WS 7 (4)

*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 10:16:31 +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/t1290507286qb11hditz9mcgvt.htm/, Retrieved Tue, 23 Nov 2010 11:14:46 +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/t1290507286qb11hditz9mcgvt.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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation eu/us[t] = + 3.14689431591185 -1.52758658767174`us/ch`[t] -0.00114121297467551t + 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) 3.14689431591185 0.051064 61.6262 0 0 `us/ch` -1.52758658767174 0.046768 -32.6629 0 0 t -0.00114121297467551 8.7e-05 -13.084 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.960534672716085 R-squared 0.922626857489797 Adjusted R-squared 0.92110973704842 F-TEST (value) 608.1434488172 F-TEST (DF numerator) 2 F-TEST (DF denominator) 102 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0270799927376016 Sum Squared Residuals 0.0747992526801926 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.3954 1.51352683400991 -0.118126834009909 2 1.479 1.46273905693591 0.0162609430640904 3 1.4619 1.46373646518397 -0.00183646518397477 4 1.467 1.46534490806711 0.00165509193289207 5 1.4799 1.47718818108764 0.00271181891235740 6 1.4508 1.45695213576707 -0.00615213576707027 7 1.4678 1.46910092610514 -0.00130092610513892 8 1.4824 1.48155523376074 0.000844766239258157 9 1.5189 1.54655852003225 -0.0276585200322531 10 1.5348 1.57046972709539 -0.0356697270953941 11 1.5666 1.60767093747128 -0.0410709374712793 12 1.5446 1.59186489325496 -0.0472648932549552 13 1.5803 1.61256816848399 -0.0322681684839853 14 1.5718 1.59111005389328 -0.0193100538932756 15 1.5832 1.60432815484271 -0.0211281548427146 16 1.5801 1.57294072743214 0.00715927256786147 17 1.5605 1.54537226649074 0.0151277335092582 18 1.5416 1.5138320804214 0.0277679195786015 19 1.5479 1.53514639028550 0.0127536097145021 20 1.558 1.52407586449096 0.0339241355090443 21 1.579 1.56188810950191 0.0171118904980901 22 1.5554 1.52897309550366 0.0264269044963377 23 1.5761 1.5617443047753 0.0143556952247007 24 1.536 1.51706687405198 0.0189331259480208 25 1.5621 1.53822842525731 0.0238715747426889 26 1.5773 1.56015376975648 0.0171462302435212 27 1.571 1.55213841713728 0.0188615828627195 28 1.5925 1.56214858625261 0.0303514137473912 29 1.5844 1.55291116436327 0.0314888356367271 30 1.5696 1.52855063525599 0.041049364744013 31 1.554 1.50632872737144 0.0476712726285585 32 1.5012 1.45859612347278 0.0426038765272222 33 1.4676 1.43347180107166 0.0341281989283441 34 1.477 1.42942817358040 0.047571826419596 35 1.466 1.42385695950148 0.0421430404985193 36 1.4241 1.40010746502926 0.0239925349707368 37 1.4214 1.37559417726321 0.0458058227367899 38 1.4469 1.42012780325992 0.0267721967400806 39 1.4618 1.43976176787758 0.0220382321224202 40 1.3834 1.38072502323015 0.00267497676985486 41 1.3412 1.36705760023656 -0.0258576002365614 42 1.3437 1.36713845653202 -0.0234384565320235 43 1.263 1.31665619677555 -0.0536561967755506 44 1.2759 1.33231843626526 -0.056418436265264 45 1.2743 1.29573721445660 -0.0214372144566043 46 1.2797 1.28329186073316 -0.00359186073315770 47 1.2573 1.22669925462600 0.0306007453740021 48 1.2705 1.2391535622816 0.0313464377183991 49 1.268 1.22273648343021 0.0452635165697921 50 1.3371 1.29232252946473 0.0447774705352658 51 1.3885 1.39658479103941 -0.00808479103940915 52 1.406 1.45731083486544 -0.0513108348654394 53 1.3855 1.43050616721788 -0.0450061672178783 54 1.3431 1.38139873539031 -0.0382987353903103 55 1.3257 1.37567476265262 -0.0499747626526192 56 1.2978 1.31556870739381 -0.0177687073938144 57 1.2793 1.30602576818694 -0.0267257681869442 58 1.2945 1.30457903789473 -0.0100790378947345 59 1.289 1.31092299919965 -0.0219229991996508 60 1.2848 1.31665592586950 -0.0318559258694980 61 1.2694 1.29244815542098 -0.0230481554209789 62 1.2636 1.30459694575905 -0.0409969457590478 63 1.29 1.25900296308312 0.0309970369168756 64 1.3559 1.34829487609862 