Ws 7 link 4 verbetering

*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: Sun, 22 Nov 2009 15:10:50 -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/22/t12589279143qutm7yg9yi31c3.htm/, Retrieved Sun, 22 Nov 2009 23:12:06 +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/22/t12589279143qutm7yg9yi31c3.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:

Ws 7 link 4 verbetering

Dataseries X:

» Textbox « » Textfile « » CSV «

106370 100.3 123297 116476 109375 106370 109375 101.9 106370 123297 116476 109375 116476 102.1 109375 106370 123297 116476 123297 103.2 116476 109375 106370 123297 114813 103.7 123297 116476 109375 106370 117925 106.2 114813 123297 116476 109375 126466 107.7 117925 114813 123297 116476 131235 109.9 126466 117925 114813 123297 120546 111.7 131235 126466 117925 114813 123791 114.9 120546 131235 126466 117925 129813 116 123791 120546 131235 126466 133463 118.3 129813 123791 120546 131235 122987 120.4 133463 129813 123791 120546 125418 126 122987 133463 129813 123791 130199 128.1 125418 122987 133463 129813 133016 130.1 130199 125418 122987 133463 121454 130.8 133016 130199 125418 122987 122044 133.6 121454 133016 130199 125418 128313 134.2 122044 121454 133016 130199 131556 135.5 128313 122044 121454 133016 120027 136.2 131556 128313 122044 121454 123001 139.1 120027 131556 128313 122044 130111 139 123001 120027 131556 128313 132524 139.6 130111 123001 120027 131556 123742 138.7 132524 13 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 Y[t] = + 59313.335440879 -345.279769827803X[t] + 0.213894954464884Y1[t] + 0.507550237696988Y2[t] -0.215173320358447Y3[t] + 0.325595448772870Y4[t] -11107.3359434972M1[t] -7270.12175937004M2[t] + 1809.7233257425M3[t] -456.589776813249M4[t] -11681.9631328455M5[t] -5738.79009446743M6[t] + 2125.90512822115M7[t] -236.514315406094M8[t] -11095.5021540931M9[t] -6801.7209454567M10[t] + 2587.76554077346M11[t] + 625.751505986009t + 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) 59313.335440879 11944.30533 4.9658 4e-06 2e-06 X -345.279769827803 79.207601 -4.3592 4.3e-05 2.1e-05 Y1 0.213894954464884 0.159533 1.3408 0.184215 0.092108 Y2 0.507550237696988 0.193956 2.6168 0.010808 0.005404 Y3 -0.215173320358447 0.188576 -1.141 0.257633 0.128816 Y4 0.325595448772870 0.166636 1.9539 0.054594 0.027297 M1 -11107.3359434972 2849.885156 -3.8975 0.000216 0.000108 M2 -7270.12175937004 4646.259177 -1.5647 0.122032 0.061016 M3 1809.7233257425 3337.080962 0.5423 0.589281 0.294641 M4 -456.589776813249 1158.548292 -0.3941 0.694668 0.347334 M5 -11681.9631328455 2756.939703 -4.2373 6.6e-05 3.3e-05 M6 -5738.79009446743 4554.875333 -1.2599 0.211766 0.105883 M7 2125.90512822115 3348.765179 0.6348 0.52755 0.263775 M8 -236.514315406094 1191.153196 -0.1986 0.843167 0.421584 M9 -11095.5021540931 2810.4122 -3.948 0.000182 9.1e-05 M10 -6801.7209454567 4565.700972 -1.4897 0.14066 0.07033 M11 2587.76554077346 3264.589379 0.7927 0.43057 0.215285 t 625.751505986009 129.810358 4.8205 8e-06 4e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.997952047794552 R-squared 0.99590828969734 Adjusted R-squared 0.994942191431434 F-TEST (value) 1030.85609905699 F-TEST (DF numerator) 17 F-TEST (DF denominator) 72 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2217.37818625887 Sum Squared Residuals 354007153.504561 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 106370 110789.223748055 -4419.22374805503 2 109375 113991.610659245 -4616.6106592452 3 116476 116523.958824619 -47.9588246193766 4 123297 123410.771366961 -113.771366960822 5 114813 111543.651365237 3269.34863476347 6 117925 118347.160438379 -422.160438379289 7 126466 123523.628457557 2942.37154244318 8 131235 128480.135178093 2754.86482190718 9 120546 119548.475717370 997.524282629702 10 123791 122672.754791245 1118.24520875481 11 129813 129331.818836326 481.181163674124 12 133463 133363.489584558 99.5104154420338 13 122987 121815.47456912 1171.52543087991 14 125418 123717.451868887 1700.54813111313 15 130199 129076.196460739 1122.80353926059 