11.3

*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: Wed, 26 Nov 2008 12:36:19 -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/26/t1227728308keax9ox9ab4lq33.htm/, Retrieved Wed, 26 Nov 2008 19:38:38 +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/26/t1227728308keax9ox9ab4lq33.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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User-defined keywords:

Dataseries X:

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78,40 97,80 114,60 107,40 113,30 117,50 117,00 105,60 99,60 97,40 99,40 99,50 101,90 98,00 115,20 104,30 108,50 100,60 113,80 101,10 121,00 103,90 92,20 96,90 90,20 95,50 101,50 108,40 126,60 117,00 93,90 103,80 89,80 100,80 93,40 110,60 101,50 104,00 110,40 112,60 105,90 107,30 108,40 98,90 113,90 109,80 86,10 104,90 69,40 102,20 101,20 123,90 100,50 124,90 98,00 112,70 106,60 121,90 90,10 100,60 96,90 104,30 125,90 120,40 112,00 107,50 100,00 102,90 123,90 125,60 79,80 107,50 83,40 108,80 113,60 128,40 112,90 121,10 104,00 119,50 109,90 128,70 99,00 108,70 106,30 105,50 128,90 119,80 111,10 111,30 102,90 110,60 130,00 120,10 87,00 97,50 87,50 107,70 117,60 127,30 103,40 117,20 110,80 119,80 112,60 116,20 102,50 111,00 112,40 112,40 135,60 130,60 105,10 109,10 127,70 118,80 137,00 123,90 91,00 101,60 90,50 112,80 122,40 128,00 123,30 129,60 124,30 125,80 120,00 119,50 118,10 115,70 119,00 113,60 142,70 129,70 123,60 112,00 129,60 116,80 151,60 127,00 110,40 112,10 99,20 114,20 130,50 121,10 136,20 131,60 129,70 125,00 128,00 120,40 121,60 117,70 135,80 117,50 143,80 120,60 147,50 127,50 136,20 112,30 156,60 124,50 123,30 115,20 100,40 105,40

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 5 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation investerings[t] = + 53.60002807051 + 0.082379831183076consumptie[t] + 0.411324526534124V3[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) 53.60002807051 14.57535 3.6774 0.00042 0.00021 consumptie 0.082379831183076 0.132577 0.6214 0.536077 0.268039 V3 0.411324526534124 0.101541 4.0508 0.000115 5.8e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.461354168942973 R-squared 0.212847669201062 Adjusted R-squared 0.193648831864502 F-TEST (value) 11.0864874507659 F-TEST (DF numerator) 2 F-TEST (DF denominator) 82 p-value 5.47532894894509e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.8214665322626 Sum Squared Residuals 13479.9803311102 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 78.4 108.794566301026 -30.3945663010255 2 107.4 111.264294811312 -3.86429481131201 3 117 103.267261086242 13.7327389137585 4 97.4 102.715373680253 -5.31537368025304 5 101.9 109.057836983182 -7.15783698318247 6 104.3 103.917487123207 0.382512876793430 7 113.8 111.698896713748 2.10110328625207 8 103.9 101.052795126746 2.84720487325383 9 90.2 103.216741391707 -13.0167413917073 10 108.4 112.154284302780 -3.75428430277987 11 93.9 99.0879970300776 -5.18799703007757 12 100.8 106.786796937683 -5.98679693768336 13 101.5 107.577758242917 -6.07775824291714 14 112.6 106.459173889909 6.1408261100908 15 108.4 108.597256946753 -0.197256946752883 16 109.8 103.840874368802 5.9591256311976 17 69.4 103.645288902674 -34.2452889026736 18 123.9 113.253634468521 10.6463655314788 19 98 106.731429573380 -8.73142957338023 20 121.9 102.401698229438 19.4983017705620 21 96.9 113.978002353551 -17.0780023535510 22 120.4 107.043955765433 13.3560442345672 23 100 113.040021536826 -13.0400215368264 24 125.6 104.391325201338 21.2086747986623 25 83.4 109.289419917505 -25.8894199175051 26 128.4 112.712111174362 15.6878888256384 27 104 108.648983362988 -4.64898336298776 28 128.7 106.466607391894 22.2333926081062 29 106.3 115.310831730573 -9.01083173057305 30 119.8 108.532847118198 11.2671528818023 31 102.9 116.183425848794 -13.2834258487943 32 120.1 100.871214720515 19.2287852794853 33 87.5 110.844100209340 -23.3441002093402 34 127.3 110.325337124639 16.9746628753607 35 110.8 109.784273533985 1.01572646601519 36 116.2 107.700983212063 8.499016787937 37 112.4 118.635126893515 -6.23512689351489 38 130.6 107.133654172724 23.4663458272758 39 127.7 119.738212150234 7.96178784976564 40 123.9 102.887164604037 21.0128353959631 41 90.5 113.238595075738 -22.7385950757377 42 128 117.065119894206 10.9348801057943 43 124.3 113.322354017436 10.9776459825642 44 119.5 110.919333853229 8.58066614677063 45 119 121.654386829327 -2.65438682932686 46 129.7 109.85052217656 19.8494778234400 47 129.6 125.578790575266 4.02120942473358 48 127 108.804240857597 18.1957591424032 49 99.2 116.685655504320 -17.4856555043204 50 121.1 118.950468769536 2.14953123046439 51 129.7 116.547046364762 13.1529536352377 52 120.4 112.030312315438 8.36968768456162 53 135.8 122.428125150128 13.3718748498716 54 120.6 118.194930303114 2.40506969688553 55 136.2 127.264703967613 8.9352960323868 56 124.5 111.142046712114 13.3579532878857 57 100.4 94.5307051574815 5.86929484251852 58 97.8 107.217010873855 -9.41701087385538 59 113.3 111.404627839014 1.89537216098612 60 105.6 101.868068140768 3.73193185923200 61 99.4 103.710790527053 -4.31079052705324 62 98 105.991332740309 -7.99133274030944 63 108.5 108.696170207111 -0.196170207110701 64 101.1 106.304605950558 -5.20460595055763 65 92.2 98.684106005528 -6.484106005528 66 95.5 106.549159611891 -11.0491596118912 67 126.6 101.861841360484 24.7381586395159 68 103.8 102.459249185390 1.34075081461013 69 93.4 104.460676842572 -11.0606768425717 70 104 109.009903120864 -5.0099031208639 71 105.9 107.026962632753 -1.12696263275304 72 98.9 108.146523855709 -9.24652385570911 73 86.1 90.7875945030828 -4.68759450308284 74 102.2 112.899975823815 -10.6999758238152 75 100.5 104.199072585620 -3.69907258562029 76 112.7 112.522177859136 0.177822140864439 77 90.1 101.744785708684 -11.6447857086840 78 104.3 113.495121811168 -9.19512181116775 79 112 103.588312576103 8.41168742389698 80 102.9 115.469249686779 -12.5692496867790 81 79.8 96.7603254356366 -16.9603254356366 82 108.8 115.772446099889 -6.9724460998889 83 112.9 106.353976386329 6.54602361367066 84 119.5 115.591038082472 3.90896191752825 85 99 106.278512890688 -7.27851289068769 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 0.532153679622959 0.935692640754081 0.467846320377041 7 0.649669320961572 0.700661358076856 0.350330679038428 8 0.55854700007939 0.88290599984122 0.44145299992061 9 0.48741931580555 0.9748386316111 0.51258068419445 10 0.409424504233182 0.818849008466365 0.590575495766818 11 0.35335477172318 0.70670954344636 0.64664522827682 12 0.266282958383101 0.532565916766201 0.7337170416169 13 0.188705675369864 0.377411350739728 0.811294324630136 14 0.164013519556475 0.328027039112951 0.835986480443525 15 0.12758506702938 0.25517013405876 0.87241493297062 16 0.12811116093443 0.25622232186886 0.87188883906557 17 0.552536391790849 0.8949272164183 0.44746360820915 18 0.569837637039852 0.860324725920297 0.430162362960148 19 0.505834117060464 0.988331765879072 0.494165882939536 20 0.637358793127455 0.725282413745089 0.362641206872545 21 0.648391203230609 0.703217593538783 0.351608796769391 22 0.685282372826413 0.629435254347175 0.314717627173587 23 0.656856182006184 0.686287635987632 0.343143817993816 24 0.736889167806026 0.526221664387949 0.263110832193974 25 0.8352512606237 0.329497478752601 0.164748739376301 26 0.888732044118172 0.222535911763655 0.111267955881828 27 0.85600296124982 0.28799407750036 0.14399703875018 28 0.917108075141813 0.165783849716374 0.082891924858187 29 0.897323388822166 0.205353222355668 0.102676611177834 30 0.897485368273358 0.205029263453284 0.102514631726642 31 0.888103410452731 0.223793179094537 0.111896589547269 32 0.908904858344938 0.182190283310125 0.0910951416550624 33 0.947727445049362 0.104545109901276 0.0522725549506379 34 0.963712868381356 0.0725742632372872 0.0362871316186436 35 0.951521763768394 0.0969564724632115 0.0484782362316058 36 0.942940211591518 0.114119576816963 0.0570597884084816 37 0.927014293277079 0.145971413445842 0.072985706722921 38 0.969314243030008 0.0613715139399838 0.0306857569699919 39 0.967187450935626 0.0656250981287478 0.0328125490643739 40 