MR geslacht

*The author of this computation has been verified*

R Software Module: /rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Sun, 12 Dec 2010 20:16:35 +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/12/t1292184868kn0y233bma23v3x.htm/, Retrieved Sun, 12 Dec 2010 21:14:28 +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/12/t1292184868kn0y233bma23v3x.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:

» Textbox « » Textfile « » CSV «

1 26 9 15 6 25 25 13 1 20 9 15 6 25 24 16 1 21 9 14 13 19 21 19 0 31 14 10 8 18 23 15 1 21 8 10 7 18 17 14 1 18 8 12 9 22 19 13 1 26 11 18 5 29 18 19 1 22 10 12 8 26 27 15 1 22 9 14 9 25 23 14 1 29 15 18 11 23 23 15 0 15 14 9 8 23 29 16 1 16 11 11 11 23 21 16 0 24 14 11 12 24 26 16 1 17 6 17 8 30 25 17 0 19 20 8 7 19 25 15 0 22 9 16 9 24 23 15 1 31 10 21 12 32 26 20 0 28 8 24 20 30 20 18 1 38 11 21 7 29 29 16 0 26 14 14 8 17 24 16 1 25 11 7 8 25 23 19 1 25 16 18 16 26 24 16 0 29 14 18 10 26 30 17 1 28 11 13 6 25 22 17 0 15 11 11 8 23 22 16 1 18 12 13 9 21 13 15 0 21 9 13 9 19 24 14 1 25 7 18 11 35 17 15 0 23 13 14 12 19 24 12 1 23 10 12 8 20 21 14 1 19 9 9 7 21 23 16 0 18 9 12 8 21 24 14 0 18 13 8 9 24 24 7 0 26 16 5 4 23 24 10 0 18 12 10 8 19 23 14 1 18 6 11 8 17 26 16 0 28 14 11 8 24 24 16 0 17 14 12 6 15 21 16 1 29 10 12 8 25 23 14 0 12 4 15 4 27 28 20 1 28 12 16 14 27 22 14 1 20 14 14 10 18 24 11 1 17 9 17 9 25 21 15 1 17 9 13 6 22 23 16 0 20 10 10 8 26 23 14 1 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Gender[t] = + 0.597949944197336 + 0.00437351222682402Concern_mistakes[t] -0.0173002748928004Doubts_actions[t] + 0.0141160678171409Parental_expectations[t] + 0.006185694746909Parental_criticism[t] + 0.0199299315322437Personal_standards[t] -0.0432498272067169Organization[t] + 0.0262634935492487PLC[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) 0.597949944197336 0.411535 1.453 0.148534 0.074267 Concern_mistakes 0.00437351222682402 0.008598 0.5086 0.611823 0.305912 Doubts_actions -0.0173002748928004 0.016643 -1.0395 0.300408 0.150204 Parental_expectations 0.0141160678171409 0.014181 0.9954 0.321287 0.160643 Parental_criticism 0.006185694746909 0.017773 0.348 0.728349 0.364174 Personal_standards 0.0199299315322437 0.011492 1.7342 0.085152 0.042576 Organization -0.0432498272067169 0.011566 -3.7394 0.000271 0.000135 PLC 0.0262634935492487 0.018669 1.4068 0.161758 0.080879 Multiple Linear Regression - Regression Statistics Multiple R 0.424855834673263 R-squared 0.180502480255915 Adjusted R-squared 0.138322460857322 F-TEST (value) 4.27933611291648 F-TEST (DF numerator) 7 F-TEST (DF denominator) 136 p-value 0.000264199339875648 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.455533348009159 Sum Squared Residuals 28.2214458361869 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1 0.563241998076528 0.436758001923472 2 1 0.659041232570046 0.340958767429954 3 1 0.781558913282527 0.218441086717473 4 0 0.439916355940984 -0.439916355940984 5 1 0.72703265797369 0.272967342026310 6 1 0.72147222458761 0.27852777541239 7 1 1.10485343486693 -0.104853434866925 8 1 0.484428124536134 0.515571875463866 9 1 0.6629521135555 0.3370478864445 10 1 0.645004341033608 0.354995658966392 11 0 0.222238280464825 -0.222238280464825 12 1 0.671300454898794 0.328699545101206 13 0 0.464254218280553 -0.464254218280553 14 1 0.821088369699826 0.178911630300174 15 0 0.182645006599913 -0.182645006599913 16 0 0.697517811206787 -0.697517811206787 17 1 0.969724008065876 0.0302759919341237 18 0 1.25014989567502 -1.25014989567502 19 1 0.657516594612422 0.342483405387578 20 0 0.437596800885716 -0.437596800885716 21 1 0.66779139872972 0.33220860127028 22 1 0.68394195190732 0.316058048092680 23 0 0.46568691242771 -0.46568691242771 24 1 0.743959792927438 0.256040207072562 25 0 0.605120031224526 -0.605120031224526 26 1 0.958483211640104 0.0415167883598956 27 0 0.481633117111356 -0.481633117111356 28 1 1.26457063289594 -0.264570632895941 29 0 0.401325206953173 -0.401325206953173 30 1 0.602457517260548 0.397542482739452 31 1 0.539687109265028 0.460312890734972 32 0 0.488070680931322 -0.488070680931322 33 0 0.244536344590455 -0.244536344590455 34 0 0.213207489656181 -0.213207489656181 35 0 0.411327684760868 -0.411327684760868 