11.3.2

*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: Thu, 27 Nov 2008 08:33:45 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz.htm/, Retrieved Thu, 27 Nov 2008 15:35:04 +0000

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/2008/Nov/27/t12278000894iuq2aisrr6jjjz.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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78.40 97.80 114.60 107.40 113.30 117.50 117.00 105.60 99.60 97.40 99.40 99.50 101.90 98.00 115.20 104.30 108.50 100.60 113.80 101.10 121.00 103.90 92.20 96.90 90.20 95.50 101.50 108.40 126.60 117.00 93.90 103.80 89.80 100.80 93.40 110.60 101.50 104.00 110.40 112.60 105.90 107.30 108.40 98.90 113.90 109.80 86.10 104.90 69.40 102.20 101.20 123.90 100.50 124.90 98.00 112.70 106.60 121.90 90.10 100.60 96.90 104.30 125.90 120.40 112.00 107.50 100.00 102.90 123.90 125.60 79.80 107.50 83.40 108.80 113.60 128.40 112.90 121.10 104.00 119.50 109.90 128.70 99.00 108.70 106.30 105.50 128.90 119.80 111.10 111.30 102.90 110.60 130.00 120.10 87.00 97.50 87.50 107.70 117.60 127.30 103.40 117.20 110.80 119.80 112.60 116.20 102.50 111.00 112.40 112.40 135.60 130.60 105.10 109.10 127.70 118.80 137.00 123.90 91.00 101.60 90.50 112.80 122.40 128.00 123.30 129.60 124.30 125.80 120.00 119.50 118.10 115.70 119.00 113.60 142.70 129.70 123.60 112.00 129.60 116.80 151.60 127.00 110.40 112.10 99.20 114.20 130.50 121.10 136.20 131.60 129.70 125.00 128.00 120.40 121.60 117.70 135.80 117.50 143.80 120.60 147.50 127.50 136.20 112.30 156.60 124.50 123.30 115.20 100.40 105.40

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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation investerings[t] = + 66.1505894651654 + 0.025180716423023consumptie[t] + 0.400773220002877V3[t] -18.2608604869474M1[t] -1.45328247179579M2[t] -1.27651989429140M3[t] + 1.11405923780997M4[t] -16.6343188842857M5[t] -2.58000142953887M6[t] -0.294549633253951M7[t] + 1.80960814707014M8[t] -16.678029531183M9[t] -1.36461534589584M10[t] -5.89777655442444M11[t] + 0.00368835171866561t + 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) 66.1505894651654 14.113009 4.6872 1.3e-05 7e-06 consumptie 0.025180716423023 0.140632 0.1791 0.858412 0.429206 V3 0.400773220002877 0.092534 4.3311 4.9e-05 2.4e-05 M1 -18.2608604869474 5.927365 -3.0808 0.00295 0.001475 M2 -1.45328247179579 6.311027 -0.2303 0.818548 0.409274 M3 -1.27651989429140 6.330316 -0.2017 0.840774 0.420387 M4 1.11405923780997 6.101213 0.1826 0.855643 0.427821 M5 -16.6343188842857 6.093701 -2.7298 0.008011 0.004005 M6 -2.58000142953887 6.435418 -0.4009 0.68971 0.344855 M7 -0.294549633253951 6.252135 -0.0471 0.962558 0.481279 M8 1.80960814707014 6.08816 0.2972 0.767168 0.383584 M9 -16.678029531183 6.132893 -2.7194 0.00824 0.00412 M10 -1.36461534589584 6.330505 -0.2156 0.829956 0.414978 M11 -5.89777655442444 6.241581 -0.9449 0.347951 0.173976 t 0.00368835171866561 0.055355 0.0666 0.947066 0.473533 Multiple Linear Regression - Regression Statistics Multiple R 0.68955263982766 R-squared 0.475482843093295 Adjusted R-squared 0.370579411711954 F-TEST (value) 4.53257664532286 F-TEST (DF numerator) 14 F-TEST (DF denominator) 70 p-value 9.81164605140528e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 11.3278128582093 Sum Squared Residuals 8982.3540905429 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 78.4 96.2847024084382 -17.8847024084382 2 107.4 114.648512217873 -7.24851221787348 3 117 107.461230992588 9.53876900741221 4 97.4 109.659300712585 -12.2593007125848 5 101.9 98.1714974932607 3.72850250673928 6 104.3 106.642611810126 -2.34261181012595 7 113.8 116.921188344658 -3.12118834465784 8 103.9 109.146291498466 -5.24629149846637 9 90.2 92.5889953481411 -2.38899534814111 10 108.4 114.901203075948 -6.50120307594754 11 93.9 98.8965783006144 -4.99657830061442 12 100.8 112.872246732018 -12.0722467320179 