Bouwvergunningen (BouwV)

*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, 19 Nov 2009 08:48:21 -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/19/t1258645769wrz17j6s0llelaf.htm/, Retrieved Thu, 19 Nov 2009 16:49:42 +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/19/t1258645769wrz17j6s0llelaf.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:

Bouwvergunningen volgens effectieve datum van toekenning - woongebouwen - koninkrijk, Ruimte

Dataseries X:

» Textbox « » Textfile « » CSV «

100 0 108.1560276 0 114.0150276 0 102.1880309 0 110.3672031 0 96.8602511 0 94.1944583 0 99.51621961 0 94.06333487 0 97.5541476 0 78.15062422 0 81.2434643 0 92.36262465 0 96.06324371 0 114.0523777 0 110.6616666 0 104.9171949 0 90.00187193 0 95.7008067 0 86.02741157 0 84.85287668 0 100.04328 0 80.91713823 0 74.06539709 0 77.30281369 0 97.23043249 0 90.75515676 0 100.5614455 0 92.01293267 0 99.24012138 0 105.8672755 0 90.9920463 0 93.30624423 0 91.17419413 0 77.33295039 0 91.1277721 0 85.01249943 0 83.90390242 0 104.8626302 0 110.9039108 0 95.43714373 0 111.6238727 0 108.8925403 0 96.17511682 0 101.9740205 0 99.11953031 0 86.78158147 0 118.4195003 0 118.7441447 0 106.5296192 0 134.7772694 0 104.6778714 0 105.2954304 0 139.4139849 0 103.6060491 0 99.78182974 0 103.4610301 0 120.0594945 0 96.71377168 0 107.1308929 0 105.3608372 0 111.6942359 0 132.0519998 0 126.8037879 0 154.4824253 0 141.5570984 0 109.9506882 0 127.904198 0 133.0888617 0 120.079 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 BouwV[t] = + 100.737929464167 + 18.0026589708333X[t] + 1.24926445333335M1[t] + 0.741356025555553M2[t] + 13.1704820188889M3[t] + 6.56709212333335M4[t] + 6.68611955666668M5[t] + 12.8062306022222M6[t] + 0.279589323333329M7[t] -2.22710195000001M8[t] -0.0735003233333385M9[t] + 8.83081141666668M10[t] -10.2619287333333M11[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) 100.737929464167 5.344786 18.8479 0 0 X 18.0026589708333 3.206871 5.6138 0 0 M1 1.24926445333335 7.405952 0.1687 0.866404 0.433202 M2 0.741356025555553 7.405952 0.1001 0.920474 0.460237 M3 13.1704820188889 7.405952 1.7784 0.078543 0.039272 M4 6.56709212333335 7.405952 0.8867 0.377463 0.188731 M5 6.68611955666668 7.405952 0.9028 0.368914 0.184457 M6 12.8062306022222 7.405952 1.7292 0.087025 0.043512 M7 0.279589323333329 7.405952 0.0378 0.969965 0.484982 M8 -2.22710195000001 7.405952 -0.3007 0.764287 0.382143 M9 -0.0735003233333385 7.405952 -0.0099 0.992102 0.496051 M10 8.83081141666668 7.405952 1.1924 0.236077 0.118038 M11 -10.2619287333333 7.405952 -1.3856 0.169104 0.084552 Multiple Linear Regression - Regression Statistics Multiple R 0.585800390694066 R-squared 0.343162097737320 Adjusted R-squared 0.260193099556772 F-TEST (value) 4.13602797746894 F-TEST (DF numerator) 12 F-TEST (DF denominator) 95 p-value 3.41703775448288e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 15.7103976677556 Sum Squared Residuals 23447.5765135068 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 100 101.9871939175 -1.98719391749993 2 108.1560276 101.479285489722 6.67674211027781 3 114.0150276 113.908411483056 0.106616116944433 4 102.1880309 107.3050215875 -5.11699068749999 5 110.3672031 107.424049020833 2.94315407916666 6 96.8602511 113.544160066389 -16.6839089663889 7 94.1944583 101.0175187875 -6.82306048750004 8 99.51621961 98.5108275141666 1.00539209583334 9 94.06333487 100.664429140833 -6.60109427083333 10 97.5541476 109.568740880833 -12.0145932808333 11 78.15062422 90.4760007308333 -12.3253765108333 12 81.2434643 100.737929464167 -19.4944651641667 13 92.36262465 101.9871939175 -9.6245692675 14 96.06324371 101.479285489722 -5.41604177972223 15 114.0523777 113.908411483056 0.143966216944441 16 110.6616666 107.3050215875 3.35664501250000 17 104.9171949 107.424049020833 -2.50685412083333 18 90.00187193 113.544160066389 -23.5422881363889 19 95.7008067 101.0175187875 -5.31671208749999 20 86.02741157 98.5108275141667 -12.4834159441667 21 84.85287668 100.664429140833 -15.8115524608333 22 100.04328 109.568740880833 -9.52546088083333 23 80.91713823 90.4760007308333 -9.55886250083333 24 74.06539709 100.737929464167 -26.6725323741667 25 77.30281369 101.9871939175 -24.6843802275 26 97.23043249 101.479285489722 -4.24885299972222 27 90.75515676 113.908411483056 -23.1532547230555 28 100.5614455 107.3050215875 -6.7435760875 29 92.01293267 107.424049020833 -15.4111163508333 30 