Seatbelt Q3: eigen tijdreeks

*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, 27 Nov 2008 07:44:31 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o.htm/, Retrieved Thu, 27 Nov 2008 14:45:36 +0000

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/2008/Nov/27/t1227797122fxta2ovv8ffo22o.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

101,02 0 100,67 0 100,47 0 100,38 0 100,33 0 100,34 0 100,37 0 100,39 0 100,21 0 100,21 0 100,22 0 100,28 0 100,25 0 100,25 0 100,21 0 100,16 0 100,18 0 100,1 1 99,96 1 99,88 1 99,88 1 99,86 1 99,84 1 99,8 1 99,82 1 99,81 1 99,92 1 100,03 1 99,99 1 100,02 1 100,01 1 100,13 1 100,33 1 100,13 1 99,96 1 100,05 1 99,83 1 99,8 1 100,01 1 100,1 1 100,13 1 100,16 1 100,41 1 101,34 1 101,65 1 101,85 1 102,07 1 102,12 1 102,14 1 102,21 1 102,28 1 102,19 1 102,33 1 102,54 1 102,44 1 102,78 1 102,9 1 103,08 1 102,77 1 102,65 1 102,71 1 103,29 1 102,86 1 103,45 1 103,72 1 103,65 1 103,83 1 104,45 1 105,14 1 105,07 1 105,31 1 105,19 1 105,3 1 105,02 1 105,17 1 105,28 1 105,45 1 105,38 1 105,8 1 105,96 1 105,08 1 105,11 1 105,61 1 105,5 1

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Suiker[t] = + 100.349411764706 + 1.92864793678665Cotonou[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) 100.349411764706 0.467365 214.7132 0 0 Cotonou 1.92864793678665 0.523309 3.6855 0.000408 0.000204 Multiple Linear Regression - Regression Statistics Multiple R 0.376968236042663 R-squared 0.142105050985117 Adjusted R-squared 0.131642917460545 F-TEST (value) 13.5827984465466 F-TEST (DF numerator) 1 F-TEST (DF denominator) 82 p-value 0.000408476657244750 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.92699482285318 Sum Squared Residuals 304.491341878841 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.02 100.349411764706 0.670588235294208 2 100.67 100.349411764706 0.320588235294116 3 100.47 100.349411764706 0.120588235294112 4 100.38 100.349411764706 0.0305882352941085 5 100.33 100.349411764706 -0.0194117647058887 6 100.34 100.349411764706 -0.00941176470588355 7 100.37 100.349411764706 0.0205882352941176 8 100.39 100.349411764706 0.0405882352941136 9 100.21 100.349411764706 -0.139411764705893 10 100.21 100.349411764706 -0.139411764705893 11 100.22 100.349411764706 -0.129411764705888 12 100.28 100.349411764706 -0.0694117647058858 13 100.25 100.349411764706 -0.099411764705887 14 100.25 100.349411764706 -0.099411764705887 15 100.21 100.349411764706 -0.139411764705893 16 100.16 100.349411764706 -0.189411764705890 17 100.18 100.349411764706 -0.16941176470588 18 100.1 102.278059701493 -2.17805970149254 19 99.96 102.278059701493 -2.31805970149254 20 99.88 102.278059701493 -2.39805970149254 21 99.88 102.278059701493 -2.39805970149254 22 99.86 102.278059701493 -2.41805970149254 23 99.84 102.278059701493 -2.43805970149253 24 99.8 102.278059701493 -2.47805970149254 25 99.82 102.278059701493 -2.45805970149254 26 99.81 102.278059701493 -2.46805970149253 27 99.92 102.278059701493 -2.35805970149253 28 100.03 102.278059701493 -2.24805970149254 29 99.99 102.278059701493 -2.28805970149254 30 100.02 102.278059701493 -2.25805970149254 31 100.01 102.278059701493 -2.26805970149253 32 100.13 102.278059701493 -2.14805970149254 33 100.33 102.278059701493 -1.94805970149254 34 100.13 102.278059701493 -2.14805970149254 35 99.96 102.278059701493 -2.31805970149254 36 100.05 102.278059701493 -2.22805970149254 37 99.83 102.278059701493 -2.44805970149254 38 99.8 102.278059701493 -2.47805970149254 39 100.01 102.278059701493 -2.26805970149253 40 100.1 102.278059701493 -2.17805970149254 41 100.13 102.278059701493 -2.14805970149254 42 100.16 102.278059701493 -2.11805970149254 43 100.41 102.278059701493 -1.86805970149254 44 101.34 102.278059701493 -0.938059701492533 45 101.65 102.278059701493 -0.62805970149253 46 101.85 102.278059701493 -0.428059701492542 47 