*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: Fri, 20 Nov 2009 07:12:55 -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/20/t1258726546tm5ozhp193wsv3m.htm/, Retrieved Fri, 20 Nov 2009 15:15:58 +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/20/t1258726546tm5ozhp193wsv3m.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:

Rob_WS7_4

Dataseries X:

» Textbox « » Textfile « » CSV «

103,2 123297 116476 109375 106370 103,7 114813 123297 116476 109375 106,2 117925 114813 123297 116476 107,7 126466 117925 114813 123297 109,9 131235 126466 117925 114813 111,7 120546 131235 126466 117925 114,9 123791 120546 131235 126466 116 129813 123791 120546 131235 118,3 133463 129813 123791 120546 120,4 122987 133463 129813 123791 126 125418 122987 133463 129813 128,1 130199 125418 122987 133463 130,1 133016 130199 125418 122987 130,8 121454 133016 130199 125418 133,6 122044 121454 133016 130199 134,2 128313 122044 121454 133016 135,5 131556 128313 122044 121454 136,2 120027 131556 128313 122044 139,1 123001 120027 131556 128313 139 130111 123001 120027 131556 139,6 132524 130111 123001 120027 138,7 123742 132524 130111 123001 140,9 124931 123742 132524 130111 141,3 133646 124931 123742 132524 141,8 136557 133646 124931 123742 142 127509 136557 133646 124931 144,5 128945 127509 136557 133646 144,6 137191 128945 127509 136557 145,5 139716 137191 128945 127509 146,8 129083 1397 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation HFCE[t] = + 71477.0285422472 -421.673162748552RPI[t] -0.104860415997025`HFCE-1`[t] + 0.870028900662522`HFCE-2`[t] + 0.0742366888686009`HFCE-3`[t] + 2800.16997442852Q1[t] -13222.0530399475Q2[t] -14003.3472016326Q3[t] + 695.827997756174t + 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) 71477.0285422472 11082.756006 6.4494 0 0 RPI -421.673162748552 79.354233 -5.3138 1e-06 0 `HFCE-1` -0.104860415997025 0.158377 -0.6621 0.509862 0.254931 `HFCE-2` 0.870028900662522 0.155598 5.5915 0 0 `HFCE-3` 0.0742366888686009 0.161787 0.4589 0.647615 0.323807 Q1 2800.16997442852 2548.788347 1.0986 0.27531 0.137655 Q2 -13222.0530399475 2824.834412 -4.6806 1.2e-05 6e-06 Q3 -14003.3472016326 2380.838002 -5.8817 0 0 t 695.827997756174 124.345516 5.5959 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.998029220778848 R-squared 0.996062325528435 Adjusted R-squared 0.995658461480069 F-TEST (value) 2466.33076046058 F-TEST (DF numerator) 8 F-TEST (DF denominator) 78 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2004.35192048201 Sum Squared Residuals 313359276.448913 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 123297 122298.601910029 998.398089971375 2 114813 112447.273888173 2365.72611182688 3 117925 118658.882445767 -733.882445766559 4 126466 125524.265548002 941.73445199829 5 131235 129274.675619610 1960.32438039044 6 120546 120351.131002470 194.868997529791 7 123791 124820.387091224 -1029.38709122434 8 129813 129769.745611712 43.2543882879276 9 133463 133694.153699774 -231.153699774423 10 122987 122579.716618162 407.283381838195 11 125418 124854.057288611 563.942711389308 12 130199 129569.344325969 629.65567403141 13 133016 133057.995028698 -41.9950286975279 14 121454 121481.114570997 -27.1145709971728 15 122044 124233.156703777 -2189.15670377696 16 128313 128767.310963161 -454.310963161168 17 131556 130712.756330580 843.243669420483 18 120027 120249.138595643 -222.138595643181 19 123001 123436.649523139 -435.649523138921 20 130111 128076.32354789 2034.67645210992 21 132524 132305.351229291 218.648770708957 22 123742 123512.119271750 229.880728250156 23 124931 126047.058918214 -1116.05891821434 24 133646 132991.425142505 654.574857495075 25 136557 135745.245769145 811.75423085505 26 127509 127699.836741347 -190.836741346731 27 128945 130688.591587806 -1743.59158780591 28 137191 137539.101421650 -348.101421650137 29 139716 140368.582497518 -652.582497517941 30 129083 131510.102019011 -2427.10201901097 31 131604 134210.077829541 -2606.07782954062 32 139413 139412.66099375 0.339006249869755 33 143125 143409.453490695 -284.453490694735 34 133948 134337.684457606 -389.684457606482 35 137116 