model 4 ws 7

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Fri, 20 Nov 2009 07:57:59 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729414a9cvtyc3f2ac3h0.htm/, Retrieved Fri, 20 Nov 2009 16:03:46 +0100

BibTeX entries for LaTeX users:

@Manual{KEY, author = {{YOUR NAME}}, publisher = {Office for Research Development and Education}, title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2009/Nov/20/t1258729414a9cvtyc3f2ac3h0.htm/}, year = {2009}, } @Manual{R, title = {R: A Language and Environment for Statistical Computing}, author = {{R Development Core Team}}, organization = {R Foundation for Statistical Computing}, address = {Vienna, Austria}, year = {2009}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

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95.43 0 104.48 103.84 100.01 104.80 0 95.43 104.48 103.84 108.64 0 104.80 95.43 104.48 105.65 0 108.64 104.80 95.43 108.42 0 105.65 108.64 104.80 115.35 0 108.42 105.65 108.64 113.64 0 115.35 108.42 105.65 115.24 0 113.64 115.35 108.42 100.33 0 115.24 113.64 115.35 101.29 0 100.33 115.24 113.64 104.48 0 101.29 100.33 115.24 99.26 0 104.48 101.29 100.33 100.11 0 99.26 104.48 101.29 103.52 0 100.11 99.26 104.48 101.18 0 103.52 100.11 99.26 96.39 0 101.18 103.52 100.11 97.56 0 96.39 101.18 103.52 96.39 0 97.56 96.39 101.18 85.10 0 96.39 97.56 96.39 79.77 0 85.10 96.39 97.56 79.13 0 79.77 85.10 96.39 80.84 0 79.13 79.77 85.10 82.75 0 80.84 79.13 79.77 92.55 0 82.75 80.84 79.13 96.60 0 92.55 82.75 80.84 96.92 0 96.60 92.55 82.75 95.32 0 96.92 96.60 92.55 98.52 0 95.32 96.92 96.60 100.22 0 98.52 95.32 96.92 104.91 0 100.22 98.52 95.32 103.10 0 104.91 100.22 98.52 97.13 0 103.10 104.91 100.22 103.42 0 97.13 103.10 104.91 111.72 0 103.42 97.13 103.10 118.11 0 111.72 103.42 97.13 111 etc...

