Q3

*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 07:00:34 -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/t1227535282o8yzfy8louhiod0.htm/, Retrieved Mon, 24 Nov 2008 14:01:32 +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/t1227535282o8yzfy8louhiod0.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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Original text written by user:

IsPrivate?

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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 9 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] = -38.7020016944876 + 0.0103197910467146Yen[t] + 5.1604990419276DeenseKroon[t] + 0.148107290491013`(Y/N)`[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) -38.7020016944876 7.879726 -4.9116 6e-06 3e-06 Yen 0.0103197910467146 0.000794 12.9999 0 0 DeenseKroon 5.1604990419276 1.056379 4.8851 6e-06 3e-06 `(Y/N)` 0.148107290491013 0.030059 4.9272 5e-06 3e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.906281906334077 R-squared 0.821346893748529 Adjusted R-squared 0.813690332052037 F-TEST (value) 107.273594376558 F-TEST (DF numerator) 3 F-TEST (DF denominator) 70 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.0597122498843697 Sum Squared Residuals 0.249588695037739 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1.3322 1.14483691411901 0.187363085880990 2 1.4369 1.36754029827770 0.0693597017223019 3 1.4975 1.48136831774524 0.0161316822547573 4 1.577 1.53317391020718 0.0438260897928225 5 1.5553 1.50386491906036 0.0514350809396357 6 1.5557 1.47497089222228 0.080729107777725 7 1.575 1.46413474951209 0.110865250487915 8 1.5527 1.39117129203382 0.161528707966182 9 1.4748 1.39457555569023 0.0802244443097662 10 1.4718 1.38384086068666 0.0879591393133444 11 1.457 1.48260693407828 -0.0256069340782764 12 1.4684 1.44689707735265 0.0215029226473512 13 1.4227 1.46351139777115 -0.0408113977711475 14 1.3896 1.39612147238410 -0.00652147238410165 15 1.3622 1.34843939065529 0.0137606093447100 16 1.3716 1.41820003144580 -0.0466000314457967 17 1.3419 1.41706738920865 -0.0751673892086534 18 1.3511 1.43791741069744 -0.0868174106974398 19 1.3516 1.41738169038491 -0.0657816903849058 20 1.3242 1.34266423053984 -0.0184642305398365 21 1.3074 1.39127328290715 -0.0838732829071457 22 1.2999 1.37950860041017 -0.0796086004101748 23 1.3213 1.36671266303082 -0.0454126630308208 24 1.2881 1.33616698681040 -0.0480669868104015 25 1.2611 1.31645564274446 -0.0553556427444603 26 1.2727 1.33338287624650 -0.0606828762464955 27 1.2811 1.33276417159855 -0.0516641715985484 28 1.2684 1.31026660465372 -0.0418666046537151 29 1.265 1.27528034033850 -0.0102803403384973 30 1.277 1.24989359401172 0.0271064059882766 31 1.2271 1.28642885296552 -0.059328852965516 32 1.202 1.25619150308750 -0.0541915030874984 33 1.1938 1.26919619001021 -0.0753961900102144 34 1.2103 1.24483979328797 -0.0345397932879681 35 1.1856 1.21563043929206 -0.0300304392920638 36 1.1786 1.23379659088642 -0.055196590886416 37 1.2015 1.2302893103751 -0.0287893103751006 38 1.2256 1.1911751296412 0.0344248703587988 39 1.2292 1.19654214520778 0.0326578547922237 40 1.2037 1.17765620337001 0.0260437966299949 41 1.2165 1.0813643450516 0.135135654948399 42 1.2694 1.25939872781880 0.0100012721811981 43 1.2938 1.32410719738570 -0.0303071973856974 44 1.3201 1.30697435263687 0.0131256473631323 45 1.3014 1.26331928278684 0.0380807172131579 46 1.3119 1.24247197713162 0.0694280228683774 47 1.3408 1.24411910012467 0.096680899875326 48 1.2991 1.19974248982738 0.0993575101726217 49 1.249 1.23256340857849 0.0164365914215070 50 1.2218 1.21852861345868 0.00327138654132258 51 1.2176 1.21058140872299 0.00701859127700897 52 1.2266 1.20067380579958 0.0259261942004221 53 1.2138 1.18137501196808 0.0324249880319225 54 1.2007 1.2306042174279 -0.0299042174279008 55 1.1985 1.19087489280562 0.00762510719438257 56 1.2262 1.26208489108380 -0.0358848910837987 57 1.2646 1.28840144458635 -0.0238014445863488 58 1.2613 1.2662120832802 -0.00491208328020069 59 1.2286 1.21667334444084 0.0119266555591636 60 1.1702 1.14401905823097 0.0261809417690294 61 1.1692 1.11130115633475 0.0578988436652494 62 1.1222 1.10531398767566 0.0168860123243438 63 1.1139 1.1661005141818 -0.0522005141818003 64 1.1372 1.19819566785565 -0.0609956678556547 65 1.1663 1.18745813631479 -0.0211581363147929 66 1.1582 1.16248400057432 -0.0042840005743158 67 1.0848 1.10820244283531 -0.0234024428353132 68 1.0807 1.09778060056341 -0.0170806005634104 69 1.0773 1.12451145450426 -0.0472114545042569 70 1.0622 1.10253072203775 -0.0403307220377548 71 1.0183 1.05175372897650 -0.0334537289764961 72 1.0014 1.03369506027446 -0.0322950602744571 73 0.9811 1.041642325362 -0.0605423253619993 74 0.9808 0.995304894413967 -0.0145048944139673 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.347496581799829 0.694993163599659 0.652503418200171 8 0.569130133282187 0.861739733435626 0.430869866717813 9 0.527355813275624 0.945288373448751 0.472644186724376 10 0.467514954721527 0.935029909443054 0.532485045278473 11 0.703025859275234 0.593948281449532 0.296974140724766 12 