WS 7 (1)

*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: Tue, 23 Nov 2010 09:44:46 +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/Nov/23/t1290505393pp49bte87ob6vwh.htm/, Retrieved Tue, 23 Nov 2010 10:43:24 +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/Nov/23/t1290505393pp49bte87ob6vwh.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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1.3954 1.0685 1.4790 1.1010 1.4619 1.0996 1.4670 1.0978 1.4799 1.0893 1.4508 1.1018 1.4678 1.0931 1.4824 1.0842 1.5189 1.0409 1.5348 1.0245 1.5666 0.9994 1.5446 1.0090 1.5803 0.9947 1.5718 1.0080 1.5832 0.9986 1.5801 1.0184 1.5605 1.0357 1.5416 1.0556 1.5479 1.0409 1.5580 1.0474 1.5790 1.0219 1.5554 1.0427 1.5761 1.0205 1.5360 1.0490 1.5621 1.0344 1.5773 1.0193 1.5710 1.0238 1.5925 1.0165 1.5844 1.0218 1.5696 1.0370 1.5540 1.0508 1.5012 1.0813 1.4676 1.0970 1.4770 1.0989 1.4660 1.1018 1.4241 1.1166 1.4214 1.1319 1.4469 1.1020 1.4618 1.0884 1.3834 1.1263 1.3412 1.1345 1.3437 1.1337 1.2630 1.1660 1.2759 1.1550 1.2743 1.1782 1.2797 1.1856 1.2573 1.2219 1.2705 1.2130 1.2680 1.2230 1.3371 1.1767 1.3885 1.1077 1.4060 1.0672 1.3855 1.0840 1.3431 1.1154 1.3257 1.1184 1.2978 1.1570 1.2793 1.1625 1.2945 1.1627 1.2890 1.1578 1.2848 1.1533 1.2694 1.1684 1.2636 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation eu/us[t] = + 3.06886493595529 -1.51139049137114`us/ch`[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) 3.06886493595529 0.082594 37.156 0 0 `us/ch` -1.51139049137114 0.07614 -19.8502 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.890375586990514 R-squared 0.792768685908702 Adjusted R-squared 0.790756731402962 F-TEST (value) 394.029131198881 F-TEST (DF numerator) 1 F-TEST (DF denominator) 103 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0441024371267238 Sum Squared Residuals 0.200337570933212 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.3954 1.45394419592521 -0.0585441959252147 2 1.479 1.40482400495566 0.074175995044338 3 1.4619 1.40693995164358 0.0549600483564183 4 1.467 1.40966045452805 0.0573395454719506 5 1.4799 1.42250727370470 0.0573927262952955 6 1.4508 1.40361489256257 0.0471851074374349 7 1.4678 1.41676398983749 0.0510360101625059 8 1.4824 1.43021536521070 0.0521846347893028 9 1.5189 1.49565857348707 0.0232414265129321 10 1.5348 1.52044537754555 0.0143546224544455 11 1.5666 1.55838127887897 0.0082187211210298 12 1.5446 1.54387193016181 0.00072806983819268 13 1.5803 1.56548481418841 0.0148151858115856 14 1.5718 1.54538332065318 0.0264166793468218 15 1.5832 1.55959039127207 0.023609608727933 16 1.5801 1.52966485954292 0.0504351404570816 17 1.5605 1.50351780404220 0.0569821959578025 18 1.5416 1.47344113326391 0.0681588667360883 19 1.5479 1.49565857348707 0.0522414265129323 20 1.558 1.48583453529316 0.072165464706845 21 1.579 1.52437499282312 0.0546250071768806 22 1.5554 1.4929380706026 0.0624619293974002 23 1.5761 1.52649093951104 0.049609060488961 24 1.536 1.48341631050696 0.0525836894930385 25 1.5621 1.50548261168098 0.0566173883190199 26 1.5773 1.52830460810068 0.0489953918993157 27 1.571 1.52150335088951 0.0494966491104858 28 1.5925 1.53253650147652 0.0599634985234764 29 1.5844 1.52452613187226 0.0598738681277436 30 1.5696 