Q3 Task 6 deel 2

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 20 Nov 2008 10:58:57 -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/20/t1227204033m62frcjgn7po4z1.htm/, Retrieved Thu, 20 Nov 2008 18:00:49 +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/20/t1227204033m62frcjgn7po4z1.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 «

3353 0 3480 0 3098 0 2944 0 3389 0 3497 0 4404 0 3849 0 3734 0 3060 0 3507 0 3287 0 3215 0 3764 0 2734 0 2837 0 2766 0 3851 0 3289 0 3848 0 3348 0 3682 0 4058 0 3655 1 3811 1 3341 1 3032 1 3475 1 3353 1 3186 1 3902 1 4164 1 3499 1 4145 1 3796 1 3711 1 3949 1 3740 1 3243 1 4407 1 4814 1 3908 1 5250 1 3937 1 4004 1 5560 1 3922 1 3759 1 4138 1 4634 1 3996 1 4308 1 4142 1 4429 1 5219 1 4929 1 5754 1 5592 1 4163 1 4962 1 5208 1 4755 1 4491 1 5732 1 5730 1 5024 1 6056 1 4901 1 5353 1 5578 1 4618 1 4724 1 5011 1 5298 1 4143 1 4617 1 4727 1 4207 1 5112 1 4190 1 4098 1 5071 1 4177 1 4598 1 3757 1 5591 1 4218 1 3780 1 4336 1 4870 1 4422 1 4727 1 4459 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 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation y[t] = + 3217.70420865441 + 1028.67842323652d[t] + 66.036973918197M1[t] + 336.161973918197M2[t] -369.838026081803M3[t] + 23.2869739181971M4[t] + 167.911973918197M5[t] + 132.286973918197M6[t] + 717.536973918197M7[t] + 328.911973918197M8[t] + 291.911973918197M9[t] + 717.239774748073M10[t] + 81.9540604623588M11[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) 3217.70420865441 268.951581 11.9639 0 0 d 1028.67842323652 150.492539 6.8354 0 0 M1 66.036973918197 323.557174 0.2041 0.838796 0.419398 M2 336.161973918197 323.557174 1.039 0.301955 0.150977 M3 -369.838026081803 323.557174 -1.143 0.256432 0.128216 M4 23.2869739181971 323.557174 0.072 0.942804 0.471402 M5 167.911973918197 323.557174 0.519 0.605224 0.302612 M6 132.286973918197 323.557174 0.4089 0.683741 0.34187 M7 717.536973918197 323.557174 2.2177 0.029415 0.014708 M8 328.911973918197 323.557174 1.0165 0.312432 0.156216 M9 291.911973918197 323.557174 0.9022 0.369661 0.184831 M10 717.239774748073 334.444927 2.1446 0.035022 0.017511 M11 81.9540604623588 334.444927 0.245 0.807049 0.403525 Multiple Linear Regression - Regression Statistics Multiple R 0.671239540120704 R-squared 0.450562520221454 Adjusted R-squared 0.368146898254672 F-TEST (value) 5.46695528674231 F-TEST (DF numerator) 12 F-TEST (DF denominator) 80 p-value 1.04655709964874e-06 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 624.395081779015 Sum Squared Residuals 31189537.4519858 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 3353 3283.74118257262 69.2588174273813 2 3480 3553.86618257262 -73.8661825726207 3 3098 2847.86618257261 250.133817427385 4 2944 3240.99118257261 -296.991182572615 5 3389 3385.61618257261 3.38381742738651 6 3497 3349.99118257261 147.008817427386 7 4404 3935.24118257261 468.758817427386 8 3849 3546.61618257261 302.383817427387 9 3734 3509.61618257261 224.383817427388 10 3060 3934.94398340249 -874.943983402489 11 3507 3299.65826911678 207.341730883225 12 3287 3217.70420865442 69.2957913455837 13 3215 3283.74118257261 -68.7411825726129 14 3764 3553.86618257261 210.133817427387 15 2734 2847.86618257261 -113.866182572613 16 2837 3240.99118257261 -403.991182572613 17 2766 3385.61618257261 -619.616182572614 18 3851 3349.99118257261 501.008817427387 19 3289 3935.24118257261 -646.241182572614 20 3848 3546.61618257261 301.383817427386 21 3348 3509.61618257261 -161.616182572613 22 3682 3934.94398340249 -252.943983402489 23 4058 3299.65826911678 758.341730883225 24 3655 4246.38263189093 -591.382631890931 25 3811 4312.41960580913 -501.419605809128 26 3341 4582.54460580913 -1241.54460580913 27 3032 3876.54460580913 -844.544605809128 28 3475 4269.66960580913 -794.669605809128 29 3353 4414.29460580913 -1061.29460580913 30 3186 4378.66960580913 -1192.66960580913 31 3902 4963.91960580913 -1061.91960580913 32 4164 4575.29460580913 -411.294605809129 33 3499 4538.29460580913 -1039.29460580913 34 4145 4963.622406639 -818.622406639004 35 3796 4328.33669235329 -532.33669235329 36 3711 4246.38263189093 -535.382631890931 37 3949 4312.41960580913 -363.419605809128 38 3740 4582.54460580913 -842.544605809128 39 3243 3876.54460580913 -633.544605809128 40 4407 4269.66960580913 137.330394190871 41 4814 4414.29460580913 399.705394190871 42 3908 4378.66960580913 -470.669605809129 43 5250 4963.91960580913 286.080394190871 44 3937 4575.29460580913 -638.294605809129 45 4004 4538.29460580913 -534.294605809129 46 5560 4963.622406639 596.377593360996 47 3922 4328.33669235329 -406.33669235329 48 3759 4246.38263189093 -487.382631890931 49 4138 4312.41960580913 -174.419605809128 50 4634 4582.54460580913 51.4553941908722 51 3996 3876.54460580913 119.455394190871 52 4308 4269.66960580913 38.3303941908714 53 4142 4414.29460580913 -272.294605809129 54 4429 4378.66960580913 50.3303941908713 55 5219 4963.91960580913 255.080394190871 56 4929 4575.29460580913 353.705394190871 57 5754 4538.29460580913 1215.70539419087 58 5592 4963.622406639 628.377593360996 59 4163 4328.33669235329 -165.33669235329 60 4962 4246.38263189093 715.617368109069 61 5208 4312.41960580913 895.580394190872 62 4755 4582.54460580913 172.455394190872 63 4491 3876.54460580913 614.455394190871 64 5732 4269.66960580913 1462.33039419087 65 5730 4414.29460580913 1315.70539419087 66 5024 4378.66960580913 645.330394190871 67 6056 4963.91960580913 1092.08039419087 68 4901 4575.29460580913 325.705394190871 69 5353 4538.29460580913 814.705394190871 70 5578 4963.622406639 614.377593360996 71 4618 4328.33669235329 289.66330764671 72 4724 4246.38263189093 477.617368109069 73 5011 4312.41960580913 698.580394190872 74 5298 4582.54460580913 715.455394190872 75 4143 3876.54460580913 266.455394190871 76 4617 4269.66960580913 347.330394190872 77 4727 4414.29460580913 312.705394190871 78 4207 4378.66960580913 -171.669605809129 79 5112 4963.91960580913 148.080394190871 80 4190 4575.29460580913 -385.294605809129 81 4098 4538.29460580913 -440.294605809129 82 5071 4963.622406639 107.377593360996 83 4177 4328.33669235329 -151.33669235329 84 4598 4246.38263189093 351.617368109069 85 3757 4312.41960580913 -555.419605809128 86 5591 4582.54460580913 1008.45539419087 87 4218 3876.54460580913 341.455394190872 88 3780 4269.66960580913 -489.669605809128 89 4336 4414.29460580913 -78.2946058091285 90 4870 4378.66960580913 491.330394190871 91 4422 4963.91960580913 -541.919605809129 92 4727 4575.29460580913 151.705394190871 93 4459 4538.29460580913 -79.2946058091287 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0339530072348682 0.0679060144697363 0.966046992765132 17 0.048367205804919 0.096734411609838 0.951632794195081 18 0.0253077519401250 0.0506155038802499 0.974692248059875 19 0.100120867476793 0.200241734953586 0.899879132523207 20 0.051533411971894 0.103066823943788 0.948466588028106 21 0.0312124511414957 0.0624249022829913 0.968787548858504 22 0.0287388519704409 0.0574777039408819 0.97126114802956 23 0.0213993156069768 0.0427986312139536 0.978600684393023 24 0.0111591740594609 0.0223183481189219 0.988840825940539 25 0.00566773906122474 0.0113354781224495 0.994332260938775 26 0.00757199663304763 0.0151439932660953 0.992428003366952 27 0.00439076378590419 0.00878152757180839 0.995609236214096 28 0.00352345771222771 0.00704691542445542 0.996476542287772 29 0.00258765365434724 0.00517530730869448 0.997412346345653 30 0.00506919733564805 0.0101383946712961 0.994930802664352 31 0.00418071107075065 0.0083614221415013 0.99581928892925 32 0.00237365519425795 0.0047473103885159 0.997626344805742 33 0.00210328766002444 0.00420657532004889 0.997896712339976 34 0.00401795161747175 0.0080359032349435 0.995982048382528 35 0.00244088307178035 0.0048817661435607 0.99755911692822 36 0.00171710232374748 0.00343420464749496 0.998282897676253 37 0.00138238464483455 0.00276476928966909 0.998617615355165 38 0.00160909382946956 0.00321818765893913 0.99839090617053 39 