Toon Wouters

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Tue, 25 Nov 2008 04:35:36 -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/25/t12276130396pnytyv935upchq.htm/, Retrieved Tue, 25 Nov 2008 11:37:31 +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/25/t12276130396pnytyv935upchq.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)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

124 0 113 0 109 0 109 0 106 0 101 0 98 0 93 0 91 0 122 1 139 1 140 1 132 1 117 0 114 0 113 0 110 0 107 0 103 0 98 0 98 0 137 1 148 1 147 1 139 1 130 0 128 0 127 0 123 0 118 0 114 0 108 0 111 0 151 1 159 1 158 1 148 1 138 0 137 0 136 0 133 0 126 0 120 0 114 0 116 0 153 1 162 1 161 1 149 1 139 0 135 0 130 0 127 0 122 0 117 0 112 0 113 0 149 1 157 1 157 1 147 1 137 0 132 0 125 0 123 0 117 0 114 0 111 0 112 0 144 1 150 1 149 1 134 1 123 0 116 0 117 0 111 0 105 0 102 0 95 0 93 0 124 1 130 1 124 1 115 1 106 0 105 0 105 0 101 0 95 0 93 0 84 0 87 0 116 1 120 1 117 1 109 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 6 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 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 Y[t] = + 113.646153846154 + 26.5725961538462X[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) 113.646153846154 1.754501 64.7741 0 0 X 26.5725961538462 3.054671 8.699 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.665867135086476 R-squared 0.443379041588271 Adjusted R-squared 0.43751987360499 F-TEST (value) 75.6726966786779 F-TEST (DF numerator) 1 F-TEST (DF denominator) 95 p-value 9.93649607039515e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 14.1452354963416 Sum Squared Residuals 19008.3302884615 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 124 113.646153846154 10.3538461538459 2 113 113.646153846154 -0.646153846153948 3 109 113.646153846154 -4.64615384615384 4 109 113.646153846154 -4.64615384615384 5 106 113.646153846154 -7.64615384615384 6 101 113.646153846154 -12.6461538461538 7 98 113.646153846154 -15.6461538461538 8 93 113.646153846154 -20.6461538461538 9 91 113.646153846154 -22.6461538461538 10 122 140.21875 -18.21875 11 139 140.21875 -1.21875 12 140 140.21875 -0.21875 13 132 140.21875 -8.21875 14 117 113.646153846154 3.35384615384616 15 114 113.646153846154 0.353846153846158 16 113 113.646153846154 -0.646153846153842 17 110 113.646153846154 -3.64615384615384 18 107 113.646153846154 -6.64615384615384 19 103 113.646153846154 -10.6461538461538 20 98 113.646153846154 -15.6461538461538 21 98 113.646153846154 -15.6461538461538 22 137 140.21875 -3.21875 23 148 140.21875 7.78125 24 147 140.21875 6.78125 25 139 140.21875 -1.21875 26 130 113.646153846154 16.3538461538462 27 128 113.646153846154 14.3538461538462 28 127 113.646153846154 13.3538461538462 29 123 113.646153846154 9.35384615384616 30 118 113.646153846154 4.35384615384616 31 114 113.646153846154 0.353846153846158 32 108 113.646153846154 -5.64615384615384 33 111 113.646153846154 -2.64615384615384 34 151 140.21875 10.78125 35 159 140.21875 18.78125 36 158 140.21875 17.78125 37 148 140.21875 7.78125 38 138 113.646153846154 24.3538461538462 39 137 113.646153846154 23.3538461538462 40 136 113.646153846154 22.3538461538462 41 133 113.646153846154 19.3538461538462 42 126 113.646153846154 12.3538461538462 43 120 113.646153846154 6.35384615384616 44 114 113.646153846154 0.353846153846158 45 116 113.646153846154 2.35384615384616 46 153 140.21875 12.78125 47 162 140.21875 21.78125 48 161 140.21875 20.78125 49 149 140.21875 8.78125 50 139 113.646153846154 25.3538461538462 51 135 113.646153846154 21.3538461538462 52 130 113.646153846154 16.3538461538462 53 127 113.646153846154 13.3538461538462 54 122 113.646153846154 8.35384615384616 55 117 113.646153846154 3.35384615384616 56 112 113.646153846154 -1.64615384615384 57 113 113.646153846154 -0.646153846153842 58 149 140.21875 8.78125 59 157 140.21875 16.78125 60 157 140.21875 16.78125 61 147 140.21875 6.78125 62 137 113.646153846154 23.3538461538462 63 132 113.646153846154 18.3538461538462 64 125 113.646153846154 