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 10:49:16 -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/t1258653172tuaja8ju40o7qox.htm/, Retrieved Thu, 19 Nov 2009 18:53:05 +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/t1258653172tuaja8ju40o7qox.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 0 0 0 96,8602511 0 110,3672031 102,1880309 114,0150276 0 0 0 94,1944583 0 96,8602511 110,3672031 102,1880309 0 0 0 99,51621961 0 94,1944583 96,8602511 110,3672031 0 0 0 94,06333487 0 99,51621961 94,1944583 96,8602511 0 0 0 97,5541476 0 94,06333487 99,51621961 94,1944583 0 0 0 78,15062422 0 97,5541476 94,06333487 99,51621961 0 0 0 81,2434643 0 78,15062422 97,5541476 94,06333487 0 0 0 92,36262465 0 81,2434643 78,15062422 97,5541476 0 0 0 96,06324371 0 92,36262465 81,2434643 78,15062422 0 0 0 114,0523777 0 96,06324371 92,36262465 81,2434643 0 0 0 110,6616666 0 114,0523777 96,06324371 92,36262465 0 0 0 104,9171949 0 110,6616666 114,0523777 96,06324371 0 0 0 90,00187193 0 104,9171949 110,6616666 114,0523777 0 0 0 95,7008067 0 90,00187193 104,9171949 110,6616666 0 0 0 86,02741157 0 95,7008067 90,00187193 104,9171949 0 0 0 84,85287668 0 86,02741157 95,7008067 90,00187193 0 0 0 100,04328 0 84,85287668 86,02741157 95,7008067 0 0 0 80,91713823 0 100,04 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation BouwV[t] = + 31.8082471358567 -7.02863629197244X[t] + 0.238965295567245Y1[t] + 0.0273747630402027Y2[t] + 0.402192343482086Y3[t] + 35.1646947722934D1[t] + 46.4488639594472D2[t] + 48.3693352090327D3[t] -2.53493234791146M1[t] + 2.46209784069195M2[t] -9.05815573602356M3[t] -8.97051564784286M4[t] -8.52014242938935M5[t] -0.581790688040753M6[t] -15.662456704794M7[t] -2.1316338495595M8[t] -9.61420426024542M9[t] + 1.19846234618739M10[t] + 11.0501843155142M11[t] + 0.183692662157701t + 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) 31.8082471358567 9.395879 3.3853 0.001083 0.000541 X -7.02863629197244 3.593639 -1.9559 0.053806 0.026903 Y1 0.238965295567245 0.083335 2.8675 0.005229 0.002614 Y2 0.0273747630402027 0.084011 0.3258 0.74535 0.372675 Y3 0.402192343482086 0.081214 4.9523 4e-06 2e-06 D1 35.1646947722934 10.239791 3.4341 0.000926 0.000463 D2 46.4488639594472 10.674105 4.3515 3.8e-05 1.9e-05 D3 48.3693352090327 10.191496 4.746 8e-06 4e-06 M1 -2.53493234791146 4.924658 -0.5147 0.608085 0.304042 M2 2.46209784069195 4.752438 0.5181 0.605771 0.302885 M3 -9.05815573602356 4.620506 -1.9604 0.053261 0.02663 M4 -8.97051564784286 4.913921 -1.8255 0.071475 0.035737 M5 -8.52014242938935 4.904506 -1.7372 0.086016 0.043008 M6 -0.581790688040753 4.812185 -0.1209 0.904059 0.45203 M7 -15.662456704794 4.608836 -3.3984 0.001039 0.000519 M8 -2.1316338495595 5.154911 -0.4135 0.680283 0.340142 M9 -9.61420426024542 5.289551 -1.8176 0.072693 0.036347 M10 1.19846234618739 4.842068 0.2475 0.805117 0.402559 M11 11.0501843155142 4.846427 2.2801 0.025137 0.012569 t 0.183692662157701 0.063583 2.889 0.004915 0.002458 Multiple Linear Regression - Regression Statistics Multiple R 0.888787196164306 R-squared 0.78994268006561 Adjusted R-squared 0.742429714842354 F-TEST (value) 16.6258341560837 F-TEST (DF numerator) 19 F-TEST (DF denominator) 84 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9.42444946867422 Sum Squared Residuals 7460.90081415787 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 110.3672031 100.497061023286 9.87014207671448 2 96.8602511 109.665005884637 -12.8047547846368 3 94.1944583 90.5679275748986 3.62653072510141 4 99.51621961 93.12207918533 6.39414042466996 5 94.06333487 89.5224932059295 4.54084166407054 6 97.5541476 95.4150078971956 2.1391397028044 7 78.15062422 83.3433178634725 -5.19269364347253 8 81.2434643 90.3235163603003 -9.08005206030026 9 92.36262465 84.6365313534947 7.72609329650527 10 96.06324371 90.5707012858822 5.4925424241178 11 114.0523777 103.038736424399 11.0136412756008 12 110.6616666 101.044368220551 9.61729837944905 13 104.9171949 99.8636751869531 5.05351971304688 14 90.00187193 110.813940703797 -20.8120687737971 15 95.7008067 94.392163632274 1.30864306772604 16 86.02741157 93.3066580472825 -7.2792464772825 17 84.85287668 85.7862964913284 -0.933419811328375 18 100.04328 95.6549288487116 4.38835115128838 19 80.91713823 80.4652166422064 0.451921587793574 20 74.06539709 89.552692789929 -15.4872956999291 21 77.30281369 86.2023770056123 -8.89956331561229 22 97.23043249 90.092413918529 7.1380185714711 23 90.75515676 102.222743552765 -11.4675867927650 24 100.5614455 