*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: Fri, 20 Nov 2009 06:12:38 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx.htm/, Retrieved Fri, 20 Nov 2009 14:20:30 +0100

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/2009/Nov/20/t12587232179px1963yn45jgsx.htm/}, year = {2009}, } @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 = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Rob_WS7_1

Dataseries X:

» Textbox « » Textfile « » CSV «

106370 100,3 109375 101,9 116476 102,1 123297 103,2 114813 103,7 117925 106,2 126466 107,7 131235 109,9 120546 111,7 123791 114,9 129813 116 133463 118,3 122987 120,4 125418 126 130199 128,1 133016 130,1 121454 130,8 122044 133,6 128313 134,2 131556 135,5 120027 136,2 123001 139,1 130111 139 132524 139,6 123742 138,7 124931 140,9 133646 141,3 136557 141,8 127509 142 128945 144,5 137191 144,6 139716 145,5 129083 146,8 131604 149,5 139413 149,9 143125 150,1 133948 150,9 137116 152,8 144864 153,1 149277 154 138796 154,9 143258 156,9 150034 158,4 154708 159,7 144888 160,2 148762 163,2 156500 163,7 161088 164,4 152772 163,7 158011 165,5 163318 165,6 169969 166,8 162269 167,5 165765 170,6 170600 170,9 174681 172 166364 171,8 170240 173,9 176150 174 182056 173,8 172218 173,9 177856 176 182253 176,6 188090 178,2 176863 179,2 183273 181,3 187969 181,8 194650 182,9 183036 183,8 189516 186,3 193805 187,4 200499 189,2 188142 189,7 193732 191,9 1971 etc...