99.24012138 113.544160066389 -14.3040386863889 31 105.8672755 101.0175187875 4.84975671250001 32 90.9920463 98.5108275141667 -7.51878121416667 33 93.30624423 100.664429140833 -7.35818491083332 34 91.17419413 109.568740880833 -18.3945467508333 35 77.33295039 90.4760007308333 -13.1430503408333 36 91.1277721 100.737929464167 -9.61015736416667 37 85.01249943 101.9871939175 -16.9746944875 38 83.90390242 101.479285489722 -17.5753830697222 39 104.8626302 113.908411483056 -9.04578128305556 40 110.9039108 107.3050215875 3.59888921250001 41 95.43714373 107.424049020833 -11.9869052908333 42 111.6238727 113.544160066389 -1.92028736638888 43 108.8925403 101.0175187875 7.8750215125 44 96.17511682 98.5108275141667 -2.33571069416667 45 101.9740205 100.664429140833 1.30959135916666 46 99.11953031 109.568740880833 -10.4492105708333 47 86.78158147 90.4760007308333 -3.69441926083333 48 118.4195003 100.737929464167 17.6815708358333 49 118.7441447 101.9871939175 16.7569507825 50 106.5296192 101.479285489722 5.05033371027778 51 134.7772694 113.908411483056 20.8688579169444 52 104.6778714 107.3050215875 -2.6271501875 53 105.2954304 107.424049020833 -2.12861862083333 54 139.4139849 113.544160066389 25.8698248336111 55 103.6060491 101.0175187875 2.58853031250001 56 99.78182974 98.5108275141667 1.27100222583334 57 103.4610301 100.664429140833 2.79660095916667 58 120.0594945 109.568740880833 10.4907536191667 59 96.71377168 90.4760007308333 6.23777094916666 60 107.1308929 100.737929464167 6.39296343583333 61 105.3608372 101.9871939175 3.37364328249999 62 111.6942359 101.479285489722 10.2149504102778 63 132.0519998 113.908411483056 18.1435883169445 64 126.8037879 107.3050215875 19.4987663125 65 154.4824253 107.424049020833 47.0583762791667 66 141.5570984 113.544160066389 28.0129383336111 67 109.9506882 101.0175187875 8.93316941250001 68 127.904198 98.5108275141667 29.3933704858333 69 133.0888617 100.664429140833 32.4244325591667 70 120.0796299 109.568740880833 10.5108890191667 71 117.5557142 90.4760007308333 27.0797134691667 72 143.0362309 100.737929464167 42.2983014358333 73 159.982927 119.989852888333 39.9930741116666 74 128.5991124 119.481944460556 9.11716793944444 75 149.7373327 131.911070453889 17.8262622461111 76 126.8169313 125.307680558333 1.50925074166666 77 140.9639674 125.426707991667 15.5372594083333 78 137.6691981 131.546819037222 6.12237906277777 79 117.9402337 119.020177758333 -1.07994405833333 80 122.3095247 116.513486485 5.796038215 81 127.7804207 118.667088111667 9.11333258833333 82 136.1677176 127.571399851667 8.59631774833334 83 116.2405856 108.478659701667 7.76192589833334 84 123.1576893 118.740588435 4.41710086499999 85 116.3400234 119.989852888333 -3.64982948833334 86 108.6119282 119.481944460556 -10.8700162605556 87 125.8982264 131.911070453889 -6.01284405388889 88 112.8003105 125.307680558333 -12.5073700583333 89 107.5182447 125.426707991667 -17.9084632916667 90 135.0955413 131.546819037222 3.54872226277779 91 115.5096488 119.020177758333 -3.51052895833333 92 115.8640759 116.513486485 -0.649410584999995 93 104.5883906 118.667088111667 -14.0786975116667 94 163.7213386 127.571399851667 36.1499387483333 95 113.4482275 108.478659701667 4.96956779833334 96 98.0428844 118.740588435 -20.697704035 97 116.7868521 119.989852888333 -3.20300078833334 98 126.5330444 119.481944460556 7.05109993944444 99 113.0336597 131.911070453889 -18.8774107538889 100 124.3392163 125.307680558333 -0.96846425833333 101 109.8298759 125.426707991667 -15.5968320916667 102 124.4434777 131.546819037222 -7.10334133722222 103 111.5039454 119.020177758333 -7.51623235833332 104 102.0350019 116.513486485 -14.478484585 105 116.8726598 118.667088111667 -1.79442831166667 106 112.2073122 127.571399851667 -15.3640876516667 107 101.1513902 108.478659701667 -7.32726950166667 108 124.4255108 118.740588435 5.68492236499999 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.080675621898115 0.16135124379623 0.919324378101885 17 0.031345006379653 0.062690012759306 0.968654993620347 18 0.0144620332424114 0.0289240664848227 0.98553796675759 19 0.00454749338596286 0.00909498677192572 0.995452506614037 20 0.00497785386225539 0.00995570772451077 0.995022146137745 21 0.00292920340748893 0.00585840681497787 0.997070796592511 22 0.00105620742820457 0.00211241485640913 0.998943792571795 23 0.000371018841528166 0.000742037683056331 0.999628981158472 24 0.000218495046068637 