*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: Thu, 02 Dec 2010 14:13:24 +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/02/t1291299175ktyshbg1iv3pfm0.htm/, Retrieved Thu, 02 Dec 2010 15:13:04 +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/02/t1291299175ktyshbg1iv3pfm0.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 6282929 5627 37 29790 444 81767 4324047 13346 138 87550 412 153198 4108272 8533 45 54660 312 126942 1485329 4299 24 42634 166 157214 1779876 10127 37 42312 237 234817 2519076 7180 55 37704 228 60448 912684 1577 19 16275 129 47818 1443586 11409 76 18014 393 -1710 1457425 5261 70 24524 275 95350 929144 1467 30 20813 255 114337 774497 1144 28 37597 234 37884 990576 3188 21 12988 73 82340 876607 5686 52 22330 67 79801 711969 6095 23 26088 224 74996 588864 1456 15 3369 26 87161 688779 17456 145 11819 70 106113 608419 4805 35 6620 40 80570 696348 11281 75 4519 42 102129 597793 8118 88 5336 80 112477 697458 6141 93 2365 83 191778 700368 6334 212 3653 24 101792 336260 5677 37 1265 19 210568 636765 16799 345 7489 149 136996 481231 873 38 3160 90 108094 563925 3370 115 4150 136 134759 511939 2080 75 7285 97 188873 521016 2791 44 1134 63 146216 543856 2123 303 4658 114 156608 329304 818 28 9327 85 87419 406339 2043 22 5565 43 94355 493408 6243 53 3122 25 94670 416002 6353 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 4 seconds R Server 'Herman Ole Andreas Wold' @ www.wessa.org Multiple Linear Regression - Estimated Regression Equation Wealth[t] = -51843.9526262499 + 19.7292480567881Costs[t] + 3247.59353619237Orders[t] + 2.14055513624031Dividends[t] + 6.25156768943924`Profit/Trades`[t] -5.77246339770221`Profit/Cost`[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) -51843.9526262499 98846.605805 -0.5245 0.601407 0.300704 Costs 19.7292480567881 3.97359 4.9651 4e-06 2e-06 Orders 3247.59353619237 685.482696 4.7377 9e-06 5e-06 Dividends 2.14055513624031 1.006173 2.1274 0.036504 0.018252 `Profit/Trades` 6.25156768943924 3.01548 2.0732 0.041417 0.020709 `Profit/Cost` -5.77246339770221 30.374536 -0.19 0.849763 0.424882 Multiple Linear Regression - Regression Statistics Multiple R 0.923991244554472 R-squared 0.853759820013322 Adjusted R-squared 0.844504112419228 F-TEST (value) 92.2414425190076 F-TEST (DF numerator) 5 F-TEST (DF denominator) 79 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 362485.57554555 Sum Squared Residuals 10380267605808.5 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6282929 6267226.49796825 15702.5020317534 2 4324047 2235485.47131421 2088561.52868579 3 4108272 3394473.88365963 713798.116340369 4 1485329 2338269.22992978 -852940.229929776 5 1779876 1728014.61606891 51861.3839310949 6 2519076 2099827.16518 419248.834820002 7 912684 1571620.26467589 -658936.26467589 8 1443586 861431.619722117 582154.380277883 9 1457425 1608688.05748566 -151263.057485659 10 929144 1538184.15731031 -609040.15731031 11 774497 1438652.05598262 -664155.055982624 12 990576 1550755.04087773 -560179.040877733 13 876607 652973.404980854 223633.595019146 14 711969 815087.902242814 -103118.902242814 15 588864 1359862.39138866 -770998.391388663 16 688779 393924.600642096 294854.399357904 17 608419 665645.051391097 -57226.0513910974 18 696348 451222.938633999 245125.061366001 19 597793 442566.453095411 155226.546904589 20 697458 591856.056043921 105601.943956079 21 700368 713251.032954509 -12883.0329545092 22 336260 351337.192449218 -15077.1924492184 23 636765 588578.823023774 48186.1769762262 24 481231 878285.579391843 -397054.579391843 25 563925 554569.006209947 9355.99379005269 26 511939 772734.543375396 -260795.543375396 27 521016 828387.399756952 -307371.399756952 28 543856 499633.839045955 44222.1609540448 29 329304 750460.760119346 -421156.760119346 30 406339 607986.34262553 -201647.34262553 31 493408 