1.50155299640342 0.0680470035965848 31 1.554 1.48069580762249 0.0733041923775066 32 1.5012 1.43459839763567 0.0666016023643265 33 1.4676 1.41086956692115 0.0567304330788534 34 1.477 1.40799792498754 0.0690020750124587 35 1.466 1.40361489256257 0.0623851074374347 36 1.4241 1.38124631329027 0.0428536867097278 37 1.4214 1.35812203877229 0.0632779612277062 38 1.4469 1.40331261446429 0.0435873855357094 39 1.4618 1.42386752514694 0.0379324748530617 40 1.3834 1.36658582552397 0.0168141744760280 41 1.3412 1.35419242349473 -0.0129924234947286 42 1.3437 1.35540153588783 -0.0117015358878258 43 1.263 1.30658362301654 -0.0435836230165379 44 1.2759 1.32320891842162 -0.0473089184216202 45 1.2743 1.28814465902181 -0.0138446590218099 46 1.2797 1.27696036938566 0.00273963061433678 47 1.2573 1.22209689454889 0.0352031054511093 48 1.2705 1.23554826992209 0.0349517300779062 49 1.268 1.22043436500838 0.0475656349916176 50 1.3371 1.29041174475887 0.0466882552411336 51 1.3885 1.39469768866348 -0.00619768866347539 52 1.406 1.45590900356401 -0.0499090035640068 53 1.3855 1.43051764330897 -0.0450176433089713 54 1.3431 1.38305998187992 -0.0399599818799176 55 1.3257 1.37852581040580 -0.0528258104058039 56 1.2978 1.32018613743888 -0.0223861374388779 57 1.2793 1.31187348973634 -0.0325734897363364 58 1.2945 1.31157121163806 -0.0170712116380624 59 1.289 1.31897702504578 -0.0299770250457812 60 1.2848 1.32577828225695 -0.0409782822569513 61 1.2694 1.30295628583725 -0.0335562858372467 62 1.2636 1.31610538311218 -0.0525053831121759 63 1.29 1.27212391981328 0.0178760801867245 64 1.3559 1.36159823690245 -0.00569823690244727 65 1.3305 1.34225243861290 -0.0117524386128965 66 1.3482 1.35842431687057 -0.0102243168705679 67 1.3146 1.31852360789837 -0.00392360789836986 68 1.3027 1.30476995442689 -0.00206995442689231 69 1.3247 1.34724002723442 -0.0225400272344214 70 1.3267 1.35237875490508 -0.0256787549050834 71 1.3621 1.39651135725312 -0.0344113572531205 72 1.3479 1.37127113604722 -0.0233711360472225 73 1.4011 1.42976194806329 -0.0286619480632858 74 1.4135 1.45530444736746 -0.041804447367458 75 1.3964 1.42704144517882 -0.0306414451788176 76 1.401 1.43777231766755 -0.036772317667553 77 1.3955 1.43671434432359 -0.041214344323593 78 1.4077 1.43399384143912 -0.0262938414391249 79 1.3975 1.42613461088400 -0.0286346108839952 80 1.3949 1.43006422616156 -0.03516422616156 81 1.4138 1.44457357487872 -0.0307735748787231 82 1.421 1.45001458064766 -0.0290145806476592 83 1.4253 1.45349077877781 -0.0281907787778128 84 1.4169 1.43233131189862 -0.0154313118986168 85 1.4174 1.44472471392786 -0.0273247139278602 86 1.4346 1.47162746467427 -0.0370274646742665 87 1.4296 1.46679101510188 -0.0371910151018788 88 1.4311 1.46784898844584 -0.0367489884458387 89 1.4594 1.50155299640342 -0.0421529964034152 90 1.4722 1.51394639843266 -0.0417463984326587 91 1.4669 1.51288842508870 -0.0459884250886985 92 1.4571 1.50427349928788 -0.0471734992878833 93 1.4709 1.50850539266372 -0.0376053926637223 94 1.4893 1.52936258144464 -0.0400625814446442 95 1.4997 1.54326737396526 -0.0435673739652585 96 1.4713 1.51848056990677 -0.0471805699067718 97 1.4846 1.53178080623084 -0.0471808062308382 98 1.4914 1.53979117583511 -0.048391175835105 99 1.4859 1.53102511098515 -0.0451251109851523 100 1.4957 1.54780154543937 -0.0521015454393721 101 1.4843 1.53208308432911 -0.0477830843291124 102 1.4619 1.50608716787753 -0.0441871678775285 103 1.434 1.49354262679915 -0.0595426267991481 104 1.4426 1.50366894309133 -0.0610689430913345 105 1.4318 1.50487805548443 -0.0730780554844318 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.0983310122554686 0.196662024510937 0.901668987744531 6 0.0822416416228463 0.164483283245693 0.917758358377154 7 0.0394134972671759 0.0788269945343517 0.960586502732824 8 0.0663025057069408 0.132605011413882 0.933697494293059 9 0.277372984922148 0.554745969844295 0.722627015077852 10 0.234287537694288 0.468575075388575 0.765712462305712 11 0.180259048392263 0.360518096784525 0.819740951607737 12 0.118347838885477 0.236695677770955 0.881652161114522 13 0.086063377242487 0.172126754484974 0.913936622757513 14 0.0626544915964111 0.125308983192822 0.937345508403589 15 0.0436799704504399 0.0873599409008797 0.95632002954956 16 0.0431587462529280 0.0863174925058561 0.956841253747072 17 0.0415107804720085 0.083021560944017 0.958489219527991 18 0.044491547648054 0.088983095296108 0.955508452351946 19 0.0366510308825039 0.0733020617650079 0.963348969117496 20 0.0458877531816991 0.0917755063633981 0.95411224681830 21 0.0437955719846287 0.0875911439692575 0.956204428015371 22 0.0442413166619724 0.0884826333239447 0.955758683338028 23 0.0393945955475148 0.0787891910950295 0.960605404452485 24 0.033840335452031 0.067680670904062 0.966159664547969 25 0.0338003279295784 0.0676006558591569 0.966199672070422 26 0.032309034505866 0.064618069011732 0.967690965494134 27 0.0318697583587357 0.0637395167174715 0.968130241641264 28 0.0434589075525112 0.0869178151050225 0.95654109244749 29 0.0611687147655088 0.122337429531018 0.93883128523449 30 0.104165952326951 0.208331904653901 0.89583404767305 31 0.195726456781621 0.391452913563241 0.804273543218379 32 0.278546080212467 0.557092160424935 0.721453919787533 33 0.342383253979891 0.684766507959782 0.657616746020109 34 0.497110701736885 0.99422140347377 0.502889298263115 35 0.657110319937642 0.685779360124716 0.342889680062358 36 0.746684663620162 0.506630672759676 0.253315336379838 37 0.881148452702595 0.237703094594809 0.118851547297405 38 0.95473120802408 0.0905375839518388 0.0452687919759194 39 0.990515352369575 0.0189692952608496 0.0094846476304248 40 0.996810431446568 0.0063791371068647 0.00318956855343235 41 0.999302860542528 0.00139427891494461 0.000697139457472303 42 0.99973481392507 0.000530372149857276 0.000265186074928638 43 0.99998813390465 2.37321907017121e-05 1.18660953508560e-05 44 0.999999350502616 1.29899476830146e-06 6.49497384150728e-07 45 0.999999199326676 1.60134664708633e-06 8.00673323543164e-07 46 0.999998438009867 3.12398026666057e-06 1.56199013333028e-06 47 0.999998770132118 2.45973576310743e-06 1.22986788155371e-06 48 0.999999235113376 1.52977324734788e-06 7.64886623673941e-07 49 0.999999911803938 1.76392123260769e-07 8.81960616303845e-08 50 0.999999999737283 5.25433433684218e-10 2.62716716842109e-10 51 0.999999999903366 1.93267201430468e-10 9.66336007152342e-11 52 0.999999999989197 2.16067688100372e-11 1.08033844050186e-11 53 0.999999999997045 5.91047143820719e-12 