Q3 vervolg

*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: Mon, 24 Nov 2008 07:05:06 -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/24/t1227535613vfr3qc7btb6zvhg.htm/, Retrieved Mon, 24 Nov 2008 14:07:03 +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/24/t1227535613vfr3qc7btb6zvhg.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}, }

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User-defined keywords:

Dataseries X:

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1,3322 133,52 7,4545 0 1,4369 153,2 7,4583 0 1,4975 163,63 7,4595 0 1,577 168,45 7,4599 0 1,5553 166,26 7,4586 0 1,5557 162,31 7,4609 0 1,575 161,56 7,4603 0 1,5527 156,59 7,4561 0 1,4748 157,97 7,454 0 1,4718 158,68 7,4505 0 1,457 163,55 7,4599 0 1,4684 162,89 7,4543 0 1,4227 164,95 7,4534 0 1,3896 159,82 7,4506 0 1,3622 159,05 7,4429 0 1,3716 166,76 7,441 0 1,3419 164,55 7,4452 0 1,3511 163,22 7,4519 0 1,3516 160,68 7,453 0 1,3242 155,24 7,4494 0 1,3074 157,6 7,4541 0 1,2999 156,56 7,4539 0 1,3213 154,82 7,4549 0 1,2881 151,11 7,4564 0 1,2611 149,65 7,4555 0 1,2727 148,99 7,4601 0 1,2811 148,53 7,4609 0 1,2684 146,7 7,4602 0 1,265 145,11 7,4566 0 1,277 142,7 7,4565 0 1,2271 143,59 7,4618 0 1,202 140,96 7,4612 0 1,1938 140,77 7,4641 0 1,2103 139,81 7,4613 0 1,1856 140,58 7,4541 0 1,1786 139,59 7,4596 0 1,2015 138,05 7,462 0 1,2256 136,06 7,4584 0 1,2292 135,98 7,4596 0 1,2037 134,75 7,4584 0 1,2165 132,22 7,4448 0 1,2694 135,37 7,4443 1 1,2938 138,84 7,4499 1 1,3201 138,83 7,4466 1 1,3014 136,55 7,4427 1 1,3119 135,63 7,4405 1 1,3408 139,14 7,4338 1 1,2991 136,09 7,4313 1 1,249 135,97 7,4379 1 1,2218 134,51 7,4381 1 1,2176 134,54 7,4365 1 1,2266 134,08 7,4355 1 1,2138 132,86 7,4342 1 1,2007 134,48 7,4405 1 1,1985 129,08 7,4436 1 1,2262 133,13 7,4493 1 1,2646 134,78 7,4511 1 1,2613 134,13 7,4481 1 1,2286 132,43 7,4419 1 1,1702 127,84 7,437 1 1,1692 128,12 7,4301 1 1,1222 128,94 7,4273 1 1,1139 132,38 7,4322 1 1,1372 134,99 7,4332 1 1,1663 138,05 7,425 1 1,1582 135,83 7,4246 1 1,0848 130,12 7,4255 1 1,0807 128,16 7,4274 1 1,0773 128,6 7,4317 1 1,0622 126,12 7,4324 1 1,0183 124,2 7,4264 1 1,0014 121,65 7,428 1 0,9811 121,57 7,4297 1 0,9808 118,38 7,4271 1