102.07 102.278059701493 -0.208059701492543 48 102.12 102.278059701493 -0.158059701492531 49 102.14 102.278059701493 -0.138059701492535 50 102.21 102.278059701493 -0.0680597014925422 51 102.28 102.278059701493 0.00194029850746515 52 102.19 102.278059701493 -0.0880597014925383 53 102.33 102.278059701493 0.0519402985074623 54 102.54 102.278059701493 0.261940298507470 55 102.44 102.278059701493 0.161940298507462 56 102.78 102.278059701493 0.501940298507465 57 102.9 102.278059701493 0.62194029850747 58 103.08 102.278059701493 0.801940298507462 59 102.77 102.278059701493 0.49194029850746 60 102.65 102.278059701493 0.37194029850747 61 102.71 102.278059701493 0.431940298507458 62 103.29 102.278059701493 1.01194029850747 63 102.86 102.278059701493 0.581940298507463 64 103.45 102.278059701493 1.17194029850747 65 103.72 102.278059701493 1.44194029850746 66 103.65 102.278059701493 1.37194029850747 67 103.83 102.278059701493 1.55194029850746 68 104.45 102.278059701493 2.17194029850747 69 105.14 102.278059701493 2.86194029850746 70 105.07 102.278059701493 2.79194029850746 71 105.31 102.278059701493 3.03194029850747 72 105.19 102.278059701493 2.91194029850746 73 105.3 102.278059701493 3.02194029850746 74 105.02 102.278059701493 2.74194029850746 75 105.17 102.278059701493 2.89194029850747 76 105.28 102.278059701493 3.00194029850746 77 105.45 102.278059701493 3.17194029850747 78 105.38 102.278059701493 3.10194029850746 79 105.8 102.278059701493 3.52194029850746 80 105.96 102.278059701493 3.68194029850746 81 105.08 102.278059701493 2.80194029850746 82 105.11 102.278059701493 2.83194029850746 83 105.61 102.278059701493 3.33194029850746 84 105.5 102.278059701493 3.22194029850746 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0059400665704052 0.0118801331408104 0.994059933429595 6 0.00101782766826165 0.00203565533652330 0.998982172331738 7 0.000150172458016267 0.000300344916032533 0.999849827541984 8 1.97626063681139e-05 3.95252127362279e-05 0.999980237393632 9 3.96522215154569e-06 7.93044430309137e-06 0.999996034777848 10 7.16035966539098e-07 1.43207193307820e-06 0.999999283964033 11 1.15489003962649e-07 2.30978007925298e-07 0.999999884510996 12 1.49726377121867e-08 2.99452754243733e-08 0.999999985027362 13 1.97820688799911e-09 3.95641377599823e-09 0.999999998021793 14 2.48680714049294e-10 4.97361428098587e-10 0.99999999975132 15 3.34272498666322e-11 6.68544997332644e-11 0.999999999966573 16 5.10498478378584e-12 1.02099695675717e-11 0.999999999994895 17 6.86017002298311e-13 1.37203400459662e-12 0.999999999999314 18 7.50457285853327e-14 1.50091457170665e-13 0.999999999999925 19 9.21106102713646e-15 1.84221220542729e-14 0.99999999999999 20 1.22088197496684e-15 2.44176394993369e-15 0.999999999999999 21 1.49736145456893e-16 2.99472290913786e-16 1 22 1.85609796365598e-17 3.71219592731196e-17 1 23 2.35942993920493e-18 4.71885987840985e-18 1 24 3.29442970707436e-19 6.58885941414872e-19 1 25 4.31731096590950e-20 8.63462193181901e-20 1 26 5.85156560794638e-21 1.17031312158928e-20 1 27 7.39902452353312e-22 1.47980490470662e-21 1 28 1.22829205155309e-22 2.45658410310618e-22 1 29 1.81503829954691e-23 3.63007659909382e-23 1 30 2.99020552204966e-24 5.98041104409931e-24 1 31 4.95118706222304e-25 9.90237412444608e-25 1 32 1.42735216773018e-25 2.85470433546037e-25 1 33 2.07325334747668e-25 4.14650669495336e-25 1 34 5.64749496223981e-26 1.12949899244796e-25 1 35 1.25536805384387e-26 2.51073610768774e-26 1 36 3.27042227751821e-27 6.54084455503642e-27 1 37 1.44799862388066e-27 2.89599724776132e-27 1 38 9.19338887941163e-28 1.83867777588233e-27 1 39 4.27544339972204e-28 8.55088679944408e-28 1 40 3.12046393508372e-28 6.24092787016744e-28 1 41 3.47589238353969e-28 6.95178476707939e-28 1 42 6.51746308035426e-28 1.30349261607085e-27 1 43 1.26466599587788e-26 2.52933199175576e-26 1 44 8.21566507053968e-20 