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*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: Tue, 25 Nov 2008 00:21:29 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv.htm/, Retrieved Tue, 25 Nov 2008 07:22:24 +0000

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv.htm/}, year = {2008}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

multiple linear regression

Dataseries X:

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11178,4 1190,8 -1,3 9516,4 728,8 -1,4 12102,8 995,6 -1,3 12989,0 1260,3 -1 11610,2 994 -0,8 10205,5 957,3 -0,7 11356,2 975,6 -0,6 11307,1 884,9 -0,8 12648,6 908,4 -0,9 11947,2 1022,8 -1 11714,1 958,6 -1,2 12192,5 825,1 -1,3 11268,8 1116,6 -1,3 9097,4 724,2 -1,4 12639,8 1004,5 -1,4 13040,1 1058,9 -1,8 11687,3 854,7 -1,9 11191,7 943,4 -2 11391,9 792,4 -2,4 11793,1 873,2 -2,5 13933,2 1101,4 -2,5 12778,1 987,1 -2,3 11810,3 1038,8 -1,7 13698,4 1060,7 -1,1 11956,6 1047,7 -0,7 10723,8 840 -0,2 13938,9 1044 0,3 13979,8 1097,4 1,1 13807,4 987,5 1,6 12973,9 934 2,2 12509,8 977 3 12934,1 881,1 3,8 14908,3 1083,3 4,6 13772,1 1074,7 5,1 13012,6 1182,2 5,3 14049,9 1117,5 5,5 11816,5 1117,4 5,7 11593,2 936,2 5,9 14466,2 1246,3 6,1 13615,9 1175,1 6,1 14733,9 1177,7 6,3 13880,7 1035,8 6,5 13527,5 1091,6 6,7 13584,0 998,7 6,6 16170,2 1247,9 6,5 13260,6 1034,7 6,4 14741,9 1287,7 6,3 15486,5 994,0 6,3 13154,5 1122,8 6,3 12621,2 1017,3 6,2 15031,6 1106,0 6 15452,4 1191,8 5,6 15428 1030,1 5,3 13105,9 989,4 5,1 14716,8 979,6 4,5 14180,0 1088,0 4 16202,2 1389,2 3,5 14392,4 1043,9 3,5 15140,6 1182,1 3,3 15960,1 1109,6 3,1 14351,3 1463,3 2,9 13230,2 1276,2 2,5 15202,1 1082,4 2,6 17157,3 1360,4 2,8 16159,1 1130,2 2,8 13405,7 1019,6 2,9 17224,7 1077,0 3,1 17338,4 958,8 3,3 17370,6 959,6 3,5 18817,8 907,2 3,4 16593,2 880,8 3,5 17979,5 759,6 3,7 17015,2 1137,2 3,8