16 133016 132444.140821748 571.85917825189 17 121454 120697.938642843 756.061357156643 18 122044 125019.574279092 -2975.57427909211 19 128313 128511.283917885 -198.283917885046 20 131556 131371.150698427 184.849301573219 21 120027 120880.225466575 -853.225466574991 22 123001 122821.617108971 179.382891028670 23 130111 128999.310772775 1111.68922722507 24 132524 133397.01566003 -873.015660029913 25 123742 122957.278346865 784.721653135353 26 124931 125445.360333711 -514.360333710849 27 133646 132605.630349032 1040.36965096777 28 136557 135935.314545609 621.685454391414 29 127509 127197.364966544 311.635033456173 30 128945 130957.144781943 -2012.14478194311 31 137191 137339.096938056 -148.096938056247 32 139716 140678.993697401 -962.99369740135 33 129083 131467.261175466 -2384.26117546578 34 131604 133154.993675675 -1550.99367567498 35 139413 140316.11469941 -903.114699409919 36 143125 144344.95098283 -1219.95098283015 37 133948 134340.072259181 -392.072259180559 38 137116 137208.656539505 -92.656539505202 39 144864 144574.351378352 289.648621648318 40 149277 149071.471115929 205.528884070750 41 138796 139367.856636484 -571.856636483645 42 143258 144608.531317978 -1350.53131797765 43 150034 149788.977311781 245.022688218931 44 154708 155009.571331533 -301.571331533269 45 144888 144669.931287694 218.068712305775 46 148762 148820.258524659 -58.2585246594785 47 156500 155707.857012904 792.142987096144 48 161088 160760.351051209 327.648948791323 49 152772 151398.307492943 1373.69250705708 50 158011 155386.005262202 2624.99473779769 51 163318 163489.124154871 -171.12415487135 52 169969 168511.636304219 1457.36369578133 53 162269 157951.558291544 4317.44170845621 54 165765 165742.710775963 22.2892240365053 55 170600 171266.030797166 -666.030797165741 56 174681 175780.502745089 -1099.50274508871 57 166364 165683.902191265 680.097808734801 58 170240 170268.614257983 -28.6142579830335 59 176150 177553.217464230 -1403.21746423047 60 182056 182009.994817473 46.0051825271638 61 172218 172214.778771684 3.22122831564263 62 177856 176836.283723097 1019.71627690272 63 182253 183200.82843932 -947.828439319855 64 188090 188849.725412082 -759.725412082309 65 176863 176968.671831365 -105.671831365079 66 183273 184263.270993316 -990.270993316145 67 187969 188429.554493895 -460.554493894696 68 194650 194887.178041399 -237.178041399191 69 183036 183120.956935987 -84.956935987502 70 189516 189160.670277170 355.329722830374 71 193805 194378.874641017 -573.874641017446 72 200499 200676.004156411 -177.004156410861 73 188142 188454.686970685 -312.686970685221 74 193732 194099.458643029 -367.458643029133 75 197126 198443.342576793 -1317.34257679263 76 205140 204824.571191347 315.428808652804 77 191751 192262.887306922 -511.887306922197 78 196700 199951.309002789 -3251.30900278881 79 199784 201498.428083660 -1714.42808366038 80 207360 207698.468308058 -338.468308057885 81 196101 194674.247225642 1426.75277435799 82 200824 200839.091364296 -15.0913642963660 83 205743 205247.806573337 495.193426662506 84 212489 210692.193747490 1796.80625251040 85 200810 199019.177841467 1790.82215853283 86 203683 203437.172970323 245.827029676841 87 207286 207254.567816273 31.4321837265343 88 210910 213208.369242105 -2298.36924210505 89 194915 202380.070959062 -7465.07095906157 90 217920 206940.298410539 10979.7015894606 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 21 0.00373156064466383 0.00746312128932767 0.996268439355336 22 0.00067902338990544 0.00135804677981088 0.999320976610095 23 0.000113433925108827 0.000226867850217653 0.999886566074891 24 0.0165692479386536 0.0331384958773071 0.983430752061346 25 0.0159420301413825 0.0318840602827649 0.984057969858618 26 0.193316254295535 0.38663250859107 0.806683745704465 27 0.178941192711826 0.357882385423653 0.821058807288173 28 0.216027420947 0.432054841894 0.783972579053 29 0.262602890040785 0.52520578008157 0.737397109959215 30 0.213259936718751 0.426519873437502 0.786740063281249 31 0.193155950906202 0.386311901812404 0.806844049093798 