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 8.2339349299459 -2.78755263787447X[t] + 1.31883197290955Y1[t] -0.154452435378261Y2[t] -0.256484237853682Y3[t] -3.13382616025963M1[t] + 0.968052253349415M2[t] -0.053936219421836M3[t] -4.64989216064404M4[t] -7.24376686972142M5[t] -8.614266794937M6[t] -9.13615037441491M7[t] -4.51485066691514M8[t] -3.88401004887181M9[t] + 2.93988204549788M10[t] -1.78358143139068M11[t] + 0.210428913856814t + 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) 8.2339349299459 5.156068 1.5969 0.113532 0.056766 X -2.78755263787447 4.438695 -0.628 0.531472 0.265736 Y1 1.31883197290955 0.099241 13.2891 0 0 Y2 -0.154452435378261 0.169109 -0.9133 0.363333 0.181666 Y3 -0.256484237853682 0.101598 -2.5245 0.013209 0.006605 M1 -3.13382616025963 5.920954 -0.5293 0.597822 0.298911 M2 0.968052253349415 5.912037 0.1637 0.870274 0.435137 M3 -0.053936219421836 5.941597 -0.0091 0.992776 0.496388 M4 -4.64989216064404 5.938704 -0.783 0.435546 0.217773 M5 -7.24376686972142 5.945821 -1.2183 0.226067 0.113033 M6 -8.614266794937 5.966356 -1.4438 0.152015 0.076008 M7 -9.13615037441491 6.213281 -1.4704 0.144684 0.072342 M8 -4.51485066691514 6.224649 -0.7253 0.470003 0.235002 M9 -3.88401004887181 6.191525 -0.6273 0.531929 0.265965 M10 2.93988204549788 6.124509 0.48 0.632294 0.316147 M11 -1.78358143139068 6.125483 -0.2912 0.77154 0.38577 t 0.210428913856814 0.070131 3.0005 0.003426 0.001713 Multiple Linear Regression - Regression Statistics Multiple R 0.989709517238206 R-squared 0.979524928511882 Adjusted R-squared 0.976147597132399 F-TEST (value) 290.029262293389 F-TEST (DF numerator) 16 F-TEST (DF denominator) 97 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 12.8403394174577 Sum Squared Residuals 15992.8086864852 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 95.43 101.412772695707 -5.98277269570738 2 104.8 92.7084664787205 12.0915335212795 3 108.64 105.488007133916 3.15199286608399 4 105.65 107.040757915604 -1.39075791560419 5 108.42 97.7176498608426 10.7023501391574 6 115.35 99.6876567228661 15.6623432771339 7 113.64 108.854762254693 4.78523774530708 8 115.24 109.650471486348 5.58952851365186 9 100.33 111.088550071074 -10.7585500710744 10 101.29 98.650550513344 2.63944948665595 11 104.48 97.2961056752295 7.18389432477054 12 99.26 107.173095662494 -7.91309566249369 13 100.11 96.6264673803068 3.48353261969316 14 103.52 102.047838878667 1.47216112133292 15 101.18 106.941059498899 -5.7610594988989 16 96.39 98.7247712481097 -2.33477124810969 17 97.56 89.5109277503564 8.04907224964357 18 96.39 91.2338904293413 5.15610957065868 19 85.1 90.4272525053426 -5.32725250534263 20 79.77 80.2499909436541 -0.479990943654141 21 79.13 76.1057406136558 3.02425938634423 22 80.84 86.0149476851543 -5.17494768515434 23 82.75 85.2230263422002 -2.47302634220016 24 92.55 89.6360420034344 2.91395799656558 25 96.6 98.903605893243 -2.30360589324292 26 96.92 106.553663949985 -9.63366394998497 27 95.32 103.025052728154 -7.70505272815356 28 98.52 95.4412086015044 3.07879139849559 29 100.22 97.4430740600865 2.77692593991354 30 104.91 98.4411443900294 6.46885560997061 31 103.1 103.231692976079 -0.131692976079254 32 97.13 104.515930600194 -7.38593060019425 33 103.42 96.5604210863252 6.85957891367475 34 111.72 113.276512713876 -1.55651271387623 35 118.11 120.269488607451 -2.15948860745096 36 111.62 127.795594189851 -16.1755941898513 37 100.22 113.197207203013 -12.9772072030128 38 102.03 101.838292065030 0.191707934970322 39 105.76 106.839158844064 -1.0791588440641 40 107.68 110.017236479149 -2.33723647914866 41 110.77 109.125604017438 1.64439598256163 42 105.44 110.787488919250 -5.3474889192496 43 112.26 102.476952076023 9.7830479239773 44 114.07 116.333809938221 -2.26380993822070 45 117.9 119.875860719567 -1.97586071956749 46 124.72 129.932526773841 -5.21252677384082 47 126.42 133.358136968038 -6.9381369680383 48 134.73 135.558461426973 -0.828461426972701 49 135.79 141.582766233143 -5.79276623314308 50 143.36 