11.3538461538462 65 123 113.646153846154 9.35384615384616 66 117 113.646153846154 3.35384615384616 67 114 113.646153846154 0.353846153846158 68 111 113.646153846154 -2.64615384615384 69 112 113.646153846154 -1.64615384615384 70 144 140.21875 3.78125 71 150 140.21875 9.78125 72 149 140.21875 8.78125 73 134 140.21875 -6.21875 74 123 113.646153846154 9.35384615384616 75 116 113.646153846154 2.35384615384616 76 117 113.646153846154 3.35384615384616 77 111 113.646153846154 -2.64615384615384 78 105 113.646153846154 -8.64615384615384 79 102 113.646153846154 -11.6461538461538 80 95 113.646153846154 -18.6461538461538 81 93 113.646153846154 -20.6461538461538 82 124 140.21875 -16.21875 83 130 140.21875 -10.21875 84 124 140.21875 -16.21875 85 115 140.21875 -25.21875 86 106 113.646153846154 -7.64615384615384 87 105 113.646153846154 -8.64615384615384 88 105 113.646153846154 -8.64615384615384 89 101 113.646153846154 -12.6461538461538 90 95 113.646153846154 -18.6461538461538 91 93 113.646153846154 -20.6461538461538 92 84 113.646153846154 -29.6461538461538 93 87 113.646153846154 -26.6461538461538 94 116 140.21875 -24.21875 95 120 140.21875 -20.21875 96 117 140.21875 -23.21875 97 109 140.21875 -31.21875 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.187142182112489 0.374284364224979 0.81285781788751 6 0.165115374667785 0.33023074933557 0.834884625332215 7 0.16315230799391 0.32630461598782 0.83684769200609 8 0.210434085666461 0.420868171332922 0.789565914333539 9 0.254804304174191 0.509608608348381 0.74519569582581 10 0.176203868516853 0.352407737033707 0.823796131483147 11 0.168725393902883 0.337450787805766 0.831274606097117 12 0.129408000535193 0.258816001070385 0.870591999464807 13 0.084168685349761 0.168337370699522 0.915831314650239 14 0.0788016305116944 0.157603261023389 0.921198369488306 15 0.0586548082439145 0.117309616487829 0.941345191756086 16 0.0403927942865213 0.0807855885730425 0.959607205713479 17 0.0249205453452760 0.0498410906905521 0.975079454654724 18 0.0147742655780624 0.0295485311561248 0.985225734421938 19 0.00948760276448714 0.0189752055289743 0.990512397235513 20 0.00827603151946175 0.0165520630389235 0.991723968480538 21 0.00703953317626683 0.0140790663525337 0.992960466823733 22 0.00414782404252630 0.00829564808505259 0.995852175957474 23 0.00469600756841265 0.0093920151368253 0.995303992431587 24 0.00397606292588818 0.00795212585177637 0.996023937074112 25 0.00222744798203251 0.00445489596406502 0.997772552017967 26 0.00950956131783725 0.0190191226356745 0.990490438682163 27 0.0184680122467897 0.0369360244935794 0.98153198775321 28 0.0263681820780477 0.0527363641560955 0.973631817921952 29 0.0258005926850024 0.0516011853700049 0.974199407314998 30 0.0194441211915451 0.0388882423830901 0.980555878808455 31 0.0130516233846358 0.0261032467692716 0.986948376615364 32 0.00871319010834918 0.0174263802166984 0.99128680989165 33 0.00553063279260746 0.0110612655852149 0.994469367207393 34 0.00524992153844176 0.0104998430768835 0.994750078461558 35 0.00876257403945527 0.0175251480789105 0.991237425960545 36 0.0114477064249179 0.0228954128498358 0.988552293575082 37 0.0082204839063498 0.0164409678126996 0.99177951609365 38 0.0261124982753662 0.0522249965507325 0.973887501724634 39 0.0550405650572494 0.110081130114499 0.94495943494275 40 0.0910273424233643 0.182054684846729 0.908972657576636 41 0.116439589115241 0.232879178230483 0.883560410884759 42 0.109338294230871 0.218676588461742 0.890661705769129 43 0.0881477518078275 0.176295503615655 0.911852248192172 44 0.0665213591577179 0.133042718315436 0.933478640842282 45 0.0496247600716455 0.099249520143291 0.950375239928354 46 0.0451873041075775 0.090374608215155 0.954812695892423 47 0.064624926774462 0.129249853548924 0.935375073225538 48 0.0868123473804336 0.173624694760867 0.913187652619566 49 0.0751372210143826 0.150274442028765 0.924862778985617 50 0.138730278984931 0.277460557969862 0.861269721015069 51 0.190716099495937 0.381432198991874 0.809283900504063 52 0.210686545673137 0.421373091346275 