Ws 7 (3)

*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 09:58:08 +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/t1290506219j74orxo6nuxm327.htm/, Retrieved Tue, 23 Nov 2010 10:56:59 +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/t1290506219j74orxo6nuxm327.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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0 1.3954 1.0685 0 1.4790 1.1010 0 1.4619 1.0996 0 1.4670 1.0978 0 1.4799 1.0893 0 1.4508 1.1018 0 1.4678 1.0931 0 1.4824 1.0842 0 1.5189 1.0409 0 1.5348 1.0245 0 1.5666 0.9994 0 1.5446 1.0090 0 1.5803 0.9947 0 1.5718 1.0080 0 1.5832 0.9986 0 1.5801 1.0184 0 1.5605 1.0357 0 1.5416 1.0556 0 1.5479 1.0409 0 1.5580 1.0474 0 1.5790 1.0219 0 1.5554 1.0427 0 1.5761 1.0205 0 1.5360 1.0490 0 1.5621 1.0344 0 1.5773 1.0193 0 1.5710 1.0238 0 1.5925 1.0165 0 1.5844 1.0218 0 1.5696 1.0370 0 1.5540 1.0508 0 1.5012 1.0813 0 1.4676 1.0970 0 1.4770 1.0989 0 1.4660 1.1018 0 1.4241 1.1166 0 1.4214 1.1319 1 1.4469 1.1020 1 1.4618 1.0884 1 1.3834 1.1263 1 1.3412 1.1345 1 1.3437 1.1337 1 1.2630 1.1660 1 1.2759 1.1550 1 1.2743 1.1782 1 1.2797 1.1856 1 1.2573 1.2219 1 1.2705 1.2130 1 1.2680 1.2230 1 1.3371 1.1767 1 1.3885 1.1077 1 1.4060 1.0672 1 1.3855 1.0840 1 1.3431 1.1154 1 1.3 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 7 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation eu/us[t] = + 2.81718821954907 -0.0858813976806043Crisis[t] -1.22772246144353`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) 2.81718821954907 0.044156 63.8003 0 0 Crisis -0.0858813976806043 0.004943 -17.3734 0 0 `us/ch` -1.22772246144353 0.041776 -29.3883 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.97347709088338 R-squared 0.947657646474767 Adjusted R-squared 0.94663132581741 F-TEST (value) 923.354352931696 F-TEST (DF numerator) 2 F-TEST (DF denominator) 102 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0222730682933475 Sum Squared Residuals 0.0506011362624124 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.3954 1.50536676949665 -0.109966769496650 2 1.479 1.46546578949974 0.0135342105002572 3 1.4619 1.46718460094576 -0.00528460094576385 4 1.467 1.46939450137636 -0.00239450137636192 5 1.4799 1.47983014229863 6.98577013677639e-05 6 1.4508 1.46448361153059 -0.0136836115305881 7 1.4678 1.47516479694515 -0.00736479694514679 8 1.4824 1.48609152685199 -0.00369152685199415 9 1.5189 1.5392519094325 -0.0203519094324992 10 1.5348 1.55938655780017 -0.0245865578001731 11 1.5666 1.59020239158241 -0.0236023915824056 12 1.5446 1.57841625595255 -0.0338162559525478 13 1.5803 1.59597268715119 -0.0156726871511901 14 1.5718 1.57964397841399 -0.0078439784139911 15 1.5832 1.59118456955156 -0.00798456955156039 16 1.5801 1.56687566481498 0.0132243351850216 17 1.5605 1.54563606623201 0.0148639337679947 18 1.5416 1.52120438924928 0.0203956107507211 19 1.5479 1.5392519094325 0.00864809056750093 20 1.558 1.53127171343312 0.0267282865668841 21 1.579 1.56257863619993 0.0164213638000739 22 1.5554 1.5370420090019 0.0183579909980992 23 1.5761 1.56429744764595 0.0118025523540529 24 1.536 1.52930735749481 0.0066926425051935 25 1.5621 1.54723210543188 0.0148678945681180 26 1.5773 1.56577071459968 0.0115292854003207 27 1.571 1.56024596352318 0.0107540364768166 28 1.5925 1.56920833749172 