2.95523571910359e-12 54 0.999999999998557 2.88658274863735e-12 1.44329137431868e-12 55 0.999999999999887 2.25236871341928e-13 1.12618435670964e-13 56 0.999999999999778 4.43212246736198e-13 2.21606123368099e-13 57 0.999999999999822 3.56433092443109e-13 1.78216546221555e-13 58 0.999999999999542 9.15270914480455e-13 4.57635457240227e-13 59 0.999999999999467 1.06631345153693e-12 5.33156725768467e-13 60 0.99999999999988 2.40689570161308e-13 1.20344785080654e-13 61 0.999999999999962 7.65541770082074e-14 3.82770885041037e-14 62 1 1.27939285670383e-17 6.39696428351916e-18 63 1 9.85034280193608e-18 4.92517140096804e-18 64 1 1.6176404780976e-17 8.088202390488e-18 65 1 6.55158131620528e-17 3.27579065810264e-17 66 1 1.81101051449675e-16 9.05505257248376e-17 67 1 5.29840368619815e-16 2.64920184309908e-16 68 1 1.31812658880133e-15 6.59063294400663e-16 69 0.999999999999998 4.67218010782898e-15 2.33609005391449e-15 70 0.999999999999994 1.27997080192796e-14 6.3998540096398e-15 71 0.99999999999999 2.19861664497143e-14 1.09930832248572e-14 72 0.999999999999962 7.56661062782402e-14 3.78330531391201e-14 73 0.999999999999886 2.28385171437755e-13 1.14192585718878e-13 74 0.99999999999979 4.18636116655575e-13 2.09318058327788e-13 75 0.999999999999326 1.34714552853916e-12 6.73572764269581e-13 76 0.99999999999831 3.3801490942439e-12 1.69007454712195e-12 77 0.999999999997522 4.95613083956597e-12 2.47806541978298e-12 78 0.999999999991466 1.70688305530175e-11 8.53441527650875e-12 79 0.99999999996924 6.15219274500657e-11 3.07609637250328e-11 80 0.999999999920824 1.58352008640905e-10 7.91760043204523e-11 81 0.999999999721504 5.56991647949637e-10 2.78495823974818e-10 82 0.999999999054979 1.89004270219084e-09 9.45021351095422e-10 83 0.999999997049589 5.90082188604896e-09 2.95041094302448e-09 84 0.999999997154775 5.69044890302593e-09 2.84522445151297e-09 85 0.999999994867223 1.02655535860365e-08 5.13277679301823e-09 86 0.99999998618277 2.76344618185567e-08 1.38172309092783e-08 87 0.999999968044158 6.39116831120946e-08 3.19558415560473e-08 88 0.999999965594873 6.88102549004331e-08 3.44051274502165e-08 89 0.99999992214524 1.55709520505766e-07 7.78547602528831e-08 90 0.99999979841551 4.03168982296953e-07 2.01584491148477e-07 91 0.99999928918323 1.42163353973835e-06 7.10816769869175e-07 92 0.99999766418139 4.67163722084478e-06 2.33581861042239e-06 93 0.999998191921065 3.61615787026824e-06 1.80807893513412e-06 94 0.999995288040872 9.42391825643227e-06 4.71195912821614e-06 95 0.999977068217156 4.58635656884915e-05 2.29317828442457e-05 96 0.999912906884839 0.000174186230322551 8.70931151612753e-05 97 0.999597995488137 0.00080400902372625 0.000402004511863125 98 0.99811029418265 0.00377941163469956 0.00188970581734978 99 0.99301593969603 0.0139681206079381 0.00698406030396907 100 0.973156370856907 0.0536872582861867 0.0268436291430933 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 59 0.614583333333333 NOK 5% type I error level 61 0.635416666666667 NOK 10% type I error level 78 0.8125 NOK

Charts produced by software:

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Parameters (Session):

par1 = 0 ; par2 = 36 ;

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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