Paper - Multiple Regression Model 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, 14 Dec 2010 10:32:11 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/14/t1292322836254vhj3afwnjukq.htm/, Retrieved Tue, 14 Dec 2010 11:33:56 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/14/t1292322836254vhj3afwnjukq.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:

» Textbox « » Textfile « » CSV «

10.81 -0.2643 24563400 24.45 2772.73 0.0373 115.7 5.98 9.12 -0.2643 14163200 23.62 2151.83 0.0353 109.2 5.49 11.03 -0.2643 18184800 21.90 1840.26 0.0292 116.9 5.31 12.74 -0.1918 20810300 27.12 2116.24 0.0327 109.9 4.8 9.98 -0.1918 12843000 27.70 2110.49 0.0362 116.1 4.21 11.62 -0.1918 13866700 29.23 2160.54 0.0325 118.9 3.97 9.40 -0.2246 15119200 26.50 2027.13 0.0272 116.3 3.77 9.27 -0.2246 8301600 22.84 1805.43 0.0272 114.0 3.65 7.76 -0.2246 14039600 20.49 1498.80 0.0265 97.0 3.07 8.78 0.3654 12139700 23.28 1690.20 0.0213 85.3 2.49 10.65 0.3654 9649000 25.71 1930.58 0.019 84.9 2.09 10.95 0.3654 8513600 26.52 1950.40 0.0155 94.6 1.82 12.36 0.0447 15278600 25.51 1934.03 0.0114 97.8 1.73 10.85 0.0447 15590900 23.36 1731.49 0.0114 95.0 1.74 11.84 0.0447 9691100 24.15 1845.35 0.0148 110.7 1.73 12.14 -0.0312 10882700 20.92 1688.23 0.0164 108.5 1.75 11.65 -0.0312 10294800 20.38 1615.73 0.0118 110.3 1.75 8.86 -0.0312 16031900 21.90 1463.21 0.0107 106.3 1.75 7.63 -0.0048 13683600 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 9 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Apple[t] = -85.2045348232552 + 28.1570131846653Omzetgroei[t] + 4.38917615134892e-07Volume[t] + 6.50659755634622Microsoft[t] + 0.08815121892656NASDAQ[t] -961.803925838047Inflatie[t] -1.9810344949101Cons_vertrouwen[t] + 1.03003237831383Fed_funds_rate[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) -85.2045348232552 28.425357 -2.9975 0.003372 0.001686 Omzetgroei 28.1570131846653 15.886281 1.7724 0.07912 0.03956 Volume 4.38917615134892e-07 0 1.2703 0.206671 0.103335 Microsoft 6.50659755634622 1.543888 4.2144 5.2e-05 2.6e-05 NASDAQ 0.08815121892656 0.018421 4.7854 5e-06 3e-06 Inflatie -961.803925838047 268.539097 -3.5816 0.000512 0.000256 Cons_vertrouwen -1.9810344949101 0.216208 -9.1626 0 0 Fed_funds_rate 1.03003237831383 3.809839 0.2704 0.787394 0.393697 Multiple Linear Regression - Regression Statistics Multiple R 0.913298070864655 R-squared 0.8341133662451 Adjusted R-squared 0.823460096187447 F-TEST (value) 78.2964631264382 F-TEST (DF numerator) 7 F-TEST (DF denominator) 109 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 31.9226167034062 Sum Squared Residuals 111076.826833992 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 10.81 62.7193311740069 -51.9093311740069 2 9.12 12.3165485930305 -3.19654859303055 3 11.03 -34.1472908950989 45.1772908950989 4 12.74 37.3144946138027 -24.5744946138027 5 9.98 20.8280166605106 -10.8480166605106 6 11.62 33.4089695606655 -21.7889695606655 7 9.4 13.5541424133925 -4.15414241339251 8 9.27 -28.3627191589011 37.6327191589011 9 7.76 -34.4110920179855 42.1710920179855 10 8.78 43.9752618941058 -35.1952618941058 11 10.65 82.4754217336431 -71.8254217336431 12 10.95 72.8667462508443 -61.9167462508443 13 12.36 56.3027536013476 -43.9427536013476 14 10.85 30.1536918545558 -19.3036918545558 15 11.84 8.35860032355354 3.48139967644646 16 12.14 -25.2821421166795 37.4221421166795 17 11.65 -34.5862718672031 46.2362718672031 18 8.86 -26.6408309443831 35.5008309443831 19 7.63 -42.4673794062746 