0.0232916625082788 29 1.5844 1.56270140844607 0.0216985915539296 30 1.5696 1.54404002703213 0.0255599729678712 31 1.554 1.52709745706421 0.0269025429357919 32 1.5012 1.48965192199018 0.0115480780098196 33 1.4676 1.47037667934552 -0.00277667934551697 34 1.477 1.46804400666877 0.00895599333122582 35 1.466 1.46448361153059 0.00151638846941182 36 1.4241 1.44631331910122 -0.0222133191012238 37 1.4214 1.42752916544114 -0.00612916544113788 38 1.4469 1.37835666935769 0.0685433306423051 39 1.4618 1.39505369483333 0.066746305166673 40 1.3834 1.34852301354462 0.0348769864553828 41 1.3412 1.33845568936078 0.00274431063921973 42 1.3437 1.33943786732994 0.00426213267006469 43 1.263 1.29978243182531 -0.0367824318253092 44 1.2759 1.31328737890119 -0.0373873789011878 45 1.2743 1.28480421779570 -0.0105042177956981 46 1.2797 1.27571907158102 0.0039809284189842 47 1.2573 1.23115274623062 0.0261472537693844 48 1.2705 1.24207947613746 0.0284205238625369 49 1.268 1.22980225152303 0.0381977484769723 50 1.3371 1.28664580148786 0.0504541985121368 51 1.3885 1.37135865132747 0.0171413486725330 52 1.406 1.42108141101593 -0.0150814110159301 53 1.3855 1.40045567366368 -0.0149556736636785 54 1.3431 1.36190518837435 -0.0188051883743518 55 1.3257 1.35822202099002 -0.0325220209900210 56 1.2978 1.3108319339783 -0.0130319339783007 57 1.2793 1.30407946044036 -0.0247794604403612 58 1.2945 1.30383391594807 -0.00933391594807263 59 1.289 1.30984975600915 -0.0208497560091461 60 1.2848 1.31537450708564 -0.0305745070856420 61 1.2694 1.29683589791784 -0.0274358979178444 62 1.2636 1.30751708333240 -0.0439170833324033 63 1.29 1.27179035970440 0.0182096402956036 64 1.3559 1.34447152942185 0.0114284705781464 65 1.3305 1.32875668191538 0.00174331808462372 66 1.3482 1.34189331225282 0.0063066877471778 67 1.3146 1.30948143927071 0.00511856072928693 68 1.3027 1.29830916487158 0.00439083512842319 69 1.3247 1.33280816603814 -0.00810816603814005 70 1.3267 1.33698242240705 -0.0102824224070481 71 1.3621 1.3728319182812 -0.010731918281199 72 1.3479 1.35232895317509 -0.00442895317509212 73 1.4011 1.39984181243296 0.00125818756704322 74 1.4135 1.42059032203135 -0.00709032203135238 75 1.3964 1.39763191200236 -0.00123191200235833 76 1.401 1.40634874147861 -0.00534874147860759 77 1.3955 1.40548933575560 -0.009989335755597 78 1.4077 1.403279435325 0.00442056467500137 79 1.3975 1.39689527852549 0.000604721474507587 80 1.3949 1.40008735692525 -0.00518735692524544 81 1.4138 1.41187349255510 0.00192650744489651 82 1.421 1.4162932934163 0.00470670658369984 83 1.4253 1.41911705507762 0.00618294492237972 84 1.4169 1.40192894061741 0.0149710593825892 85 1.4174 1.41199626480125 0.00540373519875222 86 1.4346 1.43384972461494 0.00075027538505742 87 1.4296 1.42992101273832 -0.000321012738323278 88 1.4311 1.43078041846133 0.000319581538666129 89 1.4594 1.45815862935152 0.00124137064847540 90 1.4722 1.46822595353536 0.00397404646463837 91 1.4669 1.46736654781235 -0.00046654781235083 92 1.4571 1.46036852978212 -0.00326852978212295 93 1.4709 1.46380615267416 0.0070938473258353 94 1.4893 1.48074872264209 0.0085512773579145 95 1.4997 1.49204376928737 0.00765623071263413 96 1.4713 1.47190912091969 -0.000609120919691987 97 1.4846 1.48271307858040 0.00188692141960465 98 1.4914 1.48922000762605 