145.573512509548 -2.21351250954844 51 140.37 152.450407387494 -12.0804073874943 52 144.74 142.680494533191 2.05950546680902 53 151.98 144.580571560814 7.3994284391862 54 150.92 153.060774761900 -2.14077476189966 55 163.38 149.112286453435 14.2677135465648 56 154.43 168.683435156685 -14.2534351566851 57 146.66 156.068554478357 -9.4085544783566 58 157.95 151.042106750054 6.90789324994557 59 162.1 164.914314512851 -2.81431451285103 60 180.42 172.630592078376 7.78940792162427 61 179.57 190.331511923488 -10.7615119234880 62 171.58 189.628833870758 -18.0488338707582 63 185.43 173.712300180889 11.7176998191114 64 190.64 189.044682539168 1.59531746083159 65 203 193.442494153269 9.55750584673143 66 202.36 204.226182444478 -1.86618244447761 67 193.41 199.825360335701 -6.41536033570145 68 186.17 189.782247178288 -3.61224717828812 69 192.24 182.621672435185 9.61832756481513 70 209.6 201.075073079901 8.52492692009854 71 206.41 220.376381165894 -13.9663811658941 72 209.82 213.925163915622 -4.10516391562168 73 230.37 211.539120596557 18.8308794034428 74 235.8 243.244926881428 -7.44492688142766 75 232.07 245.546016137308 -13.4760161373078 76 244.64 230.131818038993 14.5081819610074 77 242.19 243.509488315661 -1.31948831566053 78 217.48 238.133498065163 -20.6534980651629 79 209.39 202.388106945803 7.00189305419727 80 211.73 200.995390967259 10.7346090327406 81 221 212.509973035343 8.4900269646575 82 203.11 233.483405217892 -30.3734052178917 83 214.71 203.344519466974 11.3654805330260 84 224.19 221.022525881986 3.16747411801425 85 238.04 233.39851050358 4.64148949641999 86 238.36 251.537214409355 -13.1772144093545 87 246.24 246.577044276929 -0.337044276929331 88 259.87 248.982181722497 10.8878182775033 89 249.97 263.275255571139 -13.3052555711394 90 266.48 244.932465539483 21.5475344605167 91 282.98 264.428125694898 18.5518743051019 92 306.31 291.009766115919 15.3002338840814 93 301.73 313.048812987219 -11.3188129872186 94 314.62 306.207518317559 8.41248168244122 95 332.62 313.417842770237 19.2021572297628 96 355.51 338.334634545201 17.1753654547993 97 370.32 359.513075495955 10.8069245040452 98 408.13 375.205151815036 32.9248481849636 99 433.58 416.100264379409 17.4797356205907 100 440.51 435.640632918327 4.86936708167322 101 386.29 428.768209181745 -42.478209181745 102 342.84 348.503189368683 -5.66318936868292 103 254.97 297.485460758025 -42.5154607580250 104 203.42 207.048957613432 -3.62895761343153 105 170.09 164.620414573275 5.4695854267254 106 174.03 158.197358948378 15.8326410516218 107 167.85 177.250184491125 -9.40018449112482 108 177.01 179.033890296064 -2.02389029606416 109 188.19 188.134962075007 0.0550379249930264 110 211.2 207.362099141473 3.83790085852747 111 240.91 232.820689432938 8.08931056706187 112 230.26 261.196216003458 -30.9362160034576 113 251.25 234.276725528649 16.9732744713511 114 241.66 254.823709358807 -13.1637093588072 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 20 0.0453334140488965 0.090666828097793 0.954666585951103 21 0.132146517318799 0.264293034637597 0.867853482681201 22 0.0613815041829615 0.122763008365923 0.938618495817038 23 0.0266070463657822 0.0532140927315645 0.973392953634218 24 0.0288401304807753 0.0576802609615505 0.971159869519225 25 0.0185471781070875 0.0370943562141750 0.981452821892912 26 0.008846946656842 0.017693893313684 0.991153053343158 27 0.00385348783260667 0.00770697566521335 0.996146512167393 28 0.00232835448045329 0.00465670896090659 0.997671645519547 29 0.000953661793598913 0.00190732358719783 0.9990463382064 30 0.000449876958391226 0.000899753916782453 0.99955012304161 31 0.000210584519787064 0.000421169039574128 0.999789415480213 32 8.51823470650232e-05 0.000170364694130046 0.999914817652935 33 0.000186529403287792 0.000373058806575584 0.999813470596712 34 8.92139617674816e-05 0.000178427923534963 0.999910786038233 35 3.93403388073272e-05 7.86806776146545e-05 0.999960659661193 36 3.04112315640955e-05 6.08224631281909e-05 0.999969588768436 37 2.11654226183901e-05 