Multiple Regressie: Allochtonen

*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: Mon, 20 Dec 2010 13:37:34 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/20/t1292852167hak0hmq9i61lue9.htm/, Retrieved Mon, 20 Dec 2010 14:36:22 +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/2010/Dec/20/t1292852167hak0hmq9i61lue9.htm/}, year = {2010}, } @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 = {2010}, 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:

Dataseries X:

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22.037 17.759 14.116 104.708 158.620 21.732 17.530 13.504 101.817 154.583 21.172 17.139 13.168 97.898 149.377 21.388 16.916 13.064 95.559 146.927 22.053 16.543 12.828 92.822 144.246 22.687 16.317 12.541 90.848 142.393 24.793 18.161 13.547 101.141 157.642 26.113 19.144 13.710 105.841 164.808 23.708 16.947 12.535 93.647 146.837 23.554 16.491 12.386 90.923 143.354 23.222 16.428 12.253 89.130 141.033 23.363 16.639 12.484 90.212 142.698 24.023 16.821 12.966 93.196 147.006 23.355 16.765 12.770 91.861 144.751 23.276 16.533 12.660 90.593 143.062 23.085 16.554 12.514 89.895 142.048 23.173 16.494 12.430 88.819 140.916 23.487 16.612 12.372 87.924 140.395 25.576 17.933 13.085 96.906 153.500 26.311 19.070 13.454 101.217 160.052 27.109 18.179 13.361 98.709 157.358 27.060 17.830 13.713 98.139 156.742 26.490 17.349 13.601 95.529 152.969 27.157 17.919 14.090 98.577 157.743 26.973 18.269 14.452 100.772 160.466 27.589 18.385 14.108 100.180 160.262 27.246 18.260 14.036 99.200 158.742 26.845 17.905 13.3 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 12 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Allochtonen[t] = + 5.95971907838046e-14 -1.00000000000000PmAH[t] -1`50+`[t] -1`Kort-geschoolden`[t] + 1Totaal_NWW[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) 5.95971907838046e-14 0 1.84 0.068122 0.034061 PmAH -1.00000000000000 0 -302126877091037 0 0 `50+` -1 0 -847499136595611 0 0 `Kort-geschoolden` -1 0 -995783378701965 0 0 Totaal_NWW 1 0 1172554188227856 0 0 Multiple Linear Regression - Regression Statistics Multiple R 1 R-squared 1 Adjusted R-squared 1 F-TEST (value) 2.07693327813918e+30 F-TEST (DF numerator) 4 F-TEST (DF denominator) 126 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.83887387350201e-14 Sum Squared Residuals 1.01545981357619e-25 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 22.037 22.0370000000001 -1.39436549705867e-13 2 21.732 21.7319999999997 2.64988403957292e-13 3 21.172 21.1720000000000 -1.63708577568790e-14 4 21.388 21.3880000000000 -2.83244468699482e-14 5 22.053 22.053 -2.19086514796257e-14 6 22.687 22.687 -4.70488549618069e-15 7 24.793 24.793 6.32464805708135e-15 8 26.113 26.113 -2.55942390497803e-15 9 23.708 23.708 1.25329906294968e-14 10 23.554 23.554 -1.89517626303593e-14 11 23.222 23.222 9.0995267982118e-15 12 23.363 23.363 -7.4410747227253e-15 13 24.023 24.023 -9.27529025607326e-15 14 23.355 23.355 -1.81055653441793e-15 15 23.276 23.276 -6.42702660725644e-15 16 23.085 23.085 -1.34987256723788e-14 17 23.173 23.173 2.67075489194684e-15 18 23.487 23.487 -1.29294747517701e-14 19 25.576 25.576 7.18821858850646e-15 20 26.311 26.311 5.6017887642539e-15 21 27.109 27.109 -4.06738836503146e-15 22 27.06 27.06 1.7766533621944e-15 23 26.49 26.49 2.18172716581063e-15 24 27.157 27.157 7.19267980525531e-15 25 26.973 26.973 -6.90657330022654e-15 26 27.589 27.589 6.05155331606072e-15 27 27.246 27.246 6.95565589504913e-15 28 26.845 26.845 7.52633754254068e-15 29 26.582 26.582 -1.42612980764935e-14 30 26.544 26.544 -3.58898580658815e-15 31 29.105 29.105 -8.67059108819859e-15 32 28.703 28.703 -1.13440623138335e-15 33 27.921 27.921 -7.194622204126e-15 