Multiple Regressions

*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, 21 Dec 2010 15:03:21 +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/21/t1292943801aduq0viqpc034dz.htm/, Retrieved Tue, 21 Dec 2010 16:03:36 +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/21/t1292943801aduq0viqpc034dz.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 «

143827 829461 4.93 5.01 639.98 3536.15 0.94 109.57 9113 145191 837669 4.92 5.02 597.33 3240.92 0.92 107.08 9140 146832 854793 4.83 4.94 558.36 3121.58 0.91 110.33 9309 148577 850092 5.02 5.10 593.09 3302.70 0.89 110.36 9395 149873 848783 5.22 5.26 585.15 3292.49 0.87 106.50 10027 151847 846150 5.17 5.21 573.50 3162.62 0.85 104.30 10202 153252 828543 5.17 5.25 548.72 3051.60 0.86 107.21 10003 154292 830389 4.98 5.06 523.63 2848.11 0.90 109.34 9745 155657 848989 4.98 5.04 453.87 2577.68 0.91 108.20 9966 156523 841106 4.77 4.82 460.33 2680.55 0.91 109.86 10035 156416 854616 4.62 4.67 492.67 2775.70 0.89 108.68 9999 156693 832714 4.89 4.95 506.78 2879.30 0.89 113.38 9943 160312 839290 4.97 5.02 500.92 2790.11 0.88 117.12 10258 160438 840572 5.03 5.07 494.91 2764.18 0.87 116.23 10926 160882 869186 5.27 5.31 531.21 2868.37 0.88 114.75 10807 161668 856979 5.25 5.29 511.28 2740.50 0.89 115.81 10992 164391 872126 5.30 5.31 484.55 2622.87 0.92 115.86 11034 168556 868281 5.16 5.17 439.66 2376.70 0.96 11 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 17 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation SpaarNL[t] = + 25533.6968586138 + 0.0675790252249236Leningen[t] + 11377.5610545139`10jNL`[t] -12780.5380104956`10JEUR`[t] -284.080217654409AEX[t] + 48.3210098962368EURO[t] + 36746.7729427624USD[t] + 108.679230765155YEN[t] + 4.30611868642121GOLD[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) 25533.6968586138 11382.219803 2.2433 0.0269 0.01345 Leningen 0.0675790252249236 0.013923 4.8536 4e-06 2e-06 `10jNL` 11377.5610545139 9101.474273 1.2501 0.213948 0.106974 `10JEUR` -12780.5380104956 9790.550389 -1.3054 0.194507 0.097254 AEX -284.080217654409 52.322441 -5.4294 0 0 EURO 48.3210098962368 9.860403 4.9005 3e-06 2e-06 USD 36746.7729427624 12892.841021 2.8502 0.005226 0.002613 YEN 108.679230765155 103.532406 1.0497 0.296172 0.148086 GOLD 4.30611868642121 0.335582 12.8318 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.99094578367694 R-squared 0.981973546187107 Adjusted R-squared 0.980650503705427 F-TEST (value) 742.208628811386 F-TEST (DF numerator) 8 F-TEST (DF denominator) 109 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 6230.06816412887 Sum Squared Residuals 4230698676.93644 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 143827 148405.034187474 -4578.03418747407 2 145191 145679.069828239 -488.069828238617 3 146832 152852.206191663 -6020.2061916633 4 148577 151175.814191128 -2598.81419112809 5 149873 154647.248513677 -4774.24851367682 6 151847 151353.087772094 493.912227906421 7 153252 151153.718301691 2098.28169830898 8 154292 149430.144219200 4861.85578079977 9 155657 158887.935761980 -3230.93576198033 10 156523 162390.794641405 -5867.79464140506 11 156416 157906.626444405 -1490.62644440506 12 156693 157187.235968012 -494.235968011862 13 160312 156397.582044184 3914.41795581642 14 160438 159394.498476482 1043.50152351770 15 160882 155408.138699278 5473.8613007224 16 161668 155373.471950924 6294.5280490762 17 164391 159908.516696980 4482.48330301968 18 168556 161379.653054636 7176.34694536353 19 169738 168974.221176522 763.778823477707 20 170387 167677.783504600 