workshop 7

*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: Fri, 03 Dec 2010 16:03:28 +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/03/t12913921695t0euqjb8tf219f.htm/, Retrieved Fri, 03 Dec 2010 17:02:49 +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/03/t12913921695t0euqjb8tf219f.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 «

0 162556 1081 213118 6282154 0 29790 309 81767 4321023 0 87550 458 153198 4111912 1 84738 588 -26007 223193 0 54660 302 126942 1491348 0 42634 156 157214 1629616 1 40949 481 129352 1398893 0 45187 353 234817 1926517 0 37704 452 60448 983660 0 16275 109 47818 1443586 1 25830 115 245546 1073089 1 12679 110 48020 984885 0 18014 239 -1710 1405225 1 43556 247 32648 227132 0 24811 505 95350 929118 1 6575 159 151352 1071292 1 7123 109 288170 638830 0 21950 519 114337 856956 0 37597 248 37884 992426 1 17821 373 122844 444477 0 12988 119 82340 857217 0 22330 84 79801 711969 1 13326 102 165548 702380 1 16189 295 116384 358589 1 7146 105 134028 297978 1 15824 64 63838 585715 0 27664 282 74996 657954 1 11920 182 31080 209458 1 8568 37 32168 786690 1 14416 361 49857 439798 0 3369 28 87161 688779 0 11819 85 106113 574339 0 6984 45 80570 741409 0 4519 49 102129 597793 1 2220 22 301670 644190 1 18562 155 102313 377934 1 10327 91 88577 640273 0 5336 81 112477 697458 0 2365 79 191778 55060 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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Wealth[t] = + 205204.989348910 -210280.941099507Group[t] + 30.6772483966378Costs[t] -324.567875602376Trades[t] + 2.3730252473845Dividends[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) 205204.989348910 126632.659901 1.6205 0.108445 0.054223 Group -210280.941099507 100241.326173 -2.0977 0.038582 0.019291 Costs 30.6772483966378 3.190638 9.6148 0 0 Trades -324.567875602376 358.607505 -0.9051 0.367714 0.183857 Dividends 2.3730252473845 0.926933 2.5601 0.012042 0.006021 Multiple Linear Regression - Regression Statistics Multiple R 0.821002770953783 R-squared 0.67404554991379 Adjusted R-squared 0.660321152015423 F-TEST (value) 49.1129414131897 F-TEST (DF numerator) 4 F-TEST (DF denominator) 95 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 487513.442644005 Sum Squared Residuals 22578588892067.9 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 6282154 5346852.30085869 935301.69914131 2 4321023 1212823.90092650 3108199.09907350 3 4111912 3105888.72129747 1006023.27870253 4 223193 2341891.54442077 -2118698.54442077 5 1491348 2085240.4592307 -593892.459230699 6 1629616 1835539.00013950 -205923.000139503 7 1398893 1401965.10647826 -3072.10647826221 8 1926517 2034072.02207523 -107555.022075230 9 983660 1358599.91327737 -374939.913277366 10 1443586 782572.629842963 661013.370157037 11 1073089 1332678.92603456 -259589.926034559 12 984885 462131.086733516 522753.913266484 13 1405225 676195.346523948 729029.653476052 14 227132 1328408.54241618 -1101276.54241618 15 929118 1028699.37947680 -99581.379476803 16 1071292 504182.781478658 567109.218521342 17 638830 861894.875676787 -223064.875676787 18 856956 981444.451927678 -124488.451927678 19 992426 1367984.35263983 -375558.352639827 20 444477 712071.387815901 -267594.387815901 21 857217 760412.413197399 96804.5868026011 22 711969 1052334.03226176 -340365.032261763 23 702380 763472.720725566 -61092.7207255656 24 358589 671992.66963147 -313403.669631470 25 297978 498115.866209977 -200137.866209977 26 585715 611077.66858178 -25362.6685817795 27 657954 1140299.64952648 -482345.649526476 28 209458 375279.120466404 -165821.120466404 29 786690 322093.177272373 464596.822727627 30 439798 438310.177801725 1487.82219827473 31 688779 506303.992267596 182475.007732404 32 574339 791999.946798282 -217660.946798282 33 741409 596043.98193069 145365.018069309 34 597793 570286.344438931 27506.6555610687 35 644190 771757.572805169 -127567.572805169 36 377934 756838.444405076 -378904.444405076 37 640273 492387.773099243 147885.226900757 38 697458 609519.549619643 87938.4503803568 39 550608 707209.855527275 -156601.855527275 40 207393 262064.336855251 -54671.3368552506 41 301607 286792.132850683 14814.867149317 42 345783 624829.440402476 -279046.440402476 43 501749 283002.128840440 218746.871159560 44 379983 450444.367620285 -70461.3676202853 45 387475 280891.19862556 106583.801374440 46 377305 551814.14777581 -174509.147775810 47 370837 784499.36254241 -413662.362542409 48 430866 655531.013438052 -224665.013438052 49 469107 407328.779251844 61778.2207481563 50 194493 235586.391668363 -41093.3916683634 51 530670 523601.554808009 7068.44519199109 52 518365 603616.898166888 -85251.8981668882 53 491303 839563.835773396 -348260.835773396 54 527021 534387.252754684 -7366.25275468426 55 233773 668452.625977665 -434679.625977665 56 405972 182155.877370101 223816.122629899 57 652925 226463.085565390 426461.91443461 58 446211 346431.844298556 99779.1557014437 59 341340 235705.458586665 105634.541413335 60 387699 665997.823809616 -278298.823809616 61 493408 584575.983739852 -91167.9837398521 62 146494 172620.425600293 -26126.4256002927 63 414462 514598.351242623 -100136.351242623 64 364304 604514.865314149 -240210.865314149 65 355178 191393.656562713 163784.343437287 66 357760 309663.353249281 48096.6467507191 67 261216 189621.086805310 71594.9131946903 68 397144 489289.597318375 -92145.597318375 69 374943 319240.647995658 55702.3520043418 70 424898 546511.456244787 -121613.456244787 71 202055 344647.053942752 -142592.053942752 72 378525 228697.589519919 149827.410480081 73 310768 281428.686501203 29339.3134987966 74 325738 196494.303968394 129243.696031606 75 394510 326259.352620996 68250.6473790036 76 247060 437060.314034655 -190000.314034655 77 368078 283062.095065313 85015.9049346867 78 236761 181455.664484941 55305.3355150587 79 312378 161058.683561186 151319.316438814 80 339836 449841.042937028 -110005.042937028 81 347385 173531.711907103 173853.288092897 82 426280 538223.028001819 -111943.028001819 83 352850 332429.504838589 20420.495161411 84 301881 108084.232111034 193796.767888966 85 377516 249592.654328812 127923.345671188 86 357312 508891.769045934 -151579.769045934 87 458343 292931.754487573 165411.245512427 88 354228 253645.516843247 100582.483156753 89 308636 291533.843244303 17102.1567556972 90 386212 253988.095649622 132223.904350378 91 393343 266176.628172588 127166.371827412 92 378509 479095.824054814 -100586.824054814 93 452469 182815.463759315 269653.536240685 94 364839 682437.496599533 -317598.496599533 95 358649 127320.334527143 231328.665472857 96 376641 427935.21662891 -51294.21662891 97 429112 268811.704636681 160300.295363319 98 330546 350866.583174919 -20320.5831749190 99 403560 375265.525569749 28294.474430251 100 317892 417816.658888574 -99924.6588885743 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 1 1.14950965585681e-15 5.74754827928404e-16 9 1 6.50192402395504e-24 3.25096201197752e-24 10 1 9.8389847131465e-26 4.91949235657325e-26 11 1 1.09331675281288e-25 5.46658376406438e-26 12 1 5.56955056448243e-28 2.78477528224121e-28 13 1 1.19906892278859e-31 5.99534461394296e-32 14 1 6.14506189946576e-34 3.07253094973288e-34 15 1 6.80467709672857e-35 3.40233854836428e-35 16 1 1.81358795447156e-38 9.06793977235778e-39 17 1 1.00611277651984e-37 5.03056388259921e-38 18 1 4.14645866930642e-38 2.07322933465321e-38 19 1 6.45718189579459e-38 3.22859094789729e-38 20 1 4.8059319749975e-37 2.40296598749875e-37 21 1 1.28743242062180e-37 6.43716210310901e-38 22 1 3.96508430295402e-37 1.98254215147701e-37 23 1 1.39043656624749e-36 6.95218283123747e-37 24 1 6.30361347947488e-36 3.15180673973744e-36 25 1 2.30446578924454e-35 1.15223289462227e-35 26 1 1.07255870838167e-34 5.36279354190836e-35 27 1 2.8229844597628e-34 1.4114922298814e-34 28 1 4.27595018248717e-34 2.13797509124359e-34 29 1 5.71056452836322e-36 2.85528226418161e-36 30 1 3.73588069179919e-35 1.86794034589960e-35 31 1 1.52453543166418e-35 7.6226771583209e-36 32 1 6.1702786543498e-35 3.0851393271749e-35 33 1 6.03071074959444e-36 3.01535537479722e-36 34 1 8.7183147117415e-36 4.35915735587075e-36 35 1 4.62526601527646e-35 2.31263300763823e-35 36 1 1.46046141829268e-34 7.30230709146342e-35 37 1 1.12673394782425e-34 5.63366973912124e-35 38 1 8.82644793150257e-36 4.41322396575128e-36 39 1 2.49612257980424e-35 1.24806128990212e-35 40 1 4.68516129621032e-35 2.34258064810516e-35 41 1 3.15945586510931e-34 1.57972793255466e-34 42 1 8.85011631103996e-34 4.42505815551998e-34 43 1 2.13197647725125e-33 1.06598823862563e-33 44 1 1.45530460245198e-32 