*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: Sat, 27 Nov 2010 14:19:13 +0000

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Nov/27/t1290867804oyb0x8ehs2j770d.htm/, Retrieved Sat, 27 Nov 2010 15:23:24 +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/2010/Nov/27/t1290867804oyb0x8ehs2j770d.htm/}, year = {2010}, } @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 = {2010}, 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 «

1579 0 2146 0 2462 0 3695 0 4831 0 5134 0 6250 0 5760 0 6249 0 2917 0 1741 0 2359 0 1511 1 2059 0 2635 0 2867 0 4403 0 5720 0 4502 0 5749 0 5627 0 2846 0 1762 0 2429 0 1169 0 2154 1 2249 0 2687 0 4359 0 5382 0 4459 0 6398 0 4596 0 3024 0 1887 0 2070 0 1351 0 2218 0 2461 1 3028 0 4784 0 4975 0 4607 0 6249 0 4809 0 3157 0 1910 0 2228 0 1594 0 2467 0 2222 0 3607 1 4685 0 4962 0 5770 0 5480 0 5000 0 3228 0 1993 0 2288 0 1580 0 2111 0 2192 0 3601 0 4665 1 4876 0 5813 0 5589 0 5331 0 3075 0 2002 0 2306 0 1507 0 1992 0 2487 0 3490 0 4647 0 5594 1 5611 0 5788 0 6204 0 3013 0 1931 0 2549 0 1504 0 2090 0 2702 0 2939 0 4500 0 6208 0 6415 1 5657 0 5964 0 3163 0 1997 0 2422 0 1376 0 2202 0 2683 0 3303 0 5202 0 5231 0 4880 0 7998 1 4977 0 3531 0 2025 0 2205 0 1442 0 2238 0 2179 0 3218 0 5139 0 4990 0 4914 0 6084 0 5672 1 3548 0 1793 0 2086 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] = + 2187.30135021097 + 471.720675105485X[t] -862.255625879044M1[t] -157.475302390999M2[t] + 100.405021097046M3[t] + 915.085344585091M4[t] + 2391.46566807314M5[t] + 2975.54599156118M6[t] + 2988.82631504923M7[t] + 3740.30663853727M8[t] + 3106.38696202532M9[t] + 859.23935302391M10[t] -388.480323488046M11[t] + 1.61967651195499t + 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) 2187.30135021097 138.566337 15.7852 0 0 X 471.720675105485 134.877022 3.4974 0.000688 0.000344 M1 -862.255625879044 172.390428 -5.0018 2e-06 1e-06 M2 -157.475302390999 172.323461 -0.9138 0.362877 0.181439 M3 100.405021097046 172.262469 0.5829 0.561225 0.280613 M4 915.085344585091 172.207458 5.3139 1e-06 0 M5 2391.46566807314 172.158433 13.8911 0 0 M6 2975.54599156118 172.115399 17.2881 0 0 M7 2988.82631504923 172.078362 17.369 0 0 M8 3740.30663853727 172.047324 21.74 0 0 M9 3106.38696202532 172.02229 18.058 0 0 M10 859.23935302391 171.465587 5.0111 2e-06 1e-06 M11 -388.480323488046 171.456544 -2.2658 0.025498 0.012749 t 1.61967651195499 1.016674 1.5931 0.114112 0.057056 Multiple Linear Regression - Regression Statistics Multiple R 0.974833105636288 R-squared 0.95029958384449 Adjusted R-squared 0.944204249787683 F-TEST (value) 155.906070936853 F-TEST (DF numerator) 13 F-TEST (DF denominator) 106 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 383.381748113083 Sum Squared Residuals 15580045.8673418 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 1579 1326.66540084389 252.334599156115 2 2146 2033.06540084388 112.934599156119 3 2462 2292.56540084388 169.434599156118 4 3695 3108.86540084388 586.134599156118 5 4831 4586.86540084388 244.134599156118 6 5134 5172.56540084388 -38.5654008438817 7 6250 5187.46540084388 1062.53459915612 8 5760 5940.56540084388 -180.565400843881 9 6249 5308.26540084388 940.734599156118 10 2917 3062.73746835443 -145.737468354431 11 1741 1816.63746835443 -75.6374683544286 12 2359 2206.73746835443 