2709.21649539962 21 171294 175078.093797074 -3784.09379707369 22 172202 171678.388659177 523.611340822628 23 172651 173232.578610673 -581.578610672689 24 172770 175835.553102918 -3065.55310291823 25 178366 180541.014773351 -2175.01477335128 26 180014 187979.615865739 -7965.61586573948 27 181067 187509.449837263 -6442.44983726338 28 182586 185774.608789225 -3188.60878922513 29 184957 193027.032894278 -8070.03289427842 30 186417 192910.901922809 -6493.90192280897 31 188599 187153.615026960 1445.38497303974 32 189490 186135.048910183 3354.95108981702 33 190264 191245.112206034 -981.112206034347 34 191221 190655.421919650 565.578080350328 35 191110 192549.306265688 -1439.30626568817 36 190674 197794.962450050 -7120.96245005026 37 195438 196893.900191873 -1455.90019187305 38 196393 197757.471173246 -1364.47117324613 39 197172 200766.954084996 -3594.95408499601 40 198760 201006.215642499 -2246.21564249907 41 200945 201102.261853487 -157.261853487097 42 203845 200578.583418356 3266.41658164397 43 204613 205330.253471913 -717.253471913398 44 205487 207062.937869157 -1575.93786915733 45 206100 209412.043690639 -3312.04369063904 46 206315 211419.143733932 -5104.14373393160 47 206291 214356.258985778 -8065.25898577801 48 207801 213981.955295070 -6180.95529506965 49 211653 211394.35096377 258.649036230201 50 211325 210170.338725319 1154.66127468050 51 211893 213171.05155761 -1278.05155760984 52 212056 217290.338562238 -5234.33856223824 53 214696 217166.338510297 -2470.33851029655 54 217455 215091.764313945 2363.23568605471 55 218884 204505.995653208 14378.0043467924 56 219816 207936.753012490 11879.2469875098 57 219984 214051.967378932 5932.03262106766 58 219062 215925.204973045 3136.79502695512 59 218550 213551.071948848 4998.92805115239 60 218179 214664.536317834 3514.46368216638 61 222218 218164.87530598 4053.12469401983 62 222196 217915.607614437 4280.39238556332 63 223393 219437.020907734 3955.97909226588 64 223292 226013.351848435 -2721.35184843492 65 226236 234517.464429847 -8281.46442984735 66 228831 225355.172320519 3475.82767948059 67 228745 228737.055228772 7.9447712278511 68 229140 227118.343781794 2021.65621820594 69 229270 222040.66070402 7229.33929598014 70 229359 226030.871108660 3328.12889133956 71 230006 232235.157747131 -2229.15774713095 72 228810 231527.215706028 -2717.21570602789 73 232677 235440.963964112 -2763.96396411195 74 232961 238042.854798049 -5081.85479804878 75 234629 234914.213927539 -285.213927539195 76 235660 236847.453330115 -1187.45333011472 77 240024 237893.419168560 2130.58083144033 78 243554 234816.351849372 8737.64815062765 79 244368 234789.608585449 9578.39141455077 80 244356 238215.544397749 6140.45560225092 81 245126 239516.662135224 5609.3378647765 82 246321 247616.453612925 -1295.45361292538 83 246797 257402.570039116 -10605.5700391164 84 246735 252551.17700358 -5816.17700357996 85 251083 263344.966527308 -12261.9665273077 86 251786 264323.896133391 -12537.8961333906 87 252732 262492.158232545 -9760.15823254543 88 255051 259445.105938424 -4394.10593842411 89 259022 255824.063381529 3197.93661847102 90 261698 260074.577946531 1623.42205346914 91 263891 269844.441866128 -5953.4418661281 92 265247 259510.736048295 5736.26395170457 93 262228 272357.708296821 -10129.7082968209 94 263429 274445.963866315 -11016.9638663153 95 264305 266173.74335452 -1868.7433545199 96 266371 262486.219898713 3884.78010128693 97 273248 265093.423665467 8154.57633453305 98 275472 274184.421459799 1287.57854020126 99 278146 