workshop 7 berekening 1

*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, 19 Nov 2009 08:24:52 -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/19/t1258644418gc5wjt2luqynn3s.htm/, Retrieved Thu, 19 Nov 2009 16:27:10 +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/19/t1258644418gc5wjt2luqynn3s.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:

» Textbox « » Textfile « » CSV «

4716.99 0 4926.65 0 4920.10 0 5170.09 0 5246.24 0 5283.61 0 4979.05 0 4825.20 0 4695.12 0 4711.54 0 4727.22 0 4384.96 0 4378.75 0 4472.93 0 4564.07 0 4310.54 0 4171.38 0 4049.38 0 3591.37 0 3720.46 0 4107.23 0 4101.71 0 4162.34 0 4136.22 0 4125.88 0 4031.48 0 3761.36 0 3408.56 0 3228.47 0 3090.45 0 2741.14 0 2980.44 0 3104.33 0 3181.57 0 2863.86 0 2898.01 0 3112.33 0 3254.33 0 3513.47 0 3587.61 0 3727.45 0 3793.34 0 3817.58 0 3845.13 0 3931.86 0 4197.52 0 4307.13 0 4229.43 0 4362.28 0 4217.34 0 4361.28 0 4327.74 0 4417.65 0 4557.68 0 4650.35 0 4967.18 0 5123.42 0 5290.85 0 5535.66 0 5514.06 0 5493.88 0 5694.83 0 5850.41 0 6116.64 0 6175.00 0 6513.58 0 6383.78 0 6673.66 0 6936.61 0 7300.68 0 7392.93 0 7497.31 0 7584.71 0 7160.79 0 7196.19 0 7245.63 0 7347.51 0 7425.75 0 7778.51 0 7822.33 0 8181.22 0 8371.47 0 8347.71 0 8672.11 0 8802.79 0 9138.46 0 9123.29 0 9023.21 1 8850.41 1 8864.58 1 9163.74 1 8516.66 1 8553.44 1 7 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] = + 5156.77183908046 + 1866.29530377668X[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) 5156.77183908046 182.028745 28.3294 0 0 X 1866.29530377668 412.802393 4.521 1.6e-05 8e-06 Multiple Linear Regression - Regression Statistics Multiple R 0.402065013518833 R-squared 0.161656275095899 Adjusted R-squared 0.153747372030766 F-TEST (value) 20.4397846028196 F-TEST (DF numerator) 1 F-TEST (DF denominator) 106 p-value 1.60881629114318e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 1697.85110548636 Sum Squared Residuals 305566027.898534 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 4716.99 5156.77183908045 -439.781839080449 2 4926.65 5156.77183908046 -230.121839080459 3 4920.1 5156.77183908046 -236.671839080460 4 5170.09 5156.77183908046 13.3181609195402 5 5246.24 5156.77183908046 89.4681609195398 6 5283.61 5156.77183908046 126.838160919540 7 4979.05 5156.77183908046 -177.72183908046 8 4825.2 5156.77183908046 -331.57183908046 9 4695.12 5156.77183908046 -461.65183908046 10 4711.54 5156.77183908046 -445.23183908046 11 4727.22 5156.77183908046 -429.551839080460 12 4384.96 5156.77183908046 -771.81183908046 13 4378.75 5156.77183908046 -778.02183908046 14 4472.93 5156.77183908046 -683.84183908046 15 4564.07 5156.77183908046 -592.70183908046 16 4310.54 5156.77183908046 -846.23183908046 17 4171.38 5156.77183908046 -985.39183908046 18 4049.38 5156.77183908046 -1107.39183908046 19 3591.37 5156.77183908046 -1565.40183908046 20 3720.46 5156.77183908046 -1436.31183908046 21 4107.23 5156.77183908046 -1049.54183908046 22 4101.71 5156.77183908046 -1055.06183908046 23 4162.34 5156.77183908046 -994.43183908046 24 4136.22 5156.77183908046 -1020.55183908046 25 4125.88 5156.77183908046 -1030.89183908046 26 4031.48 5156.77183908046 -1125.29183908046 27 3761.36 5156.77183908046 -1395.41183908046 28 3408.56 5156.77183908046 -1748.21183908046 29 3228.47 5156.77183908046 -1928.30183908046 30 3090.45 