438290.511271092 55117.4887289076 32 416002 332904.862516287 83097.1374837126 33 337430 519390.022123477 -181960.022123477 34 408247 370494.668322099 37752.3316779009 35 418799 539860.047624924 -121061.047624924 36 247405 230880.234241452 16524.7657585484 37 283662 225282.016309311 58379.9836906893 38 420383 261967.121270628 158415.878729372 39 431809 459404.8879643 -27595.8879643005 40 357257 432290.204029609 -75033.2040296091 41 373177 339765.83038037 33411.1696196303 42 369419 456857.497180685 -87438.4971806855 43 376641 260165.368757228 116475.631242772 44 364885 202265.161271942 162619.838728058 45 329118 234247.589335322 94870.410664678 46 317365 258336.229610467 59028.7703895333 47 153661 290348.227959363 -136687.227959363 48 513294 347050.41947074 166243.58052926 49 264512 218399.202759938 46112.7972400621 50 129302 216648.662318246 -87346.6623182457 51 268673 226606.200199045 42066.7998009548 52 353179 223958.382230196 129220.617769804 53 211742 197765.361315542 13976.6386844581 54 485538 352911.279961908 132626.720038092 55 279268 203489.870356426 75778.1296435741 56 219060 255309.333730768 -36249.3337307681 57 325806 855783.040699587 -529977.040699587 58 349729 239684.205841234 110044.794158766 59 305442 220070.620257208 85371.3797427918 60 329537 228308.74138653 101228.25861347 61 327055 181276.406672442 145778.593327558 62 356245 193040.487166975 163204.512833025 63 404480 497618.38912923 -93138.3891292297 64 318314 226048.651662464 92265.3483375364 65 311807 122588.084774973 189218.915225027 66 337724 233020.967691249 104703.032308751 67 326431 418636.660181122 -92205.6601811217 68 327556 284118.014360667 43437.985639333 69 356850 188718.506292663 168131.493707337 70 321024 194864.426163936 126159.573836064 71 355822 156032.257272854 199789.742727146 72 324047 261659.125096242 62387.8749037582 73 328576 209291.4544658 119284.5455342 74 332013 219368.287137922 112644.712862078 75 319634 324993.19938525 -5359.19938525022 76 279230 265096.619081615 14133.3809183849 77 532682 1028447.69601692 -495765.696016921 78 171493 166986.529655007 4506.47034499305 79 302211 220081.637088631 82129.3629113688 80 286146 282977.582973631 3168.41702636893 81 306844 181291.451859568 125552.548140432 82 307705 298631.440145438 9073.55985456208 83 299446 204888.785507001 94557.214492999 84 -78375 340572.859397513 -418947.859397513 85 235098 438512.302578535 -203414.302578535 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 9 0.999952070493982 9.5859012036465e-05 4.79295060182325e-05 10 0.999945740800299 0.00010851839940221 5.42591997011051e-05 11 0.99987428171779 0.000251436564418748 0.000125718282209374 12 0.999886342987903 0.000227314024194875 0.000113657012097438 13 0.999971971310401 5.60573791979386e-05 2.80286895989693e-05 14 0.999994175595394 1.16488092113403e-05 5.82440460567014e-06 15 0.99999384322557 1.23135488589638e-05 6.15677442948188e-06 16 1 1.71201097727476e-19 8.5600548863738e-20 17 1 5.65087461543498e-20 2.82543730771749e-20 18 1 1.70912462204404e-22 8.54562311022019e-23 19 1 4.0463030590122e-23 2.0231515295061e-23 20 1 2.0964684362509e-25 1.04823421812545e-25 21 1 5.5237360166608e-25 2.7618680083304e-25 22 1 1.76252791852425e-24 8.81263959262126e-25 23 1 3.50272226630326e-25 1.75136113315163e-25 24 1 2.2211104661777e-24 1.11055523308885e-24 25 1 1.54972275234054e-25 7.7486137617027e-26 26 1 8.828740328167e-25 4.4143701640835e-25 27 1 4.0952804144437e-24 2.04764020722185e-24 28 1 4.76215559591186e-25 2.38107779795593e-25 29 1 1.37462386351657e-26 6.87311931758283e-27 30 1 2.19296181165224e-26 1.09648090582612e-26 31 1 6.43972121349973e-27 3.21986060674986e-27 32 1 2.5877901644983e-26 1.29389508224915e-26 33 1 8.59855689561831e-26 4.29927844780916e-26 34 1 3.8835645151734e-25 1.9417822575867e-25 35 1 2.21129173327592e-24 1.10564586663796e-24 