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 EU[t] = + 11681.0078496593 -0.121106302339442VS[t] -41.7372997987009Price[t] -1518.85729897612M1[t] -2982.91706376903M2[t] -274.229848568124M3[t] + 136.175800976025M4[t] -430.032646657245M5[t] -1956.87539195070M6[t] -1042.60909329050M7[t] -1059.83567468688M8[t] + 563.25808866321M9[t] -572.625391015120M10[t] -968.492550445997M11[t] + 82.0137590534028t + 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) 11681.0078496593 842.524785 13.8643 0 0 VS -0.121106302339442 0.842108 -0.1438 0.886146 0.443073 Price -41.7372997987009 41.216606 -1.0126 0.315441 0.157721 M1 -1518.85729897612 447.764477 -3.3921 0.001255 0.000627 M2 -2982.91706376903 431.888596 -6.9067 0 0 M3 -274.229848568124 441.655291 -0.6209 0.537089 0.268544 M4 136.175800976025 470.548355 0.2894 0.773309 0.386654 M5 -430.032646657245 432.929775 -0.9933 0.324686 0.162343 M6 -1956.87539195070 429.479744 -4.5564 2.7e-05 1.4e-05 M7 -1042.60909329050 429.233787 -2.429 0.01826 0.00913 M8 -1059.83567468688 429.306606 -2.4687 0.016528 0.008264 M9 563.25808866321 445.327853 1.2648 0.210996 0.105498 M10 -572.625391015120 429.810963 -1.3323 0.187982 0.093991 M11 -968.492550445997 438.997474 -2.2061 0.031349 0.015674 t 82.0137590534028 5.804249 14.13 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.945812309989402 R-squared 0.894560925727488 Adjusted R-squared 0.869110114696192 F-TEST (value) 35.1486215754571 F-TEST (DF numerator) 14 F-TEST (DF denominator) 58 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 742.230790716727 Sum Squared Residuals 31952579.7079027 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 11178.4 10154.2094146491 1024.19058535093 2 9516.4 8832.28825057027 684.111749429731 3 12102.8 11586.5043333805 516.295666619454 4 12989 12034.3457138092 954.654286190765 5 11610.2 11574.0541735826 36.1458264173805 6 10205.5 10129.4960586586 76.0039413414454 7 11356.2 11119.3861410595 236.81385894052 8 11307.1 11203.5051202984 103.594879701576 9 12648.6 12909.9403745768 -261.340374576814 10 11947.2 11846.3898229441 100.810177055877 11 11714.1 11548.6589071366 165.441092863419 12 12192.5 12619.5066379782 -427.006637978169 13 11268.8 11147.3606109235 121.439389076490 14 9097.4 9817.01044820187 -719.610448201868 15 12639.8 12573.7653259104 66.034674089567 16 13040.1 13076.2914715802 -36.1914715801941 17 11687.3 12621.0004199179 -933.700419917911 18 11191.7 11169.6030346402 22.0969653597856 19 11391.9 12200.8650639266 -808.965063926562 20 11793.1 12260.0405823344 -466.940582334421 21 13933.2 13937.5116465441 -4.31164654405717 22 12778.1 12889.1369163168 -111.036916316787 23 11810.3 12543.9799402291 -733.679940229145 24 13698.4 13566.7916418281 131.608358171910 25 11956.6 12114.8275639163 -158.227563916310 26 10723.8 10737.0666872734 -13.2666872733547 27 13938.9 13482.1933259511 456.706674048939 28 13979.8 13934.7558181647 45.0441818352726 29 13807.4 13443.0020623126 364.397937687386 30 12973.9 11979.6098833685 994.290116631504 31 12509.8 12937.2925302426 -427.49253024255 32 12934.1 12980.3039624550 -46.2039624549565 33 14908.3 14627.5339506865 280.766049313541 34 13772.1 13553.8370943623 218.262905637702 35 13012.6 13218.6173065236 -206.017306523594 36 14049.9 14268.6117338246 -218.711733824616 37 11816.5 12823.4328445724 -1006.93284457240 38 11593.2 11454.9838408571 138.216159142944 39 14466.2 14199.7822907962 266.417709203841 40 13615.9 14700.8244681203 -1084.92446812028 41 14733.9 14207.9674431946 525.932556805409 42 13880.7 12771.9759812968 1108.72401870324 43 13527.5 13753.1508473801 -225.650847380089 44 13584 13833.3625305043 -249.36253050431 45 16170.2 15512.4640923447 657.735907655313 46 13260.6 14488.5879653584 -1227.98796535840 47 14741.9 14148.2684004689 593.631599531083 48 15486.5 15234.3436309654 252.15636903459 49 13154.5 13781.9015993014 -627.401599301378 50 12621.2 12416.8060384386 204.393961561448 51 15031.6 15205.1123436351 -173.512343635087 52 15452.4 15703.8357514114 -251.435751411397 53 15428 15251.7451418594 176.254858140573 54 13105.9 13820.1926420843 -714.292642084326 55 14716.8 14842.7019214401 -125.901921440083 56 14180 14915.2298258229 -735.229825822855 57 16202.2 16604.7287798611 -402.528779861063 58 14392.4 15592.6770654339 -1200.27706543394 59 15140.6 15270.4342340329 -129.834234032898 60 15960.1 16338.0682104116 -377.968210411649 61 14351.3 14866.7368313112 -515.436831311219 62 13230.2 13524.0447346589 -293.8447346589 63 15202.1 16334.0423803267 -1131.94238032671 64 17157.3 16784.4467769142 