0.000436990092137273 0.999781504953931 25 0.00134503107187715 0.00269006214375430 0.998654968928123 26 0.000624660703603694 0.00124932140720739 0.999375339296396 27 0.00421669043307113 0.00843338086614226 0.995783309566929 28 0.00236963962121652 0.00473927924243305 0.997630360378783 29 0.0031174765878233 0.0062349531756466 0.996882523412177 30 0.00215030303165335 0.0043006060633067 0.997849696968347 31 0.00160525032949522 0.00321050065899045 0.998394749670505 32 0.000846738924814078 0.00169347784962816 0.999153261075186 33 0.000468379519406068 0.000936759038812135 0.999531620480594 34 0.000390555156622182 0.000781110313244364 0.999609444843378 35 0.000237659368707838 0.000475318737415677 0.999762340631292 36 0.000300178453759093 0.000600356907518186 0.99969982154624 37 0.000280113333371505 0.00056022666674301 0.999719886666628 38 0.000649640182979537 0.00129928036595907 0.99935035981702 39 0.00044264261703862 0.00088528523407724 0.999557357382961 40 0.00026778472761326 0.00053556945522652 0.999732215272387 41 0.000240551659172522 0.000481103318345044 0.999759448340827 42 0.000451696801934846 0.000903393603869691 0.999548303198065 43 0.000347068456707538 0.000694136913415075 0.999652931543292 44 0.000222042942790004 0.000444085885580008 0.99977795705721 45 0.000204021748433508 0.000408043496867016 0.999795978251567 46 0.000211188319790218 0.000422376639580437 0.99978881168021 47 0.00019736520142584 0.00039473040285168 0.999802634798574 48 0.0040219729088763 0.0080439458177526 0.995978027091124 49 0.0138560778051192 0.0277121556102384 0.98614392219488 50 0.0115108571624711 0.0230217143249423 0.98848914283753 51 0.0253922024538745 0.0507844049077491 0.974607797546125 52 0.0204731365157338 0.0409462730314675 0.979526863484266 53 0.0194268713474965 0.0388537426949931 0.980573128652503 54 0.0723486062699253 0.144697212539851 0.927651393730075 55 0.0561258968995103 0.112251793799021 0.94387410310049 56 0.0501576630434541 0.100315326086908 0.949842336956546 57 0.0477977337580721 0.0955954675161443 0.952202266241928 58 0.0611689261240849 0.122337852248170 0.938831073875915 59 0.0656068613711289 0.131213722742258 0.934393138628871 60 0.0710862364681818 0.142172472936364 0.928913763531818 61 0.103328485430880 0.206656970861761 0.89667151456912 62 0.106665389777356 0.213330779554712 0.893334610222644 63 0.108471049159053 0.216942098318105 0.891528950840947 64 0.108680830618568 0.217361661237136 0.891319169381432 65 0.363185256234709 0.726370512469418 0.636814743765291 66 0.408350527182567 0.816701054365134 0.591649472817433 67 0.376009547471099 0.752019094942197 0.623990452528902 68 0.408790784859073 0.817581569718145 0.591209215140927 69 0.463552803558605 0.92710560711721 0.536447196441395 70 0.539403561557806 0.921192876884388 0.460596438442194 71 0.579375865994628 0.841248268010745 0.420624134005372 72 0.649445826350158 0.701108347299684 0.350554173649842 73 0.818268629803778 0.363462740392445 0.181731370196222 74 0.804910046652803 0.390179906694394 0.195089953347197 75 0.861563528105513 0.276872943788975 0.138436471894487 76 0.83691188709949 0.326176225801022 0.163088112900511 77 0.906385036795903 0.187229926408193 0.0936149632040967 78 0.876827324533008 0.246345350933985 0.123172675466992 79 0.839824183902006 0.320351632195988 0.160175816097994 80 0.812535920937026 0.374928158125947 0.187464079062974 81 0.799078134550673 0.401843730898655 0.200921865449327 82 0.731238970589076 0.537522058821847 0.268761029410924 83 0.667994819418275 0.66401036116345 0.332005180581725 84 0.613545010543116 0.772909978913768 0.386454989456884 85 0.52448320411332 0.95103359177336 0.47551679588668 86 0.49441273113662 0.98882546227324 0.50558726886338 87 0.428490676340661 0.856981352681322 0.571509323659339 88 0.358251133403849 0.716502266807698 0.641748866596151 89 0.27585975921516 0.55171951843032 0.72414024078484 90 0.195385738441253 0.390771476882506 0.804614261558747 91 0.118401762111011 0.236803524222021 0.88159823788899 92 0.0707532422075034 0.141506484415007 0.929246757792497 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 30 0.389610389610390 NOK 5% type I error level 35 0.454545454545455 NOK 10% type I error level 38 0.493506493506494 NOK

Charts produced by software:

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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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