34 28.566 28.566 2.06907626305457e-14 35 29.86 29.86 2.13090130407413e-15 36 30.194 30.194 -5.23099051848936e-15 37 31.33 31.33 -3.54336827745476e-16 38 31.018 31.018 -7.9611202701636e-15 39 30.954 30.954 -6.94664083384472e-15 40 31.398 31.398 3.57478356886525e-15 41 31.267 31.267 5.02829597065022e-15 42 32.069 32.069 6.45437251800447e-15 43 34.665 34.665 -6.78104387705468e-16 44 35.834 35.834 5.10858532776871e-15 45 34.034 34.034 -1.10795700859167e-14 46 34.435 34.435 -4.7747415593056e-15 47 34 34 8.50868602597298e-17 48 35.216 35.216 -6.76033964642284e-15 49 35.734 35.734 1.03583681751205e-14 50 35.347 35.347 -1.18265067764993e-14 51 35.357 35.357 -1.14514349403553e-14 52 34.802 34.802 -3.05989596168257e-15 53 34.493 34.493 -8.546966181921e-15 54 35.047 35.047 3.7522330975807e-15 55 37.386 37.386 5.36417841553899e-15 56 38.691 38.691 -7.05591602176881e-15 57 37.249 37.249 7.89923930641528e-15 58 37.668 37.668 -7.26322072956547e-15 59 36.764 36.764 1.93218718708355e-14 60 37.926 37.926 9.9281263597166e-15 61 38.145 38.145 -4.35340287074763e-15 62 37.664 37.664 -6.52408093035672e-15 63 37.449 37.449 -4.29705502239248e-15 64 37.389 37.389 -2.56569290670593e-15 65 37.121 37.121 -4.10681666736668e-15 66 37.447 37.447 -5.80212980716945e-16 67 39.751 39.751 -1.24773250931881e-14 68 40.154 40.154 1.22061902207135e-14 69 38.814 38.814 -1.22927270461425e-15 70 38.673 38.673 8.05726738434459e-15 71 37.948 37.948 -3.9577393911937e-15 72 39.161 39.161 3.19349094593801e-15 73 37.408 37.408 -5.01459276703801e-15 74 37.356 37.356 1.79278681176569e-14 75 36.606 36.606 1.80854066088765e-14 76 37.04 37.04 -3.88140633968540e-15 77 36.349 36.349 -1.21460367165643e-14 78 36.158 36.158 -4.97433209911962e-15 79 37.342 37.342 -1.20990581254355e-14 80 36.8 36.8 -2.28719225939759e-14 81 37.135 37.135 3.03512539732841e-15 82 34.265 34.265 -1.45030778748095e-14 83 33.226 33.226 -4.15571263003123e-15 84 32.357 32.357 -1.02603896327229e-15 85 36.87 36.87 -1.41072870192564e-14 86 35.88 35.88 -8.74313967220859e-16 87 34.808 34.808 -8.8964321286908e-16 88 34.025 34.025 -2.12612364044014e-15 89 33.901 33.901 1.55408387289965e-14 90 37.459 37.459 -2.08024899231367e-14 91 37.152 37.152 6.1560876688198e-16 92 34.929 34.929 1.86505646291389e-14 93 34.116 34.116 -4.52501573838639e-15 94 33.71 33.71 -2.42001630012801e-15 95 34.264 34.264 6.95437905820048e-15 96 34.826 34.826 9.64635865360283e-15 97 34.096 34.096 -5.10283280681074e-15 98 33.955 33.955 -1.0963472083258e-14 99 34.111 34.111 -1.12909829064628e-15 100 32.344 32.344 5.8772776920021e-15 101 32.871 32.871 7.56375695590457e-15 102 36.244 36.244 -4.8498490713393e-15 103 35.988 35.988 4.07385931710708e-15 104 35.439 35.439 -2.26071291692842e-16 105 35.692 35.692 -4.61896243239328e-16 106 35.804 35.804 1.27203387734614e-14 107 37.747 37.747 -4.14407304817765e-15 108 40.673 40.673 -1.00875468710497e-14 109 41.601 41.601 4.45754323316784e-15 110 42.273 42.273 5.48898593899409e-15 111 41.952 41.952 -5.54595616720295e-15 112 41.463 41.463 -1.13464192298241e-14 113 42.759 42.759 -3.41321875646778e-15 114 45.434 45.434 1.34586081960627e-14 115 45.776 45.776 1.25601586156790e-14 116 44.63 44.63 -1.19609632033471e-14 117 44.793 44.793 9.90873329165066e-15 118 44.757 44.757 -1.45312139867557e-14 119 49.099 49.099 9.33557350758775e-15 120 47.974 47.974 3.54983116452130e-15 121 47.919 47.919 1.01674814794200e-14 122 47.519 47.519 -9.60876029656346e-15 123 47.136 47.136 1.23573860063422e-15 124 45.91 45.91 -1.78353101437798e-14 125 46.436 46.436 9.64810281214765e-15 126 50.334 50.334 4.88187491482466e-15 127 50.294 50.294 7.60689157353962e-15 128 47.224 47.224 -4.36687414070035e-15 129 47.03 47.03 