paper - multiple regression - carrièremogelijkheden

*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, 19 Dec 2010 10:14:20 +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/19/t1292753557ht9wq3s0dg5jacq.htm/, Retrieved Sun, 19 Dec 2010 11:12:48 +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/19/t1292753557ht9wq3s0dg5jacq.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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46 11 52 26 23 44 8 39 25 15 42 10 42 28 25 41 12 35 30 18 48 12 32 28 21 49 10 49 40 19 51 8 33 28 15 47 10 47 27 22 49 11 46 25 19 46 7 40 27 20 51 10 33 32 26 54 9 39 28 26 52 9 37 21 21 52 11 56 40 18 45 12 36 29 19 52 5 24 27 19 56 10 56 31 18 54 11 32 33 19 50 12 41 28 24 35 9 24 26 28 48 3 42 25 20 37 10 47 37 27 47 7 25 13 18 31 9 33 32 19 45 9 43 32 24 47 10 45 38 21 44 9 44 30 22 30 19 46 33 25 40 14 31 22 19 44 5 31 29 15 43 13 42 33 34 51 7 28 31 23 48 8 38 23 19 55 11 59 42 26 48 11 43 35 15 53 12 29 31 15 49 9 38 31 17 44 13 39 38 30 45 12 50 34 19 40 11 44 33 28 44 18 29 23 23 41 8 29 18 23 46 14 36 33 21 47 10 43 26 18 48 13 28 29 19 43 13 39 23 24 46 8 35 18 15 53 10 43 36 20 33 8 28 21 24 47 9 49 31 9 43 10 33 31 20 45 9 39 29 20 49 9 36 24 10 45 9 24 35 44 37 10 47 37 20 42 8 34 29 20 43 11 33 31 11 44 11 43 34 21 39 10 41 38 21 37 23 40 27 19 53 9 39 33 17 48 12 54 36 16 47 9 43 27 14 49 9 45 33 19 47 8 29 24 21 56 9 45 31 16 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 27 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Carrièremogelijkheden[t] = + 40.7659808745792 -0.215882565886822Geen_Motivatie[t] + 0.177165440935508Leermogelijkheden[t] + 0.0943861603943652Persoonlijke_redenen[t] -0.136765672292484Ouders[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) 40.7659808745792 3.487571 11.6889 0 0 Geen_Motivatie -0.215882565886822 0.167317 -1.2903 0.199071 0.099535 Leermogelijkheden 0.177165440935508 0.066423 2.6672 0.008543 0.004271 Persoonlijke_redenen 0.0943861603943652 0.090928 1.038 0.301033 0.150516 Ouders -0.136765672292484 0.083539 -1.6372 0.103828 0.051914 Multiple Linear Regression - Regression Statistics Multiple R 0.348733567865764 R-squared 0.121615101356386 Adjusted R-squared 0.0966963808274888 F-TEST (value) 4.88047133942352 F-TEST (DF numerator) 4 F-TEST (DF denominator) 141 p-value 0.00102534486890948 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5.37718131977644 Sum Squared Residuals 4076.88513135113 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 46 46.9123052859971 -0.91230528599714 2 44 46.2565414694413 -2.25654146944133 3 42 45.2717744187325 -3.27177441873248 4 41 44.7459832272464 -3.74598322724639 5 48 43.6154175667737 4.38458243322631 6 49 48.4651604637683 0.534839536231693 7 51 45.4767073050114 5.52329269498862 8 47 46.4735124798931 0.526487520106907 9 49 46.3019891691595 2.69801083084051 10 46 46.15453343559 -0.154533435589972 11 51 43.9180644195979 7.08193558040212 12 54 44.8193949895203 9.18060501047972 13 52 44.4881893463511 7.51181065364887 14 52 49.6262016567225 2.37379834327748 15 45 44.6919968354950 0.308003164504952 16 52 43.888417184688 8.11158281531203 17 56 48.9926087790601 7.00739122093994 18 54 44.5767622792173 9.4232377207827 19 50 44.7996095183158 5.2003904816842 20 35 41.699609710114 -6.69960971011397 21 48 47.1836222602195 0.816377739780457 22 37 46.7335457223743 -9.73354572237433 23 47 42.4491769206212 4.55082307937879 24 31 45.0913066915321 -14.0913066915321 25 45 46.1791327394247 -1.17913273942474 26 47 47.2941950346526 -0.294195034652579 27 44 46.4410572041565 -2.44105720415649 