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 09:02:09 -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/t1258646615jyen1tkg97kcyi2.htm/, Retrieved Thu, 19 Nov 2009 17:03:47 +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/t1258646615jyen1tkg97kcyi2.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 «

110,3672031 0 102,1880309 114,0150276 108,1560276 100 0 0 0 96,8602511 0 110,3672031 102,1880309 114,0150276 108,1560276 0 0 0 94,1944583 0 96,8602511 110,3672031 102,1880309 114,0150276 0 0 0 99,51621961 0 94,1944583 96,8602511 110,3672031 102,1880309 0 0 0 94,06333487 0 99,51621961 94,1944583 96,8602511 110,3672031 0 0 0 97,5541476 0 94,06333487 99,51621961 94,1944583 96,8602511 0 0 0 78,15062422 0 97,5541476 94,06333487 99,51621961 94,1944583 0 0 0 81,2434643 0 78,15062422 97,5541476 94,06333487 99,51621961 0 0 0 92,36262465 0 81,2434643 78,15062422 97,5541476 94,06333487 0 0 0 96,06324371 0 92,36262465 81,2434643 78,15062422 97,5541476 0 0 0 114,0523777 0 96,06324371 92,36262465 81,2434643 78,15062422 0 0 0 110,6616666 0 114,0523777 96,06324371 92,36262465 81,2434643 0 0 0 104,9171949 0 110,6616666 114,0523777 96,06324371 92,36262465 0 0 0 90,00187193 0 104,9171949 110,6616666 114,0523777 96,06324371 0 0 0 95,7008067 0 90,00187193 104,9171949 110,6616666 114,0523777 0 0 0 86,02741157 0 95,7 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] = + 30.1351868004738 -7.37172943300236X[t] + 0.212933144893385Y1[t] + 0.0216016225641451Y2[t] + 0.377218616670764Y3[t] + 0.0813878620545899Y4[t] + 35.9249251015229D1[t] + 45.6077804994825D2[t] + 47.6216268602616D3[t] -3.09242413991911M1[t] + 2.30992298293443M2[t] -10.2226835489360M3[t] -9.88126679169627M4[t] -9.42118012801932M5[t] -2.16496647380466M6[t] -16.1234331600829M7[t] -2.77630037148207M8[t] -9.90267109162424M9[t] -0.389746923416947M10[t] + 11.3017701244168M11[t] + 0.179561185199603t + 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) 30.1351868004738 9.568743 3.1493 0.002275 0.001137 X -7.37172943300236 3.614497 -2.0395 0.044582 0.022291 Y1 0.212933144893385 0.087856 2.4236 0.017539 0.008769 Y2 0.0216016225641451 0.084291 0.2563 0.798374 0.399187 Y3 0.377218616670764 0.085489 4.4125 3e-05 1.5e-05 Y4 0.0813878620545899 0.086455 0.9414 0.349239 0.174619 D1 35.9249251015229 10.278508 3.4951 0.000763 0.000382 D2 45.6077804994825 10.718644 4.255 5.5e-05 2.7e-05 D3 47.6216268602616 10.229288 4.6554 1.2e-05 6e-06 M1 -3.09242413991911 4.963452 -0.623 0.534967 0.267484 M2 2.30992298293443 4.758407 0.4854 0.628643 0.314321 M3 -10.2226835489360 4.786261 -2.1358 0.035639 0.017819 M4 -9.88126679169627 5.011521 -1.9717 0.051974 0.025987 M5 -9.42118012801932 5.000292 -1.8841 0.063049 0.031524 M6 -2.16496647380466 5.100668 -0.4244 0.672338 0.336169 M7 -16.1234331600829 4.637884 -3.4765 0.000811 0.000405 M8 -2.77630037148207 5.203663 -0.5335 0.595094 0.297547 M9 -9.90267109162424 5.302 -1.8677 0.06533 0.032665 M10 -0.389746923416947 5.130664 -0.076 0.93963 0.469815 M11 11.3017701244168 4.857071 2.3269 0.022409 0.011205 t 0.179561185199603 0.063778 2.8154 0.006083 0.003042 Multiple Linear Regression - Regression Statistics Multiple R 0.890034725391375 R-squared 0.7921618124025 Adjusted R-squared 0.742080321415151 F-TEST (value) 15.8174566448631 F-TEST (DF numerator) 20 F-TEST (DF denominator) 83 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9.4308394231125 Sum Squared Residuals 7382.08077463624 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 110.3672031 100.381705549945 9.98549755005454 2 96.8602511 110.323673922685 -13.4634228226846 3 94.1944583 91.2867223487156 2.90773595128442 4 99.51621961 93.071054614377 6.44516499562306 5 94.06333487 90.3569079753199 3.70642689468011 6 97.5541476 94.641652978999 2.91249462100100 7 78.15062422 83.2787703156515 -5.12814609565152 8 81.2434643 91.1254153900696 -9.88195109006963 9 92.36262465 85.291027349593 7.07159730040694 10 96.06324371 90.3827003859439 5.68054332405608 11 114.0523777 102.869420550923 11.1829571490766 12 110.6616666 100.103707789849 10.5579588101514 13 104.9171949 99.1583516312563 5.75884326874369 14 90.00187193 110.530848365456 -20.5289764354558 15 95.7008067 95.062804287176 0.638002412824003 16 86.02741157 94.0321947600613 -8.00478319006127 17 84.85287668 86.6412946317926 -1.78841795179265 18 100.04328 94.5538290748344 5.4894509251656 19 80.91713823 80.7989314529706 0.118206777029389 20 74.06539709 89.3508198942467 -15.2854228042467 21 77.30281369 86.1664019181804 -8.86358822818042 22 97.23043249 90.4218095527753 6.8086229372247 23 90.75515676 102.464831675975 -11.7096749159750 24 100.5614455 91.0578560601398 9.50358943986022 25 92.01293267 97.8737557571752 -5.86082308717519 26 99.24012138 101.02650582883 -1.78638444882995 27 105.8672755 93.1998125805772 12.6674629194228 28 90.9920463 92.8615049851083 -1.86945868510829 29 93.30624423 92.5073657260184 0.798878503981583 30 91.17419413 103.202672269222 -12.0284781392221 31 77.33295039 83.9479295893513 -6.6149791993513 32 91.1277721 94.1436037025066 -3.01583160250657 33 85.01249943 89.2192742469271 -4.20677481692712 34 83.90390242 92.5129080747115 -8.60900565471151 35 104.8626302 108.092983774383 -3.23035357438289 36 110.9039108 100.225571509399 10.6783392906011 37 95.43714373 98.1359475598993 -2.69880382989934 38 111.6238727 108.370765937556 3.25310676244409 39 108.8925403 103.114973987721 5.77756631227933 40 96.17511682 98.0613547758284 -1.88623795582838 41 101.9740205 100.781168842330 1.19285165767012 42 99.11953031 109.464099332186 -10.3445690221864 43 86.78158147 90.1830977891265 -3.40151631912654 44 118.4195003 102.173382411691 16.2461178883087 45 118.7441447 101.092008249048 17.6521364509516 46 106.5296192 106.650626689001 -0.121007489000737 47 134.7772694 126.858093136903 7.91917626309703 48 104.6778714 124.184296105877 -19.5064247058772 49 105.2954304 110.891344454809 -5.59591405480864 50 139.4139849 125.615981124072 13.7980037759276 51 103.6060491 111.486209744393 -7.88016064439317 52 99.78182974 102.902736540884 -3.12090680088364 53 103.4610301 114.874987558719 -11.4139574587191 54 120.0594945 112.280992957100 7.77850154289966 55 96.71377168 97.7590293024544 -1.04525762245440 56 107.1308929 107.749816692012 -0.618923792011776 57 105.3608372 109.077544077532 -3.71670687753243 58 111.6942359 111.162624888467 0.531611011532999 59 132.0519998 126.373531137267 5.67846866273272 60 126.8037879 119.903105838815 6.90068206118511 61 154.4824253 154.4824253 -8.88178419700125e-16 62 141.5570984 138.114527238476 3.44257116152416 63 109.9506882 123.284306511538 -13.3336183115377 64 127.904198 126.809780663298 1.09441733670216 65 133.0888617 127.966607261349 5.12225443865054 66 120.0796299 123.919702733700 -3.84007283370018 67 117.5557142 111.682717718913 5.87299648108748 68 143.0362309 127.807915317888 15.2283155821116 69 159.982927 159.982927 -4.66293670342566e-15 70 128.5991124 126.215704102420 2.38340829757957 71 149.7373327 141.176513285588 8.56081941441244 72 126.8169313 142.343804792927 -15.5268734929274 73 140.9639674 124.547744777690 16.4162226223104 74 137.6691981 138.066376760411 -0.39717866041074 75 117.9402337 118.391757207436 -0.451523507435588 76 122.3095247 118.111695272093 4.19782942790694 77 127.7804207 119.164081056997 8.61633964300348 78 136.1677176 120.148887873525 16.0188297264745 79 116.2405856 108.316575782783 7.92400981721704 80 123.1576893 120.200633167339 2.95705613266098 81 116.3400234 117.905354955729 -1.56533155572921 82 108.6119282 119.461292927434 -10.8493647274336 83 125.8982264 130.526964518678 -4.62873811867777 84 112.8003105 120.909859805133 -8.10954930513275 85 107.5182447 112.111371882877 -4.59312718287737 86 135.0955413 122.177357421234 12.9181838787660 87 115.5096488 112.048448511756 3.46120028824364 88 115.8640759 105.936150194408 9.92792570559219 89 104.5883906 116.200953895234 -11.6125632952336 90 163.7213386 163.7213386 -1.55431223447522e-15 91 113.4482275 113.208240308481 0.239987191519269 92 98.0428844 113.082937909027 -15.0400535090272 93 116.7868521 123.158184372989 -6.37133227298933 94 126.5330444 122.357852099248 4.17519230075247 95 113.0336597 126.806314580283 -13.7726548802831 96 124.3392163 118.836938397861 5.5022779021395 97 109.8298759 123.241771186348 -13.4118952863481 98 124.4434777 121.679380911281 2.76409678871919 99 111.5039454 115.290610820688 -3.78666542068773 100 102.0350019 108.818952733943 -6.78395083394276 101 116.8726598 111.494472232240 5.37818756775954 102 112.2073122 118.193469020432 -5.98615682043211 103 101.1513902 99.1166912302694 2.03469896973057 104 124.4255108 115.014817605220 9.4106931947805 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 24 0.708918580180587 0.582162839638827 0.291081419819413 25 0.591812900181364 0.816374199637273 0.408187099818636 26 0.633249893786074 0.733500212427852 0.366750106213926 27 0.777664460274928 0.444671079450144 0.222335539725072 28 0.690300470876211 0.619399058247577 0.309699529123789 29 