1.64313301410794e-19 1 45 4.55478756733364e-15 9.10957513466728e-15 0.999999999999995 46 8.48904452403411e-12 1.69780890480682e-11 0.99999999999151 47 2.58727554677797e-09 5.17455109355593e-09 0.999999997412724 48 1.35570124694977e-07 2.71140249389953e-07 0.999999864429875 49 2.49100124339276e-06 4.98200248678551e-06 0.999997508998757 50 2.58509916033928e-05 5.17019832067857e-05 0.999974149008397 51 0.000177074634522146 0.000354149269044292 0.999822925365478 52 0.000825340028137781 0.00165068005627556 0.999174659971862 53 0.00327363605751329 0.00654727211502658 0.996726363942487 54 0.0109574255461947 0.0219148510923894 0.989042574453805 55 0.0300993411499933 0.0601986822999866 0.969900658850007 56 0.0701984816932284 0.140396963386457 0.929801518306772 57 0.137254465552137 0.274508931104274 0.862745534447863 58 0.229515785375244 0.459031570750487 0.770484214624756 59 0.361259166678149 0.722518333356297 0.638740833321851 60 0.546728450821727 0.906543098356545 0.453271549178273 61 0.74906830491408 0.501863390171839 0.250931695085920 62 0.860727139899326 0.278545720201348 0.139272860100674 63 0.966213810552047 0.0675723788959054 0.0337861894479527 64 0.991281839129048 0.0174363217419033 0.00871816087095164 65 0.997931645521143 0.00413670895771415 0.00206835447885708 66 0.999848035881223 0.000303928237553216 0.000151964118776608 67 0.999998016319566 3.96736086776315e-06 1.98368043388157e-06 68 0.999999867505457 2.64989085617677e-07 1.32494542808838e-07 69 0.999999775389678 4.49220644404499e-07 2.24610322202250e-07 70 0.999999625053172 7.49893656919947e-07 3.74946828459973e-07 71 0.999998915715907 2.16856818678781e-06 1.08428409339391e-06 72 0.999996933616078 6.13276784407334e-06 3.06638392203667e-06 73 0.999989275108745 2.14497825090934e-05 1.07248912545467e-05 74 0.999978809319379 4.23813612417772e-05 2.11906806208886e-05 75 0.99993498973664 0.000130020526717000 6.50102633585002e-05 76 0.999745606932747 0.000508786134505967 0.000254393067252983 77 0.998826251172473 0.00234749765505436 0.00117374882752718 78 0.994862833406348 0.0102743331873034 0.00513716659365169 79 0.982620103354064 0.0347597932918713 0.0173798966459357 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 61 0.813333333333333 NOK 5% type I error level 66 0.88 NOK 10% type I error level 68 0.906666666666667 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/10l28u1227797066.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/10l28u1227797066.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/13wxp1227797066.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/13wxp1227797066.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/2qqi31227797066.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/2qqi31227797066.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/3gwze1227797066.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/3gwze1227797066.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/4f6551227797066.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/4f6551227797066.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/5e1ra1227797066.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/5e1ra1227797066.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/6qpuh1227797066.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/6qpuh1227797066.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/7u5nz1227797066.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/7u5nz1227797066.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/8xfz31227797066.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/8xfz31227797066.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/9696y1227797066.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227797122fxta2ovv8ffo22o/9696y1227797066.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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