152.26253164557 13 1511 1817.82219409283 -306.822194092826 14 2059 2052.50151898734 6.49848101265803 15 2635 2312.00151898734 322.998481012658 16 2867 3128.30151898734 -261.301518987342 17 4403 4606.30151898734 -203.301518987342 18 5720 5192.00151898734 527.998481012658 19 4502 5206.90151898734 -704.901518987342 20 5749 5960.00151898734 -211.001518987342 21 5627 5327.70151898734 299.298481012658 22 2846 3082.17358649789 -236.17358649789 23 1762 1836.07358649789 -74.0735864978905 24 2429 2226.17358649789 202.82641350211 25 1169 1365.53763713080 -196.537637130801 26 2154 2543.65831223629 -389.658312236287 27 2249 2331.4376371308 -82.4376371308018 28 2687 3147.7376371308 -460.737637130801 29 4359 4625.7376371308 -266.737637130802 30 5382 5211.4376371308 170.562362869198 31 4459 5226.3376371308 -767.337637130802 32 6398 5979.4376371308 418.562362869198 33 4596 5347.1376371308 -751.137637130802 34 3024 3101.60970464135 -77.60970464135 35 1887 1855.50970464135 31.4902953586496 36 2070 2245.60970464135 -175.609704641351 37 1351 1384.97375527426 -33.9737552742612 38 2218 2091.37375527426 126.626244725738 39 2461 2822.59443037975 -361.594430379747 40 3028 3167.17375527426 -139.173755274262 41 4784 4645.17375527426 138.826244725738 42 4975 5230.87375527426 -255.873755274262 43 4607 5245.77375527426 -638.773755274262 44 6249 5998.87375527426 250.126244725738 45 4809 5366.57375527426 -557.573755274262 46 3157 3121.04582278481 35.9541772151900 47 1910 1874.94582278481 35.0541772151897 48 2228 2265.04582278481 -37.0458227848103 49 1594 1404.40987341772 189.590126582279 50 2467 2110.80987341772 356.190126582279 51 2222 2370.30987341772 -148.309873417722 52 3607 3658.33054852321 -51.3305485232071 53 4685 4664.60987341772 20.3901265822786 54 4962 5250.30987341772 -288.309873417722 55 5770 5265.20987341772 504.790126582279 56 5480 6018.30987341772 -538.309873417722 57 5000 5386.00987341772 -386.009873417722 58 3228 3140.48194092827 87.51805907173 59 1993 1894.38194092827 98.6180590717298 60 2288 2284.48194092827 3.51805907172975 61 1580 1423.84599156118 156.154008438819 62 2111 2130.24599156118 -19.2459915611815 63 2192 2389.74599156118 -197.745991561182 64 3601 3206.04599156118 394.954008438818 65 4665 5155.76666666667 -490.766666666667 66 4876 5269.74599156118 -393.745991561182 67 5813 5284.64599156118 528.354008438819 68 5589 6037.74599156118 -448.745991561182 69 5331 5405.44599156118 -74.4459915611815 70 3075 3159.91805907173 -84.9180590717297 71 2002 1913.81805907173 88.1819409282699 72 2306 2303.91805907173 2.0819409282701 73 1507 1443.28210970464 63.7178902953591 74 1992 2149.68210970464 -157.682109704641 75 2487 2409.18210970464 77.8178902953588 76 3490 3225.48210970464 264.517890295359 77 4647 4703.48210970464 -56.4821097046412 78 5594 5760.90278481013 -166.902784810127 79 5611 5304.08210970464 306.917890295359 80 5788 6057.18210970464 -269.182109704641 81 6204 5424.88210970464 779.117890295359 82 3013 3179.35417721519 -166.354177215190 83 1931 1933.25417721519 -2.25417721519004 84 2549 2323.35417721519 225.645822784810 85 1504 1462.7182278481 41.2817721518992 86 2090 2169.1182278481 -79.1182278481013 87 2702 2428.6182278481 273.381772151899 88 2939 3244.9182278481 -305.918227848101 89 4500 4722.9182278481 -222.918227848101 90 6208 5308.6182278481 