*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 06:40:56 -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/t1258724566bk3k9wt04mibeh3.htm/, Retrieved Fri, 20 Nov 2009 14:42:59 +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/t1258724566bk3k9wt04mibeh3.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_2

Dataseries X:

» Textbox « » Textfile « » CSV «

106370 100.3 109375 101.9 116476 102.1 123297 103.2 114813 103.7 117925 106.2 126466 107.7 131235 109.9 120546 111.7 123791 114.9 129813 116 133463 118.3 122987 120.4 125418 126 130199 128.1 133016 130.1 121454 130.8 122044 133.6 128313 134.2 131556 135.5 120027 136.2 123001 139.1 130111 139 132524 139.6 123742 138.7 124931 140.9 133646 141.3 136557 141.8 127509 142 128945 144.5 137191 144.6 139716 145.5 129083 146.8 131604 149.5 139413 149.9 143125 150.1 133948 150.9 137116 152.8 144864 153.1 149277 154 138796 154.9 143258 156.9 150034 158.4 154708 159.7 144888 160.2 148762 163.2 156500 163.7 161088 164.4 152772 163.7 158011 165.5 163318 165.6 169969 166.8 162269 167.5 165765 170.6 170600 170.9 174681 172 166364 171.8 170240 173.9 176150 174 182056 173.8 172218 173.9 177856 176 182253 176.6 188090 178.2 176863 179.2 183273 181.3 187969 181.8 194650 182.9 183036 183.8 189516 186.3 193805 187.4 200499 189.2 188142 189.7 193732 191.9 1971 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] = + 13234.0835008933 + 940.277242456398RPI[t] -10550.7208770042Q1[t] -8526.67838512695Q2[t] -3873.4172652533Q3[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) 13234.0835008933 5055.524122 2.6177 0.010477 0.005238 RPI 940.277242456398 29.264403 32.1304 0 0 Q1 -10550.7208770042 2595.899657 -4.0644 0.000107 5.4e-05 Q2 -8526.67838512695 2595.133711 -3.2856 0.00148 0.00074 Q3 -3873.4172652533 2623.978033 -1.4762 0.143595 0.071798 Multiple Linear Regression - Regression Statistics Multiple R 0.962082470218056 R-squared 0.925602679500876 Adjusted R-squared 0.922101629124447 F-TEST (value) 264.378566424652 F-TEST (DF numerator) 4 F-TEST (DF denominator) 85 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 8702.07384687471 Sum Squared Residuals 6436717585.09917 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 106370 96993.170042266 9376.8299577340 2 109375 100521.656122073 8853.34387792677 3 116476 105362.972690438 11113.0273095619 4 123297 110270.694922394 13026.3050776065 5 114813 100190.112666617 14622.8873333825 6 117925 104564.848264636 13360.1517353642 7 126466 110628.525248194 15837.474751806 8 131235 116570.552446851 14664.4475531486 9 120546 107712.330606269 12833.6693937313 10 123791 112745.260274006 11045.7397259936 11 129813 118432.826360582 11380.1736394179 12 133463 124468.881283485 8994.1187165149 13 122987 115892.742615639 7094.25738436068 14 125418 123182.337665272 2235.66233472758 15 130199 129810.180994304 388.8190056955 16 133016 135564.152744471 -2548.15274447060 17 121454 125671.625937186 -4217.62593718586 18 122044 130328.444707941 -8284.44470794104 19 128313 135545.872173289 -7232.87217328852 20 131556 140641.649853735 -9085.64985373514 21 120027 130749.123046450 -10722.1230464504 22 123001 135499.969541451 -12498.9695414512 23 130111 140059.202937079 -9948.20293707924 24 132524 144496.786547806 -11972.7865478064 25 123742 133099.816152591 -9357.81615259137 26 124931 137192.468577873 -12261.4685778727 27 133646 142221.840594729 -8575.84059472896 28 136557 146565.396481210 -10008.3964812105 29 127509 136202.731052697 -8693.7310526975 30 128945 140577.466650716 -11632.4666507158 31 137191 145324.755494835 -8133.75549483506 32 139716 150044.422278299 -10328.4222782991 33 129083 140716.061816488 -11633.0618164882 34 131604 145278.852862998 -13674.8528629978 35 139413 150308.224879854 -10895.2248798540 36 143125 154369.697593599 -11244.6975935985 37 133948 144571.198510559 -10623.1985105594 38 137116 148381.767763104 -11265.7677631039 39 144864 153317.112055714 -8453.11205571443 40 149277 158036.778839178 -8759.7788391785 41 138796 148332.307480385 -9536.30748038503 42 143258 152236.904457175 -8978.9044571751 43 150034 158300.581440733 -8266.58144073336 44 154708 163396.35912118 -8688.35912117996 45 144888 