0.619504806310847 0.760990387378305 0.380495193689152 30 0.532961736682935 0.93407652663413 0.467038263317065 31 0.466649958403214 0.933299916806427 0.533350041596786 32 0.579463749899548 0.841072500200904 0.420536250100452 33 0.502308880420015 0.99538223915997 0.497691119579985 34 0.493000040086988 0.986000080173976 0.506999959913012 35 0.43082807459467 0.86165614918934 0.56917192540533 36 0.399792152503094 0.799584305006189 0.600207847496906 37 0.323162227212795 0.64632445442559 0.676837772787205 38 0.430204402435549 0.860408804871098 0.569795597564451 39 0.392282506325119 0.784565012650239 0.607717493674881 40 0.334310512883195 0.66862102576639 0.665689487116805 41 0.272736893674097 0.545473787348194 0.727263106325903 42 0.259519462242349 0.519038924484698 0.740480537757651 43 0.245652733072183 0.491305466144365 0.754347266927817 44 0.521414133872698 0.957171732254604 0.478585866127302 45 0.645009722597245 0.70998055480551 0.354990277402755 46 0.596418523257793 0.807162953484415 0.403581476742207 47 0.555346713028399 0.889306573943201 0.444653286971601 48 0.768923329354022 0.462153341291956 0.231076670645978 49 0.719774502786993 0.560450994426015 0.280225497213007 50 0.798416167861262 0.403167664277477 0.201583832138738 51 0.764360930598174 0.471278138803653 0.235639069401826 52 0.71801798088434 0.56396403823132 0.28198201911566 53 0.776419741536896 0.447160516926208 0.223580258463104 54 0.786054450264631 0.427891099470737 0.213945549735368 55 0.775061782166247 0.449876435667506 0.224938217833753 56 0.775662034486019 0.448675931027963 0.224337965513981 57 0.747776536741487 0.504446926517025 0.252223463258513 58 0.722528091523733 0.554943816952535 0.277471908476267 59 0.67026941525965 0.6594611694807 0.32973058474035 60 0.614324817555916 0.771350364888168 0.385675182444084 61 0.539923748445791 0.920152503108417 0.460076251554209 62 0.485980561452198 0.971961122904396 0.514019438547802 63 0.481822320953751 0.963644641907502 0.518177679046249 64 0.417048939483659 0.834097878967318 0.582951060516341 65 0.361753791935867 0.723507583871735 0.638246208064132 66 0.333425514544925 0.666851029089851 0.666574485455075 67 0.332715500302361 0.665431000604722 0.667284499697639 68 0.306152313295780 0.612304626591561 0.69384768670422 69 0.236013344205789 0.472026688411578 0.763986655794211 70 0.223946237394934 0.447892474789869 0.776053762605066 71 0.330137222346767 0.660274444693533 0.669862777653233 72 0.350023235148893 0.700046470297785 0.649976764851107 73 0.526716525540576 0.946566948918847 0.473283474459424 74 0.427274836478215 0.85454967295643 0.572725163521785 75 0.329028461917012 0.658056923834024 0.670971538082988 76 0.236973362661385 0.47394672532277 0.763026637338615 77 0.178463264556933 0.356926529113866 0.821536735443067 78 0.266600859921709 0.533201719843417 0.733399140078291 79 0.207179089565937 0.414358179131874 0.792820910434063 80 0.142580988365912 0.285161976731824 0.857419011634088 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/10gvua1258646524.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/10gvua1258646524.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/1r61b1258646524.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/1r61b1258646524.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/2oyma1258646524.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/2oyma1258646524.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/3t4fa1258646524.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/3t4fa1258646524.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/4a07e1258646524.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/4a07e1258646524.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/5isd91258646524.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/5isd91258646524.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/6kpze1258646524.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/6kpze1258646524.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/7k35f1258646524.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/7k35f1258646524.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/8nerh1258646524.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/8nerh1258646524.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/9yt751258646524.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258646615jyen1tkg97kcyi2/9yt751258646524.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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