ds

*Unverified author*

R Software Module: rwasp_multipleregression.wasp (opens new window with default values)

Title produced by software: Multiple Regression

Date of computation: Thu, 27 Nov 2008 05:42:10 -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/27/t1227789867li91lqdxcdw2sky.htm/, Retrieved Thu, 27 Nov 2008 12:44:37 +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/27/t1227789867li91lqdxcdw2sky.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?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

7.5 0 7.2 0 6.9 0 6.7 0 6.4 0 6.3 0 6.8 0 7.3 0 7.1 0 7.1 0 6.8 0 6.5 0 6.3 0 6.1 0 6.1 0 6.3 0 6.3 0 6 0 6.2 0 6.4 0 6.8 0 7.5 0 7.5 0 7.6 0 7.6 0 7.4 0 7.3 0 7.1 0 6.9 0 6.8 0 7.5 0 7.6 0 7.8 0 8.0 0 8.1 0 8.2 0 8.3 0 8.2 0 8.0 0 7.9 0 7.6 0 7.6 0 8.2 0 8.3 0 8.4 0 8.4 0 8.4 0 8.6 0 8.9 0 8.8 0 8.3 0 7.5 0 7.2 0 7.5 0 8.8 0 9.3 0 9.3 0 8.7 1 8.2 1 8.3 1 8.5 1 8.6 1 8.6 1 8.2 1 8.1 1 8.0 1 8.6 1 8.7 1 8.8 1 8.5 1 8.4 1 8.5 1 8.7 1 8.7 1 8.6 1 8.5 1 8.3 1 8.1 1 8.2 1 8.1 1 8.1 1 7.9 1 7.9 1 7.9 1 8.0 1 8.0 1 7.9 1 8.0 1 7.7 1 7.2 1 7.5 1 7.3 1 7.0 1 7.0 1 7.0 1 7.2 1 7.3 1 7.1 1 6.8 1 6.6 1 6.2 1 6.2 1 6.8 1 6.9 1 6.8 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 3 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation w[t] = + 7.46491228070175 + 0.372587719298245d[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) 7.46491228070175 0.10582 70.5437 0 0 d 0.372587719298245 0.156509 2.3806 0.019122 0.009561 Multiple Linear Regression - Regression Statistics Multiple R 0.228369753763038 R-squared 0.0521527444337905 Adjusted R-squared 0.0429503438943127 F-TEST (value) 5.6672978110503 F-TEST (DF numerator) 1 F-TEST (DF denominator) 103 p-value 0.0191224707430224 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.798921144883387 Sum Squared Residuals 65.7423245614035 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 7.5 7.46491228070174 0.0350877192982603 2 7.2 7.46491228070176 -0.264912280701755 3 6.9 7.46491228070176 -0.564912280701754 4 6.7 7.46491228070176 -0.764912280701755 5 6.4 7.46491228070176 -1.06491228070175 6 6.3 7.46491228070176 -1.16491228070175 7 6.8 7.46491228070176 -0.664912280701755 8 7.3 7.46491228070175 -0.164912280701755 9 7.1 7.46491228070175 -0.364912280701755 10 7.1 7.46491228070175 -0.364912280701755 11 6.8 7.46491228070176 -0.664912280701755 12 6.5 7.46491228070176 -0.964912280701755 13 6.3 7.46491228070176 -1.16491228070175 14 6.1 7.46491228070176 -1.36491228070176 15 6.1 7.46491228070176 -1.36491228070176 16 6.3 7.46491228070176 -1.16491228070175 17 6.3 7.46491228070176 -1.16491228070175 18 6 7.46491228070176 -1.46491228070175 19 6.2 7.46491228070176 -1.26491228070175 20 6.4 7.46491228070176 -1.06491228070175 21 6.8 7.46491228070176 -0.664912280701755 22 7.5 7.46491228070175 0.0350877192982453 23 7.5 7.46491228070175 0.0350877192982453 24 7.6 7.46491228070175 0.135087719298245 25 7.6 7.46491228070175 0.135087719298245 26 7.4 7.46491228070175 -0.0649122807017544 27 7.3 7.46491228070175 -0.164912280701755 28 7.1 7.46491228070175 -0.364912280701755 29 6.9 7.46491228070176 -0.564912280701754 30 6.8 7.46491228070176 -0.664912280701755 31 7.5 7.46491228070175 0.0350877192982453 32 7.6 7.46491228070175 0.135087719298245 33 7.8 7.46491228070175 0.335087719298245 34 8 7.46491228070176 0.535087719298245 35 8.1 7.46491228070176 0.635087719298245 36 8.2 7.46491228070176 0.735087719298245 37 8.3 7.46491228070176 0.835087719298246 38 8.2 7.46491228070176 0.735087719298245 39 8 7.46491228070176 0.535087719298245 40 7.9 7.46491228070175 0.435087719298246 41 7.6 7.46491228070175 0.135087719298245 42 7.6 7.46491228070175 0.135087719298245 43 8.2 7.46491228070176 0.735087719298245 44 8.3 7.46491228070176 0.835087719298246 45 8.4 7.46491228070176 0.935087719298246 46 8.4 7.46491228070176 0.935087719298246 47 8.4 7.46491228070176 0.935087719298246 48 8.6 7.46491228070176 1.13508771929824 49 8.9 7.46491228070176 1.43508771929825 50 8.8 7.46491228070176 1.33508771929825 51 8.3 7.46491228070176 0.835087719298246 52 7.5 7.46491228070175 0.0350877192982453 53 7.2 7.46491228070175 -0.264912280701754 54 7.5 7.46491228070175 0.0350877192982453 55 8.8 7.46491228070176 1.33508771929825 56 9.3 7.46491228070176 