899.3817721519 91 6415 5795.23890295359 619.761097046414 92 5657 6076.6182278481 -419.618227848101 93 5964 5444.3182278481 519.681772151899 94 3163 3198.79029535865 -35.7902953586497 95 1997 1952.69029535865 44.30970464135 96 2422 2342.79029535865 79.2097046413504 97 1376 1482.15434599156 -106.154345991561 98 2202 2188.55434599156 13.4456540084387 99 2683 2448.05434599156 234.945654008439 100 3303 3264.35434599156 38.6456540084388 101 5202 4742.35434599156 459.645654008439 102 5231 5328.05434599156 -97.0543459915613 103 4880 5342.95434599156 -462.954345991562 104 7998 6567.77502109705 1430.22497890295 105 4977 5463.75434599156 -486.754345991561 106 3531 3218.22641350211 312.77358649789 107 2025 1972.12641350211 52.8735864978902 108 2205 2362.22641350211 -157.22641350211 109 1442 1501.59046413502 -59.5904641350206 110 2238 2207.99046413502 30.0095358649788 111 2179 2467.49046413502 -288.490464135021 112 3218 3283.79046413502 -65.790464135021 113 5139 4761.79046413502 377.209535864979 114 4990 5347.49046413502 -357.490464135021 115 4914 5362.39046413502 -448.390464135022 116 6084 6115.49046413502 -31.4904641350208 117 5672 5954.9111392405 -282.911139240506 118 3548 3237.66253164557 310.337468354430 119 1793 1991.56253164557 -198.562531645570 120 2086 2381.66253164557 -295.66253164557 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.443024576432498 0.886049152864996 0.556975423567502 18 0.648999851226522 0.702000297546955 0.351000148773478 19 0.970014206300081 0.0599715873998378 0.0299857936999189 20 0.95258516882444 0.09482966235112 0.04741483117556 21 0.933117010759867 0.133765978480267 0.0668829892401333 22 0.902521128519988 0.194957742960024 0.097478871480012 23 0.86859800449697 0.262803991006059 0.131401995503029 24 0.837252512334933 0.325494975330135 0.162747487665067 25 0.777806270471462 0.444387459057076 0.222193729528538 26 0.729919159699901 0.540161680600197 0.270080840300099 27 0.655570596145894 0.688858807708213 0.344429403854107 28 0.603122741748647 0.793754516502706 0.396877258251353 29 0.529302422797248 0.941395154405503 0.470697577202752 30 0.48962076276346 0.97924152552692 0.51037923723654 31 0.555642056298079 0.888715887403841 0.444357943701921 32 0.752963366224755 0.49407326755049 0.247036633775245 33 0.881016246037458 0.237967507925084 0.118983753962542 34 0.872581882552799 0.254836234894402 0.127418117447201 35 0.858788099900733 0.282423800198535 0.141211900099267 36 0.817326914674931 0.365346170650138 0.182673085325069 37 0.793786710969723 0.412426578060553 0.206213289030277 38 0.783090001931405 0.43381999613719 0.216909998068595 39 0.758454533825266 0.483090932349469 0.241545466174734 40 0.71539097425275 0.5692180514945 0.28460902574725 41 0.7105548564248 0.578890287150401 0.289445143575201 42 0.661942361487671 0.676115277024658 0.338057638512329 43 0.675636356072574 0.648727287854853 0.324363643927427 44 0.68354029977541 0.632919400449179 0.316459700224589 45 0.694449810533577 0.611100378932847 0.305550189466423 46 0.677531425197789 0.644937149604421 0.322468574802211 47 0.640126622228259 0.719746755543483 0.359873377771741 48 0.585109465662682 0.829781068674637 0.414890534337318 49 0.568474796633989 0.863050406732023 0.431525203366011 50 0.58427182563981 0.831456348720379 0.415728174360190 51 0.527721548527181 