28 30 44.5094238914649 -14.5094238914649 29 40 42.7137013762833 -2.71370137628331 30 44 45.8644102811952 -1.86441028119520 31 43 43.8651664724115 -0.865166472411477 32 51 43.9957957690639 7.00420423093611 33 48 45.3435410185472 2.65645898145284 34 55 49.2523449219779 5.74765507802209 35 48 47.2614171394665 0.738582860533452 36 53 44.1876737589052 8.81232624109484 37 49 46.1562790804002 2.84372091959978 38 44 44.3526636407467 -0.352663640746715 39 45 47.644243810564 -2.64424381056398 40 40 45.471856519811 -5.47185651981104 41 44 41.0431637020894 2.95683629791056 42 41 42.7300585589858 -1.73005855898583 43 46 44.3642450007139 1.63575499928610 44 47 46.2175272449266 0.782472755073368 45 48 43.0587907421242 4.94120925787584 46 43 43.7574652685861 -0.75746526858614 47 46 44.8871765829387 1.11282341706126 48 53 46.8878575042853 6.11214249571468 49 33 42.6992859269409 -9.69928592694093 50 47 49.1992243090307 -2.19922430903068 51 43 44.6442722929584 -1.64427229295841 52 45 45.7343751836695 -0.734375183669551 53 49 46.098604781816 2.90139521818396 54 45 40.3608343969835 4.63916560301648 55 37 47.6909054284217 -10.6909054284217 56 42 45.0644305448788 -3.06443054487884 57 43 45.6592807777039 -2.65928077770395 58 44 46.3464369453173 -2.34643694531728 59 39 46.5855332709105 -7.58553327091055 60 37 42.8371780536933 -5.83717805369331 61 53 46.5222168421245 6.47778315787554 62 48 48.9519749119722 -0.951974911972193 63 47 47.0748586603778 -0.0748586603777539 64 49 47.3116781431525 1.68832185684746 65 47 43.569906865937 3.43009313406301 66 56 47.5332028392413 8.46679716075874 67 51 47.4772367042348 3.52276329576517 68 43 45.1276584526603 -2.12765845266034 69 51 49.0022989167767 1.99770108322327 70 36 43.748223752556 -7.74822375255598 71 55 48.6750407987713 6.3249592012287 72 33 43.2000726188211 -10.2000726188211 73 42 46.9938505445329 -4.99385054453290 74 43 43.3559410421867 -0.355941042186717 75 44 46.2910650094376 -2.29106500943761 76 47 45.7999684552741 1.20003154472588 77 43 44.67253398374 -1.67253398373999 78 47 48.9695465414767 -1.96954654147674 79 41 41.8597982493342 -0.859798249334152 80 53 42.1131204922517 10.8868795077483 81 47 45.6239289401361 1.37607105986395 82 23 44.078575049605 -21.0785750496050 83 43 48.3263265272161 -5.32632652721615 84 47 44.7326562224690 2.26734377753105 85 47 47.1940782230517 -0.194078223051731 86 49 44.3992997387184 4.60070026128159 87 50 46.6996793826234 3.30032061737664 88 43 47.3580171415609 -4.35801714156085 89 44 45.2703888746041 -1.27038887460414 90 49 48.2294294255585 0.77057057444154 91 47 44.3557312999043 2.6442687000957 92 39 42.7547208639391 -3.75472086393914 93 49 46.1344763847081 2.86552361529186 94 41 45.9500236511115 -4.95002365111153 95 40 44.447024281255 -4.44702428125504 96 38 42.8524723117169 -4.85247231171693 97 43 47.2093474898518 -4.20934748985176 98 55 44.4053470646914 10.5946529353086 99 46 46.9166503930751 -0.916650393075118 100 54 46.7093065192215 7.29069348077852 101 47 46.2235549956965 0.776445004303501 102 35 45.1183539355116 -10.1183539355116 103 41 43.8677404147933 -2.86774041479334 104 53 48.3071607750245 4.69283922497553 105 44 43.7142314105679 0.285768589432078 106 48 43.2243748230925 4.77562517690751 107 49 45.0039482054679 3.99605179453206 108 39 42.8431677945682 -3.84316779456822 109 45 42.9358457913848 2.06415420861516 110 34 44.5427069361107 -10.5427069361107 111 46 45.3828783911737 0.61712160882627 112 45 44.7852945513245 0.214705448675528 113 53 47.5522230139927 5.44777698600732 114 51 45.6377121804567 5.36228781954329 115 45 49.8480234523746 -4.84802345237463 116 50 45.2879349842227 4.71206501577735 117 41 44.4246643390062 -3.42466433900625 118 44 47.9567893796636 -3.95678937966365 119 43 45.1237619672687 -2.12376196726869 120 42 41.2638595719441 0.7361404280559 121 48 48.4535535839151 -0.453553583915084 122 45 43.8944650393235 1.10553496067654 123 48 45.2498381069466 2.75016189305340 124 48 44.4880182490248 3.51198175097517 125 53 48.8138236955514 4.18617630444862 126 45 46.7600992495782 -1.76009924957815 127 45 41.878691893186 3.12130810681397 128 50 45.8089758728594 4.19102412714057 129 48 43.0478035812838 4.95219641871625 130 41 46.4873332014463 -5.48733320144625 131 53 46.6402224866589 6.35977751334114 132 40 44.8389208423939 -4.83892084239389 133 49 44.5800645654822 4.41993543451782 134 46 43.3818884089173 2.61811159108269 135 48 46.6513351210739 1.34866487892609 136 43 48.7154780486468 -5.71547804864683 137 53 44.965553699966 8.03444630003395 138 51 46.8279063628827 4.17209363711735 139 41 45.8304439877556 -4.83044398775563 140 45 45.5029262514193 -0.502926251419318 141 44 43.3944875984742 0.605512401525817 142 43 45.0426023293007 -2.04260232930072 143 34 43.769925965897 -9.76992596589701 144 38 46.1042094307854 -8.10420943078543 145 40 42.3982842365759 -2.39828423657595 146 48 42.4992673556434 5.50073264435664 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.468365866722610 0.936731733445221 0.53163413327739 9 0.370426664413245 0.740853328826489 0.629573335586755 10 0.235782968899663 0.471565937799326 0.764217031100337 11 0.230647652435456 0.461295304870911 0.769352347564544 12 0.25990450403263 0.51980900806526 0.74009549596737 13 0.244272175793062 0.488544351586124 0.755727824206938 14 0.241005526802587 0.482011053605175 0.758994473197413 15 0.169404333944410 0.338808667888819 0.83059566605559 16 0.121775904436008 0.243551808872017 0.878224095563992 17 0.194312113542997 0.388624227085994 0.805687886457003 18 0.246020637377311 0.492041274754622 0.753979362622689 19 0.201710923620229 0.403421847240459 0.79828907637977 20 0.473250929263986 0.946501858527972 0.526749070736014 21 0.414098413771174 0.828196827542349 0.585901586228826 22 0.594316063596618 0.811367872806763 0.405683936403382 23 0.534086306174347 0.931827387651306 0.465913693825653 24 0.901121640460748 0.197756719078504 0.0988783595392519 25 0.869632282126646 0.260735435746707 0.130367717873354 26 0.83139572676901 0.337208546461979 0.168604273230990 27 0.799588509133897 0.400822981732206 0.200411490866103 28 0.921984633936028 0.156030732127945 0.0780153660639723 29 0.901986919108323 0.196026161783354 0.0980130808916768 30 0.900362660030955 0.19927467993809 0.099637339969045 31 0.879390141156417 0.241219717687165 0.120609858843583 32 0.887954187661497 0.224091624677005 0.112045812338503 33 0.860912043805699 0.278175912388603 0.139087956194301 34 0.872864747736117 0.254270504527766 0.127135252263883 35 0.84098076134966 0.31803847730068 0.15901923865034 36 0.889207330077978 0.221585339844044 0.110792669922022 37 0.864937224379329 0.270125551241343 0.135062775620671 38 0.834939194543257 0.330121610913485 0.165060805456743 39 0.80747356896973 0.38505286206054 0.19252643103027 40 0.801931951680696 0.396136096638609 0.198068048319304 41 0.788110905773134 0.423778188453733 0.211889094226866 42 0.760752357700799 0.478495284598402 0.239247642299201 43 0.721372756101877 0.557254487796245 0.278627243898123 44 0.675565297966269 0.648869404067462 0.324434702033731 45 0.659071656362817 0.681856687274365 0.340928343637183 46 0.610007125298187 0.779985749403626 0.389992874701813 47 0.567037971274587 