Multiple Regression - Totale Productie (A)

*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: Thu, 11 Dec 2008 08:10:14 -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/Dec/11/t1229008309f8te1y8fmbpz1sa.htm/, Retrieved Thu, 11 Dec 2008 15:11:49 +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/Dec/11/t1229008309f8te1y8fmbpz1sa.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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101,5 1 100,7 1 110,6 1 96,8 1 100,0 1 104,8 1 86,8 1 92,0 1 100,2 1 106,6 1 102,1 1 93,7 1 97,6 1 96,9 1 105,6 1 102,8 1 101,7 1 104,2 1 92,7 1 91,9 1 106,5 1 112,3 1 102,8 1 96,5 1 101,0 0 98,9 0 105,1 0 103,0 0 99,0 0 104,3 0 94,6 0 90,4 0 108,9 0 111,4 0 100,8 0 102,5 0 98,2 0 98,7 0 113,3 0 104,6 0 99,3 0 111,8 0 97,3 0 97,7 0 115,6 0 111,9 0 107,0 0 107,1 0 100,6 0 99,2 0 108,4 0 103,0 0 99,8 0 115,0 0 90,8 0 95,9 0 114,4 0 108,2 0 112,6 0 109,1 0 105,0 0 105,0 0 118,5 0 103,7 0 112,5 0 116,6 0 96,6 0 101,9 0 116,5 0 119,3 0 115,4 0 108,5 0 111,5 0 108,8 0 121,8 0 109,6 0 112,2 0 119,6 0 104,1 0 105,3 0 115,0 0 124,1 0 116,8 0 107,5 0 115,6 0 116,2 0 116,3 0 119,0 0 111,9 0 118,6 0 106,9 0 103,2 0

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 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 107.689705882353 -7.38553921568628X[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) 107.689705882353 0.90388 119.1416 0 0 X -7.38553921568628 1.769697 -4.1733 6.9e-05 3.5e-05 Multiple Linear Regression - Regression Statistics Multiple R 0.402668140869686 R-squared 0.162141631671449 Adjusted R-squared 0.152832094245577 F-TEST (value) 17.4167226849349 F-TEST (DF numerator) 1 F-TEST (DF denominator) 90 p-value 6.91247867468103e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.45358405768734 Sum Squared Residuals 5000.03237745098 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 101.5 100.304166666667 1.19583333333329 2 100.7 100.304166666667 0.395833333333338 3 110.6 100.304166666667 10.2958333333333 4 96.8 100.304166666667 -3.50416666666667 5 100 100.304166666667 -0.304166666666664 6 104.8 100.304166666667 4.49583333333333 7 86.8 100.304166666667 -13.5041666666667 8 92 100.304166666667 -8.30416666666666 9 100.2 100.304166666667 -0.104166666666661 10 106.6 100.304166666667 6.29583333333333 11 102.1 100.304166666667 1.79583333333333 12 93.7 100.304166666667 -6.60416666666666 13 97.6 100.304166666667 -2.70416666666667 14 96.9 100.304166666667 -3.40416666666666 15 105.6 100.304166666667 5.29583333333333 16 102.8 100.304166666667 2.49583333333333 17 101.7 100.304166666667 1.39583333333334 18 104.2 100.304166666667 3.89583333333334 19 92.7 100.304166666667 -7.60416666666666 20 91.9 100.304166666667 -8.40416666666666 21 106.5 100.304166666667 6.19583333333334 22 112.3 100.304166666667 11.9958333333333 23 102.8 100.304166666667 2.49583333333333 24 96.5 100.304166666667 -3.80416666666666 25 101 107.689705882353 -6.68970588235294 26 98.9 107.689705882353 -8.78970588235294 27 105.1 107.689705882353 -2.58970588235295 28 103 107.689705882353 -4.68970588235294 29 99 107.689705882353 -8.68970588235294 30 104.3 107.689705882353 -3.38970588235294 31 94.6 107.689705882353 -13.0897058823529 32 90.4 107.689705882353 -17.2897058823529 33 108.9 107.689705882353 1.21029411764707 34 111.4 107.689705882353 3.71029411764707 35 100.8 107.689705882353 -6.88970588235294 36 102.5 107.689705882353 -5.18970588235294 37 98.2 107.689705882353 -9.48970588235294 38 98.7 107.689705882353 -8.98970588235294 39 113.3 107.689705882353 5.61029411764706 40 104.6 107.689705882353 -3.08970588235295 41 99.3 107.689705882353 -8.38970588235294 42 111.8 107.689705882353 4.11029411764706 43 97.3 107.689705882353 -10.3897058823529 44 97.7 107.689705882353 -9.98970588235294 45 115.6 107.689705882353 7.91029411764705 46 111.9 107.689705882353 4.21029411764706 47 107 107.689705882353 -0.689705882352941 48 107.1 107.689705882353 -0.589705882352947 49 100.6 107.689705882353 -7.08970588235295 50 99.2 107.689705882353 -8.48970588235294 51 108.4 107.689705882353 0.710294117647065 52 103 107.689705882353 -4.68970588235294 53 99.8 107.689705882353 -7.88970588235294 54 115 107.689705882353 7.31029411764706 55 90.8 107.689705882353 -16.8897058823529 56 95.9 107.689705882353 -11.7897058823529 57 114.4 107.689705882353 6.71029411764706 58 108.2 107.689705882353 0.510294117647062 59 112.6 107.689705882353 4.91029411764705 60 109.1 107.689705882353 1.41029411764705 61 105 107.689705882353 -2.68970588235294 62 105 107.689705882353 -2.68970588235294 63 118.5 107.689705882353 10.8102941176471 64 103.7 107.689705882353 -3.98970588235294 65 112.5 107.689705882353 4.81029411764706 66 116.6 107.689705882353 8.91029411764705 67 96.6 107.689705882353 -11.0897058823529 68 101.9 107.689705882353 -5.78970588235294 69 116.5 107.689705882353 8.81029411764706 70 119.3 107.689705882353 11.6102941176471 71 115.4 107.689705882353 7.71029411764706 72 108.5 107.689705882353 0.81029411764706 73 111.5 107.689705882353 3.81029411764706 74 108.8 107.689705882353 1.11029411764706 75 121.8 107.689705882353 14.1102941176471 76 109.6 107.689705882353 1.91029411764705 77 112.2 107.689705882353 4.51029411764706 78 119.6 107.689705882353 11.9102941176471 79 104.1 107.689705882353 -3.58970588235295 80 105.3 107.689705882353 -2.38970588235294 81 115 107.689705882353 7.31029411764706 82 124.1 107.689705882353 16.4102941176471 83 116.8 107.689705882353 9.11029411764706 84 107.5 107.689705882353 -0.189705882352941 85 115.6 107.689705882353 7.91029411764705 86 116.2 107.689705882353 8.51029411764706 87 116.3 107.689705882353 8.61029411764706 88 119 107.689705882353 11.3102941176471 89 111.9 107.689705882353 4.21029411764706 90 118.6 107.689705882353 10.9102941176471 91 106.9 107.689705882353 -0.789705882352935 92 103.2 107.689705882353 -4.48970588235294 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.396755940756828 0.793511881513657 0.603244059243172 6 0.258929953758343 0.517859907516686 0.741070046241657 7 0.656240717345003 0.687518565309993 0.343759282654997 8 0.648025094424758 0.703949811150484 0.351974905575242 9 0.533936301150155 0.93212739769969 0.466063698849845 10 0.512654555979625 0.97469088804075 0.487345444020375 11 0.413715836120659 0.827431672241318 0.586284163879341 12 0.386371182041248 0.772742364082496 0.613628817958752 13 0.303708910956958 0.607417821913916 0.696291089043042 14 0.236687961518628 0.473375923037255 0.763312038481372 15 0.215734567689162 0.431469135378324 0.784265432310838 16 0.165009754598555 0.330019509197111 0.834990245401445 17 0.118755431461356 0.237510862922713 0.881244568538644 18 0.092975414467066 0.185950828934132 0.907024585532934 19 0.100020014766402 0.200040029532804 0.899979985233598 20 0.116793149234852 0.233586298469704 0.883206850765148 21 0.111382579053815 0.222765158107631 0.888617420946185 22 0.192996147945937 0.385992295891873 0.807003852054063 23 0.152748532114543 0.305497064229086 0.847251467885457 24 0.122176978585788 0.244353957171576 0.877823021414212 25 0.0938686034719178 0.187737206943836 0.906131396528082 26 0.0752518245389675 0.150503649077935 0.924748175461032 27 0.0597598764769105 0.119519752953821 0.94024012352309 28 0.0434757149049109 0.0869514298098218 0.95652428509509 29 0.0354598136646631 0.0709196273293262 0.964540186335337 30 0.0258139460767983 0.0516278921535966 0.974186053923202 31 0.0329125408964895 0.065825081792979 0.96708745910351 32 0.0720711102730608 0.144142220546122 0.92792888972694 33 0.0785306410851927 0.157061282170385 0.921469358914807 34 0.0959238713128163 0.191847742625633 0.904076128687184 35 0.0798258630723467 0.159651726144693 0.920174136927653 36 0.0634872714045217 0.126974542809043 0.936512728595478 37 0.0613013894488931 0.122602778897786 0.938698610551107 38 0.0579014494572758 0.115802898914552 0.942098550542724 39 0.0820518037268579 0.164103607453716 0.917948196273142 40 0.0655684574158591 0.131136914831718 0.93443154258414 41 0.0624061284921972 0.124812256984394 0.937593871507803 42 0.0687121978511154 0.137424395702231 0.931287802148885 43 0.0796435442429089 0.159287088485818 0.920356455757091 44 0.0908488233840628 0.181697646768126 0.909151176615937 