5156.77183908046 -2066.32183908046 31 2741.14 5156.77183908046 -2415.63183908046 32 2980.44 5156.77183908046 -2176.33183908046 33 3104.33 5156.77183908046 -2052.44183908046 34 3181.57 5156.77183908046 -1975.20183908046 35 2863.86 5156.77183908046 -2292.91183908046 36 2898.01 5156.77183908046 -2258.76183908046 37 3112.33 5156.77183908046 -2044.44183908046 38 3254.33 5156.77183908046 -1902.44183908046 39 3513.47 5156.77183908046 -1643.30183908046 40 3587.61 5156.77183908046 -1569.16183908046 41 3727.45 5156.77183908046 -1429.32183908046 42 3793.34 5156.77183908046 -1363.43183908046 43 3817.58 5156.77183908046 -1339.19183908046 44 3845.13 5156.77183908046 -1311.64183908046 45 3931.86 5156.77183908046 -1224.91183908046 46 4197.52 5156.77183908046 -959.25183908046 47 4307.13 5156.77183908046 -849.64183908046 48 4229.43 5156.77183908046 -927.34183908046 49 4362.28 5156.77183908046 -794.49183908046 50 4217.34 5156.77183908046 -939.43183908046 51 4361.28 5156.77183908046 -795.49183908046 52 4327.74 5156.77183908046 -829.03183908046 53 4417.65 5156.77183908046 -739.12183908046 54 4557.68 5156.77183908046 -599.09183908046 55 4650.35 5156.77183908046 -506.42183908046 56 4967.18 5156.77183908046 -189.591839080460 57 5123.42 5156.77183908046 -33.3518390804599 58 5290.85 5156.77183908046 134.078160919540 59 5535.66 5156.77183908046 378.88816091954 60 5514.06 5156.77183908046 357.288160919540 61 5493.88 5156.77183908046 337.10816091954 62 5694.83 5156.77183908046 538.05816091954 63 5850.41 5156.77183908046 693.63816091954 64 6116.64 5156.77183908046 959.86816091954 65 6175 5156.77183908046 1018.22816091954 66 6513.58 5156.77183908046 1356.80816091954 67 6383.78 5156.77183908046 1227.00816091954 68 6673.66 5156.77183908046 1516.88816091954 69 6936.61 5156.77183908046 1779.83816091954 70 7300.68 5156.77183908046 2143.90816091954 71 7392.93 5156.77183908046 2236.15816091954 72 7497.31 5156.77183908046 2340.53816091954 73 7584.71 5156.77183908046 2427.93816091954 74 7160.79 5156.77183908046 2004.01816091954 75 7196.19 5156.77183908046 2039.41816091954 76 7245.63 5156.77183908046 2088.85816091954 77 7347.51 5156.77183908046 2190.73816091954 78 7425.75 5156.77183908046 2268.97816091954 79 7778.51 5156.77183908046 2621.73816091954 80 7822.33 5156.77183908046 2665.55816091954 81 8181.22 5156.77183908046 3024.44816091954 82 8371.47 5156.77183908046 3214.69816091954 83 8347.71 5156.77183908046 3190.93816091954 84 8672.11 5156.77183908046 3515.33816091954 85 8802.79 5156.77183908046 3646.01816091954 86 9138.46 5156.77183908046 3981.68816091954 87 9123.29 5156.77183908046 3966.51816091954 88 9023.21 7023.06714285714 2000.14285714286 89 8850.41 7023.06714285714 1827.34285714286 90 8864.58 7023.06714285714 1841.51285714286 91 9163.74 7023.06714285714 2140.67285714286 92 8516.66 7023.06714285714 1493.59285714286 93 8553.44 7023.06714285714 1530.37285714286 94 7555.2 7023.06714285714 532.132857142857 95 7851.22 7023.06714285714 828.152857142857 96 7442 7023.06714285714 418.932857142857 97 7992.53 7023.06714285714 969.462857142857 98 8264.04 7023.06714285714 1240.97285714286 99 7517.39 7023.06714285714 494.322857142857 100 7200.4 7023.06714285714 177.332857142857 101 7193.69 7023.06714285714 170.622857142857 102 6193.58 7023.06714285714 -829.487142857143 103 5104.21 7023.06714285714 -1918.85714285714 