36 8.7488389352732e-37 21 1 1.40727592010912e-35 7.0363796005456e-36 22 1 1.19605331367845e-34 5.98026656839227e-35 23 1 3.69649144541978e-35 1.84824572270989e-35 24 1 1.65856971604540e-34 8.29284858022698e-35 25 1 3.08651513651337e-34 1.54325756825669e-34 26 1 1.26315425421803e-33 6.31577127109013e-34 27 1 8.54969255949084e-33 4.27484627974542e-33 28 1 5.92093833158143e-32 2.96046916579072e-32 29 1 2.58762243026473e-34 1.29381121513237e-34 30 1 1.33205873213767e-33 6.66029366068835e-34 31 1 1.13757385650665e-33 5.68786928253325e-34 32 1 7.13952185974708e-33 3.56976092987354e-33 33 1 3.32650705944629e-32 1.66325352972314e-32 34 1 2.83082453492643e-31 1.41541226746322e-31 35 1 1.68276211599889e-30 8.41381057999446e-31 36 1 1.43051819965174e-29 7.15259099825872e-30 37 1 5.0692392910498e-29 2.5346196455249e-29 38 1 8.41528363429975e-29 4.20764181714988e-29 39 1 2.58657078873766e-28 1.29328539436883e-28 40 1 4.47284654580559e-28 2.23642327290280e-28 41 1 2.73879860556358e-27 1.36939930278179e-27 42 1 1.87342665700691e-26 9.36713328503454e-27 43 1 3.26548871633738e-26 1.63274435816869e-26 44 1 2.51272530537169e-25 1.25636265268584e-25 45 1 1.86322292629407e-24 9.31611463147035e-25 46 1 1.30904224303498e-23 6.54521121517492e-24 47 1 7.79982579019146e-24 3.89991289509573e-24 48 1 5.206287457898e-24 2.603143728949e-24 49 1 2.13472232813641e-23 1.06736116406821e-23 50 1 6.68813329732947e-23 3.34406664866473e-23 51 1 4.90805846056538e-22 2.45402923028269e-22 52 1 1.07333717468823e-21 5.36668587344117e-22 53 1 8.72034268917672e-22 4.36017134458836e-22 54 1 3.07937476036408e-21 1.53968738018204e-21 55 1 1.09302964624663e-20 5.46514823123317e-21 56 1 1.68287914556258e-20 8.4143957278129e-21 57 1 1.49381293721967e-19 7.46906468609834e-20 58 1 1.30875679923841e-18 6.54378399619206e-19 59 1 7.85564798470758e-18 3.92782399235379e-18 60 1 6.58078593332526e-17 3.29039296666263e-17 61 1 4.9307696682389e-16 2.46538483411945e-16 62 0.999999999999999 2.34801982755305e-15 1.17400991377652e-15 63 0.999999999999992 1.55968676591284e-14 7.79843382956418e-15 64 0.999999999999986 2.72036920766179e-14 1.36018460383089e-14 65 0.999999999999887 2.26882128013848e-13 1.13441064006924e-13 66 0.999999999999541 9.17157861154847e-13 4.58578930577423e-13 67 0.999999999996337 7.32553665072463e-12 3.66276832536232e-12 68 0.99999999997328 5.34410709795205e-11 2.67205354897602e-11 69 0.999999999920012 1.59975133318336e-10 7.99875666591682e-11 70 0.999999999381434 1.23713117032138e-09 6.18565585160692e-10 71 0.999999995903724 8.19255216934098e-09 4.09627608467049e-09 72 0.99999996976276 6.04744818360933e-08 3.02372409180467e-08 73 0.999999809962232 3.80075536010676e-07 1.90037768005338e-07 74 0.999998722792703 2.55441459423084e-06 1.27720729711542e-06 75 0.9999918683904 1.62632191982958e-05 8.1316095991479e-06 76 0.999951232288627 9.75354227467746e-05 4.87677113733873e-05 77 0.999723592354253 0.000552815291494835 0.000276407645747417 78 0.99909829601244 0.00180340797511824 0.000901703987559118 79 0.99576036394617 0.00847927210765814 0.00423963605382907 80 0.990676690829087 0.0186466183418254 0.00932330917091271 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 73 0.986486486486487 NOK 5% type I error level 74 1 NOK 10% type I error level 74 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/106yfh1291230639.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/106yfh1291230639.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/1hxin1291230639.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/1hxin1291230639.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/2r6hq1291230639.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/2r6hq1291230639.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/3r6hq1291230639.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/3r6hq1291230639.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/4r6hq1291230639.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/4r6hq1291230639.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/52fzt1291230639.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/52fzt1291230639.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/62fzt1291230639.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/62fzt1291230639.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/7d6gw1291230639.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/7d6gw1291230639.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/8d6gw1291230639.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/8d6gw1291230639.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/9d6gw1291230639.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/01/t12912305939xoyypzvtm31ues/9d6gw1291230639.ps (open in new window)

Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 4 ; 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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