372.853223085834 65 16159.1 16328.1307591328 -169.030759132836 66 13405.7 14892.5223999516 -1486.82239995165 67 17224.7 15873.5034959512 1351.19650404876 68 17338.4 15944.2579785850 1394.14202141497 69 17370.6 17640.9211559869 -270.321155986921 70 18817.8 16597.5711355845 2220.22886441555 71 16593.2 16282.7412116089 310.458788391136 72 17979.5 17339.5781449921 639.921855007934 73 17015.2 15852.8311353261 1162.36886467389 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 18 0.0613199038986478 0.122639807797296 0.938680096101352 19 0.0300623384021378 0.0601246768042755 0.969937661597862 20 0.0117731599855683 0.0235463199711367 0.988226840014432 21 0.00340810551994913 0.00681621103989827 0.99659189448005 22 0.00133486103520570 0.00266972207041141 0.998665138964794 23 0.000839743170779501 0.00167948634155900 0.99916025682922 24 0.000915666948586163 0.00183133389717233 0.999084333051414 25 0.00087048297261453 0.00174096594522906 0.999129517027386 26 0.000417610483327612 0.000835220966655224 0.999582389516672 27 0.000314451345983037 0.000628902691966075 0.999685548654017 28 0.000107958251512330 0.000215916503024661 0.999892041748488 29 0.000131577857490696 0.000263155714981392 0.99986842214251 30 0.000319385741231361 0.000638771482462722 0.999680614258769 31 0.000595481467792671 0.00119096293558534 0.999404518532207 32 0.000307656615232048 0.000615313230464097 0.999692343384768 33 0.000209762884077033 0.000419525768154066 0.999790237115923 34 0.000185978453125182 0.000371956906250363 0.999814021546875 35 0.000213855471737889 0.000427710943475778 0.999786144528262 36 0.000217392131089039 0.000434784262178078 0.99978260786891 37 0.000832263529988121 0.00166452705997624 0.999167736470012 38 0.00105952473581415 0.00211904947162831 0.998940475264186 39 0.0014073301125802 0.0028146602251604 0.99859266988742 40 0.00275217733612303 0.00550435467224607 0.997247822663877 41 0.00255380488464346 0.00510760976928692 0.997446195115357 42 0.101472116041304 0.202944232082609 0.898527883958696 43 0.0795373877501026 0.159074775500205 0.920462612249897 44 0.0512864143981203 0.102572828796241 0.94871358560188 45 0.07006903811915 0.1401380762383 0.92993096188085 46 0.234613290079605 0.469226580159209 0.765386709920395 47 0.203956203335031 0.407912406670062 0.796043796664969 48 0.190062051846944 0.380124103693888 0.809937948153056 49 0.155654801078018 0.311309602156036 0.844345198921982 50 0.104780158101467 0.209560316202933 0.895219841898533 51 0.137018813476363 0.274037626952725 0.862981186523637 52 0.110600548663421 0.221201097326842 0.889399451336579 53 0.0737196495300433 0.147439299060087 0.926280350469957 54 0.0913693819631028 0.182738763926206 0.908630618036897 55 0.394140717611957 0.788281435223914 0.605859282388043 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 21 0.552631578947368 NOK 5% type I error level 22 0.578947368421053 NOK 10% type I error level 23 0.605263157894737 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/10m9fd1227597683.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/10m9fd1227597683.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/1sog91227597682.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/1sog91227597682.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/2qvmi1227597682.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/2qvmi1227597682.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/36ltt1227597682.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/36ltt1227597682.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/4pdd31227597682.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/4pdd31227597682.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/5cua41227597682.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/5cua41227597682.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/61xwt1227597682.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/61xwt1227597682.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/7b3re1227597683.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/7b3re1227597683.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/8a59q1227597683.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/8a59q1227597683.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/9xrml1227597683.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/25/t1227597734cc5v2s6od7y9sqv/9xrml1227597683.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 = 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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