104 4800.46 7023.06714285714 -2222.60714285714 105 4461.61 7023.06714285714 -2561.45714285714 106 4398.59 7023.06714285714 -2624.47714285714 107 4243.63 7023.06714285714 -2779.43714285714 108 4293.82 7023.06714285714 -2729.24714285714 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.00386587539863812 0.00773175079727623 0.996134124601362 6 0.00083319264282347 0.00166638528564694 0.999166807357177 7 0.000104205394468802 0.000208410788937605 0.999895794605531 8 1.76408811240380e-05 3.52817622480760e-05 0.999982359118876 9 4.83299699929965e-06 9.6659939985993e-06 0.999995167003001 10 1.03730720163209e-06 2.07461440326419e-06 0.999998962692798 11 1.9017653217698e-07 3.8035306435396e-07 0.999999809823468 12 2.00468676949373e-07 4.00937353898745e-07 0.999999799531323 13 1.22150316652121e-07 2.44300633304242e-07 0.999999877849683 14 3.8681971028116e-08 7.7363942056232e-08 0.99999996131803 15 8.5218669285961e-09 1.70437338571922e-08 0.999999991478133 16 4.41973977526035e-09 8.8394795505207e-09 0.99999999558026 17 3.53411828546817e-09 7.06823657093634e-09 0.999999996465882 18 3.69187023893153e-09 7.38374047786306e-09 0.99999999630813 19 2.35486569933385e-08 4.70973139866771e-08 0.999999976451343 20 3.51800137012001e-08 7.03600274024001e-08 0.999999964819986 21 1.44502534467465e-08 2.8900506893493e-08 0.999999985549747 22 5.7655327651551e-09 1.15310655303102e-08 0.999999994234467 23 2.00116463288809e-09 4.00232926577618e-09 0.999999997998835 24 7.05926352202411e-10 1.41185270440482e-09 0.999999999294074 25 2.46950405100206e-10 4.93900810200411e-10 0.99999999975305 26 1.00413978200115e-10 2.00827956400231e-10 0.999999999899586 27 7.73114166708188e-11 1.54622833341638e-10 0.999999999922689 28 1.7140715224743e-10 3.4281430449486e-10 0.999999999828593 29 5.327347041672e-10 1.0654694083344e-09 0.999999999467265 30 1.85492649864778e-09 3.70985299729557e-09 0.999999998145074 31 1.40870654481176e-08 2.81741308962351e-08 0.999999985912934 32 3.35277371633315e-08 6.70554743266631e-08 0.999999966472263 33 5.15867452475614e-08 1.03173490495123e-07 0.999999948413255 34 6.39256096138433e-08 1.27851219227687e-07 0.99999993607439 35 1.45627018457859e-07 2.91254036915719e-07 0.999999854372982 36 2.84062364311938e-07 5.68124728623876e-07 0.999999715937636 37 3.65361132474051e-07 7.30722264948103e-07 0.999999634638868 38 3.85621805641011e-07 7.71243611282022e-07 0.999999614378194 39 3.06762615661152e-07 6.13525231322305e-07 0.999999693237384 40 2.35309129812231e-07 4.70618259624462e-07 0.99999976469087 41 1.66512336136506e-07 3.33024672273013e-07 0.999999833487664 42 1.1690093419261e-07 2.3380186838522e-07 0.999999883099066 43 8.43651206713238e-08 1.68730241342648e-07 0.99999991563488 44 6.28265197535801e-08 1.25653039507160e-07 0.99999993717348 45 4.68115822839482e-08 9.36231645678964e-08 0.999999953188418 46 3.32534873483773e-08 6.65069746967545e-08 0.999999966746513 47 2.44525035962369e-08 4.89050071924739e-08 0.999999975547496 48 1.90830671003401e-08 3.81661342006802e-08 0.999999980916933 49 1.55759856972772e-08 3.11519713945544e-08 0.999999984424014 50 1.39210980960704e-08 2.78421961921409e-08 0.999999986078902 51 1.30911604925300e-08 2.61823209850601e-08 0.99999998690884 52 1.35130485479267e-08 2.70260970958534e-08 0.999999986486951 53 1.51758859736619e-08 3.03517719473238e-08 