Ws 7 link 2 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 14:56:19 -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/t1258927240cy07vnakrs5clye.htm/, Retrieved Sun, 22 Nov 2009 23:00:52 +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/t1258927240cy07vnakrs5clye.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 2 verbetering

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 5 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 12578.3467133000 + 940.617859768512X[t] -9273.41189991766M1[t] -8171.45512129261M2[t] -3304.95713235452M3[t] + 1066.79085658356M4[t] -9907.20142016934M5[t] -6338.00379283771M6[t] -2779.3081961687M7[t] + 669.666289876279M8[t] -10768.7734064769M9[t] -9459.79795468256M10[t] -3727.14219083930M11[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) 12578.3467133000 6086.449334 2.0666 0.042132 0.021066 X 940.617859768512 30.685034 30.654 0 0 M1 -9273.41189991766 4718.068005 -1.9655 0.052961 0.026481 M2 -8171.45512129261 4714.746288 -1.7332 0.087069 0.043534 M3 -3304.95713235452 4714.031264 -0.7011 0.485361 0.24268 M4 1066.79085658356 4713.448009 0.2263 0.821545 0.410773 M5 -9907.20142016934 4713.455984 -2.1019 0.038832 0.019416 M6 -6338.00379283771 4712.480309 -1.3449 0.182593 0.091296 M7 -2779.3081961687 4871.583131 -0.5705 0.56999 0.284995 M8 669.666289876279 4869.874883 0.1375 0.890985 0.445493 M9 -10768.7734064769 4868.963309 -2.2117 0.029952 0.014976 M10 -9459.79795468256 4867.264228 -1.9436 0.055603 0.027802 M11 -3727.14219083930 4867.109485 -0.7658 0.446147 0.223074 Multiple Linear Regression - Regression Statistics Multiple R 0.962400410778208 R-squared 0.926214550666064 Adjusted R-squared 0.914715519601035 F-TEST (value) 80.5471822302383 F-TEST (DF numerator) 12 F-TEST (DF denominator) 77 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9105.2857716034 Sum Squared Residuals 6383779631.65739 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 106370 97648.9061481644 8721.09385183561 2 109375 100255.851502419 9119.14849758123 3 116476 105310.473063311 11165.5269366895 4 123297 110716.900697994 12580.099302006 5 114813 100213.217351125 14599.7826488746 6 117925 106133.959627878 11791.0403721217 7 126466 111103.5820142 15362.4179857999 8 131235 116621.915791736 14613.0842082642 9 120546 106876.588242966 13669.4117570341 10 123791 111195.540846019 12595.4591539805 11 129813 117962.876255608 11850.1237443919 12 133463 123853.439523915 9609.56047608502 13 122987 116555.325129511 6431.6748704888 14 125418 122924.74192284 2493.25807716008 15 130199 129766.537417292 432.462582708131 16 133016 136019.521125767 -3003.52112576698 17 121454 125703.961350852 -4249.96135085205 18 122044 131906.888985535 -9862.88898553549 19 128313 136029.955298066 -7716.95529806561 20 131556 140701.733001810 -9145.73300180967 21 120027 129921.725807294 -9894.72580729445 22 123001 133958.493052417 -10957.4930524175 23 130111 139597.087030284 -9486.08703028389 24 132524 143888.599936984 -11364.5999369843 25 123742 133768.631963275 -10026.6319632750 26 124931 136939.948033391 -12008.9480333907 27 133646 142182.693166236 -8536.69316623624 28 136557 147024.750085059 -10467.7500850586 29 127509 136238.881380259 -8729.88138025937 30 128945 142159.623657012 -13214.6236570123 31 137191 145812.381039658 -8621.38103965815 32 139716 150107.911599495 -10391.9115994948 33 129083 139892.275120841 -10809.2751208407 34 131604 143740.91879401 -12136.91879401 35 139413 149849.821701761 -10436.8217017607 36 143125 153765.087464554 -10640.0874645537 37 133948 145244.169852451 -11296.1698524508 38 137116 148133.300564636 -11017.3005646361 39 144864 153281.983911505 -8417.98391150467 40 149277 158500.287974234 -9223.28797423442 41 138796 148372.851771273 -9576.8517712732 42 143258 153823.285118142 -10565.2851181418 43 150034 158792.907504464 -8758.90750446362 44 154708 163464.685208208 -8756.68520820764 45 144888 152496.554441739 -7608.55444173874 46 148762 156627.383472839 -7865.3834728386 47 156500 162830.348166566 -6330.34816656612 48 161088 167215.922859243 -6127.92285924339 49 152772 157284.078457488 -4512.07845748775 50 158011 160079.147383696 -2068.14738369614 51 163318 165039.707158611 -1721.70715861107 52 169969 170540.196579271 -571.196579271382 53 162269 160224.636804356 2044.36319564357 54 165765 166709.749796970 -944.749796970428 55 170600 170550.63075157 49.3692484299818 56 174681 175034.284883360 -353.284883360355 57 166364 163407.721615054 2956.2783849465 58 170240 166691.994572362 3548.00542763831 59 176150 172518.712122182 3631.28787781819 60 182056 176057.730741067 5998.26925893259 61 172218 166878.380627127 5339.6193728734 62 177856 169955.634911266 7900.36508873449 63 182253 175386.503616065 6866.4963839353 64 188090 181263.240180632 6826.75981936761 65 176863 171229.865763648 5633.13423635199 66 183273 176774.360896494 6498.63910350648 67 187969 180803.365423047 7165.6345769532 68 194650 185287.019554837 9362.98044516286 69 183036 174695.135932276 8340.86406772436 70 189516 178355.656033491 11160.3439665087 71 193805 185122.99144308 8682.00855692013 72 200499 190543.245781502 9955.75421849753 73 188142 181740.142811469 6401.85718853093 74 193732 184911.458881585 8820.54111841515 75 197126 190436.389372361 6689.61062763911 76 205140 195842.817007044 9297.18299295567 77 191751 185339.133660176 6411.86633982431 78 196700 192106.432010720 4593.56798927975 79 199784 197264.177968996 2519.82203100424 80 207360 202688.449960555 4671.55003944539 81 196101 192754.998839831 3346.00116016894 82 200824 197168.013228861 3655.98677113851 83 205743 203653.163280520 2089.83671948046 84 212489 