91.656463732497 8.90498176750306 25 92.01293267 99.486063300376 -7.47313063037595 26 99.24012138 100.288126766076 -1.04800538607567 27 105.8672755 94.3886138738585 11.4786616261415 28 90.9920463 93.003302637251 -2.01125633725107 29 93.30624423 93.1708417131818 0.135402516818164 30 91.17419413 104.104083880653 -12.9298897506527 31 77.33295039 82.7782718719272 -5.44532148192725 32 91.1277721 94.0575988104428 -2.92982671044282 33 85.01249943 88.8188097154378 -3.80631028543779 34 83.90390242 93.1646187619632 -9.26071634196322 35 104.8626302 108.315884712483 -3.4532545124831 36 110.9039108 99.9679382111526 10.9359725888474 37 95.43714373 99.188225906755 -3.75108217675502 38 111.6238727 109.267746660290 2.35612603971050 39 108.8925403 103.805609936922 5.08693036307776 40 96.17511682 97.6467416089771 -1.47162478897713 41 101.9740205 101.677193509356 0.296826990644036 42 99.11953031 109.738317211688 -10.6187869016880 43 86.78158147 89.2031130268029 -2.42153155680293 44 118.4195003 102.223420621085 16.1960796789148 45 118.7441447 100.999104972041 17.7450397279594 46 106.5296192 107.976894958966 -1.44727575896614 47 134.7772694 127.826877674786 6.95039172521446 48 104.6778714 123.506793851375 -18.8289224513755 49 105.2954304 109.823526721456 -4.52809632145642 50 139.4139849 125.688849485080 13.7251354149198 51 103.6060491 110.416597143210 -6.81054804321038 52 99.78182974 103.313440777609 -3.53161103760905 53 103.4610301 115.775638581636 -12.3146084816363 54 120.0594945 110.270519473169 9.78897502683065 55 96.71377168 97.9026485615266 -1.18887688152666 56 107.1308929 107.972471769716 -0.841578869716332 57 105.3608372 109.199616137525 -3.83877893752545 58 111.6942359 110.668688776295 1.02554712370545 59 132.0519998 126.358797440523 5.69320235947691 60 126.8037879 119.818577293193 6.98521060680693 61 154.4824253 154.4824253 3.33066907387547e-16 62 141.5570984 139.156755359506 2.40034304049400 63 109.9506882 123.378395378936 -13.4277071789356 64 127.904198 126.875201253204 1.02899674679640 65 133.0888617 125.735847403682 7.35301429631823 66 120.0796299 122.876463389796 -2.7968334897962 67 117.5557142 112.233428233432 5.32228596656794 68 143.0362309 127.073922895396 15.9623080046036 69 159.982927 159.982927 1.88737914186277e-15 70 128.5991124 128.262518088238 0.336594311761745 71 149.7373327 141.510270702565 8.2270619974346 72 126.8169313 141.651787041729 -14.8348557417290 73 140.9639674 121.779690691327 19.1842767086729 74 137.6691981 138.215254007511 -0.546055907511498 75 117.9402337 117.260219381696 0.680014318304081 76 122.3095247 118.416650396478 3.89287430352231 77 127.7804207 118.229618480845 9.55080221915521 78 136.1677176 119.843787043277 16.3239305567232 79 116.2405856 108.858146439663 7.38243916033725 80 123.1576893 120.240721717240 2.91696758276046 81 116.3400234 117.422597777971 -1.0825743779713 82 108.6119282 118.964585656445 -10.3526574564447 83 125.8982264 129.748627893001 -3.85040149300129 84 112.8003105 120.059393798020 -7.2590832980203 85 107.5182447 111.943234365835 -4.42498966583474 86 135.0955413 122.455591240598 12.6399500594017 87 115.5096488 112.296570368815 3.21307843118489 88 115.8640759 106.518070066129 9.34600583387147 89 104.5883906 117.792050103784 -13.2036595037845 90 163.7213386 163.7213386 -2.10942374678780e-15 91 113.4482275 114.419631085693 -0.971403585693479 92 98.0428844 113.204373894184 -15.1614894941843 93 116.7868521 124.630758207918 -7.84390610791782 94 126.5330444 119.465097273682 7.06794712631802 95 113.0336597 126.146714259477 -13.1130545594774 96 124.3392163 119.859818151482 4.47939814851835 97 109.8298759 123.760515604012 -13.9306397040121 98 124.4434777 120.354147402505 4.08933029749514 99 111.5039454 116.659548709390 -5.15560330938978 100 102.0350019 108.403280567740 -6.36827866774037 101 116.8726598 112.297859690257 4.57480010974299 102 112.2073122 118.502198495510 -6.29488629550977 103 101.1513902 99.088209765276 2.06318043472409 104 124.4255108 116.000623231706 8.42488756829401 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 23 0.699670597687469 0.600658804625062 0.300329402312531 24 0.563216946749266 0.873566106501469 0.436783053250734 25 0.446006903159457 0.892013806318914 0.553993096840543 26 0.511025052331027 0.977949895337945 0.488974947668973 27 0.695246684550143 0.609506630899713 0.304753315449857 28 0.596184662748985 0.80763067450203 0.403815337251015 29 0.531672470987528 0.936655058024945 0.468327529012472 30 0.468667670067788 0.937335340135576 