1.60700817220776e-15 130 45.79 45.79 1.61975627163457e-14 131 38.252 38.252 1.04944286653876e-14 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.027976881628882 0.055953763257764 0.972023118371118 9 0.000531968463002966 0.00106393692600593 0.999468031536997 10 0.000138068385548253 0.000276136771096506 0.999861931614452 11 0.00330689380912324 0.00661378761824647 0.996693106190877 12 0.920376506707688 0.159246986584625 0.0796234932923125 13 0.0189094088751939 0.0378188177503879 0.981090591124806 14 0.721702492813446 0.556595014373107 0.278297507186554 15 0.0207059609814779 0.0414119219629558 0.979294039018522 16 1.20904919381343e-08 2.41809838762686e-08 0.999999987909508 17 4.60128861656144e-08 9.20257723312289e-08 0.999999953987114 18 0.00102069959545445 0.00204139919090891 0.998979300404546 19 0.179238678598225 0.358477357196449 0.820761321401775 20 0.997699245051446 0.00460150989710883 0.00230075494855442 21 1.03887465157732e-11 2.07774930315464e-11 0.999999999989611 22 0.000353483463817436 0.000706966927634873 0.999646516536183 23 7.0172294904418e-06 1.40344589808836e-05 0.99999298277051 24 3.12219380973743e-16 6.24438761947486e-16 1 25 0.999965839056258 6.83218874838026e-05 3.41609437419013e-05 26 5.4002030424953e-20 1.08004060849906e-19 1 27 1.52845707203577e-12 3.05691414407155e-12 0.999999999998472 28 0.542274970237323 0.915450059525354 0.457725029762677 29 2.0012914112302e-07 4.0025828224604e-07 0.99999979987086 30 3.07314842685570e-16 6.14629685371139e-16 1 31 0.999999999979767 4.0466935571821e-11 2.02334677859105e-11 32 0.0117916589110054 0.0235833178220109 0.988208341088995 33 4.61356224759908e-11 9.22712449519816e-11 0.999999999953864 34 6.07328668138620e-08 1.21465733627724e-07 0.999999939267133 35 1.57921930731923e-25 3.15843861463845e-25 1 36 3.66102752688546e-12 7.32205505377092e-12 0.99999999999634 37 1.91751650693865e-06 3.83503301387729e-06 0.999998082483493 38 9.7703249138767e-05 0.000195406498277534 0.999902296750861 39 6.39446454256804e-07 1.27889290851361e-06 0.999999360553546 40 1.85116022335405e-45 3.70232044670809e-45 1 41 7.22580195938298e-05 0.000144516039187660 0.999927741980406 42 2.69944630911712e-22 5.39889261823424e-22 1 43 0.191542000682657 0.383084001365314 0.808457999317343 44 5.66396905209328e-12 1.13279381041866e-11 0.999999999994336 45 3.56308295974313e-14 7.12616591948626e-14 0.999999999999964 46 8.57333592588901e-08 1.71466718517780e-07 0.99999991426664 47 0.999999840635038 3.18729923309085e-07 1.59364961654542e-07 48 0.999398995496538 0.00120200900692400 0.000601004503461998 49 0.00394741600442405 0.0078948320088481 0.996052583995576 50 4.03405950669529e-22 8.06811901339058e-22 1 51 0.789968204418178 0.420063591163644 0.210031795581822 52 0.0122879796866863 0.0245759593733725 0.987712020313314 53 1.37258618850530e-14 2.74517237701059e-14 0.999999999999986 54 0.00130924555773581 0.00261849111547161 0.998690754442264 55 4.80805033002905e-42 9.6161006600581e-42 1 56 0.464167432860348 0.928334865720696 0.535832567139652 57 0.997268515550378 0.00546296889924456 0.00273148444962228 58 3.57031536401388e-15 7.14063072802777e-15 0.999999999999996 59 4.76548349423128e-27 9.53096698846255e-27 1 60 2.80340330323686e-09 5.60680660647373e-09 0.999999997196597 61 0.999999961305605 7.73887895038263e-08 3.86943947519131e-08 62 0.490929597177802 0.981859194355603 0.509070402822198 63 0.99999936676356 1.26647287753143e-06 6.33236438765713e-07 64 0.999854088353431 0.000291823293138083 0.000145911646569041 65 0.74101958019986 0.517960839600279 0.258980419800139 66 0.820530442528544 0.358939114942912 0.179469557471456 67 1 4.52278779009158e-27 2.26139389504579e-27 68 0.0783578334816332 