met lineaire trend

*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, 01 Dec 2008 05:52:41 -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/Dec/01/t12281360529b42t3y22w00ard.htm/, Retrieved Mon, 01 Dec 2008 12:54:23 +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/Dec/01/t12281360529b42t3y22w00ard.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}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

2008-11-27 13:41:43 [a2386b643d711541400692649981f2dc]  [reply]  test Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

9.103 0 9.155 0 9.308 0 9.394 0 9.948 0 10.177 0 10.002 0 9.728 0 10.002 0 10.063 0 10.018 0 9.96 0 10.236 0 10.893 0 10.756 0 10.94 0 10.997 0 10.827 0 10.166 0 10.186 0 10.457 0 10.368 0 10.244 0 10.511 0 10.812 0 10.738 0 10.171 0 9.721 0 9.897 0 9.828 0 9.924 0 10.371 0 10.846 0 10.413 0 10.709 0 10.662 0 10.57 0 10.297 0 10.635 0 10.872 0 10.296 0 10.383 0 10.431 0 10.574 0 10.653 0 10.805 0 10.872 0 10.625 0 10.407 0 10.463 0 10.556 0 10.646 0 10.702 0 11.353 0 11.346 1 11.451 1 11.964 1 12.574 1 13.031 1 13.812 1 14.544 1 14.931 1 14.886 1 16.005 1 17.064 1 15.168 1 16.05 1 15.839 1 15.137 1 14.954 1 15.648 1 15.305 1 15.579 1 16.348 1 15.928 1 16.171 1 15.937 1 15.713 1 15.594 1 15.683 1 16.438 1 17.032 1 17.696 1 17.745 1 19.394 1 20.148 1 20.108 1 18.584 1 18.441 1 18.391 1 19.178 1 18.079 1 18.483 1 19.644 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 Multiple Linear Regression - Estimated Regression Equation goudprijs[t] = + 8.40703454617756 + 2.91842397355602dummy[t] + 0.389699591679924M1[t] + 0.618153501632036M2[t] + 0.477482411584153M3[t] + 0.41306132153627M4[t] + 0.469140231488386M5[t] + 0.226344141440501M6[t] -0.0946299453018855M7[t] -0.254676035349769M8[t] -0.0585971253976533M9[t] + 0.112981784554463M10[t] + 0.00511751861931323M11[t] + 0.0625460900478841t + 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) 8.40703454617756 0.560317 15.0041 0 0 dummy 2.91842397355602 0.518575 5.6278 0 0 M1 0.389699591679924 0.660052 0.5904 0.556582 0.278291 M2 0.618153501632036 0.659785 0.9369 0.351629 0.175815 M3 0.477482411584153 0.659653 0.7238 0.471276 0.235638 M4 0.41306132153627 0.659657 0.6262 0.532982 0.266491 M5 0.469140231488386 0.659794 0.711 0.479128 0.239564 M6 0.226344141440501 0.660067 0.3429 0.732565 0.366283 M7 -0.0946299453018855 0.660136 -0.1433 0.886375 0.443187 M8 -0.254676035349769 0.659885 -0.3859 0.700565 0.350283 M9 -0.0585971253976533 0.659768 -0.0888 0.929451 0.464726 M10 0.112981784554463 0.659787 0.1712 0.864468 0.432234 M11 0.00511751861931323 0.681176 0.0075 0.994024 0.497012 t 0.0625460900478841 0.009433 6.6308 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.931653160516201 R-squared 0.867977611499826 Adjusted R-squared 0.846523973368548 F-TEST (value) 40.4582945880104 F-TEST (DF numerator) 13 F-TEST (DF denominator) 80 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1.27424094827436 Sum Squared Residuals 129.895199540732 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 9.103 8.85928022790534 0.243719772094665 2 9.155 9.15028022790537 0.00471977209463401 3 9.308 9.07215522790536 0.235844772094642 4 9.394 9.07028022790536 0.323719772094642 5 9.948 9.18890522790536 0.759094772094642 6 10.177 9.00865522790536 1.16834477209464 7 10.002 8.75022723121086 1.25177276878914 8 9.728 8.65272723121086 1.07527276878914 9 10.002 8.91135223121086 1.09064776878914 10 10.063 9.14547723121086 0.917522768789144 11 10.018 9.1001590553236 0.917840944676408 12 9.96 9.15758762675216 0.802412373247837 13 10.236 9.60983330847997 0.626166691520029 14 10.893 9.90083330847997 0.992166691520035 15 10.756 9.82270830847997 0.933291691520032 16 10.94 9.82083330847997 1.11916669152003 17 10.997 9.93945830847997 1.05754169152003 18 10.827 9.75920830847997 1.06779169152003 19 10.166 9.50078031178547 0.665219688214534 20 10.186 9.40328031178547 0.782719688214533 21 10.457 9.66190531178547 0.795094688214534 22 10.368 9.89603031178547 0.471969688214534 23 10.244 9.8507121358982 0.393287864101799 24 10.511 9.90814070732677 0.602859292673227 25 10.812 10.3603863890546 0.451613610945418 26 10.738 10.6513863890546 0.0866136109454223 27 10.171 10.5732613890546 -0.402261389054579 28 9.721 10.5713863890546 -0.850386389054579 29 9.897 10.6900113890546 -0.793011389054578 30 9.828 10.5097613890546 -0.681761389054579 31 9.924 10.2513333923601 -0.327333392360076 32 10.371 10.1538333923601 0.217166607639924 33 10.846 10.4124583923601 0.433541607639924 34 10.413 10.6465833923601 -0.233583392360075 35 10.709 10.6012652164728 0.107734783527189 36 10.662 10.6586937879014 0.00330621209861895 37 10.57 11.1109394696292 -0.540939469629191 38 10.297 11.4019394696292 -1.10493946962919 39 10.635 11.3238144696292 -0.688814469629187 40 10.872 11.3219394696292 -0.449939469629188 41 10.296 11.4405644696292 -1.14456446962919 42 10.383 11.2603144696292 -0.877314469629188 43 10.431 11.0018864729347 -0.570886472934686 44 10.574 10.9043864729347 -0.330386472934686 45 10.653 11.1630114729347 -0.510011472934685 46 10.805 11.3971364729347 -0.592136472934685 47 10.872 11.3518182970474 -0.47981829704742 48 10.625 11.409246868476 -0.784246868475991 49 10.407 11.8614925502038 -1.4544925502038 50 10.463 12.1524925502038 -1.68949255020380 51 10.556 12.0743675502038 -1.51836755020380 52 10.646 12.0724925502038 -1.42649255020380 53 10.702 12.1911175502038 -1.48911755020380 54 11.353 12.0108675502038 -0.657867550203796 55 11.346 14.6708635270653 -3.32486352706532 56 11.451 14.5733635270653 -3.12236352706531 57 11.964 14.8319885270653 -2.86798852706532 58 12.574 15.0661135270653 -2.49211352706531 59 13.031 15.0207953511780 -1.98979535117805 60 13.812 15.0782239226066 -1.26622392260662 61 14.544 15.5304696043344 -0.98646960433443 62 14.931 15.8214696043344 -0.890469604334427 63 14.886 15.7433446043344 -0.857344604334428 64 16.005 15.7414696043344 0.263530395665572 65 17.064 15.8600946043344 1.20390539566557 66 15.168 15.6798446043344 -0.511844604334427 67 16.05 15.4214166076399 0.628583392360077 68 15.839 15.3239166076399 0.515083392360076 69 15.137 15.5825416076399 -0.445541607639924 70 14.954 15.8166666076399 -0.862666607639924 71 15.648 15.7713484317527 -0.123348431752659 72 15.305 15.8287770031812 -0.523777003181231 73 15.579 16.2810226849090 -0.702022684909038 74 16.348 16.5720226849090 -0.224022684909036 75 15.928 16.4938976849090 -0.565897684909035 76 16.171 16.4920226849090 -0.321022684909037 77 15.937 16.6106476849090 -0.673647684909037 78 15.713 16.4303976849090 -0.717397684909037 79 15.594 16.1719696882145 -0.577969688214534 80 15.683 16.0744696882145 -0.391469688214534 81 16.438 16.3330946882145 0.104905311785465 82 17.032 16.5672196882145 0.464780311785466 83 17.696 16.5219015123273 1.17409848767273 84 17.745 16.5793300837558 1.16566991624416 85 19.394 17.0315757654836 2.36242423451635 86 20.148 17.3225757654836 2.82542423451635 87 20.108 17.2444507654836 2.86354923451635 88 18.584 17.2425757654836 1.34142423451635 89 18.441 17.3612007654836 1.07979923451635 90 18.391 17.1809507654836 1.21004923451635 91 19.178 16.9225227687891 2.25547723121086 92 18.079 16.8250227687891 1.25397723121086 93 18.483 17.0836477687891 1.39935223121086 94 19.644 17.3177727687891 2.32622723121086 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00895093798282302 0.0179018759656460 0.991049062017177 18 0.00793165202940176 0.0158633040588035 0.992068347970598 19 0.0133393109433466 0.0266786218866931 0.986660689056653 20 0.00754779006346329 0.0150955801269266 0.992452209936537 21 0.00416954944999587 0.00833909889999173 0.995830450550004 22 0.00263880398504086 0.00527760797008172 0.99736119601496 23 0.00171907683148921 0.00343815366297841 0.99828092316851 24 0.000817460734584546 0.00163492146916909 0.999182539265415 25 0.000357778134731837 0.000715556269463675 0.999642221865268 26 0.000216262945752350 0.000432525891504700 0.999783737054248 27 0.000360411802110775 0.000720823604221551 0.99963958819789 28 0.00144241248564833 0.00288482497129667 0.998557587514352 29 0.00326571343389015 0.00653142686778029 0.99673428656611 30 0.00572087338919466 0.0114417467783893 0.994279126610805 31 0.00453467406658749 0.00906934813317499 0.995465325933413 32 0.00353898570142306 0.00707797140284612 0.996461014298577 33 0.00361641407228672 0.00723282814457345 0.996383585927713 34 0.00284259656648763 0.00568519313297526 0.997157403433512 35 0.00229661998447412 0.00459323996894824 0.997703380015526 36 0.00189634419320765 