50.0973794062746 20 7.38 -40.4988144645257 47.8788144645257 21 7.25 -62.1003459874336 69.3503459874336 22 8.03 2.60107370905163 5.42892629094837 23 7.75 13.0691199253445 -5.31911992534445 24 7.16 -8.75761969894247 15.9176196989425 25 7.18 -20.5841839477854 27.7641839477854 26 7.51 4.02820990595916 3.48179009404084 27 7.07 13.0014100041374 -5.93141000413742 28 7.11 5.43598330232106 1.67401669767894 29 8.98 10.6249259484946 -1.64492594849463 30 9.53 14.4448804594925 -4.91488045949246 31 10.54 43.1917031351318 -32.6517031351318 32 11.31 40.1342530329536 -28.8242530329536 33 10.36 53.3078493361767 -42.9478493361767 34 11.44 57.2727598180085 -45.8327598180085 35 10.45 37.8791384671795 -27.4291384671795 36 10.69 50.7481661995593 -40.0581661995593 37 11.28 48.424642990894 -37.1446429908940 38 11.96 55.3586848232578 -43.3986848232578 39 13.52 47.0782193405312 -33.5582193405312 40 12.89 31.7444969980815 -18.8544969980815 41 14.03 27.8845805751168 -13.8545805751168 42 16.27 26.3456642223851 -10.0756642223851 43 16.17 11.8759221521456 4.29407784785439 44 17.25 17.6123477229756 -0.362347722975618 45 19.38 29.8862157063213 -10.5062157063213 46 26.2 56.8750375008186 -30.6750375008186 47 33.53 76.4614339307149 -42.9314339307149 48 32.2 63.3780485465358 -31.1780485465358 49 38.45 57.7205143361653 -19.2705143361653 50 44.86 48.6570358397455 -3.79703583974553 51 41.67 32.4114393714118 9.2585606285882 52 36.06 45.3062202578867 -9.24622025788674 53 39.76 52.4968457444182 -12.7368457444182 54 36.81 40.8095751396519 -3.99957513965192 55 42.65 50.6967007326058 -8.04670073260578 56 46.89 49.0340009471973 -2.14400094719728 57 53.61 67.854316825496 -14.2443168254960 58 57.59 80.2028101607748 -22.6128101607748 59 67.82 81.059542518191 -13.2395425181910 60 71.89 58.9085139027248 12.9814860972752 61 75.51 67.4921295217136 8.01787047828637 62 68.49 68.985320509384 -0.495320509383977 63 62.72 68.8908034214519 -6.17080342145186 64 70.39 40.9522363175904 29.4377636824096 65 59.77 18.9301141685140 40.839885831486 66 57.27 20.1362799202347 37.1337200797653 67 67.96 20.0663745464459 47.8936254535541 68 67.85 53.4497379286833 14.4002620713167 69 76.98 77.1836686205179 -0.203668620517921 70 81.08 97.9838305532285 -16.9038305532285 71 91.66 101.832201322439 -10.1722013224393 72 84.84 91.7395551694772 -6.89955516947724 73 85.73 113.226251384054 -27.4962513840536 74 84.61 78.2402093002779 6.36979069972211 75 92.91 79.073309042405 13.8366909575951 76 99.8 107.483918444190 -7.68391844418985 77 121.19 116.378136668521 4.81186333147942 78 122.04 120.115475042355 1.92452495764462 79 131.76 105.903843396301 25.8561566036990 80 138.48 123.284004977196 15.1959950228039 81 153.47 142.019318045368 11.4506819546317 82 189.95 202.944147671640 -12.9941476716404 83 182.22 177.583491244225 4.63650875577466 84 198.08 178.970274556766 19.1097254432340 85 135.36 155.196026860577 -19.8360268605768 86 125.02 129.76855374878 -4.74855374878012 87 143.5 156.726784695558 -13.2267846955583 88 173.95 170.776522023380 3.17347797662046 89 188.75 184.64364342077 4.10635657922988 90 167.44 165.515260422009 1.92473957799142 91 158.95 159.162411053746 -0.212411053746257 92 169.53 157.958338507458 11.5716614925423 93 113.66 137.1109921974 -23.4509921974001 94 107.59 126.845763624126 -19.2557636241261 95 92.67 100.868788778525 -8.19878877852456 96 85.35 116.481247877928 -31.1312478779284 97 90.13 161.991059648354 -71.8610596483544 98 89.31 101.212722717554 -11.9027227175536 99 105.12 