153315.776865404 -8427.77686540392 46 148762 158160.651084650 -9398.6510846504 47 156500 163284.050825752 -6784.05082575224 48 161088 167815.662160725 -6727.66216072504 49 152772 156606.747214001 -3834.74721400132 50 158011 160323.2887423 -2312.28874230012 51 163318 165070.577586419 -1752.57758641940 52 169969 170072.327542620 -103.327542620400 53 162269 160179.800735336 2089.19926466436 54 165765 165118.702678828 646.297321172258 55 170600 170054.046971438 545.953028561677 56 174681 174961.769203394 -280.769203393654 57 166364 164222.992877898 2141.00712210184 58 170240 168221.617578934 2018.38242106614 59 176150 172968.906423053 3181.09357694685 60 182056 176654.268239815 5401.73176018482 61 172218 166197.575087057 6020.42491294341 62 177856 170196.199788092 7659.8002119077 63 182253 175413.627253440 6839.37274656022 64 188090 180791.488106623 7298.51189337669 65 176863 171181.044472075 5681.95552792452 66 183273 175179.669173111 8093.33082688879 67 187969 180303.068914213 7665.93108578694 68 194650 185210.791146168 9439.2088538316 69 183036 175506.319787375 7529.68021262507 70 189516 179881.055385393 9634.9446146068 71 193805 185568.621471969 8236.37852803112 72 200499 191134.537773644 9364.46222635632 73 188142 181053.955517868 7088.04448213235 74 193732 185146.607943149 8585.39205685098 75 197126 190458.063132742 6667.93686725786 76 205140 195365.785364697 9774.21463530253 77 191751 185285.203108921 6465.79689107856 78 196700 190506.188225150 6193.81177484953 79 199784 196757.9206572 3026.07934279998 80 207360 202605.920131612 4754.07986838825 81 196101 193559.642842538 2541.35715746225 82 200824 198686.600234521 2137.39976547885 83 205743 204092.08314836 1650.91685164010 84 212489 210504.248968245 1984.75103175451 85 200810 201175.888506435 -365.888506434555 86 203683 207149.095416629 -3466.09541662872 87 207286 213776.938745661 -6490.9387456608 88 210910 215863.829250247 -4953.82925024695 89 194915 200987.833057943 -6072.8330579433 90 217920 204610.346861996 13309.6531380036 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.0192848176423678 0.0385696352847357 0.980715182357632 9 0.0181763931884281 0.0363527863768561 0.981823606811572 10 0.0112702233605839 0.0225404467211678 0.988729776639416 11 0.0102077110764797 0.0204154221529594 0.98979228892352 12 0.0158809618751603 0.0317619237503205 0.98411903812484 13 0.0172090379648927 0.0344180759297853 0.982790962035107 14 0.0251250165764936 0.0502500331529873 0.974874983423506 15 0.0455787232890775 0.091157446578155 0.954421276710922 16 0.0735902740144406 0.147180548028881 0.92640972598556 17 0.0751607088262933 0.150321417652587 0.924839291173707 18 0.0834701144693265 0.166940228938653 0.916529885530673 19 0.0798232833913233 0.159646566782647 0.920176716608677 20 0.0778766171474317 0.155753234294863 0.922123382852568 21 0.0650559044170906 0.130111808834181 0.93494409558291 22 0.0496304190255959 0.0992608380511919 0.950369580974404 23 0.0335536141697677 0.0671072283395354 0.966446385830232 24 0.0253091709037404 0.0506183418074809 0.97469082909626 25 0.0155650766830487 0.0311301533660973 0.984434923316951 26 0.00975555190337596 0.0195111038067519 0.990244448096624 27 0.00593156347242214 0.0118631269448443 0.994068436527578 28 0.00344571791298487 0.00689143582596975 0.996554282087015 29 0.00235304902093843 0.00470609804187686 0.997646950979062 30 0.00153846876112074 0.00307693752224149 0.99846153123888 31 0.00109485600223659 0.00218971200447318 0.998905143997763 32 0.000692075082627765 0.00138415016525553 0.999307924917372 33 0.000459743771719157 0.000919487543438314 0.99954025622828 34 0.000398369372905523 0.000796738745811047 0.999601630627095 35 0.000309172425042226 0.000618344850084453 0.999690827574958 36 0.000275550062023894 0.000551100124047788 0.999724449937976 37 0.00032090384592909 0.00064180769185818 0.999679096154071 38 0.000570602524740464 0.00114120504948093 0.99942939747526 39 0.000823769675781935 0.00164753935156387 0.999176230324218 40 0.00145088656597857 0.00290177313195714 0.998549113434021 41 0.00244683232963447 0.00489366465926893 0.997553167670366 42 0.00695144482273385 0.0139028896454677 0.993048555177266 43 0.0120405041319055 