0.697086724912941 0.605826550174118 0.302913275087059 13 0.795359207824841 0.409281584350317 0.204640792175159 14 0.782545506297007 0.434908987405987 0.217454493702993 15 0.731765518134624 0.536468963730753 0.268234481865376 16 0.652709701619442 0.694580596761116 0.347290298380558 17 0.657692023185599 0.684615953628802 0.342307976814401 18 0.809042426514784 0.381915146970432 0.190957573485216 19 0.878526689698539 0.242946620602922 0.121473310301461 20 0.870457487591643 0.259085024816714 0.129542512408357 21 0.956312771511507 0.0873744569769855 0.0436872284884927 22 0.983185515492482 0.0336289690150359 0.0168144845075180 23 0.987537209303362 0.0249255813932762 0.0124627906966381 24 0.993108561448308 0.0137828771033840 0.00689143855169202 25 0.99599565018613 0.00800869962774127 0.00400434981387064 26 0.998152426056643 0.00369514788671337 0.00184757394335669 27 0.998689933310895 0.00262013337821042 0.00131006668910521 28 0.998697725214676 0.00260454957064715 0.00130227478532358 29 0.997896556895482 0.00420688620903553 0.00210344310451777 30 0.996575587763335 0.00684882447333065 0.00342441223666532 31 0.99728886404438 0.00542227191124136 0.00271113595562068 32 0.997407078488185 0.00518584302362996 0.00259292151181498 33 0.998503262267808 0.00299347546438341 0.00149673773219170 34 0.998127999628197 0.00374400074360604 0.00187200037180302 35 0.998082509756126 0.00383498048774754 0.00191749024387377 36 0.999123681536134 0.00175263692773195 0.000876318463865974 37 0.999419167749717 0.00116166450056523 0.000580832250282614 38 0.999157253323118 0.00168549335376443 0.000842746676882217 39 0.998894306934964 0.00221138613007208 0.00110569306503604 40 0.999287845679309 0.00142430864138283 0.000712154320691417 41 0.99938515534694 0.00122968930612147 0.000614844653060737 42 0.99883949314445 0.00232101371110145 0.00116050685555073 43 0.998584011185748 0.002831977628505 0.0014159888142525 44 0.99748030373196 0.00503939253608187 0.00251969626804093 45 0.996021453750408 0.00795709249918455 0.00397854624959227 46 0.996510562409067 0.00697887518186626 0.00348943759093313 47 0.998375261949136 0.00324947610172762 0.00162473805086381 48 0.999803064208592 0.000393871582816847 0.000196935791408423 49 0.999652447535908 0.000695104928183365 0.000347552464091682 50 0.999333625313314 0.00133274937337247 0.000666374686686234 51 0.998785264383461 0.00242947123307703 0.00121473561653851 52 0.998432551418311 0.00313489716337782 0.00156744858168891 53 0.99850811640884 0.00298376718231843 0.00149188359115921 54 0.997354580998896 0.00529083800220787 0.00264541900110393 55 0.995561705581484 0.00887658883703223 0.00443829441851611 56 0.992620835042743 0.0147583299145133 0.00737916495725665 57 0.98706978014328 0.0258604397134416 0.0129302198567208 58 0.975800001812229 0.0483999963755429 0.0241999981877714 59 0.962499329126336 0.0750013417473273 0.0375006708736636 60 0.977749213067665 0.0445015738646703 0.0222507869323352 61 0.999741055890796 0.000517888218407668 0.000258944109203834 62 0.999974409424227 5.11811515457472e-05 2.55905757728736e-05 63 0.999879777456586 0.000240445086828324 0.000120222543414162 64 0.999518820821578 0.000962358356844033 0.000481179178422016 65 0.998162773796142 0.00367445240771512 0.00183722620385756 66 0.992199728653944 0.0156005426921113 0.00780027134605565 67 0.968998098287596 0.062003803424808 0.031001901712404 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 36 0.59016393442623 NOK 5% type I error level 44 0.721311475409836 NOK 10% type I error level 47 0.770491803278688 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/108yga1227535224.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/108yga1227535224.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/177hq1227535224.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/177hq1227535224.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/2246f1227535224.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/2246f1227535224.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/3qp9x1227535224.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/3qp9x1227535224.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/4ipnv1227535224.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/4ipnv1227535224.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/5xcec1227535224.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/5xcec1227535224.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/63a5l1227535224.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/63a5l1227535224.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/7m28j1227535224.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/7m28j1227535224.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/8udfr1227535224.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/8udfr1227535224.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/9ptq01227535224.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/24/t1227535282o8yzfy8louhiod0/9ptq01227535224.ps (open in new window)

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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