0.986256864280036 0.0274862714399275 0.0137431357199637 41 0.994530716741016 0.0109385665179671 0.00546928325898353 42 0.994715584880919 0.0105688302381628 0.00528441511908141 43 0.994298507389187 0.0114029852216264 0.00570149261081321 44 0.992843483910612 0.0143130321787757 0.00715651608938783 45 0.989240496890475 0.0215190062190509 0.0107595031095255 46 0.994565170837368 0.0108696583252645 0.00543482916263225 47 0.992141277544858 0.0157174449102832 0.00785872245514158 48 0.99629346554743 0.00741306890514049 0.00370653445257025 49 0.998027675391671 0.00394464921665762 0.00197232460832881 50 0.996750269299907 0.00649946140018595 0.00324973070009297 51 0.99686888234407 0.00626223531185907 0.00313111765592953 52 0.99592999524947 0.00814000950106155 0.00407000475053078 53 0.996813887171314 0.00637222565737119 0.00318611282868559 54 0.994627424690887 0.0107451506182265 0.00537257530911323 55 0.995144268870452 0.00971146225909505 0.00485573112954752 56 0.997006299262283 0.00598740147543295 0.00299370073771648 57 0.995861642859998 0.00827671428000477 0.00413835714000239 58 0.99435652595601 0.0112869480879785 0.00564347404398925 59 0.99150183646485 0.0169963270702994 0.0084981635351497 60 0.988526654816595 0.0229466903668110 0.0114733451834055 61 0.98161695826883 0.0367660834623417 0.0183830417311709 62 0.973944736587173 0.0521105268256535 0.0260552634128268 63 0.962287688569797 0.0754246228604055 0.0377123114302028 64 0.944706650150955 0.110586699698091 0.0552933498490453 65 0.920461394459645 0.15907721108071 0.079538605540355 66 0.899749668722992 0.200500662554015 0.100250331277008 67 0.99468254249994 0.0106349150001203 0.00531745750006014 68 0.991731969784194 0.0165360604316122 0.0082680302158061 69 0.987779542753027 0.0244409144939453 0.0122204572469726 70 0.977365733924817 0.0452685321503659 0.0226342660751829 71 0.962560994369204 0.0748780112615924 0.0374390056307962 72 0.94149451878497 0.117010962430061 0.0585054812150307 73 0.903948264568774 0.192103470862453 0.0960517354312264 74 0.871902124250114 0.256195751499772 0.128097875749886 75 0.804885129567119 0.390229740865762 0.195114870432881 76 0.709337744309516 0.581324511380968 0.290662255690484 77 0.639904682926191 0.720190634147618 0.360095317073809 78 0.50501236342114 0.98997527315772 0.49498763657886 79 0.537452421923488 0.925095156153024 0.462547578076512 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 9 0.121621621621622 NOK 5% type I error level 26 0.351351351351351 NOK 10% type I error level 33 0.445945945945946 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/10eb861227728173.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/10eb861227728173.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/1w7bc1227728172.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/1w7bc1227728172.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/2tk921227728172.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/2tk921227728172.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/3tjv81227728172.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/3tjv81227728172.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/45qc01227728172.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/45qc01227728172.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/5ezbw1227728172.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/5ezbw1227728172.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/6vdpm1227728172.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/6vdpm1227728172.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/76f3a1227728172.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/76f3a1227728172.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/8zgip1227728173.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/8zgip1227728173.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/927sf1227728173.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/26/t1227728308keax9ox9ab4lq33/927sf1227728173.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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