13 101.5 94.8018355468727 6.69816445312733 14 112.6 110.418548292938 2.18145170706224 15 108.4 113.067837459219 -4.66783745921864 16 109.8 111.532832792798 -1.73283279279809 17 69.4 92.7106916428211 -23.3106916428211 18 123.9 116.224215545436 7.67578445456435 19 98 111.486410507747 -13.4864105077475 20 121.9 110.620533128613 11.2794668713874 21 96.9 102.633712441358 -5.73371244135788 22 120.4 110.770479246768 9.62952075323195 23 100 112.584542678556 -12.5845426785558 24 125.6 111.331652227280 14.2683477727201 25 83.4 96.2494375103364 -12.8494375103364 26 128.4 116.169743964563 12.2302560354374 27 104 112.027727558145 -8.0277275581454 28 128.7 113.42486249129 15.2751375087100 29 106.3 103.939466421721 2.36053357827921 30 119.8 111.084875568105 8.71512443189544 31 102.9 120.855884571950 -17.9558845719504 32 120.1 109.344336146316 10.7556638536836 33 87.5 99.4371693717963 -11.9371693717963 34 127.3 114.485685540182 12.8143144598180 35 110.8 108.525619620696 2.27438037930364 36 116.2 113.350220980717 2.84977901928344 37 112.4 105.201359150147 7.19864084985348 38 130.6 111.208315957052 19.3916840429475 39 127.7 122.915315539351 4.78468446064877 40 123.9 110.422187118509 13.4778128814906 41 90.5 101.562519942214 -11.0625199422142 42 128 118.770490455142 9.22950954485792 43 124.3 117.275159482176 7.0248405178244 44 119.5 117.465789251749 2.03421074825129 45 119 109.689403641388 9.3105963586117 46 129.7 112.954575488536 16.7454245114639 47 129.6 124.124493272163 5.47550672783653 48 127 114.034259403086 12.9657405969144 49 99.2 103.247001238317 -4.04700123831729 50 121.1 121.053093908497 0.0469060915027699 51 129.7 119.508737221772 10.1912627782279 52 120.4 117.689426103724 2.71057389627581 53 135.8 110.301676438088 25.4983235619121 54 120.6 118.582500251197 2.01749974880282 55 136.2 131.647779883194 4.55222011680593 56 124.5 117.440602587771 7.05939741222901 57 100.4 83.7574639411585 16.6425360588415 58 97.8 110.928652449340 -13.1286524493396 59 113.3 110.319626582184 2.98037341781596 60 105.6 107.915201552299 -2.31520155229865 61 99.4 91.4589908354406 7.94100916455944 62 98 109.627450178159 -11.6274501781592 63 108.5 113.247608237633 -4.74760823763344 64 101.1 112.187907458455 -11.0879074584547 65 92.2 88.3457693082434 3.85423069175658 66 95.5 109.813679014307 -14.3136790143072 67 126.6 106.681908576826 19.9180914231741 68 103.8 110.870174440182 -7.07017444018227 69 93.4 93.1905252692487 0.209474730751319 70 104 112.951174405002 -8.95117440500184 71 105.9 106.660393803268 -0.760393803268446 72 98.9 113.289133945808 -14.3891339458075 73 86.1 78.6140972746554 7.48590272534461 74 102.2 117.174335480917 -14.9743354809172 75 100.5 107.571542991291 -7.07154299129144 76 112.7 119.083483322639 -6.3834833226389 77 90.1 91.1683787536519 -1.06837875365188 78 104.3 115.281627355687 -10.9816273556874 79 112 108.931668633449 3.0683313665513 80 102.9 121.712272946903 -18.8122729469027 81 79.8 85.9027299869092 -6.10272998690924 82 108.8 119.408229794225 -10.6082297942250 83 112.9 105.288745742517 7.6112542574825 84 119.5 120.807285158794 -1.30728515879380 85 99 93.542576035793 5.457423964207 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.907950744759477 0.184098510481046 0.0920492552405229 19 0.860862201654627 0.278275596690745 0.139137798345373 20 0.822858276194173 0.354283447611654 0.177141723805827 21 0.744869737155135 0.510260525689731 0.255130262844865 22 0.656328706285305 0.68734258742939 0.343671293714695 23 0.625087276718785 0.749825446562429 0.374912723281215 24 0.59030823684584 0.81938352630832 0.40969176315416 25 0.571980045894123 0.856039908211754 0.428019954105877 26 0.573207767408429 0.853584465183143 0.426792232591571 27 0.522788282591745 0.95442343481651 0.477211717408255 28 0.620894898924362 0.758210202151277 0.379105101075638 29 0.572264690270468 0.855470619459064 0.427735309729532 