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 HFCE[t] = + 7117.57957043553 + 942.117029576953RPI[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) 7117.57957043553 5207.509379 1.3668 0.175172 0.087586 RPI 942.117029576953 31.942573 29.4941 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.95295985390233 R-squared 0.90813248314955 Adjusted R-squared 0.907088534094432 F-TEST (value) 869.901149578852 F-TEST (DF numerator) 1 F-TEST (DF denominator) 88 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9503.7112551085 Sum Squared Residuals 7948206430.60187 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 106370 101611.917637004 4758.08236299578 2 109375 103119.304884327 6255.69511567294 3 116476 103307.728290242 13168.2717097576 4 123297 104344.057022777 18952.9429772229 5 114813 104815.115537566 9997.88446243442 6 117925 107170.408111508 10754.5918884920 7 126466 108583.583655873 17882.4163441266 8 131235 110656.241120943 20578.7588790573 9 120546 112352.051774181 8193.94822581879 10 123791 115366.826268827 8424.17373117254 11 129813 116403.155001362 13409.8449986379 12 133463 118570.024169389 14892.9758306109 13 122987 120548.469931501 2438.5300684993 14 125418 125824.325297132 -406.32529713163 15 130199 127802.771059243 2396.22894075677 16 133016 129687.005118397 3328.99488160287 17 121454 130346.487039101 -8892.48703910101 18 122044 132984.414721916 -10940.4147219165 19 128313 133549.684939663 -5236.68493966263 20 131556 134774.437078113 -3218.43707811268 21 120027 135433.918998817 -15406.9189988165 22 123001 138166.058384590 -15165.0583845897 23 130111 138071.846681632 -7960.84668163202 24 132524 138637.116899378 -6113.11689937818 25 123742 137789.211572759 -14047.2115727589 26 124931 139861.869037828 -14930.8690378282 27 133646 140238.715849659 -6592.71584965902 28 136557 140709.774364447 -4152.7743644475 29 127509 140898.197770363 -13389.1977703629 30 128945 143253.490344305 -14308.4903443053 31 137191 143347.702047263 -6156.70204726295 32 139716 144195.607373882 -4479.60737388221 33 129083 145420.359512332 -16337.3595123323 34 131604 147964.07549219 -16360.0754921900 35 139413 148340.922304021 -8927.92230402081 36 143125 148529.345709936 -5404.34570993619 37 133948 149283.039333598 -15335.0393335978 38 137116 151073.061689794 -13957.0616897940 39 144864 151355.696798667 -6491.69679866705 40 149277 152203.602125286 -2926.60212528631 41 138796 153051.507451906 -14255.5074519056 42 143258 154935.741511059 -11677.7415110595 43 150034 156348.917055425 -6314.91705542491 44 154708 157573.669193875 -2865.66919387493 45 144888 158044.727708663 -13156.7277086634 46 148762 160871.078797394 -12109.0787973943 47 156500 161342.137312183 -4842.13731218275 48 161088 162001.619232887 -913.619232886629 49 152772 161342.137312183 -8570.13731218275 50 158011 163037.947965421 -5026.94796542127 51 163318 163132.159668379 185.840331621038 52 169969 164262.700103871 5706.29989612868 53 162269 164922.182024575 -2653.18202457518 54 165765 167842.744816264 -2077.74481626373 55 170600 168125.379925137 2474.62007486318 56 174681 169161.708657671 5519.29134232854 57 166364 168973.285251756 -2609.28525175609 58 170240 170951.731013868 -711.731013867682 59 176150 171045.942716825 5104.05728317462 60 182056 170857.51931091 11198.48068909 61 172218 170951.731013868 1266.26898613232 62 177856 172930.176775979 4925.82322402072 63 182253 173495.446993725 8757.55300627456 64 188090 175002.834241049 13087.1657589514 65 176863 175944.951270626 918.048729374482 66 183273 177923.397032737 5349.60296726286 67 187969 178394.455547526 9574.54445247439 68 194650 179430.784280060 15219.2157199397 69 183036 180278.689606680 2757.31039332048 70 189516 182633.982180622 6882.0178193781 71 193805 183670.310913157 10134.6890868435 72 200499 185366.121566395 15132.8784336050 73 188142 185837.180081184 2304.81991881647 74 193732 187909.837546253 5822.16245374717 75 197126 188569.319466957 8556.68053304331 76 205140 189605.648199491 15534.3518005087 77 191751 190076.706714280 1674.29328572019 78 196700 193279.904614841 3420.09538515854 79 199784 194881.503565122 4902.49643487771 80 207360 196859.949327234 10500.0506727661 81 196101 198367.336574557 -2266.33657455701 82 200824 201476.322772161 -652.322772160962 83 205743 202230.016395823 3512.98360417749 84 212489 204773.732375680 7715.2676243197 85 200810 205998.484514130 -5188.48451413032 86 203683 209955.376038354 -6272.37603835354 87 207286 211933.821800465 -4647.82180046513 88 210910 210143.799444269 766.200555731074 89 194915 205810.061108215 -10895.0611082149 90 217920 207411.660058496 10508.3399415042 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.168560274331752 0.337120548663505 0.831439725668248 6 0.141336574113161 0.282673148226322 0.858663425886839 7 0.0806079834539858 0.161215966907972 0.919392016546014 8 0.0488561050817873 0.0977122101635746 0.951143894918213 9 0.143854965070560 0.287709930141119 0.85614503492944 10 0.154955938019746 0.309911876039492 0.845044061980254 11 0.126207839923069 0.252415679846138 0.873792160076931 12 0.119384947484536 0.238769894969071 0.880615052515464 13 0.221792370022380 0.443584740044759 0.77820762997762 14 0.276168784401322 0.552337568802645 0.723831215598678 15 0.242918600800659 0.485837201601318 0.757081399199341 16 0.211352244392847 0.422704488785694 0.788647755607153 17 0.316760505330473 0.633521010660945 0.683239494669527 18 0.362358019044491 0.724716038088982 0.637641980955509 19 0.300489462398163 