209919.973692734 2569.02630726617 85 200810 201869.365010515 -1059.36501051522 86 203683 206921.916800168 -3238.91680016804 87 207286 213763.71229462 -6477.71229461999 88 210910 216348.286349998 -5438.28634999790 89 194915 201047.45191831 -6132.45191830986 90 217920 206215.699907248 11704.3000927521 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.179096516737757 0.358193033475514 0.820903483262243 17 0.214837659747152 0.429675319494305 0.785162340252848 18 0.249279035611104 0.498558071222208 0.750720964388896 19 0.276457763287381 0.552915526574762 0.723542236712619 20 0.288868523378219 0.577737046756437 0.711131476621781 21 0.282237803957177 0.564475607914355 0.717762196042823 22 0.262387503785132 0.524775007570264 0.737612496214868 23 0.213807800985515 0.427615601971031 0.786192199014485 24 0.179303352774691 0.358606705549382 0.820696647225309 25 0.127845629551121 0.255691259102241 0.87215437044888 26 0.0860656584281957 0.172131316856391 0.913934341571804 27 0.0629281556994619 0.125856311398924 0.937071844300538 28 0.0407958782087462 0.0815917564174923 0.959204121791254 29 0.0268181700675395 0.053636340135079 0.97318182993246 30 0.0185729383867189 0.0371458767734378 0.981427061613281 31 0.0122605301803665 0.0245210603607330 0.987739469819633 32 0.00759849237448418 0.0151969847489684 0.992401507625516 33 0.00489516129177526 0.00979032258355052 0.995104838708225 34 0.00334445000665756 0.00668890001331512 0.996655549993342 35 0.00226820103975865 0.0045364020795173 0.997731798960241 36 0.00180517318125498 0.00361034636250996 0.998194826818745 37 0.00232535053639351 0.00465070107278703 0.997674649463607 38 0.00369525931652762 0.00739051863305524 0.996304740683472 39 0.00519914622892752 0.0103982924578550 0.994800853771072 40 0.00741851847911453 0.0148370369582291 0.992581481520886 41 0.0085436635462132 0.0170873270924264 0.991456336453787 42 0.0203566047581421 0.0407132095162842 0.979643395241858 43 0.0286969661936851 0.0573939323873703 0.971303033806315 44 0.0494651361280466 0.0989302722560932 0.950534863871953 45 0.0882984257538932 0.176596851507786 0.911701574246107 46 0.178567857565682 0.357135715131365 0.821432142434318 47 0.282814913988089 0.565629827976177 0.717185086011911 48 0.474005198301033 0.948010396602067 0.525994801698967 49 0.639751976793555 0.720496046412889 0.360248023206445 50 0.794523233399651 0.410953533200698 0.205476766600349 51 0.852369291665272 0.295261416669456 0.147630708334728 52 0.9070615684156 0.1858768631688 0.0929384315844 53 0.930884648393887 0.138230703212227 0.0691153516061135 54 0.979582983454615 0.0408340330907705 0.0204170165453852 55 0.986478122411442 0.0270437551771163 0.0135218775885581 56 0.994988462138652 0.0100230757226951 0.00501153786134757 57 0.99697233170981 0.00605533658038139 0.00302766829019069 58 0.998720291268001 0.00255941746399735 0.00127970873199867 59 0.999198635753966 0.00160272849206780 0.000801364246033898 60 0.99952408130738 0.000951837385239332 0.000475918692619666 61 0.999493702866974 0.00101259426605152 0.000506297133025762 62 0.999350810583523 0.00129837883295375 0.000649189416476873 63 0.998902959622795 0.00219408075441095 0.00109704037720548 64 0.998332572786345 0.00333485442730923 0.00166742721365462 65 0.99690586588754 0.00618826822491937 0.00309413411245968 66 0.999066307144005 0.00186738571199006 0.000933692855995032 67 0.998020080908615 0.00395983818277063 0.00197991909138532 68 0.996153980147324 0.00769203970535174 0.00384601985267587 69 0.9922713572074 0.0154572855852004 0.00772864279260018 70 0.983638557903345 0.0327228841933102 0.0163614420966551 71 0.965281280964996 0.0694374380700084 0.0347187190350042 72 0.929051973803094 0.141896052393812 0.0709480261969062 73 0.859148523435805 0.281702953128389 0.140851476564195 74 0.730610403454114 0.538779193091773 0.269389596545886 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 18 0.305084745762712 NOK 5% type I error level 30 0.508474576271186 NOK 10% type I error level 35 0.593220338983051 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/10rtlc1258926973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/10rtlc1258926973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/1p48j1258926973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/1p48j1258926973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/2sgf71258926973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/2sgf71258926973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/3biau1258926973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/3biau1258926973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/45vie1258926973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/45vie1258926973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/5eqv31258926973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/5eqv31258926973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/6pqpc1258926973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/6pqpc1258926973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/78pt51258926973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/78pt51258926973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/8d6y01258926973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/8d6y01258926973.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/9mkb01258926973.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/22/t1258927240cy07vnakrs5clye/9mkb01258926973.ps (open in new window)

Parameters (Session):

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

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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