36 1 0.412163044348671 0.587836955651329 37 0 0.543505142613647 -0.543505142613647 38 0 0.447521284271863 -0.447521284271863 39 1 0.641848593869277 0.358151406130723 40 0 0.670097648160253 -0.670097648160253 41 1 0.779562661878074 0.220437338121926 42 1 0.31233958080459 0.68766041919541 43 1 0.796195903835485 0.203804096164515 44 1 0.601148592865278 0.398851407134722 45 0 0.594184779725823 -0.594184779725823 46 1 0.812948547732315 0.187051452267685 47 0 0.427491112264492 -0.427491112264492 48 0 0.703362344690401 -0.703362344690401 49 1 0.45242155651972 0.54757844348028 50 0 0.726305289335162 -0.726305289335162 51 1 0.890187793399189 0.109812206600811 52 1 0.662017093325141 0.337982906674859 53 1 0.561950467535103 0.438049532464897 54 0 0.582481678983905 -0.582481678983905 55 1 0.710484936364889 0.289515063635111 56 0 0.255725157830062 -0.255725157830062 57 0 0.650969543247306 -0.650969543247306 58 1 0.312045469435121 0.68795453056488 59 1 0.705779266312945 0.294220733687055 60 1 0.942774066714847 0.0572259332851525 61 0 0.459264310678517 -0.459264310678517 62 1 0.763893838396806 0.236106161603194 63 0 0.340975934073361 -0.340975934073361 64 1 0.456928765408543 0.543071234591457 65 0 0.558949817939918 -0.558949817939918 66 0 0.150740781722405 -0.150740781722405 67 1 0.587380147412393 0.412619852587607 68 1 0.437056838205021 0.562943161794979 69 0 0.353091636185009 -0.353091636185009 70 0 0.597952402737545 -0.597952402737545 71 1 0.529655469708148 0.470344530291852 72 1 0.758693870464313 0.241306129535687 73 0 0.664098903192731 -0.664098903192731 74 0 0.780016463815995 -0.780016463815995 75 0 0.398280480114844 -0.398280480114844 76 0 0.518530994510164 -0.518530994510164 77 1 0.769493891901017 0.230506108098983 78 0 0.723582245908354 -0.723582245908354 79 1 0.447654721514774 0.552345278485226 80 1 0.471215569104775 0.528784430895225 81 0 0.414992418804905 -0.414992418804905 82 1 0.393826483768517 0.606173516231483 83 1 0.501530328152474 0.498469671847526 84 1 0.744182468652883 0.255817531347117 85 1 0.537097973802983 0.462902026197017 86 1 0.723800001367121 0.276199998632879 87 1 0.671037226140111 0.328962773859889 88 1 0.697100896166523 0.302899103833477 89 1 0.466304151880909 0.533695848119091 90 1 0.63754467973179 0.362455320268210 91 0 0.620567465586434 -0.620567465586434 92 0 0.587068923098296 -0.587068923098296 93 1 0.57948493191375 0.420515068086249 94 0 0.817833910378806 -0.817833910378806 95 1 0.820254328464619 0.179745671535381 96 0 0.277061828745541 -0.277061828745541 97 1 0.695295579998129 0.304704420001871 98 0 0.650094385662007 -0.650094385662007 99 1 0.365378670780804 0.634621329219196 100 0 0.684208630877617 -0.684208630877617 101 1 1.2244021243924 -0.224402124392400 102 1 0.76369749636103 0.236302503638970 103 1 0.885374375556575 0.114625624443425 104 1 0.996451716522952 0.00354828347704763 105 0 0.307060546042081 -0.307060546042081 106 1 0.594063216084299 0.405936783915701 107 0 0.509438199494678 -0.509438199494678 108 1 0.508169523059286 0.491830476940714 109 1 0.597512809158334 0.402487190841666 110 1 0.513374144079433 0.486625855920567 111 1 0.769064894252639 0.230935105747361 112 1 0.682775965179448 0.317224034820552 113 1 0.891390262393137 0.108609737606863 114 1 0.623251758098538 0.376748241901462 115 0 0.559710125442704 -0.559710125442704 116 1 0.349925324836356 0.650074675163644 117 0 0.432762613857539 -0.432762613857539 118 1 0.537995916701722 0.462004083298278 119 1 0.73590068772424 0.264099312275760 120 1 0.643542291917778 0.356457708082222 121 0 0.399874907176162 -0.399874907176162 122 0 0.0737521932105159 -0.0737521932105159 123 1 0.678863749690244 0.321136250309756 124 0 0.518987979009612 -0.518987979009612 125 1 0.648430154180819 0.351569845819181 126 0 0.483782758550231 -0.483782758550231 127 0 0.602310815441038 -0.602310815441038 128 1 0.683030956764437 0.316969043235563 129 1 0.724599776814506 0.275400223185494 130 0 0.34306676049017 -0.34306676049017 131 0 0.211776615261537 -0.211776615261537 132 1 0.571204384502637 0.428795615497363 133 0 0.549466299914101 -0.549466299914101 134 1 0.684256728394437 0.315743271605563 135 1 1.10561751359219 -0.105617513592191 136 0 0.420154639207281 -0.420154639207281 137 1 