130.549655028455 -25.4296550284551 100 125.83 136.920775066969 -11.0907750669692 101 135.81 122.826729942277 12.9832700577233 102 142.43 160.272068135717 -17.8420681357169 103 163.39 173.855587606783 -10.4655876067827 104 168.21 162.753744288542 5.45625571145848 105 185.35 181.386923913572 3.96307608642804 106 188.5 195.147207171396 -6.64720717139603 107 199.91 189.458468177304 10.4515318226958 108 210.73 194.272543060002 16.4574569399980 109 192.06 173.850710333562 18.2092896664383 110 204.62 206.341607230959 -1.72160723095851 111 235 210.364260948881 24.6357390511194 112 261.09 218.590464032118 42.499535967882 113 256.88 168.36662532517 88.51337467483 114 251.53 160.900678046314 90.6293219536864 115 257.25 197.607562872832 59.6424371271679 116 243.1 162.582081626892 80.5179183731075 117 283.75 203.021988145009 80.7280118549914 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 11 9.93984868386272e-05 0.000198796973677254 0.999900601513161 12 3.75528495496602e-06 7.51056990993203e-06 0.999996244715045 13 1.40415187592706e-07 2.80830375185413e-07 0.999999859584812 14 4.43679524443318e-09 8.87359048886636e-09 0.999999995563205 15 3.27874994815992e-10 6.55749989631985e-10 0.999999999672125 16 4.83938169169556e-11 9.67876338339112e-11 0.999999999951606 17 1.87273445471079e-12 3.74546890942158e-12 0.999999999998127 18 1.01078373073411e-12 2.02156746146822e-12 0.99999999999899 19 8.59501249251241e-14 1.71900249850248e-13 0.999999999999914 20 5.04829534913246e-15 1.00965906982649e-14 0.999999999999995 21 4.49634627660947e-16 8.99269255321894e-16 1 22 2.78293446701977e-17 5.56586893403954e-17 1 23 1.19556963679628e-18 2.39113927359256e-18 1 24 6.60852797051429e-20 1.32170559410286e-19 1 25 5.80911855173079e-21 1.16182371034616e-20 1 26 7.98736029761316e-22 1.59747205952263e-21 1 27 4.9778209111119e-23 9.9556418222238e-23 1 28 4.83034863825841e-24 9.66069727651681e-24 1 29 2.84125116913214e-25 5.68250233826427e-25 1 30 1.88903654283954e-26 3.77807308567909e-26 1 31 1.29292177584208e-27 2.58584355168415e-27 1 32 9.73198006053906e-29 1.94639601210781e-28 1 33 4.39000219102557e-30 8.78000438205115e-30 1 34 3.06414982941595e-31 6.12829965883191e-31 1 35 2.20033502101284e-32 4.40067004202568e-32 1 36 1.64829608112119e-33 3.29659216224239e-33 1 37 1.15290506131906e-34 2.30581012263811e-34 1 38 1.35529818143049e-35 2.71059636286099e-35 1 39 1.34282130064479e-35 2.68564260128957e-35 1 40 3.0456357233426e-36 6.0912714466852e-36 1 41 2.48714847361314e-36 4.97429694722627e-36 1 42 1.54250888690192e-36 3.08501777380385e-36 1 43 1.97373390550936e-37 3.94746781101872e-37 1 44 6.80275674278562e-37 1.36055134855712e-36 1 45 4.48035287166176e-35 8.96070574332352e-35 1 46 1.75925932315762e-34 3.51851864631523e-34 1 47 5.41819667505582e-32 1.08363933501116e-31 1 48 5.78731021919124e-31 1.15746204383825e-30 1 49 2.11494834863360e-31 4.22989669726719e-31 1 50 1.09190077300583e-28 2.18380154601166e-28 1 51 6.05918482025963e-26 1.21183696405193e-25 1 52 9.56614976926313e-27 1.91322995385263e-26 1 53 7.71787015172632e-26 1.54357403034526e-25 1 54 1.03656652612917e-25 2.07313305225834e-25 1 55 1.92252220674624e-22 3.84504441349248e-22 1 56 5.9703239248202e-20 1.19406478496404e-19 1 57 2.81404918234567e-17 5.62809836469134e-17 1 58 8.7044144064997e-16 1.74088288129994e-15 1 59 3.28081589555319e-12 6.56163179110638e-12 0.99999999999672 60 5.44923997479046e-10 1.08984799495809e-09 0.999999999455076 61 2.46102583235247e-07 4.92205166470493e-07 