0.789313454326863 53 0.213057211063915 0.42611442212783 0.786942788936085 54 0.191833781045524 0.383667562091048 0.808166218954476 55 0.159852414191025 0.31970482838205 0.840147585808975 56 0.128971017925645 0.25794203585129 0.871028982074355 57 0.102379441791088 0.204758883582176 0.897620558208912 58 0.0941256134494164 0.188251226898833 0.905874386550584 59 0.123509174913187 0.247018349826374 0.876490825086813 60 0.174567679784827 0.349135359569655 0.825432320215173 61 0.180269342078757 0.360538684157515 0.819730657921243 62 0.32410139096223 0.64820278192446 0.67589860903777 63 0.441480774414035 0.88296154882807 0.558519225585965 64 0.483536388924504 0.967072777849009 0.516463611075496 65 0.5157490860567 0.9685018278866 0.4842509139433 66 0.500139336749722 0.999721326500556 0.499860663250278 67 0.470138321746771 0.940276643493542 0.529861678253229 68 0.430255398978616 0.860510797957231 0.569744601021384 69 0.396744985032162 0.793489970064323 0.603255014967838 70 0.425883326023814 0.851766652047627 0.574116673976186 71 0.58317426916479 0.83365146167042 0.41682573083521 72 0.789079560642217 0.421840878715566 0.210920439357783 73 0.819724527999662 0.360550944000675 0.180275472000338 74 0.916179564679044 0.167640870641911 0.0838204353209555 75 0.943761687031022 0.112476625937955 0.0562383129689776 76 0.976798194977773 0.0464036100444536 0.0232018050222268 77 0.984930827830655 0.0301383443386899 0.0150691721693450 78 0.98363211281607 0.0327357743678587 0.0163678871839294 79 0.979145770715498 0.0417084585690037 0.0208542292845019 80 0.972087922405127 0.0558241551897454 0.0279120775948727 81 0.965451207314052 0.0690975853718957 0.0345487926859479 82 0.958074749258577 0.0838505014828468 0.0419252507414234 83 0.971534244042942 0.0569315119141154 0.0284657559570577 84 0.970139552532033 0.0597208949359346 0.0298604474679673 85 0.957612716364374 0.0847745672712525 0.0423872836356262 86 0.957467803195743 0.0850643936085149 0.0425321968042574 87 0.961186954251835 0.0776260914963302 0.0388130457481651 88 0.978674027311853 0.0426519453762945 0.0213259726881472 89 0.98802349065875 0.0239530186825007 0.0119765093412504 90 0.984077679308824 0.0318446413823516 0.0159223206911758 91 0.979948038991777 0.0401039220164469 0.0200519610082234 92 0.944250526313964 0.111498947372072 0.0557494736860362 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 4 0.0454545454545455 NOK 5% type I error level 27 0.306818181818182 NOK 10% type I error level 41 0.465909090909091 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/10gc3w1227612930.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/10gc3w1227612930.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/1e0o31227612930.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/1e0o31227612930.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/29w991227612930.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/29w991227612930.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/3pvjr1227612930.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/3pvjr1227612930.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/4hmto1227612930.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/4hmto1227612930.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/5f5kd1227612930.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/5f5kd1227612930.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/6yg851227612930.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/6yg851227612930.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/75ryx1227612930.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/75ryx1227612930.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/8yhqc1227612930.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/8yhqc1227612930.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/94vmc1227612930.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t12276130396pnytyv935upchq/94vmc1227612930.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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