0.156715666963266 0.921642166518367 69 3.2748725809629e-20 6.5497451619258e-20 1 70 0.989771768804748 0.0204564623905043 0.0102282311952521 71 1.17851875153289e-24 2.35703750306577e-24 1 72 5.79165372512274e-09 1.15833074502455e-08 0.999999994208346 73 0.000140735347197839 0.000281470694395678 0.999859264652802 74 0.0030902157002204 0.0061804314004408 0.99690978429978 75 0.976330684986361 0.0473386300272774 0.0236693150136387 76 0.041411529744868 0.082823059489736 0.958588470255132 77 0.995695637266447 0.00860872546710622 0.00430436273355311 78 0.905575766218031 0.188848467563938 0.0944242337819689 79 1.24302848065296e-06 2.48605696130592e-06 0.99999875697152 80 0.0494700765751206 0.0989401531502412 0.95052992342488 81 3.00435794432421e-36 6.00871588864842e-36 1 82 6.73836151010632e-07 1.34767230202126e-06 0.99999932616385 83 0.999946463054938 0.000107073890124116 5.35369450620582e-05 84 8.71392515186244e-21 1.74278503037249e-20 1 85 0.0672769105963 0.1345538211926 0.9327230894037 86 4.47992219842564e-21 8.95984439685128e-21 1 87 1.95373742389373e-21 3.90747484778745e-21 1 88 0.997897224725084 0.00420555054983205 0.00210277527491603 89 0.999968753073257 6.24938534862937e-05 3.12469267431468e-05 90 0.999999999999999 1.99469534001525e-15 9.97347670007623e-16 91 1 3.69652991042511e-21 1.84826495521255e-21 92 1.08178177068417e-06 2.16356354136834e-06 0.99999891821823 93 0.999544812167305 0.000910375665390303 0.000455187832695152 94 0.99123935696619 0.0175212860676199 0.00876064303380995 95 7.35040528323005e-06 1.47008105664601e-05 0.999992649594717 96 4.37121078528351e-05 8.74242157056703e-05 0.999956287892147 97 0.99999999999994 1.20408929999822e-13 6.02044649999108e-14 98 0.000368852054070015 0.00073770410814003 0.99963114794593 99 0.000310359418631203 0.000620718837262406 0.99968964058137 100 6.41450429834065e-08 1.28290085966813e-07 0.999999935854957 101 0.999300384816067 0.00139923036786542 0.000699615183932712 102 0.604405541546662 0.791188916906676 0.395594458453338 103 0.996358914899006 0.00728217020198782 0.00364108510099391 104 0.948092482950687 0.103815034098626 0.0519075170493129 105 0.99999606683731 7.86632538046704e-06 3.93316269023352e-06 106 1.34989815188627e-13 2.69979630377255e-13 0.999999999999865 107 0.99999991801057 1.63978859638475e-07 8.19894298192377e-08 108 0.997871949877798 0.00425610024440392 0.00212805012220196 109 0.999999999922656 1.54687538033950e-10 7.73437690169749e-11 110 0.99999994300781 1.13984381358541e-07 5.69921906792706e-08 111 0.999392304741628 0.00121539051674352 0.000607695258371761 112 0.885704638655388 0.228590722689223 0.114295361344612 113 0.99941211799454 0.00117576401091775 0.000587882005458874 114 0.999957105427848 8.578914430385e-05 4.2894572151925e-05 115 0.999999071783016 1.85643396810402e-06 9.28216984052011e-07 116 0.730788214369567 0.538423571260866 0.269211785630433 117 0.997342510906204 0.00531497818759253 0.00265748909379627 118 0.999914275897267 0.000171448205465391 8.57241027326954e-05 119 0.58040712705674 0.83918574588652 0.41959287294326 120 0.99880486688253 0.0023902662349414 0.0011951331174707 121 0.999637115710453 0.0007257685790944 0.0003628842895472 122 0.974348961011744 0.0513020779765118 0.0256510389882559 123 0.96802685394904 0.0639462921019221 0.0319731460509610 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 86 0.741379310344828 NOK 5% type I error level 93 0.801724137931034 NOK 10% type I error level 98 0.844827586206897 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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Software written by Ed van Stee & Patrick Wessa

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