multi regression lin trend y-1

*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, 17 Dec 2010 14:32:33 +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/17/t1292596290va9uwp1xzvorzvr.htm/, Retrieved Fri, 17 Dec 2010 15:31:33 +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/17/t1292596290va9uwp1xzvorzvr.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 «

100.00 0 100,21 100.42 0 100.00 100.50 0 100.42 101.14 0 100.50 101.98 0 101.14 102.31 0 101.98 103.27 0 102.31 103.80 0 103.27 103.46 0 103.80 105.06 0 103.46 106.08 0 105.06 106.74 0 106.08 107.35 0 106.74 108.96 0 107.35 109.85 0 108.96 109.81 0 109.85 109.99 0 109.81 111.60 0 109.99 112.74 0 111.60 112.78 0 112.74 113.66 0 112.78 115.37 0 113.66 116.26 0 115.37 116.24 0 116.26 116.73 0 116.24 118.76 0 116.73 119.78 0 118.76 120.23 0 119.78 121.48 0 120.23 124.07 0 121.48 125.82 0 124.07 126.92 0 125.82 128.48 0 126.92 131.44 0 128.48 133.51 0 131.44 134.58 0 133.51 136.68 0 134.58 140.10 0 136.68 142.45 0 140.10 143.91 0 142.45 146.19 0 143.91 149.84 0 146.19 152.31 0 149.84 153.62 0 152.31 155.79 0 153.62 159.89 0 155.79 163.21 0 159.89 165.32 0 163.21 167.68 0 165.32 171.79 0 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 6 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation woningprijsindex_us[t] = + 3.26808178579541 -6.7292968674863Dummy_[t] + 0.960574149589203Y1[t] + 0.123234411445745t + 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) 3.26808178579541 1.289608 2.5342 0.013357 0.006679 Dummy_ -6.7292968674863 0.709424 -9.4856 0 0 Y1 0.960574149589203 0.015593 61.6011 0 0 t 0.123234411445745 0.031525 3.909 0.000202 0.000101 Multiple Linear Regression - Regression Statistics Multiple R 0.999235671676733 R-squared 0.998471927551252 Adjusted R-squared 0.998410804653302 F-TEST (value) 16335.4808269943 F-TEST (DF numerator) 3 F-TEST (DF denominator) 75 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.69071015269754 Sum Squared Residuals 214.38756153259 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 99.6504517275753 0.349548272424711 2 100.42 99.5719655676072 0.848034432392764 3 100.5 100.09864112188 0.401358878119529 4 101.14 100.298721465293 0.84127853470666 5 101.98 101.036723332476 0.94327666752383 6 102.31 101.966840029577 0.343159970423147 7 103.27 102.407063910387 0.862936089612962 8 103.8 103.452449505438 0.347550494561589 9 103.46 104.084788216166 -0.62478821616644 10 105.06 103.881427416752 1.17857258324815 11 106.08 105.54158046754 0.538419532459678 12 106.74 106.644600511567 0.0953994884329456 13 107.35 107.401813861742 -0.0518138617416712 14 108.96 108.110998504437 0.84900149556317 15 109.85 109.780757296721 0.0692427032788055 16 109.81 110.758902701301 -0.948902701301321 17 109.99 110.843714146764 -0.853714146763514 18 111.6 111.139851905135 0.460148094864688 19 112.74 112.80961069742 -0.0696106974196718 20 112.78 114.027899639397 -1.2478996393971 21 113.66 114.189557016826 -0.529557016826428 22 115.37 115.158096679911 0.211903320089339 23 116.26 116.923912887154 -0.663912887153949 24 116.24 117.902058291734 -1.6620582917341 25 116.73 118.006081220188 -1.27608122018804 26 118.76 118.599996964933 0.160003035067501 27 119.78 120.673196900044 -0.893196900044334 28 120.23 121.776216944071 -1.54621694407106 29 121.48 122.331709722832 -0.851709722831949 30 124.07 123.655661821264 0.414338178735787 31 125.82 126.266783280146 -0.446783280145984 32 126.92 128.071022453373 -1.15102245337283 33 128.48 129.250888429367 -0.770888429366717 34 131.44 130.872618514172 0.567381485828401 35 133.51 133.839152408401 -0.3291524084014 36 134.58 135.950775309497 -1.37077530949677 37 136.68 137.101824061003 -0.421824061002986 38 140.1 139.242264186586 0.857735813413935 39 142.45 142.650662189627 -0.20066218962688 40 143.91 145.031245852607 -1.12124585260724 