30 0.489157101989624 0.97831420397925 0.510842898010376 31 0.674026045351587 0.651947909296826 0.325973954648413 32 0.60376460525378 0.792470789492439 0.396235394746219 33 0.688273100869883 0.623453798260234 0.311726899130117 34 0.637259794692104 0.725480410615792 0.362740205307896 35 0.677800533621672 0.644398932756656 0.322199466378328 36 0.620930323618978 0.758139352762044 0.379069676381022 37 0.595224361648354 0.809551276703292 0.404775638351646 38 0.621596665691558 0.756806668616884 0.378403334308442 39 0.54666645299589 0.90666709400822 0.45333354700411 40 0.521829071862821 0.956341856274358 0.478170928137179 41 0.7627534546851 0.474493090629799 0.237246545314900 42 0.734183413228274 0.531633173543452 0.265816586771726 43 0.809916942110498 0.380166115779005 0.190083057889502 44 0.755726844769235 0.48854631046153 0.244273155230765 45 0.720130783080558 0.559738433838884 0.279869216919442 46 0.758285413581084 0.483429172837832 0.241714586418916 47 0.700669575818786 0.598660848362429 0.299330424181214 48 0.696802364881596 0.606395270236808 0.303197635118404 49 0.848519721506255 0.302960556987490 0.151480278493745 50 0.8109188297511 0.378162340497801 0.189081170248900 51 0.773102885940205 0.453794228119591 0.226897114059795 52 0.713294073937714 0.573411852124572 0.286705926062286 53 0.881867293080842 0.236265413838316 0.118132706919158 54 0.855800974549918 0.288398050900165 0.144199025450082 55 0.837622899791939 0.324754200416123 0.162377100208061 56 0.893330429478815 0.213339141042370 0.106669570521185 57 0.961401454330134 0.0771970913397328 0.0385985456698664 58 0.980125368236836 0.0397492635263282 0.0198746317631641 59 0.964731053295054 0.0705378934098915 0.0352689467049457 60 0.94822731431868 0.103545371362640 0.0517726856813199 61 0.910832053407035 0.178335893185930 0.0891679465929648 62 0.89338664469758 0.213226710604840 0.106613355302420 63 0.843617674958501 0.312764650082998 0.156382325041499 64 0.786248583482126 0.427502833035749 0.213751416517874 65 0.673454829069772 0.653090341860456 0.326545170930228 66 0.599005925627425 0.80198814874515 0.400994074372575 67 0.738594144448763 0.522811711102473 0.261405855551237 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 1 0.02 OK 10% type I error level 3 0.06 OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/10cdbl1227800020.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/10cdbl1227800020.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/1vo9l1227800020.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/1vo9l1227800020.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/28g0i1227800020.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/28g0i1227800020.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/36lht1227800020.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/36lht1227800020.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/4wwny1227800020.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/4wwny1227800020.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/5pilk1227800020.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/5pilk1227800020.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/655gf1227800020.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/655gf1227800020.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/78gir1227800020.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/78gir1227800020.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/85jka1227800020.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/85jka1227800020.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/9zhk01227800020.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t12278000894iuq2aisrr6jjjz/9zhk01227800020.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly 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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