0.600978924796326 0.699510537601837 20 0.247342267092178 0.494684534184355 0.752657732907822 21 0.310985323033706 0.621970646067412 0.689014676966294 22 0.306319505816718 0.612639011633436 0.693680494183282 23 0.245734064085329 0.491468128170658 0.754265935914671 24 0.201458472305119 0.402916944610237 0.798541527694881 25 0.183790157609529 0.367580315219057 0.816209842390471 26 0.162939308785125 0.325878617570250 0.837060691214875 27 0.135927326850338 0.271854653700675 0.864072673149662 28 0.129474441880495 0.258948883760990 0.870525558119505 29 0.105290055760070 0.210580111520141 0.89470994423993 30 0.08609179055941 0.17218358111882 0.91390820944059 31 0.0777965639701615 0.155593127940323 0.922203436029839 32 0.079687498475619 0.159374996951238 0.920312501524381 33 0.0744837511941968 0.148967502388394 0.925516248805803 34 0.0701963914970464 0.140392782994093 0.929803608502954 35 0.0643160672069142 0.128632134413828 0.935683932793086 36 0.071426842708806 0.142853685417612 0.928573157291194 37 0.0693467511580726 0.138693502316145 0.930653248841927 38 0.0680152392812337 0.136030478562467 0.931984760718766 39 0.076930743437782 0.153861486875564 0.923069256562218 40 0.109066584819248 0.218133169638496 0.890933415180752 41 0.119481201959119 0.238962403918238 0.880518798040881 42 0.13192816937339 0.26385633874678 0.86807183062661 43 0.160554813772191 0.321109627544381 0.839445186227809 44 0.218801705296822 0.437603410593643 0.781198294703178 45 0.278398328728696 0.556796657457391 0.721601671271304 46 0.372355538939091 0.744711077878182 0.627644461060909 47 0.458448820587021 0.916897641174043 0.541551179412979 48 0.561743372156219 0.876513255687561 0.438256627843781 49 0.658670504484025 0.68265899103195 0.341329495515975 50 0.73956247028942 0.52087505942116 0.26043752971058 51 0.803639136393638 0.392721727212724 0.196360863606362 52 0.875636784793912 0.248726430412177 0.124363215206088 53 0.905328701930201 0.189342596139599 0.0946712980697993 54 0.93158551806954 0.136828963860919 0.0684144819304594 55 0.946512395708525 0.106975208582951 0.0534876042914754 56 0.958027367454414 0.0839452650911726 0.0419726325455863 57 0.973420017237096 0.0531599655258076 0.0265799827629038 58 0.982505388762715 0.0349892224745693 0.0174946112372847 59 0.985008977862029 0.029982044275943 0.0149910221379715 60 0.988712597124446 0.0225748057511074 0.0112874028755537 61 0.99170873798077 0.0165825240384601 0.00829126201923006 62 0.992107232652045 0.0157855346959096 0.0078927673479548 63 0.991608369950604 0.0167832600987911 0.00839163004939553 64 0.992447699172063 0.0151046016558733 0.00755230082793667 65 0.994557912543335 0.0108841749133306 0.00544208745666531 66 0.993942213530946 0.0121155729381074 0.00605778646905371 67 0.992170841801656 0.0156583163966881 0.00782915819834406 68 0.992815457161779 0.0143690856764427 0.00718454283822133 69 0.992862859318753 0.0142742813624941 0.00713714068124703 70 0.989863397125188 0.0202732057496248 0.0101366028748124 71 0.984971050412187 0.0300578991756262 0.0150289495878131 72 0.985803690646625 0.0283926187067508 0.0141963093533754 73 0.982487949425414 0.0350241011491725 0.0175120505745862 74 0.972562285580466 0.0548754288390675 0.0274377144195337 75 0.956409617194278 0.0871807656114446 0.0435903828057223 76 0.965566497289299 0.0688670054214026 0.0344335027107013 77 0.948845314432565 0.102309371134870 0.0511546855674348 78 0.918177155135035 0.163645689729931 0.0818228448649654 79 0.869603222520928 0.260793554958144 0.130396777479072 80 0.857875918060095 0.28424816387981 0.142124081939905 81 0.79911529273791 0.40176941452418 0.20088470726209 82 0.710733388101687 0.578533223796626 0.289266611898313 83 0.584748438601267 0.830503122797466 0.415251561398733 84 0.568241895814561 0.863516208370877 0.431758104185439 85 0.410628768869048 0.821257537738097 0.589371231130952 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 0.197530864197531 NOK 10% type I error level 22 0.271604938271605 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/10pt7z1258722754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/10pt7z1258722754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/1ed1u1258722754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/1ed1u1258722754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/2bowv1258722754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/2bowv1258722754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/3u0bg1258722754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/3u0bg1258722754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/4onp71258722754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/4onp71258722754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/5s8yo1258722754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/5s8yo1258722754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/6wy071258722754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/6wy071258722754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/7poba1258722754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/7poba1258722754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/8ca0n1258722754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/8ca0n1258722754.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/9yexz1258722754.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/20/t12587232179px1963yn45jgsx/9yexz1258722754.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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