271574.798324855 6571.20167514535 100 279506 272676.123957135 6829.87604286464 101 283991 274168.736075494 9822.26392450577 102 286794 274542.790025252 12251.2099747484 103 288703 273847.666172396 14855.3338276039 104 289285 275983.083919024 13301.9160809759 105 288869 275727.901847891 13141.0981521087 106 286942 281681.108025587 5260.89197441264 107 285833 287671.335307778 -1838.33530777794 108 284095 286655.58611009 -2560.58611008991 109 289229 283076.780870442 6152.21912955754 110 289389 284237.014961953 5151.98503804671 111 290793 287584.437010873 3208.56298912739 112 291454 286771.995244801 4682.00475519878 113 294733 303981.131553162 -9248.13155316167 114 293853 306523.596047131 -12670.5960471305 115 294056 294589.167216605 -533.167216605448 116 293982 297552.923745298 -3570.92374529837 117 293075 297171.963347249 -4096.96334724889 118 292391 305102.186121918 -12711.1861219176 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 12 0.00131884379933147 0.00263768759866294 0.998681156200669 13 0.000105047016939976 0.000210094033879951 0.99989495298306 14 7.63941713092074e-06 1.52788342618415e-05 0.999992360582869 15 0.000123204398141772 0.000246408796283544 0.999876795601858 16 1.92070323985524e-05 3.84140647971049e-05 0.999980792967601 17 3.53242870016739e-06 7.06485740033479e-06 0.9999964675713 18 1.5984649228807e-06 3.1969298457614e-06 0.999998401535077 19 2.92597683812326e-06 5.85195367624652e-06 0.999997074023162 20 8.84916648589e-07 1.769833297178e-06 0.999999115083351 21 4.39869247437869e-07 8.79738494875737e-07 0.999999560130753 22 1.81200650503215e-07 3.62401301006429e-07 0.99999981879935 23 9.08909399845154e-08 1.81781879969031e-07 0.99999990910906 24 2.44299505505447e-08 4.88599011010894e-08 0.99999997557005 25 6.4115580986214e-08 1.28231161972428e-07 0.999999935884419 26 2.17400454963829e-08 4.34800909927659e-08 0.999999978259954 27 1.85082391454231e-08 3.70164782908463e-08 0.99999998149176 28 1.36310441694477e-08 2.72620883388954e-08 0.999999986368956 29 4.50968510221349e-09 9.01937020442698e-09 0.999999995490315 30 2.11760808925906e-09 4.23521617851812e-09 0.999999997882392 31 2.07153318547725e-08 4.14306637095451e-08 0.999999979284668 32 6.64798014792329e-08 1.32959602958466e-07 0.999999933520199 33 5.30709320095791e-08 1.06141864019158e-07 0.999999946929068 34 5.03480712333801e-08 1.00696142466760e-07 0.999999949651929 35 1.66105600065558e-08 3.32211200131115e-08 0.99999998338944 36 1.70396883437120e-08 3.40793766874241e-08 0.999999982960312 37 7.35029213607018e-09 1.47005842721404e-08 0.999999992649708 38 3.8273871906135e-09 7.654774381227e-09 0.999999996172613 39 2.01405021513283e-09 4.02810043026565e-09 0.99999999798595 40 1.15834072813930e-09 2.31668145627860e-09 0.99999999884166 41 4.24928529013829e-10 8.49857058027657e-10 0.999999999575071 42 2.07088588729679e-10 4.14177177459357e-10 0.999999999792911 43 7.79062790696816e-11 1.55812558139363e-10 0.999999999922094 44 3.26031795986511e-11 6.52063591973023e-11 0.999999999967397 45 1.71691061423440e-11 3.43382122846879e-11 0.99999999998283 46 1.68999223098115e-11 3.37998446196230e-11 0.9999999999831 47 4.15669546858113e-11 8.31339093716226e-11 0.999999999958433 48 4.98779561857665e-11 9.9755912371533e-11 0.999999999950122 49 6.39630624769542e-11 1.27926124953908e-10 0.999999999936037 50 6.06801414310233e-11 1.21360282862047e-10 0.99999999993932 51 5.8933839780321e-11 1.17867679560642e-10 0.999999999941066 52 1.18063050389684e-10 2.36126100779367e-10 0.999999999881937 53 