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 4 seconds R Server 'George Udny Yule' @ 72.249.76.132 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation Dollar[t] = -17.5591525902905 + 0.00350468724936866Yen[t] + 2.48338256066758DeenseKroon[t] + 0.217718242006334`(Y/N)`[t] -0.0233279490086631M1[t] -0.0145722863144368M2[t] -0.0079625062616061M3[t] + 0.00746575775475982M4[t] + 0.0239101519213048M5[t] + 0.000565823432548992M6[t] -0.00651859027095586M7[t] + 0.00440537472594684M8[t] -0.0082024995482914M9[t] + 0.00612796500313673M10[t] + 0.00642078012929746M11[t] -0.00697509153826563t + 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) -17.5591525902905 6.494117 -2.7039 0.008979 0.00449 Yen 0.00350468724936866 0.000964 3.635 0.000591 0.000296 DeenseKroon 2.48338256066758 0.863213 2.8769 0.005611 0.002805 `(Y/N)` 0.217718242006334 0.023418 9.2969 0 0 M1 -0.0233279490086631 0.023521 -0.9918 0.325427 0.162713 M2 -0.0145722863144368 0.023307 -0.6252 0.534267 0.267133 M3 -0.0079625062616061 0.024171 -0.3294 0.743026 0.371513 M4 0.00746575775475982 0.024347 0.3066 0.760213 0.380107 M5 0.0239101519213048 0.024375 0.9809 0.3307 0.16535 M6 0.000565823432548992 0.024362 0.0232 0.98155 0.490775 M7 -0.00651859027095586 0.024484 -0.2662 0.791003 0.395501 M8 0.00440537472594684 0.024305 0.1813 0.856798 0.428399 M9 -0.0082024995482914 0.024536 -0.3343 0.739357 0.369678 M10 0.00612796500313673 0.024267 0.2525 0.801531 0.400765 M11 0.00642078012929746 0.024163 0.2657 0.791395 0.395698 t -0.00697509153826563 0.000774 -9.0137 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.96326748849804 R-squared 0.927884254397322 Adjusted R-squared 0.90923363053456 F-TEST (value) 49.7508427184554 F-TEST (DF numerator) 15 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0416781296745391 Sum Squared Residuals 0.100749856603726 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.3322 1.39086550919470 -0.0586655091946951 2 1.4369 1.47105517914878 -0.034155179148775 3 1.4975 1.51022381474706 -0.0127238147470555 4 1.577 1.53656293279138 0.0404370672086207 5 1.5553 1.53512857301467 0.0201714269853274 6 1.5557 1.49667741824218 0.0590225817578199 7 1.575 1.47849936802698 0.096500631973016 8 1.5527 1.45459973910146 0.0981002608985449 9 1.4748 1.43463813831568 0.0401618616843232 10 1.4718 1.43579000031356 0.0360099996864448 11 1.457 1.46951934687615 -0.0125193468761514 12 1.4684 1.43990343928427 0.0284965607157341 13 1.4227 1.41458501016644 0.00811498983356346 14 1.3896 1.39143306456327 -0.00183306456326552 15 1.3622 1.36924709817868 -0.0070470981786768 16 1.3716 1.40000298248414 -0.0284029824841409 17 1.3419 1.41215713304612 -0.0702571330461194 18 1.3511 1.39381514213391 -0.0427151421339114 19 1.3516 1.37358545209548 -0.0219854520954791 20 1.3242 1.34952864969915 -0.0253286496991461 21 1.3074 1.34988864383029 -0.0424886438302914 22 1.2999 1.35310246559198 -0.0532024655919758 23 1.3213 1.34280541592664 -0.0215054159266379 24 1.2881 1.32013222840492 -0.0320322284049186 25 1.2611 1.28247730016931 -0.0213773001693092 26 1.2727 1.29336833751976 -0.0206683375197575 27 1.2811 1.29337757594815 -0.0122775759481467 28 1.2684 1.29367880296744 -0.0252788029674369 29 1.265 1.28863547565082 -0.0236354756508157 30 1.277 1.24962142109675 0.0273785789032505 31 1.2271 1.25184301507846 -0.0247430150784554 32 1.202 1.24508453153485 -0.0430845315348515 33 1.1938 1.23203748457090 -0.0382374845709045 34 1.2103 1.22907488665480 -0.0187748866548024 35 1.1856 1.20721086498791 -0.0216108649879068 36 1.1786 1.20400395702714 -0.0254039570271392 37 1.2015 1.17426381626178 0.0272361837382157 38 1.2256 1.1601298825731 0.0654701174269008 39 1.2292 1.16246425518052 0.0667357448194846 40 1.2037 1.16362660326909 0.0400733967309084 41 1.2165 1.13045504433139 0.0860449556686114 42 1.2694 1.32765193986588 -0.0582519398658795 43 1.2938 1.33966064171916 -0.0458606417191574 44 1.3201 1.33537930585510 -0.0152793058550971 45 1.3014 1.29812046112743 0.00327953887257013 46 1.3119 1.29678808023770 0.0151119197622962 47 1.3408 1.28576859291441 0.0550314070855907 48 1.2991 1.25547496873460 0.0436250312653957 49 1.249 1.24114169061816 0.00785830938184373 50 1.2218 1.23830209490217 -0.0165020949021734 51 1.2176 1.23406851193715 -0.0164685119371496 52 1.2266 1.23842614571987 -0.0118261457198743 53 1.2138 1.24039133257505 -0.0265913325750546 54 1.2007 1.23139481602422 -0.030694816024217 55 1.1985 1.20610848557393 -0.00760848557392517 56 1.2262 1.23840662298831 -0.0122066229883104 57 1.2646 1.22907647974647 0.0355235202535328 58 1.2613 1.22670365836554 0.0345963416344631 59 1.2286 1.19866644175337 