0.531332329932212 31 0.382392868072224 0.764785736144447 0.617607131927776 32 0.480949337697824 0.961898675395647 0.519050662302176 33 0.399479543423276 0.798959086846552 0.600520456576724 34 0.392892173898643 0.785784347797286 0.607107826101357 35 0.336232778151222 0.672465556302445 0.663767221848778 36 0.306598456452706 0.613196912905411 0.693401543547294 37 0.243966117457298 0.487932234914596 0.756033882542702 38 0.355575358336231 0.711150716672463 0.644424641663769 39 0.315832749792244 0.631665499584489 0.684167250207756 40 0.259477555781311 0.518955111562622 0.740522444218689 41 0.208319665264695 0.416639330529391 0.791680334735305 42 0.213332154068427 0.426664308136854 0.786667845931573 43 0.190069878515349 0.380139757030698 0.809930121484651 44 0.445087047144116 0.890174094288231 0.554912952855884 45 0.572293345769434 0.855413308461133 0.427706654230566 46 0.52106878784701 0.95786242430598 0.47893121215299 47 0.466579867518283 0.933159735036567 0.533420132481717 48 0.720372842724693 0.559254314550614 0.279627157275307 49 0.67647246287334 0.64705507425332 0.32352753712666 50 0.75526837514552 0.489463249708959 0.244731624854479 51 0.723881113860427 0.552237772279146 0.276118886139573 52 0.679862919174941 0.640274161650118 0.320137080825059 53 0.764455805118133 0.471088389763734 0.235544194881867 54 0.750418966533943 0.499162066932114 0.249581033466057 55 0.742268057593622 0.515463884812756 0.257731942406378 56 0.756596982292105 0.48680603541579 0.243403017707895 57 0.731858741686566 0.536282516626868 0.268141258313434 58 0.716619787484668 0.566760425030664 0.283380212515332 59 0.671597962830405 0.65680407433919 0.328402037169595 60 0.611401264672753 0.777197470654493 0.388598735327247 61 0.538334519583533 0.923330960832935 0.461665480416467 62 0.480897951097123 0.961795902194247 0.519102048902877 63 0.48156541008004 0.96313082016008 0.51843458991996 64 0.420209062847089 0.840418125694178 0.579790937152911 65 0.370984579834657 0.741969159669315 0.629015420165343 66 0.338541061807601 0.677082123615201 0.661458938192399 67 0.350121186157455 0.700242372314911 0.649878813842545 68 0.325978737221642 0.651957474443283 0.674021262778358 69 0.255285195745446 0.510570391490893 0.744714804254554 70 0.205463039909308 0.410926079818616 0.794536960090692 71 0.249228502590628 0.498457005181257 0.750771497409372 72 0.241342628624791 0.482685257249582 0.758657371375209 73 0.57091944497728 0.85816111004544 0.42908055502272 74 0.476324738038862 0.952649476077724 0.523675261961138 75 0.389374396803496 0.778748793606993 0.610625603196504 76 0.293170436957688 0.586340873915375 0.706829563042312 77 0.245225947248874 0.490451894497747 0.754774052751126 78 0.359627335046705 0.719254670093411 0.640372664953295 79 0.293648668072604 0.587297336145209 0.706351331927396 80 0.207289766117196 0.414579532234392 0.792710233882804 81 0.124240777831160 0.248481555662319 0.87575922216884 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/t1258653172tuaja8ju40o7qox/1017vp1258652951.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/1017vp1258652951.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/1aqhz1258652951.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/1aqhz1258652951.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/2pdl61258652951.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/2pdl61258652951.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/3sw111258652951.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/3sw111258652951.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/4nop51258652951.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/4nop51258652951.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/5tiu51258652951.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/5tiu51258652951.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/6m9m71258652951.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/6m9m71258652951.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/7al6r1258652951.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/7al6r1258652951.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/8f3v31258652951.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/8f3v31258652951.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/97yvt1258652951.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t1258653172tuaja8ju40o7qox/97yvt1258652951.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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