0.944556902945638 0.472278451472819 52 0.543974027274959 0.912051945450082 0.456025972725041 53 0.493823320730366 0.987646641460731 0.506176679269634 54 0.44812478027621 0.89624956055242 0.55187521972379 55 0.565774468878252 0.868451062243495 0.434225531121748 56 0.581772772842267 0.836454454315466 0.418227227157733 57 0.559888238580053 0.880223522839893 0.440111761419947 58 0.520887236985997 0.958225526028005 0.479112763014002 59 0.473564937248844 0.947129874497688 0.526435062751156 60 0.416651682848209 0.833303365696418 0.583348317151791 61 0.374102620725668 0.748205241451335 0.625897379274332 62 0.319344899611905 0.63868979922381 0.680655100388095 63 0.28005520257129 0.56011040514258 0.71994479742871 64 0.287011666546647 0.574023333093294 0.712988333453353 65 0.374994589809749 0.749989179619497 0.625005410190251 66 0.360857044323079 0.721714088646158 0.639142955676921 67 0.437548833663188 0.875097667326376 0.562451166336812 68 0.453667447853034 0.907334895706068 0.546332552146966 69 0.404650948456199 0.809301896912398 0.595349051543801 70 0.365359943144178 0.730719886288357 0.634640056855822 71 0.314317630712232 0.628635261424464 0.685682369287768 72 0.264344657971422 0.528689315942845 0.735655342028578 73 0.217928322530693 0.435856645061386 0.782071677469307 74 0.185337896231835 0.370675792463669 0.814662103768165 75 0.151238220034319 0.302476440068638 0.84876177996568 76 0.129474217743623 0.258948435487247 0.870525782256377 77 0.112470190417367 0.224940380834733 0.887529809582633 78 0.211606916296705 0.423213832593411 0.788393083703295 79 0.208791527766089 0.417583055532178 0.791208472233911 80 0.215245998362681 0.430491996725362 0.784754001637319 81 0.422602950832748 0.845205901665497 0.577397049167252 82 0.436859299923177 0.873718599846355 0.563140700076823 83 0.381155297882831 0.762310595765661 0.61884470211717 84 0.326395737031003 0.652791474062006 0.673604262968997 85 0.266771032358745 0.53354206471749 0.733228967641255 86 0.227281441172124 0.454562882344249 0.772718558827876 87 0.185359857064269 0.370719714128537 0.814640142935732 88 0.186952302305534 0.373904604611068 0.813047697694466 89 0.301678936713896 0.603357873427791 0.698321063286104 90 0.533651154455584 0.932697691088831 0.466348845544416 91 0.514002840654945 0.97199431869011 0.485997159345055 92 0.817406295603407 0.365187408793186 0.182593704396593 93 0.984769524386675 0.0304609512266500 0.0152304756133250 94 0.99662538091856 0.00674923816288166 0.00337461908144083 95 0.99429218550515 0.0114156289896999 0.00570781449484997 96 0.987667353063601 0.0246652938727971 0.0123326469363985 97 0.982750145224322 0.0344997095513566 0.0172498547756783 98 0.975265200314984 0.0494695993700329 0.0247347996850164 99 0.966159772443998 0.0676804551120045 0.0338402275560022 100 0.935473244145092 0.129053511709816 0.064526755854908 101 0.892276882229258 0.215446235541484 0.107723117770742 102 0.795049941459592 0.409900117080817 0.204950058540408 103 0.738603123706794 0.522793752586411 0.261396876293206 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 1 0.0114942528735632 NOK 5% type I error level 6 0.0689655172413793 NOK 10% type I error level 9 0.103448275862069 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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