4.23308452367802e-05 0.999978834577382 38 8.15160369826621e-06 1.63032073965324e-05 0.999991848396302 39 3.29615346306019e-06 6.59230692612038e-06 0.999996703846537 40 1.25465090036378e-06 2.50930180072755e-06 0.9999987453491 41 4.67289266998663e-07 9.34578533997326e-07 0.999999532710733 42 2.95000903525589e-07 5.90001807051177e-07 0.999999704999097 43 5.39649364492355e-07 1.07929872898471e-06 0.999999460350635 44 2.06884131027377e-07 4.13768262054754e-07 0.99999979311587 45 1.32842073425841e-07 2.65684146851682e-07 0.999999867157927 46 5.23092811987071e-08 1.04618562397414e-07 0.99999994769072 47 1.86242268423776e-08 3.72484536847552e-08 0.999999981375773 48 1.49570299965968e-08 2.99140599931937e-08 0.99999998504297 49 5.77249094827321e-09 1.15449818965464e-08 0.999999994227509 50 2.09643833194607e-09 4.19287666389214e-09 0.999999997903562 51 1.06139821096093e-09 2.12279642192187e-09 0.999999998938602 52 4.33791066591454e-10 8.67582133182908e-10 0.99999999956621 53 1.83701044822566e-10 3.67402089645133e-10 0.999999999816299 54 6.67556747819397e-11 1.33511349563879e-10 0.999999999933244 55 2.08581137516617e-10 4.17162275033234e-10 0.999999999791419 56 1.98088590100397e-10 3.96177180200793e-10 0.999999999801911 57 1.09432664195203e-10 2.18865328390406e-10 0.999999999890567 58 4.49562827808727e-11 8.99125655617454e-11 0.999999999955044 59 1.45814737809445e-11 2.91629475618890e-11 0.999999999985419 60 4.52019976949899e-11 9.04039953899797e-11 0.999999999954798 61 2.11828810939198e-11 4.23657621878396e-11 0.999999999978817 62 5.12621614638768e-11 1.02524322927754e-10 0.999999999948738 63 4.39213263054836e-11 8.78426526109672e-11 0.999999999956079 64 1.44081979592520e-11 2.88163959185040e-11 0.999999999985592 65 1.51486431002012e-11 3.02972862004024e-11 0.999999999984851 66 5.81007907994128e-12 1.16201581598826e-11 0.99999999999419 67 5.38933377964112e-12 1.07786675592822e-11 0.99999999999461 68 2.75988606779933e-12 5.51977213559865e-12 0.99999999999724 69 1.09612517444718e-12 2.19225034889437e-12 0.999999999998904 70 7.59880062041359e-13 1.51976012408272e-12 0.99999999999924 71 6.03938972030396e-13 1.20787794406079e-12 0.999999999999396 72 2.34819556667122e-13 4.69639113334244e-13 0.999999999999765 73 1.04816815345052e-12 2.09633630690105e-12 0.999999999998952 74 4.30574411433484e-13 8.61148822866969e-13 0.99999999999957 75 4.25123086537252e-13 8.50246173074504e-13 0.999999999999575 76 2.92717081855446e-13 5.85434163710892e-13 0.999999999999707 77 4.19455363885615e-13 8.3891072777123e-13 0.99999999999958 78 1.76770749265684e-11 3.53541498531367e-11 0.999999999982323 79 1.36092624988806e-11 2.72185249977611e-11 0.99999999998639 80 5.06337921936045e-12 1.01267584387209e-11 0.999999999994937 81 1.99104136118746e-12 3.98208272237491e-12 0.99999999999801 82 1.00038474400529e-09 2.00076948801059e-09 0.999999998999615 83 5.74264429946464e-10 1.14852885989293e-09 0.999999999425736 84 2.18040686316066e-10 4.36081372632132e-10 0.99999999978196 85 1.96926047734112e-10 3.93852095468225e-10 0.999999999803074 86 4.43399715939075e-09 8.8679943187815e-09 0.999999995566003 87 1.00495890680825e-07 2.00991781361650e-07 0.99999989950411 88 4.44218850699269e-08 8.88437701398537e-08 0.999999955578115 89 1.10830577218266e-07 2.21661154436531e-07 0.999999889169423 90 1.70928354369139e-06 3.41856708738277e-06 0.999998290716456 91 9.77133956858552e-07 1.95426791371710e-06 0.999999022866043 92 2.01010035288756e-06 4.02020070577512e-06 0.999997989899647 93 1.12913093090125e-06 2.25826186180250e-06 0.99999887086907 94 0.000143470359089262 0.000286940718178525 0.99985652964091 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 68 0.906666666666667 NOK 5% type I error level 70 0.933333333333333 NOK 10% type I error level 73 0.973333333333333 NOK

Charts produced by software:

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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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