0.999999753897417 62 1.54566562354621e-06 3.09133124709241e-06 0.999998454334377 63 8.08777788808017e-06 1.61755557761603e-05 0.999991912222112 64 2.32825904036498e-05 4.65651808072996e-05 0.999976717409596 65 1.76684941687331e-05 3.53369883374661e-05 0.999982331505831 66 1.04032478035942e-05 2.08064956071885e-05 0.999989596752196 67 3.17259754812921e-05 6.34519509625843e-05 0.999968274024519 68 6.14290417222864e-05 0.000122858083444573 0.999938570958278 69 0.000106924516153687 0.000213849032307374 0.999893075483846 70 0.000230885598119640 0.000461771196239281 0.99976911440188 71 0.000747245376807143 0.00149449075361429 0.999252754623193 72 0.000907406056018717 0.00181481211203743 0.999092593943981 73 0.000869099671056274 0.00173819934211255 0.999130900328944 74 0.00101196014033976 0.00202392028067953 0.99898803985966 75 0.00238993826673824 0.00477987653347647 0.997610061733262 76 0.00405959584094406 0.00811919168188811 0.995940404159056 77 0.0164223891594542 0.0328447783189084 0.983577610840546 78 0.0241143698288601 0.0482287396577202 0.97588563017114 79 0.0369119651625179 0.0738239303250359 0.963088034837482 80 0.051690949178027 0.103381898356054 0.948309050821973 81 0.101844753015718 0.203689506031435 0.898155246984282 82 0.275169385538666 0.550338771077332 0.724830614461334 83 0.369991471587955 0.73998294317591 0.630008528412045 84 0.8839451634971 0.232109673005800 0.116054836502900 85 0.927923509325336 0.144152981349329 0.0720764906746643 86 0.906832868652905 0.186334262694190 0.0931671313470951 87 0.92197760704776 0.156044785904479 0.0780223929522397 88 0.944165934368196 0.111668131263609 0.0558340656318044 89 0.944477573935532 0.111044852128936 0.0555224260644681 90 0.97871873965737 0.0425625206852594 0.0212812603426297 91 0.971319657781523 0.0573606844369537 0.0286803422184769 92 0.966549439500991 0.0669011209980173 0.0334505604990087 93 0.985215049979191 0.0295699000416178 0.0147849500208089 94 0.981659102873206 0.0366817942535877 0.0183408971267939 95 0.9688646228356 0.0622707543288006 0.0311353771644003 96 0.971569958421307 0.0568600831573867 0.0284300415786934 97 0.95929391770041 0.0814121645991801 0.0407060822995900 98 0.94051614321888 0.118967713562241 0.0594838567811206 99 0.95480854536017 0.0903829092796615 0.0451914546398308 100 0.930440343005274 0.139119313989451 0.0695596569947256 101 0.973157101778828 0.0536857964423435 0.0268428982211718 102 0.962807513084047 0.0743849738319065 0.0371924869159532 103 0.946093138827315 0.107813722345370 0.0539068611726851 104 0.933682220941975 0.132635558116050 0.0663177790580252 105 0.96257205954852 0.0748558809029609 0.0374279404514804 106 0.973977785682637 0.0520444286347256 0.0260222143173628 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 66 0.6875 NOK 5% type I error level 71 0.739583333333333 NOK 10% type I error level 82 0.854166666666667 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice) library(lmtest) n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test par1 <- as.numeric(par1) x <- t(y) k <- length(x[1,]) n <- length(x[,1]) x1 <- cbind(x[,par1], x[,1:k!