138222.604904694 -1106.60490469414 36 144864 144754.39172508 109.608274919903 37 149277 149133.406811198 143.593188802141 38 138796 139940.922684978 -1144.92268497841 39 143258 144525.775619595 -1267.77561959477 40 150034 149333.387498815 700.612501184725 41 154708 154674.670399355 33.3296006446181 42 144888 144873.881153612 14.1188463878694 43 148762 149122.667671999 -360.667671998551 44 156500 155008.075517706 1491.92448229352 45 161088 160038.980053459 1049.01994654085 46 152772 151546.533228173 1225.46677182748 47 158011 156140.210685432 1870.78931456771 48 163318 163353.292439757 -35.2924397574393 49 169969 169727.517494887 241.482505112679 50 162269 158414.694026346 3854.3059736541 51 165765 164010.002587206 1754.99741279439 52 170600 172010.609506008 -1410.60950600789 53 174681 177005.765420252 -2324.76542025151 54 166364 165801.890877485 562.109122514546 55 170240 169612.557485915 627.44251408479 56 176150 176930.055957087 -780.055957087267 57 182056 182645.468980927 -589.46898092703 58 172218 172087.213240124 130.786759875881 59 177856 177724.979725528 131.020274471795 60 182253 183459.045561617 -1206.04556161673 61 188090 189994.177621111 -1904.17762111082 62 176863 177878.102721622 -1015.10272162209 63 183273 183489.168220442 -216.168220442115 64 187969 187970.856657104 -1.85665710360944 65 194650 195254.019584062 -604.019584061857 66 183036 183409.059174851 -373.059174851137 67 189516 189648.537551693 -132.537551693487 68 193805 193595.836442435 209.163557565243 69 200499 200658.678769234 -159.678769234043 70 188142 188632.119245366 -490.119245365938 71 193732 195057.106903458 -1325.10690345774 72 197126 198620.934433299 -1494.93443329882 73 205140 205243.314464920 -103.314464920399 74 191751 192233.592672750 -482.592672750217 75 196700 199342.78479719 -2642.78479718981 76 199784 201752.277294759 -1968.27729475926 77 207360 207351.190104354 8.80989564570689 78 196101 193606.262018597 2494.73798140276 79 200824 200130.182741199 693.817258801417 80 205743 204763.525427944 979.474572056214 81 212489 209878.513092276 2610.48690772446 82 200810 197926.846641652 2883.15335834799 83 203683 203529.403229022 153.596770977609 84 207286 207381.53398375 -95.533983749771 85 210910 212933.481628626 -2023.48162862647 86 194915 202514.765149283 -7599.76514928332 87 217920 206810.156488549 11109.8435114512 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.0725192801799515 0.145038560359903 0.927480719820049 13 0.0226988108189551 0.0453976216379103 0.977301189181045 14 0.0065336173472354 0.0130672346944708 0.993466382652765 15 0.00182525292556823 0.00365050585113647 0.998174747074432 16 0.00199463432261179 0.00398926864522358 0.998005365677388 17 0.000868920070575887 0.00173784014115177 0.999131079929424 18 0.000274454076932242 0.000548908153864485 0.999725545923068 19 0.000272254202437409 0.000544508404874817 0.999727745797563 20 0.000187361300102017 0.000374722600204034 0.999812638699898 21 9.98074989927117e-05 0.000199614997985423 0.999900192501007 22 7.55750785327577e-05 0.000151150157065515 0.999924424921467 23 7.06770424835579e-05 0.000141354084967116 0.999929322957516 24 4.74754385948592e-05 9.49508771897184e-05 0.999952524561405 25 2.00542256163889e-05 4.01084512327779e-05 0.999979945774384 26 7.39743552483052e-06 1.47948710496610e-05 0.999992602564475 27 3.30913344421007e-06 6.61826688842014e-06 0.999996690866556 28 1.17629370834936e-06 2.35258741669871e-06 0.999998823706292 29 5.21892190929217e-07 1.04378438185843e-06 0.999999478107809 30 4.83192267974532e-07 9.66384535949063e-07 0.999999516807732 31 1.64789645768043e-07 3.29579291536086e-07 0.999999835210354 32 7.31198413336719e-08 1.46239682667344e-07 0.999999926880159 33 2.49329138049039e-08 4.98658276098079e-08 0.999999975067086 34 1.42147205388678e-08 2.84294410777356e-08 0.99999998578528 35 1.21874322154418e-08 2.43748644308837e-08 0.999999987812568 36 4.60648940167522e-09 9.21297880335043e-09 0.99999999539351 37 2.35840265154597e-09 4.71680530309194e-09 0.999999997641597 38 8.17807810211445e-10 1.63561562042289e-09 0.999999999182192 39 1.65029606767771e-09 3.30059213535542e-09 