36 1 2.9863965376057e-24 1.49319826880285e-24 37 1 1.06547903053862e-23 5.32739515269312e-24 38 1 2.53735404758405e-23 1.26867702379202e-23 39 1 4.21186914661241e-23 2.1059345733062e-23 40 1 3.09224241867536e-23 1.54612120933768e-23 41 1 1.47840942510266e-22 7.39204712551329e-23 42 1 8.25043819783678e-22 4.12521909891839e-22 43 1 5.6571611601275e-22 2.82858058006375e-22 44 1 1.4729799630954e-21 7.36489981547702e-22 45 1 8.49761739107581e-21 4.2488086955379e-21 46 1 5.29181360953162e-20 2.64590680476581e-20 47 1 4.23565611120482e-20 2.11782805560241e-20 48 1 3.87406416362939e-21 1.9370320818147e-21 49 1 1.64774265202473e-20 8.23871326012367e-21 50 1 1.03913295180695e-20 5.19566475903474e-21 51 1 6.45328444745121e-20 3.22664222372561e-20 52 1 1.21076067896913e-19 6.05380339484564e-20 53 1 3.80963520432642e-20 1.90481760216321e-20 54 1 2.82310574775931e-20 1.41155287387965e-20 55 1 2.6080679281463e-19 1.30403396407315e-19 56 1 2.86253116341317e-19 1.43126558170658e-19 57 1 7.78986687239409e-20 3.89493343619704e-20 58 1 8.90231388877267e-19 4.45115694438634e-19 59 1 4.81044685234348e-18 2.40522342617174e-18 60 1 5.03468250475543e-17 2.51734125237772e-17 61 1 5.54673918689674e-16 2.77336959344837e-16 62 0.999999999999998 4.03265527231981e-15 2.01632763615991e-15 63 0.999999999999978 4.45823664676965e-14 2.22911832338483e-14 64 0.999999999999884 2.32895594878605e-13 1.16447797439303e-13 65 0.999999999998776 2.44865518298563e-12 1.22432759149281e-12 66 0.999999999993839 1.23221530799729e-11 6.16107653998644e-12 67 0.99999999993344 1.33118376594874e-10 6.65591882974368e-11 68 0.999999999316181 1.36763742612205e-09 6.83818713061026e-10 69 0.999999999273175 1.45364913061697e-09 7.26824565308485e-10 70 0.999999991480031 1.70399378603937e-08 8.51996893019683e-09 71 0.999999964683617 7.06327664868426e-08 3.53163832434213e-08 72 0.999999573905265 8.52189470219706e-07 4.26094735109853e-07 73 0.999995310359418 9.37928116399244e-06 4.68964058199622e-06 74 0.999953447909168 9.31041816634891e-05 4.65520908317445e-05 75 0.999706712043823 0.0005865759123536 0.0002932879561768 76 0.999049045196858 0.00190190960628446 0.000950954803142228 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 68 1 NOK 5% type I error level 68 1 NOK 10% type I error level 68 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/10vwef1291299200.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/10vwef1291299200.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/16dz41291299200.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/16dz41291299200.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/26dz41291299200.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/26dz41291299200.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/3h4g61291299200.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/3h4g61291299200.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/4h4g61291299200.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/4h4g61291299200.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/5h4g61291299200.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/5h4g61291299200.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/6rvfr1291299200.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/6rvfr1291299200.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/724ec1291299200.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/724ec1291299200.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/824ec1291299200.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/824ec1291299200.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/924ec1291299200.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/02/t1291299175ktyshbg1iv3pfm0/924ec1291299200.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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