1.83508771929825 57 9.3 7.46491228070176 1.83508771929825 58 8.7 7.8375 0.8625 59 8.2 7.8375 0.362499999999999 60 8.3 7.8375 0.462500000000001 61 8.5 7.8375 0.6625 62 8.6 7.8375 0.7625 63 8.6 7.8375 0.7625 64 8.2 7.8375 0.362499999999999 65 8.1 7.8375 0.262500000000000 66 8 7.8375 0.1625 67 8.6 7.8375 0.7625 68 8.7 7.8375 0.8625 69 8.8 7.8375 0.9625 70 8.5 7.8375 0.6625 71 8.4 7.8375 0.5625 72 8.5 7.8375 0.6625 73 8.7 7.8375 0.8625 74 8.7 7.8375 0.8625 75 8.6 7.8375 0.7625 76 8.5 7.8375 0.6625 77 8.3 7.8375 0.462500000000001 78 8.1 7.8375 0.262500000000000 79 8.2 7.8375 0.362499999999999 80 8.1 7.8375 0.262500000000000 81 8.1 7.8375 0.262500000000000 82 7.9 7.8375 0.0625000000000003 83 7.9 7.8375 0.0625000000000003 84 7.9 7.8375 0.0625000000000003 85 8 7.8375 0.1625 86 8 7.8375 0.1625 87 7.9 7.8375 0.0625000000000003 88 8 7.8375 0.1625 89 7.7 7.8375 -0.137500000000000 90 7.2 7.8375 -0.6375 91 7.5 7.8375 -0.3375 92 7.3 7.8375 -0.5375 93 7 7.8375 -0.8375 94 7 7.8375 -0.8375 95 7 7.8375 -0.8375 96 7.2 7.8375 -0.6375 97 7.3 7.8375 -0.5375 98 7.1 7.8375 -0.7375 99 6.8 7.8375 -1.0375 100 6.6 7.8375 -1.2375 101 6.2 7.8375 -1.6375 102 6.2 7.8375 -1.6375 103 6.8 7.8375 -1.0375 104 6.9 7.8375 -0.9375 105 6.8 7.8375 -1.0375 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.229906373928257 0.459812747856514 0.770093626071743 6 0.206031377963558 0.412062755927117 0.793968622036441 7 0.109838201490088 0.219676402980176 0.890161798509912 8 0.078884155785435 0.15776831157087 0.921115844214565 9 0.0425809712240277 0.0851619424480554 0.957419028775972 10 0.0218458435289846 0.0436916870579693 0.978154156471015 11 0.0106309917333790 0.0212619834667579 0.989369008266621 12 0.00783124469797372 0.0156624893959474 0.992168755302026 13 0.00888328625818528 0.0177665725163706 0.991116713741815 14 0.0151531653649276 0.0303063307298552 0.984846834635072 15 0.0213201106775131 0.0426402213550263 0.978679889322487 16 0.0191230215283845 0.0382460430567691 0.980876978471615 17 0.0172516437829191 0.0345032875658383 0.98274835621708 18 0.027639804302403 0.055279608604806 0.972360195697597 19 0.0300199305043569 0.0600398610087139 0.969980069495643 20 0.0264248157471636 0.0528496314943272 0.973575184252836 21 0.0204968118012301 0.0409936236024603 0.97950318819877 22 0.0346251240298229 0.0692502480596459 0.965374875970177 23 0.0487535721632075 0.097507144326415 0.951246427836792 24 0.0695575028277195 0.139115005655439 0.93044249717228 25 0.0882595757874236 0.176519151574847 0.911740424212576 26 0.0881850326675148 0.176370065335030 0.911814967332485 27 0.0819904075159879 0.163980815031976 0.918009592484012 28 0.071971808627948 0.143943617255896 0.928028191372052 29 0.065403910612414 0.130807821224828 0.934596089387586 30 0.0641868165883024 0.128373633176605 0.935813183411698 31 0.0701748146062848 0.140349629212570 0.929825185393715 32 0.0805417163242038 0.161083432648408 0.919458283675796 33 0.104577043031671 0.209154086063343 0.895422956968329 34 0.150023058624765 0.300046117249531 0.849976941375235 35 0.209105407905026 0.418210815810052 0.790894592094974 36 0.28167855561811 0.56335711123622 0.71832144438189 37 0.365878203407330 0.731756406814659 0.63412179659267 38 0.417814516992907 0.835629033985814 0.582185483007093 39 0.430439774016292 0.860879548032584 0.569560225983708 40 0.428194752156725 0.85638950431345 0.571805247843275 41 0.410961519845728 0.821923039691456 0.589038480154272 42 0.396725793712011 0.793451587424023 0.603274206287989 43 0.421695666849994 0.843391333699988 0.578304333150006 44 0.452833391961770 0.905666783923541 0.54716660803823 45 0.489939735424334 0.979879470848668 0.510060264575666 46 0.517971255631802 0.964057488736395 0.482028744368198 47 0.538162572521841 0.923674854956319 0.461837427478159 48 0.578949391590387 0.842101216819227 0.421050608409613 49 0.660620625285945 0.67875874942811 0.339379374714055 50 0.709905940825401 0.580188118349198 0.290094059174599 51 0.694963927403354 0.610072145193292 0.305036072596646 52 0.675667066725526 0.648665866548948 0.324332933274474 53 0.710000972837397 0.579998054325206 0.289999027162603 54 0.753616015586092 0.492767968827816 0.246383984413908 55 0.782286303121862 0.435427393756275 0.217713696878138 56 0.837483830708917 