41 146.19 146.556918522453 -0.366918522453228 42 149.84 148.870261994962 0.969738005037648 43 152.31 152.499592052409 -0.189592052408695 44 153.62 154.99544461334 -1.37544461333977 45 155.79 156.377031160747 -0.587031160747384 46 159.89 158.584711476802 1.30528852319831 47 163.21 162.646299901563 0.563700098436855 48 165.32 165.958640489645 -0.63864048964508 49 167.68 168.108686356724 -0.42868635672402 50 171.79 170.4988757612 1.29112423879969 51 175.38 174.570069927458 0.80993007254233 52 177.81 178.141765535929 -0.331765535928649 53 181.09 180.599195130876 0.490804869123839 54 186.48 183.873112752974 2.60688724702549 55 191.07 189.173841830706 1.89615816929396 56 194.23 193.706111588766 0.523888411233762 57 197.82 196.864760312914 0.95523968708614 58 204.41 200.436455921385 3.97354407861515 59 209.26 206.889873978623 2.37012602137655 60 212.24 211.671893015577 0.568106984423206 61 214.88 214.657638392798 0.222361607201603 62 218.87 217.31678855916 1.55321144084038 63 219.86 221.272713827466 -1.41271382746628 64 219.75 222.346916647005 -2.59691664700536 65 220.89 222.364487901996 -1.47448790199629 66 224.02 223.582776843974 0.437223156026304 67 222.27 226.712608343634 -4.44260834363367 68 217.27 218.425541125812 -1.15554112581201 69 213.23 213.745904789312 -0.515904789311758 70 212.44 209.988419636417 2.4515803635829 71 207.87 209.352800469687 -1.48280046968738 72 199.46 205.08621101751 -5.62621101751047 73 198.19 197.131016830911 1.05898316908897 74 199.77 196.034322072378 3.73567792762153 75 200.1 197.675263640175 2.42473635982482 76 195.76 198.115487520985 -2.35548752098535 77 191.27 194.069830123214 -2.79983012321393 78 195.79 189.880086603004 5.90991339699581 79 192.7 194.345116170593 -1.64511617059312 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.00588985661531453 0.0117797132306291 0.994110143384686 8 0.000781420762792548 0.0015628415255851 0.999218579237207 9 0.00128005875940769 0.00256011751881538 0.998719941240592 10 0.000613127869944704 0.00122625573988941 0.999386872130055 11 0.00044175904302191 0.00088351808604382 0.999558240956978 12 0.000127004281558377 0.000254008563116754 0.999872995718442 13 2.94736549857688e-05 5.89473099715376e-05 0.999970526345014 14 4.40469075945615e-05 8.80938151891231e-05 0.999955953092405 15 1.29116341812702e-05 2.58232683625404e-05 0.999987088365819 16 6.0742547010553e-06 1.21485094021106e-05 0.9999939257453 17 2.58203455716982e-06 5.16406911433963e-06 0.999997417965443 18 1.53027137041159e-06 3.06054274082318e-06 0.99999846972863 19 6.17017313810558e-07 1.23403462762112e-06 0.999999382982686 20 2.23233138337282e-07 4.46466276674564e-07 0.999999776766862 21 5.55984247773268e-08 1.11196849554654e-07 0.999999944401575 22 6.87053255762369e-08 1.37410651152474e-07 0.999999931294674 23 2.24999632188856e-08 4.49999264377711e-08 0.999999977500037 24 8.68974475672035e-09 1.73794895134407e-08 0.999999991310255 25 2.44805361163145e-09 4.89610722326289e-09 0.999999997551946 26 4.91414748636707e-09 9.82829497273414e-09 0.999999995085852 27 1.96878444005683e-09 3.93756888011366e-09 0.999999998031216 28 5.24939717740853e-10 1.04987943548171e-09 0.99999999947506 29 2.29558422548247e-10 4.59116845096493e-10 0.999999999770442 30 6.16062661332501e-09 1.232125322665e-08 0.999999993839373 31 5.22403812452502e-09 1.044807624905e-08 0.999999994775962 32 1.67540990725043e-09 3.35081981450085e-09 0.99999999832459 33 6.54932089112843e-10 1.30986417822569e-09 0.999999999345068 34 2.22462171744387e-09 4.44924343488774e-09 0.999999997775378 35 7.17672814030436e-10 1.43534562806087e-09 0.999999999282327 36 4.11238392099702e-10 8.22476784199405e-10 0.999999999588762 37 1.4235968569187e-10 2.8471937138374e-10 0.99999999985764 38 2.29540978994238e-10 4.59081957988477e-10 0.99999999977046 39 7.002618546597e-11 1.4005237093194e-10 