45 0.135903500239880 0.271807000479759 0.86409649976012 46 0.136566593306943 0.273133186613885 0.863433406693057 47 0.112950652723537 0.225901305447075 0.887049347276463 48 0.0922048904083655 0.184409780816731 0.907795109591634 49 0.0893974733722442 0.178794946744488 0.910602526627756 50 0.0980107914674412 0.196021582934882 0.901989208532559 51 0.0819028578968989 0.163805715793798 0.918097142103101 52 0.0722311596503382 0.144462319300676 0.927768840349662 53 0.080161080200278 0.160322160400556 0.919838919799722 54 0.0957386588434404 0.191477317686881 0.90426134115656 55 0.314229720792980 0.628459441585959 0.68577027920702 56 0.483638779910688 0.967277559821376 0.516361220089312 57 0.497263447247813 0.994526894495627 0.502736552752187 58 0.458921586867123 0.917843173734245 0.541078413132877 59 0.438481973051678 0.876963946103356 0.561518026948322 60 0.397244854495306 0.794489708990611 0.602755145504694 61 0.380663895815474 0.761327791630948 0.619336104184526 62 0.368199779987773 0.736399559975546 0.631800220012227 63 0.435129345689617 0.870258691379235 0.564870654310383 64 0.444801458253777 0.889602916507553 0.555198541746223 65 0.407412287248852 0.814824574497704 0.592587712751148 66 0.416798305573016 0.833596611146032 0.583201694426984 67 0.661469786708083 0.677060426583834 0.338530213291917 68 0.75049935555955 0.499001288880899 0.249500644440450 69 0.7426308493837 0.5147383012326 0.2573691506163 70 0.778241995972573 0.443516008054854 0.221758004027427 71 0.750474542068887 0.499050915862226 0.249525457931113 72 0.714465948863316 0.571068102273369 0.285534051136684 73 0.658224497581125 0.683551004837751 0.341775502418875 74 0.615111375772331 0.769777248455338 0.384888624227669 75 0.702498010881829 0.595003978236342 0.297501989118171 76 0.646890363338795 0.706219273322409 0.353109636661205 77 0.574123754822946 0.851752490354108 0.425876245177054 78 0.591233016524038 0.817533966951923 0.408766983475962 79 0.637960790743254 0.724078418513491 0.362039209256746 80 0.67719105328618 0.645617893427639 0.322808946713820 81 0.594827691172819 0.810344617654362 0.405172308827181 82 0.75279098951626 0.494418020967481 0.247209010483741 83 0.695025165215703 0.609949669568595 0.304974834784297 84 0.652635805372613 0.694728389254774 0.347364194627387 85 0.543152131703989 0.913695736592022 0.456847868296011 86 0.432646492076043 0.865292984152086 0.567353507923957 87 0.322402010145329 0.644804020290657 0.677597989854671 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 0 0 OK 10% type I error level 4 0.0481927710843374 OK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/10ylqw1229008202.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/10ylqw1229008202.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/1md8x1229008201.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/1md8x1229008201.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/2n1u01229008201.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/2n1u01229008201.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/33twi1229008202.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/33twi1229008202.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/4pobd1229008202.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/4pobd1229008202.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/5k5v71229008202.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/5k5v71229008202.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/64mfz1229008202.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/64mfz1229008202.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/7wjg21229008202.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/7wjg21229008202.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/85kdp1229008202.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/85kdp1229008202.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/9wfje1229008202.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229008309f8te1y8fmbpz1sa/9wfje1229008202.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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