0.00760512390138371 65 1.3305 1.32760055480174 0.00289944519825767 66 1.3482 1.34280451831515 0.00539548168484533 67 1.3146 1.30133501942595 0.0132649805740548 68 1.3027 1.28629276850346 0.0164072314965433 69 1.3247 1.32807673864236 -0.00337673864235721 70 1.3267 1.33212932006577 -0.00542932006576573 71 1.3621 1.37559363545110 -0.0134936354511049 72 1.3479 1.34894172646231 -0.00104172646231135 73 1.4011 1.40691811443053 -0.00581811443053234 74 1.4135 1.43159311478751 -0.0180931147875092 75 1.3964 1.40188603262337 -0.00548603262337207 76 1.401 1.41159068442117 -0.0105906844211662 77 1.3955 1.40938016083512 -0.0138801608351203 78 1.4077 1.40548929200264 0.00221070799736438 79 1.3975 1.39640462877207 0.00109537122793278 80 1.3949 1.39923514092534 -0.00433514092533808 81 1.4138 1.41275875919231 0.00104124080768851 82 1.421 1.41711685793325 0.00388314206674577 83 1.4253 1.41948909411022 0.00581090588977628 84 1.4169 1.39696166890814 0.0199383310918563 85 1.4174 1.40834666595238 0.00905333404762343 86 1.4346 1.43439649423826 0.000203505761741944 87 1.4296 1.42836700418303 0.00123299581696705 88 1.4311 1.42829510181973 0.00280489818027217 89 1.4594 1.46121906975013 -0.00181906975013220 90 1.4722 1.47260406679436 -0.000404066794365051 91 1.4669 1.47039354320832 -0.00349354320831896 92 1.4571 1.46054508668391 -0.00344508668391482 93 1.4709 1.46368111615472 0.00721888384527997 94 1.4893 1.48362059808991 0.00567940191008534 95 1.4997 1.49653318172182 0.00316681827818095 96 1.4713 1.47033954870933 0.00096045129067302 97 1.4846 1.48264109770616 0.00195890229383685 98 1.4914 1.48959609364615 0.00180390635385247 99 1.4859 1.47959487846298 0.00630512153702406 100 1.4957 1.49540987661146 0.00029012338854309 101 1.4843 1.47838176312500 0.00591823687500459 102 1.4619 1.45096606084237 0.0109339391576343 103 1.434 1.43714587919001 -0.00314587919001482 104 1.4426 1.44623949635274 -0.00363949635273973 105 1.4318 1.44632035264820 -0.0145203526482020 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.564749258971833 0.870501482056334 0.435250741028167 7 0.401639903883082 0.803279807766165 0.598360096116918 8 0.37255779620874 0.74511559241748 0.62744220379126 9 0.676350301333858 0.647299397332283 0.323649698666142 10 0.642708826642338 0.714582346715323 0.357291173357662 11 0.642524590161945 0.714950819676111 0.357475409838055 12 0.614792758398428 0.770414483203145 0.385207241601572 13 0.582579137855629 0.834841724288743 0.417420862144371 14 0.519647679671268 0.960704640657465 0.480352320328732 15 0.470820540406278 0.941641080812555 0.529179459593722 16 0.395918859985251 0.791837719970501 0.604081140014749 17 0.380647618553827 0.761295237107654 0.619352381446173 18 0.390282338880439 0.780564677760879 0.60971766111956 19 0.387874984874965 0.77574996974993 0.612125015125035 20 0.326496293936426 0.652992587872851 0.673503706063574 21 0.268158667494986 0.536317334989972 0.731841332505014 22 0.243098925443696 0.486197850887392 0.756901074556304 23 0.209925993063705 0.41985198612741 0.790074006936295 24 0.249550716848658 0.499101433697317 0.750449283151342 25 0.216694479086696 0.433388958173393 0.783305520913304 26 0.183679339187673 0.367358678375346 0.816320660812327 27 0.160649944898048 0.321299889796097 0.839350055101952 28 0.122986177219916 0.245972354439831 0.877013822780084 29 0.0958384522965923 0.191676904593185 0.904161547703408 30 0.0802202885905023 0.160440577181005 0.919779711409498 31 0.0780651148735719 0.156130229747144 0.921934885126428 32 0.128909884767053 0.257819769534106 0.871090115232947 33 0.232106378523230 0.464212757046461 0.76789362147677 34 0.303501747689026 0.607003495378051 0.696498252310974 35 0.410954763616824 0.821909527233648 0.589045236383176 36 0.581369493380769 0.837261013238463 0.418630506619231 37 0.732904501209634 0.534190997580733 0.267095498790366 38 0.865082678951326 0.269834642097348 0.134917321048674 39 0.967176008240186 0.0656479835196271 0.0328239917598135 40 0.994120478042045 