0.00217999237395426 99 1.4859 1.48209921734967 0.00380078265032672 100 1.4957 1.49572693667170 -2.69366716965853e-05 101 1.4843 1.48295862307268 0.00134137692731601 102 1.4619 1.46184179673586 5.82032641448777e-05 103 1.434 1.45165170030587 -0.0176517003058739 104 1.4426 1.45987744079755 -0.0172774407975453 105 1.4318 1.4608596187667 -0.0290596187667004 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.774473470982979 0.451053058034042 0.225526529017021 7 0.674568253599246 0.650863492801508 0.325431746400754 8 0.849625749123968 0.300748501752064 0.150374250876032 9 0.996015287858395 0.00796942428320951 0.00398471214160476 10 0.996597232781375 0.00680553443725056 0.00340276721862528 11 0.996224567505728 0.0075508649885448 0.0037754324942724 12 0.995100942342104 0.00979811531579118 0.00489905765789559 13 0.994301710723888 0.0113965785522248 0.0056982892761124 14 0.99303478540588 0.0139304291882397 0.00696521459411985 15 0.991201208939544 0.0175975821209115 0.00879879106045577 16 0.992959374624313 0.0140812507513744 0.0070406253756872 17 0.9935833313375 0.0128333373249999 0.00641666866249996 18 0.994678438502153 0.0106431229956942 0.00532156149784708 19 0.99315662466166 0.0136867506766799 0.00684337533833993 20 0.995215094605126 0.0095698107897471 0.00478490539487355 21 0.99464707574589 0.0107058485082216 0.0053529242541108 22 0.994025073918578 0.0119498521628436 0.0059749260814218 23 0.991989627023242 0.0160207459535154 0.0080103729767577 24 0.988408033986796 0.0231839320264077 0.0115919660132039 25 0.985373619286154 0.0292527614276922 0.0146263807138461 26 0.980346529342542 0.039306941314916 0.019653470657458 27 0.973489472240153 0.0530210555196937 0.0265105277598468 28 0.971939754036665 0.0561204919266707 0.0280602459633353 29 0.968846263713574 0.062307472572852 0.031153736286426 30 0.969611722769592 0.0607765544608167 0.0303882772304083 31 0.972875334382297 0.0542493312354068 0.0271246656177034 32 0.965885042539543 0.068229914920914 0.034114957460457 33 0.952702066654112 0.094595866691776 0.047297933345888 34 0.941341999364612 0.117316001270777 0.0586580006353884 35 0.924564711224938 0.150870577550124 0.075435288775062 36 0.913685316529471 0.172629366941057 0.0863146834705287 37 0.88783550930476 0.224328981390478 0.112164490695239 38 0.954249991012774 0.0915000179744513 0.0457500089872257 39 0.989034917703384 0.0219301645932321 0.0109650822966160 40 0.993759729422393 0.0124805411552133 0.00624027057760664 41 0.996085027662017 0.00782994467596522 0.00391497233798261 42 0.996287706537415 0.00742458692516961 0.00371229346258480 43 0.99941974496814 0.00116051006372124 0.000580255031860619 44 0.999892863842208 0.000214272315583212 0.000107136157791606 45 0.999848331914522 0.000303336170956784 0.000151668085478392 46 0.999739386132474 0.000521227735052353 0.000260613867526177 47 0.999802978508538 0.00039404298292475 0.000197021491462375 48 0.999878877556572 0.000242244886856422 0.000121122443428211 49 0.999981360680259 3.72786394822724e-05 1.86393197411362e-05 50 0.999999915421997 1.69156005363751e-07 8.45780026818756e-08 51 0.999999941693555 1.16612889958859e-07 5.83064449794297e-08 52 0.999999951923418 9.6153163122692e-08 4.8076581561346e-08 53 0.999999947154967 1.05690066754035e-07 5.28450333770177e-08 54 0.999999945213838 