0.0299335582466329 60 1.1702 1.15701548106393 0.0131845189360695 61 1.1692 1.11055841327822 0.0586415867217811 62 1.1222 1.10825935680879 0.0139406431912089 63 1.1139 1.13211874400846 -0.0182187440084559 64 1.1372 1.15220253276808 -0.0150025327680768 65 1.1663 1.15203244138195 0.0142675586180508 66 1.1582 1.11293926263706 0.0452607373629376 67 1.0848 1.081103037506 0.00369696249400098 68 1.0807 1.08290115082114 -0.00220115082113971 69 1.0773 1.07553879240923 0.00176120759076976 70 1.0622 1.07594090883643 -0.013740908836426 71 1.0183 1.04762933754153 -0.0293293375415274 72 1.0014 1.02926992548514 -0.0278699254851418 73 0.9811 1.0029082603114 -0.0218082603113996 74 0.9808 0.987052084484138 -0.00625208448413824 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 19 0.853455367725448 0.293089264549104 0.146544632274552 20 0.757863126196315 0.484273747607371 0.242136873803685 21 0.69884651684523 0.602306966309541 0.301153483154771 22 0.625489726914662 0.749020546170675 0.374510273085338 23 0.787087456394185 0.425825087211631 0.212912543605815 24 0.747110046687464 0.505779906625072 0.252889953312536 25 0.800900120725382 0.398199758549236 0.199099879274618 26 0.798136723114135 0.40372655377173 0.201863276885865 27 0.758032629702828 0.483934740594344 0.241967370297172 28 0.696047514312403 0.607904971375194 0.303952485687597 29 0.70830437271273 0.583391254574539 0.291695627287270 30 0.736276258261082 0.527447483477835 0.263723741738918 31 0.685413590096438 0.629172819807123 0.314586409903562 32 0.69991235629686 0.60017528740628 0.30008764370314 33 0.710220091436293 0.579559817127413 0.289779908563707 34 0.712532156536668 0.574935686926665 0.287467843463332 35 0.831452002270014 0.337095995459971 0.168547997729986 36 0.940084002277044 0.119831995445911 0.0599159977229555 37 0.983453047483781 0.0330939050324384 0.0165469525162192 38 0.994785428182943 0.0104291436341141 0.00521457181705704 39 0.99541518845766 0.0091696230846803 0.00458481154234015 40 0.99513706565054 0.00972586869891947 0.00486293434945973 41 0.996989997117644 0.00602000576471192 0.00301000288235596 42 0.995246086318665 0.00950782736266942 0.00475391368133471 43 0.996657627803133 0.00668474439373389 0.00334237219686695 44 0.994616131890432 0.0107677362191358 0.00538386810956791 45 0.99200204304106 0.0159959139178801 0.00799795695894003 46 0.986114899273835 0.0277702014523301 0.0138851007261650 47 0.981867524881725 0.0362649502365502 0.0181324751182751 48 0.970483110536345 0.0590337789273098 0.0295168894636549 49 0.96586788340826 0.0682642331834785 0.0341321165917393 50 0.98825749925506 0.0234850014898804 0.0117425007449402 51 0.973412015876593 0.0531759682468136 0.0265879841234068 52 0.944642794420732 0.110714411158536 0.0553572055792681 53 0.892323649607464 0.215352700785072 0.107676350392536 54 0.907444636888203 0.185110726223594 0.092555363111797 55 0.82572886762195 0.3485422647561 0.17427113237805 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 5 0.135135135135135 NOK 5% type I error level 12 0.324324324324324 NOK 10% type I error level 15 0.405405405405405 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/106t2w1227535501.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/106t2w1227535501.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/1vqbg1227535501.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/1vqbg1227535501.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/2r3gh1227535501.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/2r3gh1227535501.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/3lmom1227535501.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/3lmom1227535501.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/4jiqm1227535501.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/4jiqm1227535501.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/5f2a61227535501.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/5f2a61227535501.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/6oxbj1227535501.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/6oxbj1227535501.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/7af8s1227535501.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/7af8s1227535501.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/8tbf41227535501.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/8tbf41227535501.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/91pl11227535501.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535613vfr3qc7btb6zvhg/91pl11227535501.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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