0.0117590439159095 0.00587952195795477 41 0.999313038340332 0.00137392331933531 0.000686961659667655 42 0.999827323783195 0.000345352433609302 0.000172676216804651 43 0.999991631718944 1.67365621114710e-05 8.36828105573552e-06 44 0.999999393639233 1.21272153381855e-06 6.06360766909273e-07 45 0.99999919891755 1.60216490196375e-06 8.01082450981874e-07 46 0.999998428819074 3.14236185245212e-06 1.57118092622606e-06 47 0.999998709573888 2.58085222400450e-06 1.29042611200225e-06 48 0.999999165945261 1.66810947793880e-06 8.34054738969401e-07 49 0.999999891454966 2.17090067775693e-07 1.08545033887846e-07 50 0.999999999680346 6.39307924060609e-10 3.19653962030305e-10 51 0.999999999928045 1.43910698201954e-10 7.19553491009772e-11 52 0.999999999980349 3.93022442263613e-11 1.96511221131806e-11 53 0.999999999986453 2.70944793630686e-11 1.35472396815343e-11 54 0.999999999986687 2.66264255233636e-11 1.33132127616818e-11 55 0.999999999998165 3.6699022156932e-12 1.8349511078466e-12 56 0.999999999995775 8.45003601120456e-12 4.22501800560228e-12 57 0.999999999996095 7.81046599029504e-12 3.90523299514752e-12 58 0.999999999988983 2.20341930849873e-11 1.10170965424936e-11 59 0.99999999998572 2.85600187890865e-11 1.42800093945433e-11 60 0.999999999996681 6.63745278938776e-12 3.31872639469388e-12 61 0.999999999998712 2.57666857797557e-12 1.28833428898779e-12 62 1 4.02576789407143e-16 2.01288394703572e-16 63 1 5.55663949613671e-17 2.77831974806835e-17 64 1 1.38003103621412e-16 6.9001551810706e-17 65 1 6.03209932550747e-16 3.01604966275374e-16 66 1 2.01824773121104e-15 1.00912386560552e-15 67 0.999999999999998 3.40901842958178e-15 1.70450921479089e-15 68 1 1.58772159011483e-15 7.93860795057417e-16 69 0.999999999999996 7.37740973175052e-15 3.68870486587526e-15 70 0.999999999999984 3.2311781528179e-14 1.61558907640895e-14 71 0.99999999999997 5.92805433815337e-14 2.96402716907669e-14 72 0.999999999999867 2.66395263036691e-13 1.33197631518346e-13 73 0.99999999999945 1.09925779342188e-12 5.49628896710942e-13 74 0.999999999999747 5.06846428839487e-13 2.53423214419743e-13 75 0.99999999999912 1.75827086867666e-12 8.79135434338331e-13 76 0.999999999998928 2.14477784392273e-12 1.07238892196136e-12 77 0.99999999999979 4.19042496351513e-13 2.09521248175756e-13 78 0.999999999998988 2.02413004628655e-12 1.01206502314327e-12 79 0.999999999995584 8.8320450035694e-12 4.4160225017847e-12 80 0.999999999993118 1.37635472822496e-11 6.88177364112479e-12 81 0.99999999997605 4.7898486903637e-11 2.39492434518185e-11 82 0.9999999998935 2.13000383466694e-10 1.06500191733347e-10 83 0.999999999491664 1.01667109309592e-09 5.0833554654796e-10 84 0.999999999880096 2.39807920647698e-10 1.19903960323849e-10 85 0.999999999799275 4.01450324457317e-10 2.00725162228659e-10 86 0.999999998855519 2.28896272362232e-09 1.14448136181116e-09 87 0.999999994017 1.19659981687334e-08 5.98299908436672e-09 88 0.99999997921597 4.15680591897576e-08 2.07840295948788e-08 89 0.999999894942475 2.10115049466120e-07 1.05057524733060e-07 90 0.999999503388402 9.93223195108437e-07 4.96611597554219e-07 91 0.999998550365631 2.89926873766898e-06 1.44963436883449e-06 92 0.99999707774388 5.84451223979454e-06 2.92225611989727e-06 93 0.999985203973668 2.95920526643151e-05 1.47960263321575e-05 94 0.999928330577961 0.000143338844077359 7.16694220386793e-05 95 0.999700913861095 0.00059817227780943 0.000299086138904715 96 0.999104216057235 0.00179156788553018 0.000895783942765092 97 0.997897685808405 0.00420462838318919 0.00210231419159459 98 0.995710883671363 0.00857823265727366 0.00428911632863683 99 0.9873147163652 0.0253705672695987 0.0126852836347993 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 58 0.617021276595745 NOK 5% type I error level 60 0.638297872340426 NOK 10% type I error level 61 0.648936170212766 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 = 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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