0.999999999929974 40 5.42215336188816e-11 1.08443067237763e-10 0.999999999945778 41 1.73574047261901e-11 3.47148094523801e-11 0.999999999982643 42 1.55317161560564e-11 3.10634323121129e-11 0.999999999984468 43 5.26851013804443e-12 1.05370202760889e-11 0.999999999994731 44 1.21750415792041e-11 2.43500831584082e-11 0.999999999987825 45 5.6301240966949e-12 1.12602481933898e-11 0.99999999999437 46 8.18172785393371e-12 1.63634557078674e-11 0.999999999991818 47 2.78896076198704e-12 5.57792152397409e-12 0.999999999997211 48 2.83123082700073e-12 5.66246165400146e-12 0.999999999997169 49 2.19140704667363e-12 4.38281409334726e-12 0.999999999997809 50 1.62324870006575e-12 3.24649740013149e-12 0.999999999998377 51 6.66284694285265e-13 1.33256938857053e-12 0.999999999999334 52 1.26666588465889e-12 2.53333176931777e-12 0.999999999998733 53 1.15274983513013e-12 2.30549967026026e-12 0.999999999998847 54 3.49404012061374e-12 6.98808024122749e-12 0.999999999996506 55 1.7329603306646e-12 3.4659206613292e-12 0.999999999998267 56 3.98873659709799e-12 7.97747319419599e-12 0.999999999996011 57 8.94273998232906e-12 1.78854799646581e-11 0.999999999991057 58 4.46127829040043e-11 8.92255658080086e-11 0.999999999955387 59 1.43317621121009e-11 2.86635242242018e-11 0.999999999985668 60 4.45850638457971e-11 8.91701276915943e-11 0.999999999955415 61 1.67463135741857e-10 3.34926271483715e-10 0.999999999832537 62 5.50688325514802e-11 1.1013766510296e-10 0.999999999944931 63 1.61051361747654e-09 3.22102723495309e-09 0.999999998389486 64 1.28162736254926e-07 2.56325472509853e-07 0.999999871837264 65 2.79546797539384e-07 5.59093595078768e-07 0.999999720453203 66 1.44421333950313e-07 2.88842667900626e-07 0.999999855578666 67 2.57345315764893e-06 5.14690631529786e-06 0.999997426546842 68 1.40802528835106e-06 2.81605057670212e-06 0.999998591974712 69 7.5668707566483e-07 1.51337415132966e-06 0.999999243312924 70 5.67620062428038e-05 0.000113524012485608 0.999943237993757 71 0.0027562421615711 0.0055124843231422 0.997243757838429 72 0.00325048162915557 0.00650096325831113 0.996749518370844 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 65 0.984848484848485 NOK 5% type I error level 66 1 NOK 10% type I error level 66 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/102m51292596344.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/102m51292596344.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/10631y1292596344.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/10631y1292596344.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/202m51292596344.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/202m51292596344.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/3atmp1292596344.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/3atmp1292596344.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/4atmp1292596344.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/4atmp1292596344.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/5atmp1292596344.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/5atmp1292596344.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/63k3a1292596344.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/63k3a1292596344.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/7wu2v1292596344.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/7wu2v1292596344.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/8wu2v1292596344.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/8wu2v1292596344.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/9wu2v1292596344.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292596290va9uwp1xzvorzvr/9wu2v1292596344.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

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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