32 0.197184877815873 0.394369755631746 0.802815122184127 33 0.156425731497479 0.312851462994959 0.84357426850252 34 0.1203320682098 0.2406641364196 0.8796679317902 35 0.0821555715002832 0.164311143000566 0.917844428499717 36 0.0553309186406516 0.110661837281303 0.944669081359348 37 0.0415807653514012 0.0831615307028024 0.958419234648599 38 0.0285219327149492 0.0570438654298984 0.97147806728505 39 0.0182185632492281 0.0364371264984561 0.981781436750772 40 0.0109497925238979 0.0218995850477957 0.989050207476102 41 0.00810407519849312 0.0162081503969862 0.991895924801507 42 0.0093439972396913 0.0186879944793826 0.990656002760309 43 0.00586051022678071 0.0117210204535614 0.99413948977322 44 0.00337138529567505 0.0067427705913501 0.996628614704325 45 0.00225374328942397 0.00450748657884795 0.997746256710576 46 0.00155809736228122 0.00311619472456244 0.998441902637719 47 0.000807636306405668 0.00161527261281134 0.999192363693594 48 0.000507302834898848 0.00101460566979770 0.999492697165101 49 0.000399861918192597 0.000799723836385194 0.999600138081807 50 0.000293294699989456 0.000586589399978912 0.99970670530001 51 0.000381260370033930 0.000762520740067861 0.999618739629966 52 0.000190058284843804 0.000380116569687607 0.999809941715156 53 0.00094942913698783 0.00189885827397566 0.999050570863012 54 0.000478087519433957 0.000956175038867914 0.999521912480566 55 0.000787404301887814 0.00157480860377563 0.999212595698112 56 0.000579510158167696 0.00115902031633539 0.999420489841832 57 0.000309955275186735 0.00061991055037347 0.999690044724813 58 0.000164944482550861 0.000329888965101723 0.99983505551745 59 0.000129577636116032 0.000259155272232065 0.999870422363884 60 0.000192708336390849 0.000385416672781698 0.99980729166361 61 8.71757165820747e-05 0.000174351433164149 0.999912824283418 62 6.34612230998622e-05 0.000126922446199724 0.9999365387769 63 5.35502984946583e-05 0.000107100596989317 0.999946449701505 64 3.04144292267508e-05 6.08288584535015e-05 0.999969585570773 65 0.000116116822090649 0.000232233644181299 0.99988388317791 66 0.000158035352877228 0.000316070705754457 0.999841964647123 67 0.000102752640265982 0.000205505280531964 0.999897247359734 68 7.98057807163835e-05 0.000159611561432767 0.999920194219284 69 0.000128664537644598 0.000257329075289196 0.999871335462355 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 29 0.591836734693878 NOK 5% type I error level 36 0.73469387755102 NOK 10% type I error level 38 0.775510204081633 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/10hhfy1258927845.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/10hhfy1258927845.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/10x0l1258927845.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/10x0l1258927845.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/2q2971258927845.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/2q2971258927845.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/3z5w61258927845.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/3z5w61258927845.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/49t6p1258927845.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/49t6p1258927845.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/54ppd1258927845.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/54ppd1258927845.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/6uc6m1258927845.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/6uc6m1258927845.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/78u1s1258927845.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/78u1s1258927845.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/8yg931258927845.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/8yg931258927845.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/9bzbv1258927845.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t12589279143qutm7yg9yi31c3/9bzbv1258927845.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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