=par1]) mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1]) colnames(x1) <- mycolnames #colnames(x)[par1] x <- x1 if (par3 == 'First Differences'){ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep=''))) for (i in 1:n-1) { for (j in 1:k) { x2[i,j] <- x[i+1,j] - x[i,j] } } x <- x2 } if (par2 == 'Include Monthly Dummies'){ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep =''))) for (i in 1:11){ x2[seq(i,n,12),i] <- 1 } x <- cbind(x, x2) } if (par2 == 'Include Quarterly Dummies'){ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep =''))) for (i in 1:3){ x2[seq(i,n,4),i] <- 1 } x <- cbind(x, x2) } k <- length(x[1,]) if (par3 == 'Linear Trend'){ x <- cbind(x, c(1:n)) colnames(x)[k+1] <- 't' } x k <- length(x[1,]) df <- as.data.frame(x) (mylm <- lm(df)) (mysum <- summary(mylm)) if (n > n25) { kp3 <- k + 3 nmkm3 <- n - k - 3 gqarr <- array(NA, dim=c(nmkm3-kp3+1,3)) numgqtests <- 0 numsignificant1 <- 0 numsignificant5 <- 0 numsignificant10 <- 0 for (mypoint in kp3:nmkm3) { j <- 0 numgqtests <- numgqtests + 1 for (myalt in c('greater', 'two.sided', 'less')) { j <- j + 1 gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value } if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1 if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1 if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1 } gqarr } bitmap(file='test0.png') plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index') points(x[,1]-mysum$resid) grid() dev.off() bitmap(file='test1.png') plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index') grid() dev.off() bitmap(file='test2.png') hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals') grid() dev.off() bitmap(file='test3.png') densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals') dev.off() bitmap(file='test4.png') qqnorm(mysum$resid, main='Residual Normal Q-Q Plot') qqline(mysum$resid) grid() dev.off() (myerror <- as.ts(mysum$resid)) bitmap(file='test5.png') dum <- cbind(lag(myerror,k=1),myerror) dum dum1 <- dum[2:length(myerror),] dum1 z <- as.data.frame(dum1) z plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals') lines(lowess(z)) abline(lm(z)) grid() dev.off() bitmap(file='test6.png') acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function') grid() dev.off() bitmap(file='test7.png') pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function') grid() dev.off() bitmap(file='test8.png') opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0)) plot(mylm, las = 1, sub='Residual Diagnostics') par(opar) dev.off() if (n > n25) { bitmap(file='test9.png') plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint') grid() dev.off() } load(file='createtable') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE) a<-table.row.end(a) myeq <- colnames(x)[1] myeq <- paste(myeq, '[t] = ', sep='') for (i in 1:k){ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '') myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ') if (rownames(mysum$coefficients)[i] != '(Intercept)') { myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='') if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='') } } myeq <- paste(myeq, ' + e[t]') a<-table.row.start(a) a<-table.element(a, myeq) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable1.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Variable',header=TRUE) a<-table.element(a,'Parameter',header=TRUE) a<-table.element(a,'S.D.',header=TRUE) a<-table.element(a,'T-STAT<br />H0: parameter = 0',header=TRUE) a<-table.element(a,'2-tail p-value',header=TRUE) a<-table.element(a,'1-tail p-value',header=TRUE) a<-table.row.end(a) for (i in 1:k){ a<-table.row.start(a) a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE) a<-table.element(a,mysum$coefficients[i,1]) a<-table.element(a, round(mysum$coefficients[i,2],6)) a<-table.element(a, round(mysum$coefficients[i,3],4)) a<-table.element(a, round(mysum$coefficients[i,4],6)) a<-table.element(a, round(mysum$coefficients[i,4]/2,6)) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable2.