0.999999984824114 54 1.87734261297878e-08 3.75468522595755e-08 0.999999981226574 55 2.59113568543129e-08 5.18227137086258e-08 0.999999974088643 56 4.38888268330831e-08 8.77776536661662e-08 0.999999956111173 57 8.63113297212975e-08 1.72622659442595e-07 0.99999991368867 58 1.97184110097417e-07 3.94368220194834e-07 0.99999980281589 59 5.53526236840371e-07 1.10705247368074e-06 0.999999446473763 60 1.42112839320398e-06 2.84225678640796e-06 0.999998578871607 61 3.4747544798025e-06 6.949508959605e-06 0.99999652524552 62 9.44636004492853e-06 1.88927200898571e-05 0.999990553639955 63 2.67072750991714e-05 5.34145501983428e-05 0.9999732927249 64 8.23418052466462e-05 0.000164683610493292 0.999917658194753 65 0.000227043663561289 0.000454087327122578 0.999772956336439 66 0.000672716691635665 0.00134543338327133 0.999327283308364 67 0.00153461281846401 0.00306922563692802 0.998465387181536 68 0.00352614515032068 0.00705229030064136 0.99647385484968 69 0.00780305793073897 0.0156061158614779 0.99219694206926 70 0.0171364262651728 0.0342728525303457 0.982863573734827 71 0.0318580767610296 0.0637161535220592 0.96814192323897 72 0.0523035936331341 0.104607187266268 0.947696406366866 73 0.0774208224617841 0.154841644923568 0.922579177538216 74 0.0956097089205235 0.191219417841047 0.904390291079477 75 0.114319933388210 0.228639866776420 0.88568006661179 76 0.133474719218696 0.266949438437392 0.866525280781304 77 0.153391587934711 0.306783175869421 0.84660841206529 78 0.173632581358604 0.347265162717208 0.826367418641396 79 0.196962651703467 0.393925303406934 0.803037348296533 80 0.21814319407973 0.43628638815946 0.78185680592027 81 0.242372222334677 0.484744444669354 0.757627777665323 82 0.265427968078179 0.530855936156358 0.734572031921821 83 0.281593764256394 0.563187528512789 0.718406235743606 84 0.299429126214282 0.598858252428563 0.700570873785718 85 0.313175781983826 0.626351563967652 0.686824218016174 86 0.329119486359628 0.658238972719255 0.670880513640372 87 0.334859333252534 0.669718666505068 0.665140666747466 88 0.340817706638682 0.681635413277365 0.659182293361318 89 0.34365874710204 0.68731749420408 0.65634125289796 90 0.358229112206268 0.716458224412536 0.641770887793732 91 0.421101484740138 0.842202969480276 0.578898515259862 92 0.439392261215678 0.878784522431356 0.560607738784322 93 0.480923902889451 0.961847805778901 0.519076097110549 94 0.44628549964359 0.89257099928718 0.55371450035641 95 0.442206505514392 0.884413011028784 0.557793494485608 96 0.413191176383495 0.82638235276699 0.586808823616505 97 0.460127606376481 0.920255212752962 0.539872393623519 98 0.613426452213427 0.773147095573146 0.386573547786573 99 0.700071629674654 0.599856740650692 0.299928370325346 100 0.790980211169657 0.418039577660685 0.209019788830343 101 0.949735211381868 0.100529577236265 0.0502647886181324 102 0.994238534688944 0.011522930622113 0.0057614653110565 103 0.994712106824188 0.0105757863516245 0.00528789317581225 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 64 0.646464646464647 NOK 5% type I error level 68 0.686868686868687 NOK 10% type I error level 69 0.696969696969697 NOK

Charts produced by software:

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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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