1.83401136194289e-10 3.66802272388578e-10 0.9999999998166 54 2.74695598270928e-09 5.49391196541856e-09 0.999999997253044 55 0.000423841778306819 0.000847683556613638 0.999576158221693 56 0.000613786758057848 0.00122757351611570 0.999386213241942 57 0.000414920828756868 0.000829841657513736 0.999585079171243 58 0.000492775005679136 0.000985550011358273 0.99950722499432 59 0.000459950899611913 0.000919901799223826 0.999540049100388 60 0.000827130337821357 0.00165426067564271 0.999172869662179 61 0.000974601667022637 0.00194920333404527 0.999025398332977 62 0.000777448153060612 0.00155489630612122 0.99922255184694 63 0.000528031585352299 0.00105606317070460 0.999471968414648 64 0.000676860645943713 0.00135372129188743 0.999323139354056 65 0.00252074761825818 0.00504149523651637 0.997479252381742 66 0.00381034491275492 0.00762068982550984 0.996189655087245 67 0.00419259749196283 0.00838519498392566 0.995807402508037 68 0.00355771684441329 0.00711543368882657 0.996442283155587 69 0.0023609020419866 0.0047218040839732 0.997639097958013 70 0.00150937438165022 0.00301874876330044 0.99849062561835 71 0.00160622745081485 0.0032124549016297 0.998393772549185 72 0.00172678567971758 0.00345357135943516 0.998273214320282 73 0.00132672275259548 0.00265344550519097 0.998673277247405 74 0.00167167050846344 0.00334334101692687 0.998328329491537 75 0.00145813985009062 0.00291627970018125 0.99854186014991 76 0.00115100158291714 0.00230200316583429 0.998848998417083 77 0.000890302615925965 0.00178060523185193 0.999109697384074 78 0.00190585325331064 0.00381170650662127 0.99809414674669 79 0.00269365311600302 0.00538730623200604 0.997306346883997 80 0.00258413217383586 0.00516826434767171 0.997415867826164 81 0.0031710429245831 0.0063420858491662 0.996828957075417 82 0.00722402335171808 0.0144480467034362 0.992775976648282 83 0.00936947402562146 0.0187389480512429 0.990630525974379 84 0.016640907018215 0.03328181403643 0.983359092981785 85 0.0140072203218128 0.0280144406436255 0.985992779678187 86 0.0116301909729957 0.0232603819459913 0.988369809027004 87 0.0263482556324448 0.0526965112648895 0.973651744367555 88 0.0287245099821764 0.0574490199643529 0.971275490017824 89 0.0229023810755919 0.0458047621511838 0.977097618924408 90 0.0207825873493803 0.0415651746987606 0.97921741265062 91 0.0400918916545446 0.0801837833090892 0.959908108345455 92 0.0635626108208264 0.127125221641653 0.936437389179174 93 0.53426137844838 0.93147724310324 0.46573862155162 94 0.667733423058084 0.664533153883831 0.332266576941916 95 0.83709420038377 0.32581159923246 0.16290579961623 96 0.980480838788074 0.0390383224238527 0.0195191612119264 97 0.984248565342455 0.0315028693150900 0.0157514346575450 98 0.979398347790822 0.0412033044183565 0.0206016522091783 99 0.982975342857332 0.0340493142853356 0.0170246571426678 100 0.998246122090914 0.00350775581817185 0.00175387790908593 101 0.99940856147972 0.00118287704056145 0.000591438520280725 102 0.998268682282225 0.0034626354355509 0.00173131771777545 103 0.995171611534175 0.0096567769316497 0.00482838846582485 104 0.98793760806321 0.0241247838735808 0.0120623919367904 105 0.971926255876284 0.0561474882474323 0.0280737441237162 106 0.92382114472305 0.152357710553900 0.0761788552769498 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 74 0.778947368421053 NOK 5% type I error level 86 0.905263157894737 NOK 10% type I error level 90 0.947368421052632 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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