0.00379268838641529 0.998103655806792 37 0.00122639182116019 0.00245278364232038 0.99877360817884 38 0.000843676646332152 0.00168735329266430 0.999156323353668 39 0.000495398743611748 0.000990797487223496 0.999504601256388 40 0.000360828370198226 0.000721656740396451 0.999639171629802 41 0.000250024322756206 0.000500048645512412 0.999749975677244 42 0.000182316221438861 0.000364632442877722 0.99981768377856 43 0.000116013221734028 0.000232026443468056 0.999883986778266 44 0.000107495793876168 0.000214991587752335 0.999892504206124 45 0.000117018923873826 0.000234037847747651 0.999882981076126 46 0.000106864952307011 0.000213729904614023 0.999893135047693 47 8.47654854546137e-05 0.000169530970909227 0.999915234514545 48 6.123724636518e-05 0.00012247449273036 0.999938762753635 49 3.35216739972263e-05 6.70433479944526e-05 0.999966478326003 50 2.01096850907255e-05 4.02193701814511e-05 0.99997989031491 51 1.01053232961900e-05 2.02106465923801e-05 0.999989894676704 52 4.80308727116003e-06 9.60617454232006e-06 0.999995196912729 53 2.98447959690508e-06 5.96895919381015e-06 0.999997015520403 54 1.88578683961588e-06 3.77157367923176e-06 0.99999811421316 55 2.05826770577180e-06 4.11653541154359e-06 0.999997941732294 56 1.51650980223749e-06 3.03301960447498e-06 0.999998483490198 57 9.46497949860824e-07 1.89299589972165e-06 0.99999905350205 58 1.24231526212167e-06 2.48463052424333e-06 0.999998757684738 59 2.35604046388891e-06 4.71208092777782e-06 0.999997643959536 60 1.22392037610602e-05 2.44784075221204e-05 0.99998776079624 61 0.000172867953735653 0.000345735907471306 0.999827132046264 62 0.00099581588152276 0.00199163176304552 0.999004184118477 63 0.00211551501795664 0.00423103003591328 0.997884484982043 64 0.0141441515338764 0.0282883030677528 0.985855848466124 65 0.226866662883742 0.453733325767485 0.773133337116258 66 0.240435030343010 0.480870060686021 0.75956496965699 67 0.48092345501502 0.96184691003004 0.51907654498498 68 0.875466753919124 0.249066492161751 0.124533246080876 69 0.952885365613296 0.0942292687734073 0.0471146343867037 70 0.95017808286447 0.099643834271059 0.0498219171355295 71 0.945619250016407 0.108761499967187 0.0543807499835934 72 0.918520423525479 0.162959152949042 0.0814795764745209 73 0.90480436289509 0.190391274209820 0.0951956371049102 74 0.903094528316042 0.193810943367916 0.0969054716839581 75 0.974238009798007 0.0515239804039851 0.0257619902019925 76 0.93701853134858 0.12596293730284 0.06298146865142 77 0.849145311197911 0.301709377604178 0.150854688802089 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 42 0.688524590163934 NOK 5% type I error level 48 0.78688524590164 NOK 10% type I error level 51 0.836065573770492 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/10dylt1228135955.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/10dylt1228135955.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/11uxq1228135955.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/11uxq1228135955.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/20dnb1228135955.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/20dnb1228135955.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/38h0f1228135955.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/38h0f1228135955.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/4frho1228135955.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/4frho1228135955.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/5m7i61228135955.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/5m7i61228135955.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/6wzn21228135955.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/6wzn21228135955.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/79s5l1228135955.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/79s5l1228135955.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/8i7cg1228135955.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/8i7cg1228135955.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/9ezhz1228135955.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/01/t12281360529b42t3y22w00ard/9ezhz1228135955.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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