0.00142354099295420 0.00284708198590839 0.998576459007046 40 0.00979846794336967 0.0195969358867393 0.99020153205663 41 0.0590521562854337 0.118104312570867 0.940947843714566 42 0.0504076273350961 0.100815254670192 0.949592372664904 43 0.0884149279411747 0.176829855882349 0.911585072058825 44 0.0818853317549866 0.163770663509973 0.918114668245013 45 0.0823295127181059 0.164659025436212 0.917670487281894 46 0.223057865044871 0.446115730089743 0.776942134955129 47 0.185023771910948 0.370047543821895 0.814976228089052 48 0.194067930280952 0.388135860561903 0.805932069719048 49 0.170576014343674 0.341152028687349 0.829423985656326 50 0.189826992157457 0.379653984314915 0.810173007842543 51 0.182008064614022 0.364016129228044 0.817991935385978 52 0.170310450911737 0.340620901823474 0.829689549088263 53 0.167423511218881 0.334847022437762 0.83257648878112 54 0.145187554017554 0.290375108035108 0.854812445982446 55 0.13140985061496 0.26281970122992 0.86859014938504 56 0.120083024633993 0.240166049267987 0.879916975366007 57 0.333238000948717 0.666476001897434 0.666761999051283 58 0.34823946984389 0.69647893968778 0.65176053015611 59 0.286620812649265 0.573241625298529 0.713379187350735 60 0.300137062044577 0.600274124089153 0.699862937955423 61 0.364761447698598 0.729522895397196 0.635238552301402 62 0.356758694613830 0.713517389227661 0.64324130538617 63 0.334794657250660 0.669589314501321 0.66520534274934 64 0.642988445028369 0.714023109943263 0.357011554971631 65 0.806543479405528 0.386913041188943 0.193456520594472 66 0.784262235127431 0.431475529745137 0.215737764872569 67 0.90451374886066 0.190972502278678 0.0954862511393392 68 0.876726194150564 0.246547611698871 0.123273805849436 69 0.934726005086927 0.130547989826146 0.0652739949130732 70 0.91659137775543 0.16681724448914 0.08340862224457 71 0.882902011344409 0.234195977311182 0.117097988655591 72 0.822731938360238 0.354536123279524 0.177268061639762 73 0.919558255100167 0.160883489799667 0.0804417448998334 74 0.8746318820329 0.250736235934200 0.125368117967100 75 0.784323884982592 0.431352230034815 0.215676115017408 76 0.793925483242283 0.412149033515433 0.206074516757717 77 0.678181319033172 0.643637361933657 0.321818680966828 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 12 0.193548387096774 NOK 5% type I error level 18 0.290322580645161 NOK 10% type I error level 23 0.370967741935484 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/10f04r1227203932.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/10f04r1227203932.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/1d6b31227203932.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/1d6b31227203932.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/2ugmn1227203932.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/2ugmn1227203932.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/3dhq11227203932.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/3dhq11227203932.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/4sihl1227203932.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/4sihl1227203932.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/5ra1l1227203932.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/5ra1l1227203932.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/6uo4i1227203932.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/6uo4i1227203932.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/7muap1227203932.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/7muap1227203932.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/8mwrm1227203932.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/8mwrm1227203932.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/9gwpb1227203932.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227204033m62frcjgn7po4z1/9gwpb1227203932.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 = 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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