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple R',1,TRUE) a<-table.element(a, sqrt(mysum$r.squared)) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'R-squared',1,TRUE) a<-table.element(a, mysum$r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Adjusted R-squared',1,TRUE) a<-table.element(a, mysum$adj.r.squared) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (value)',1,TRUE) a<-table.element(a, mysum$fstatistic[1]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE) a<-table.element(a, mysum$fstatistic[2]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE) a<-table.element(a, mysum$fstatistic[3]) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'p-value',1,TRUE) a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3])) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Residual Standard Deviation',1,TRUE) a<-table.element(a, mysum$sigma) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Sum Squared Residuals',1,TRUE) a<-table.element(a, sum(myerror*myerror)) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable3.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a, 'Time or Index', 1, TRUE) a<-table.element(a, 'Actuals', 1, TRUE) a<-table.element(a, 'Interpolation<br />Forecast', 1, TRUE) a<-table.element(a, 'Residuals<br />Prediction Error', 1, TRUE) a<-table.row.end(a) for (i in 1:n) { a<-table.row.start(a) a<-table.element(a,i, 1, TRUE) a<-table.element(a,x[i]) a<-table.element(a,x[i]-mysum$resid[i]) a<-table.element(a,mysum$resid[i]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable4.tab') if (n > n25) { a<-table.start() a<-table.row.start(a) a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'p-values',header=TRUE) a<-table.element(a,'Alternative Hypothesis',3,header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'breakpoint index',header=TRUE) a<-table.element(a,'greater',header=TRUE) a<-table.element(a,'2-sided',header=TRUE) a<-table.element(a,'less',header=TRUE) a<-table.row.end(a) for (mypoint in kp3:nmkm3) { a<-table.row.start(a) a<-table.element(a,mypoint,header=TRUE) a<-table.element(a,gqarr[mypoint-kp3+1,1]) a<-table.element(a,gqarr[mypoint-kp3+1,2]) a<-table.element(a,gqarr[mypoint-kp3+1,3]) a<-table.row.end(a) } a<-table.end(a) table.save(a,file='mytable5.tab') a<-table.start() a<-table.row.start(a) a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'Description',header=TRUE) a<-table.element(a,'# significant tests',header=TRUE) a<-table.element(a,'% significant tests',header=TRUE) a<-table.element(a,'OK/NOK',header=TRUE) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'1% type I error level',header=TRUE) a<-table.element(a,numsignificant1) a<-table.element(a,numsignificant1/numgqtests) if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'5% type I error level',header=TRUE) a<-table.element(a,numsignificant5) a<-table.element(a,numsignificant5/numgqtests) if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.row.start(a) a<-table.element(a,'10% type I error level',header=TRUE) a<-table.element(a,numsignificant10) a<-table.element(a,numsignificant10/numgqtests) if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK' a<-table.element(a,dum) a<-table.row.end(a) a<-table.end(a) table.save(a,file='mytable6.tab') }

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