0.0240810082638111 0.987959495868094 44 0.0256840244760831 0.0513680489521662 0.974315975523917 45 0.0516792910926784 0.103358582185357 0.948320708907322 46 0.147094042004055 0.294188084008109 0.852905957995945 47 0.250979213479409 0.501958426958818 0.749020786520591 48 0.441884346649351 0.883768693298702 0.558115653350649 49 0.619291886194836 0.761416227610329 0.380708113805165 50 0.825914259353511 0.348171481292977 0.174085740646489 51 0.898142717239935 0.203714565520129 0.101857282760065 52 0.953517165493045 0.0929656690139099 0.0464828345069549 53 0.975660094643443 0.0486798107131134 0.0243399053565567 54 0.99204327948907 0.01591344102186 0.00795672051093 55 0.995513985953163 0.00897202809367488 0.00448601404683744 56 0.998620673229667 0.00275865354066704 0.00137932677033352 57 0.999178789020233 0.00164242195953403 0.000821210979767017 58 0.999835859565138 0.000328280869724529 0.000164140434862265 59 0.999899862112056 0.000200275775887404 0.000100137887943702 60 0.999947092283945 0.00010581543211011 5.2907716055055e-05 61 0.999948551567608 0.000102896864783822 5.14484323919108e-05 62 0.999971041244766 5.79175104686515e-05 2.89587552343257e-05 63 0.99996514078639 6.97184272220875e-05 3.48592136110437e-05 64 0.999967081815012 6.5836369976e-05 3.2918184988e-05 65 0.999958374172666 8.3251654668963e-05 4.16258273344815e-05 66 0.999968160969538 6.3678060923354e-05 3.1839030461677e-05 67 0.999945149399778 0.000109701200443991 5.48506002219957e-05 68 0.99991082380113 0.000178352397740139 8.91761988700696e-05 69 0.99982604999859 0.000347900002817908 0.000173950001408954 70 0.999751226891705 0.000497546216590391 0.000248773108295196 71 0.99946209839774 0.00107580320452006 0.000537901602260028 72 0.99888658482833 0.00222683034333859 0.00111341517166930 73 0.997567170710394 0.00486565857921153 0.00243282928960576 74 0.99592084230688 0.00815831538624054 0.00407915769312027 75 0.9912108237114 0.0175783525772015 0.00878917628860073 76 0.983155580164255 0.0336888396714894 0.0168444198357447 77 0.966572215679124 0.0668555686417519 0.0334277843208759 78 0.948075455917384 0.103849088165231 0.0519245440826157 79 0.902974779412605 0.194050441174791 0.0970252205873953 80 0.824418757397785 0.35116248520443 0.175581242602215 81 0.701924453602285 0.596151092795431 0.298075546397715 82 0.687302741296146 0.625394517407707 0.312697258703854 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 34 0.453333333333333 NOK 5% type I error level 49 0.653333333333333 NOK 10% type I error level 57 0.76 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/108o3n1258724451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/108o3n1258724451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/191ti1258724451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/191ti1258724451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/21lkh1258724451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/21lkh1258724451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/328qj1258724451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/328qj1258724451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/409dh1258724451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/409dh1258724451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/5n8bg1258724451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/5n8bg1258724451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/6buxr1258724451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/6buxr1258724451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/7aidj1258724451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/7aidj1258724451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/83p5n1258724451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/83p5n1258724451.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/9tb311258724451.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t1258724566bk3k9wt04mibeh3/9tb311258724451.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Quarterly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Quarterly 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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