1.09572323338713e-07 5.47861616693566e-08 55 0.999999988664025 2.26719502176061e-08 1.13359751088031e-08 56 0.999999978727227 4.25455462903868e-08 2.12727731451934e-08 57 0.999999982889846 3.4220307354708e-08 1.7110153677354e-08 58 0.999999962841042 7.43179169981619e-08 3.71589584990810e-08 59 0.999999958130704 8.37385919195317e-08 4.18692959597659e-08 60 0.999999988369864 2.32602714549755e-08 1.16301357274877e-08 61 0.999999996234346 7.53130776529066e-09 3.76565388264533e-09 62 0.999999999998649 2.70243123690686e-12 1.35121561845343e-12 63 0.999999999999158 1.68357519050097e-12 8.41787595250484e-13 64 0.99999999999885 2.30036156786169e-12 1.15018078393085e-12 65 0.999999999996247 7.5063015720683e-12 3.75315078603415e-12 66 0.999999999991414 1.71712684685565e-11 8.58563423427826e-12 67 0.999999999980855 3.82891355668212e-11 1.91445677834106e-11 68 0.999999999963527 7.29454010259258e-11 3.64727005129629e-11 69 0.999999999890606 2.18788857636447e-10 1.09394428818224e-10 70 0.999999999737343 5.25314208593428e-10 2.62657104296714e-10 71 0.99999999950357 9.9286071683529e-10 4.96430358417645e-10 72 0.99999999854467 2.91065992221818e-09 1.45532996110909e-09 73 0.999999995536004 8.9279920791385e-09 4.46399603956925e-09 74 0.999999989460623 2.10787534049645e-08 1.05393767024822e-08 75 0.999999968469854 6.30602911966023e-08 3.15301455983012e-08 76 0.99999992095781 1.58084379509086e-07 7.90421897545432e-08 77 0.999999877867945 2.44264109674761e-07 1.22132054837381e-07 78 0.99999966354758 6.72904838162752e-07 3.36452419081376e-07 79 0.999999049911353 1.90017729437751e-06 9.50088647188754e-07 80 0.999997947055111 4.10588977777465e-06 2.05294488888733e-06 81 0.999994426294634 1.11474107323444e-05 5.57370536617218e-06 82 0.999985859973245 2.82800535091542e-05 1.41400267545771e-05 83 0.999967516538734 6.49669225314508e-05 3.24834612657254e-05 84 0.99997529226243 4.94154751398216e-05 2.47077375699108e-05 85 0.999965551808562 6.88963828765884e-05 3.44481914382942e-05 86 0.999928704104387 0.000142591791226031 7.12958956130153e-05 87 0.999874254796085 0.000251490407830391 0.000125745203915196 88 0.999886195157746 0.000227609684507569 0.000113804842253784 89 0.999803048193981 0.000393903612037528 0.000196951806018764 90 0.99962891683152 0.000742166336959546 0.000371083168479773 91 0.999111501743233 0.00177699651353296 0.00088849825676648 92 0.998065058572729 0.00386988285454253 0.00193494142727126 93 0.99877080557817 0.00245838884366056 0.00122919442183028 94 0.99789488009228 0.00421023981544188 0.00210511990772094 95 0.994068086793608 0.0118638264127837 0.00593191320639184 96 0.98663874442864 0.0267225111427197 0.0133612555713599 97 0.965976812202101 0.0680463755957972 0.0340231877978986 98 0.91554704926288 0.168905901474239 0.0844529507371193 99 0.830285184244428 0.339429631511143 0.169714815755572 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 59 0.627659574468085 NOK 5% type I error level 76 0.808510638297872 NOK 10% type I error level 85 0.904255319148936 NOK

Charts produced by software:

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Parameters (Session):

par1 = 0 ; par2 = 36 ;

Parameters (R input):

par1 = 2 ; 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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