0.325032338582166 0.162516169291083 57 0.87370452395119 0.252590952097619 0.126295476048810 58 0.86617409460814 0.267651810783718 0.133825905391859 59 0.840457140680721 0.319085718638558 0.159542859319279 60 0.812926124492215 0.374147751015571 0.187073875507785 61 0.793416420869243 0.413167158261514 0.206583579130757 62 0.781586784475965 0.436826431048071 0.218413215524035 63 0.771341891435698 0.457316217128604 0.228658108564302 64 0.737479344426711 0.525041311146579 0.262520655573289 65 0.697920993916629 0.604158012166742 0.302079006083371 66 0.653350002946494 0.693299994107012 0.346649997053506 67 0.64755902659975 0.7048819468005 0.35244097340025 68 0.659165323252298 0.681669353495404 0.340834676747702 69 0.69233021378517 0.615339572429661 0.307669786214830 70 0.688689370747626 0.622621258504747 0.311310629252374 71 0.677527802458513 0.644944395082973 0.322472197541487 72 0.68273732153419 0.634525356931621 0.317262678465811 73 0.726832727614116 0.546334544771767 0.273167272385884 74 0.779457973640678 0.441084052718645 0.220542026359322 75 0.822276652042037 0.355446695915926 0.177723347957963 76 0.856181631137708 0.287636737724583 0.143818368862292 77 0.870887791894101 0.258224416211798 0.129112208105899 78 0.870814920434566 0.258370159130869 0.129185079565434 79 0.884017442638038 0.231965114723924 0.115982557361962 80 0.891864222936025 0.21627155412795 0.108135777063975 81 0.903986946234332 0.192026107531335 0.0960130537656675 82 0.902776176470854 0.194447647058292 0.0972238235291458 83 0.90444199530852 0.191116009382959 0.0955580046914793 84 0.909741040769374 0.180517918461251 0.0902589592306256 85 0.929440153319557 0.141119693360886 0.070559846680443 86 0.952616687136885 0.0947666257262294 0.0473833128631147 87 0.96888830711652 0.0622233857669592 0.0311116928834796 88 0.989428618232237 0.0211427635355259 0.0105713817677629 89 0.994447789096223 0.0111044218075543 0.00555221090377716 90 0.991986708724453 0.0160265825510937 0.00801329127554683 91 0.99423886808621 0.0115222638275783 0.00576113191378916 92 0.993743120408926 0.0125137591821487 0.00625687959107435 93 0.98902115881821 0.0219576823635820 0.0109788411817910 94 0.980892433129478 0.0382151337410432 0.0191075668705216 95 0.967242091086873 0.0655158178262541 0.0327579089131270 96 0.959328631756623 0.0813427364867549 0.0406713682433774 97 0.968225666426534 0.0635486671469316 0.0317743335734658 98 0.965079474350051 0.0698410512998976 0.0349205256499488 99 0.928169196779815 0.14366160644037 0.071830803220185 100 0.837917284126827 0.324165431746346 0.162082715873173 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 16 0.166666666666667 NOK 10% type I error level 28 0.291666666666667 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/10bdyn1227789725.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/10bdyn1227789725.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/1r0yi1227789725.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/1r0yi1227789725.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/2ce0r1227789725.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/2ce0r1227789725.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/3xrly1227789725.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/3xrly1227789725.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/4yjqc1227789725.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/4yjqc1227789725.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/58fzo1227789725.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/58fzo1227789725.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/6ox7w1227789725.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/6ox7w1227789725.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/7jopc1227789725.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/7jopc1227789725.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/86fs61227789725.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/86fs61227789725.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/95w3r1227789725.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/27/t1227789867li91lqdxcdw2sky/95w3r1227789725.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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