7.2765230122599e-33 45 1 1.08419570529626e-31 5.42097852648129e-32 46 1 7.4121730740075e-31 3.70608653700375e-31 47 1 2.02728265934929e-30 1.01364132967465e-30 48 1 1.33020707434613e-29 6.65103537173064e-30 49 1 8.83389851278769e-29 4.41694925639384e-29 50 1 1.09850310028250e-28 5.49251550141249e-29 51 1 1.66036095582744e-28 8.30180477913721e-29 52 1 4.02085181224515e-28 2.01042590612258e-28 53 1 2.46548157216292e-27 1.23274078608146e-27 54 1 8.74873256765517e-28 4.37436628382759e-28 55 1 8.1781997733751e-28 4.08909988668755e-28 56 1 4.6803253177759e-27 2.34016265888795e-27 57 1 5.30178978807201e-30 2.65089489403600e-30 58 1 4.01979073556749e-29 2.00989536778375e-29 59 1 4.11344415346927e-28 2.05672207673463e-28 60 1 2.73768883310221e-27 1.36884441655110e-27 61 1 9.16671822180903e-27 4.58335911090451e-27 62 1 5.88655577201967e-28 2.94327788600983e-28 63 1 4.19669479827719e-27 2.09834739913859e-27 64 1 3.585170659887e-26 1.7925853299435e-26 65 1 3.88709899949494e-25 1.94354949974747e-25 66 1 2.63461669667776e-24 1.31730834833888e-24 67 1 9.03853465591332e-24 4.51926732795666e-24 68 1 5.5502768073258e-23 2.7751384036629e-23 69 1 5.82214151830326e-22 2.91107075915163e-22 70 1 3.45704794732006e-21 1.72852397366003e-21 71 1 4.96030486154134e-21 2.48015243077067e-21 72 1 5.30294113769182e-20 2.65147056884591e-20 73 1 3.13731943233893e-19 1.56865971616947e-19 74 1 3.36360402352731e-18 1.68180201176365e-18 75 1 3.71466028589896e-17 1.85733014294948e-17 76 1 1.16785932064480e-16 5.83929660322401e-17 77 1 1.31203610752780e-15 6.56018053763902e-16 78 1 6.97345101787183e-16 3.48672550893592e-16 79 0.999999999999998 3.7449693448447e-15 1.87248467242235e-15 80 0.999999999999976 4.75893121137737e-14 2.37946560568869e-14 81 0.999999999999745 5.09366704066196e-13 2.54683352033098e-13 82 0.999999999998429 3.14222237310169e-12 1.57111118655085e-12 83 0.999999999985867 2.82653649342057e-11 1.41326824671029e-11 84 0.99999999995228 9.54390588863874e-11 4.77195294431937e-11 85 0.999999999395675 1.20865028835267e-09 6.04325144176336e-10 86 0.999999993403396 1.31932072034809e-08 6.59660360174044e-09 87 0.999999958298718 8.34025633430263e-08 4.17012816715132e-08 88 0.999999590794206 8.18411587533157e-07 4.09205793766579e-07 89 0.999999479751128 1.04049774354137e-06 5.20248871770684e-07 90 0.99999282066021 1.43586795808325e-05 7.17933979041623e-06 91 0.99990807258056 0.000183854838881042 9.1927419440521e-05 92 0.999022326475942 0.00195534704811663 0.000977673524058315 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 85 1 NOK 5% type I error level 85 1 NOK 10% type I error level 85 1 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/10nn011291392198.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/10nn011291392198.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/1y4381291392198.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/1y4381291392198.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/2rwks1291392198.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/2rwks1291392198.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/3rwks1291392198.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/3rwks1291392198.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/4rwks1291392198.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/4rwks1291392198.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/5252d1291392198.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/5252d1291392198.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/6252d1291392198.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/6252d1291392198.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/7cejg1291392198.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/7cejg1291392198.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/8cejg1291392198.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/8cejg1291392198.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/9nn011291392198.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/03/t12913921695t0euqjb8tf219f/9nn011291392198.ps (open in new window)

Parameters (Session):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 5 ; 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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