0.999999998349704 40 6.13997130505105e-10 1.22799426101021e-09 0.999999999386003 41 3.05871321283437e-10 6.11742642566875e-10 0.999999999694129 42 1.49757838197939e-10 2.99515676395879e-10 0.999999999850242 43 3.19187402645567e-10 6.38374805291133e-10 0.999999999680813 44 1.93428854482545e-10 3.86857708965091e-10 0.999999999806571 45 9.24310412399491e-11 1.84862082479898e-10 0.999999999907569 46 8.4732741324961e-11 1.69465482649922e-10 0.999999999915267 47 3.79350052273352e-10 7.58700104546703e-10 0.99999999962065 48 1.00883063706315e-09 2.01766127412629e-09 0.99999999899117 49 3.86411488055981e-10 7.72822976111961e-10 0.999999999613588 50 2.15927901678716e-09 4.31855803357431e-09 0.99999999784072 51 9.8325007440201e-10 1.96650014880402e-09 0.99999999901675 52 7.59156197428184e-09 1.51831239485637e-08 0.999999992408438 53 9.88689326604604e-09 1.97737865320921e-08 0.999999990113107 54 4.10450668708562e-09 8.20901337417125e-09 0.999999995895493 55 1.92029747844126e-09 3.84059495688253e-09 0.999999998079703 56 1.55614008162405e-09 3.11228016324810e-09 0.99999999844386 57 6.76502635558374e-10 1.35300527111675e-09 0.999999999323497 58 3.48630748263141e-10 6.97261496526281e-10 0.99999999965137 59 2.71947115924948e-10 5.43894231849897e-10 0.999999999728053 60 7.3166130241806e-10 1.46332260483612e-09 0.999999999268339 61 3.64189904698123e-10 7.28379809396245e-10 0.99999999963581 62 2.27966179177347e-10 4.55932358354695e-10 0.999999999772034 63 5.65477209248383e-10 1.13095441849677e-09 0.999999999434523 64 4.10413636503102e-10 8.20827273006203e-10 0.999999999589586 65 2.52059574222443e-10 5.04119148444887e-10 0.99999999974794 66 1.25652003577676e-10 2.51304007155352e-10 0.999999999874348 67 7.55006972673888e-10 1.51001394534778e-09 0.999999999244993 68 1.78135085682623e-09 3.56270171365247e-09 0.99999999821865 69 1.62514006563992e-09 3.25028013127983e-09 0.99999999837486 70 5.97661316806998e-10 1.19532263361400e-09 0.999999999402339 71 4.68921464210029e-10 9.37842928420059e-10 0.999999999531079 72 3.00771788256793e-10 6.01543576513586e-10 0.999999999699228 73 2.16630243737373e-10 4.33260487474747e-10 0.99999999978337 74 1.97753013229126e-10 3.95506026458252e-10 0.999999999802247 75 1.12866245505971e-10 2.25732491011942e-10 0.999999999887134 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 61 0.953125 NOK 5% type I error level 63 0.984375 NOK 10% type I error level 63 0.984375 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/10as8m1258726370.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/10as8m1258726370.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/1yij11258726369.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/1yij11258726369.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/2kl4a1258726369.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/2kl4a1258726369.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/3by4t1258726370.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/3by4t1258726370.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/4lduo1258726370.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/4lduo1258726370.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/5y4ti1258726370.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/5y4ti1258726370.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/612671258726370.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/612671258726370.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/7u6re1258726370.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/7u6re1258726370.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/8dpfb1258726370.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/8dpfb1258726370.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/962yq1258726370.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258726546tm5ozhp193wsv3m/962yq1258726370.ps (open in new window)

Parameters (Session):

par1 = 2 ; par2 = Include Quarterly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Include Quarterly 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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