0.865924057450827 0.432962028725413 48 0.569902788842216 0.860194422315568 0.430097211157784 49 0.68718549746523 0.62562900506954 0.31281450253477 50 0.672958959119396 0.654082081761208 0.327041040880604 51 0.63585821308278 0.728283573834439 0.364141786917219 52 0.590118025956424 0.819763948087153 0.409881974043576 53 0.554160693994065 0.89167861201187 0.445839306005935 54 0.548452219875231 0.903095560249538 0.451547780124769 55 0.697378781232395 0.60524243753521 0.302621218767605 56 0.673883820424535 0.652232359150931 0.326116179575465 57 0.645186594212109 0.709626811575783 0.354813405787891 58 0.606483229464991 0.787033541070017 0.393516770535009 59 0.651594087316446 0.696811825367108 0.348405912683554 60 0.669161942749091 0.661676114501817 0.330838057250909 61 0.692582271599764 0.614835456800472 0.307417728400236 62 0.652216121085372 0.695567757829255 0.347783878914628 63 0.606490637712525 0.78701872457495 0.393509362287475 64 0.563915080136446 0.872169839727107 0.436084919863554 65 0.542850894946644 0.914298210106712 0.457149105053356 66 0.619715644347253 0.760568711305494 0.380284355652747 67 0.594292338899351 0.811415322201298 0.405707661100649 68 0.555552722177687 0.888894555644626 0.444447277822313 69 0.513737057541284 0.972525884917432 0.486262942458716 70 0.569554790176696 0.860890419646608 0.430445209823304 71 0.58939657220647 0.82120685558706 0.41060342779353 72 0.700348109310995 0.599303781378011 0.299651890689005 73 0.691260110649615 0.61747977870077 0.308739889350385 74 0.64942183983369 0.701156320332621 0.350578160166310 75 0.610807743578995 0.778384512842009 0.389192256421005 76 0.569156880082234 0.861686239835532 0.430843119917766 77 0.528448345741478 0.943103308517043 0.471551654258522 78 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0.0918453415748424 0.0459226707874212 113 0.946368794178218 0.107262411643564 0.053631205821782 114 0.95149196823293 0.0970160635341422 0.0485080317670711 115 0.942929622645374 0.114140754709251 0.0570703773546254 116 0.957400931748218 0.0851981365035645 0.0425990682517823 117 0.947227641041865 0.105544717916270 0.0527723589581349 118 0.93407369270229 0.131852614595421 0.0659263072977105 119 0.912948861677368 0.174102276645263 0.0870511383226316 120 0.88155818469216 0.236883630615679 0.118441815307839 121 0.843141765170672 0.313716469658656 0.156858234829328 122 0.824997946278318 0.350004107443365 0.175002053721682 123 0.796434066920221 0.407131866159557 0.203565933079779 124 0.75149677119973 0.497006457600539 0.248503228800270 125 0.754163123245575 0.491673753508851 0.245836876754425 126 0.713093340211037 0.573813319577925 0.286906659788963 127 0.718056610514349 0.563886778971302 0.281943389485651 128 0.695592273661513 0.608815452676975 0.304407726338488 129 0.674770215207954 0.650459569584092 0.325229784792046 130 0.598686232616573 0.802627534766853 0.401313767383427 131 0.57590908769747 0.848181824605061 0.424090912302531 132 0.49523398601927 0.99046797203854 0.50476601398073 133 0.460398662498829 0.920797324997657 0.539601337501171 134 0.421716929786215 0.84343385957243 0.578283070213785 135 0.347926517691047 0.695853035382095 0.652073482308953 136 0.349868319668889 0.699736639337777 0.650131680331111 137 0.697127756526979 0.605744486946042 0.302872243473021 138 0.727497451007378 0.545005097985243 0.272502548992622 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 8 0.0610687022900763 NOK 10% type I error level 23 0.175572519083969 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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