0.849189583497166 0.150810416502834 138 0 0.605120031224526 -0.605120031224526 139 1 0.556165141945324 0.443834858054676 140 1 0.585309613230341 0.414690386769659 141 1 0.771416506197661 0.228583493802339 142 1 0.744182468652883 0.255817531347117 143 1 0.73590068772424 0.264099312275760 144 1 0.544709902045385 0.455290097954615 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 0.152973121968894 0.305946243937789 0.847026878031106 12 0.0866885353756663 0.173377070751333 0.913311464624334 13 0.0955997956283977 0.191199591256795 0.904400204371602 14 0.105622964974902 0.211245929949803 0.894377035025098 15 0.0586041883769378 0.117208376753876 0.941395811623062 16 0.488818273230876 0.977636546461752 0.511181726769124 17 0.390545766752123 0.781091533504246 0.609454233247877 18 0.655993825992873 0.688012348014254 0.344006174007127 19 0.584292386050663 0.831415227898674 0.415707613949337 20 0.575717829971958 0.848564340056084 0.424282170028042 21 0.497670990092652 0.995341980185304 0.502329009907348 22 0.648273467488143 0.703453065023713 0.351726532511857 23 0.617377944785054 0.765244110429893 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0.340355456922888 59 0.624235242627704 0.751529514744592 0.375764757372296 60 0.575208988515672 0.849582022968656 0.424791011484328 61 0.601231233157914 0.797537533684171 0.398768766842086 62 0.564745976857938 0.870508046284123 0.435254023142062 63 0.543449407824872 0.913101184350257 0.456550592175128 64 0.556223550062321 0.887552899875358 0.443776449937679 65 0.576535098099019 0.846929803801962 0.423464901900981 66 0.5391289490558 0.9217421018884 0.4608710509442 67 0.541843403164799 0.916313193670401 0.458156596835201 68 0.565305836173994 0.869388327652012 0.434694163826006 69 0.54608376741333 0.90783246517334 0.45391623258667 70 0.581216424745367 0.837567150509267 0.418783575254633 71 0.580249585078183 0.839500829843633 0.419750414921817 72 0.545399081508892 0.909201836982217 0.454600918491108 73 0.60209357730516 0.795812845389681 0.397906422694841 74 0.690685498563847 0.618629002872305 0.309314501436153 75 0.681296232553333 0.637407534893335 0.318703767446667 76 0.699938238382726 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0.378495507907471 129 0.512537733331965 0.97492453333607 0.487462266668035 130 0.438963315127023 0.877926630254047 0.561036684872977 131 0.346355472891820 0.692710945783639 0.65364452710818 132 0.302752625636018 0.605505251272037 0.697247374363982 133 0.541687705688298 0.916624588623403 0.458312294311702 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/2010/Dec/12/t1292184868kn0y233bma23v3x/10939l1292184984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/10939l1292184984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/122c91292184984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/122c91292184984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/222c91292184984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/222c91292184984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/3vbbu1292184984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/3vbbu1292184984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/4vbbu1292184984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/4vbbu1292184984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/5vbbu1292184984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/5vbbu1292184984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/66lsf1292184984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/66lsf1292184984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/76lsf1292184984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/76lsf1292184984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/8ycsi1292184984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/8ycsi1292184984.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/9ycsi1292184984.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/12/t1292184868kn0y233bma23v3x/9ycsi1292184984.ps (open in new window)

Parameters (Session):

par1 = 6 ; par2 = quantiles ; par3 = 2 ; par4 = no ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = no ;

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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