*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 11:16:13 -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/t125865463536q62p1wlj07k3u.htm/, Retrieved Thu, 19 Nov 2009 19:17:29 +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/t125865463536q62p1wlj07k3u.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 «

102,1880309 0 114,0150276 108,1560276 100 0 0 0 110,3672031 0 102,1880309 114,0150276 108,1560276 0 0 0 96,8602511 0 110,3672031 102,1880309 114,0150276 0 0 0 94,1944583 0 96,8602511 110,3672031 102,1880309 0 0 0 99,51621961 0 94,1944583 96,8602511 110,3672031 0 0 0 94,06333487 0 99,51621961 94,1944583 96,8602511 0 0 0 97,5541476 0 94,06333487 99,51621961 94,1944583 0 0 0 78,15062422 0 97,5541476 94,06333487 99,51621961 0 0 0 81,2434643 0 78,15062422 97,5541476 94,06333487 0 0 0 92,36262465 0 81,2434643 78,15062422 97,5541476 0 0 0 96,06324371 0 92,36262465 81,2434643 78,15062422 0 0 0 114,0523777 0 96,06324371 92,36262465 81,2434643 0 0 0 110,6616666 0 114,0523777 96,06324371 92,36262465 0 0 0 104,9171949 0 110,6616666 114,0523777 96,06324371 0 0 0 90,00187193 0 104,9171949 110,6616666 114,0523777 0 0 0 95,7008067 0 90,00187193 104,9171949 110,6616666 0 0 0 86,02741157 0 95,7008067 90,00187193 104,9171949 0 0 0 84,85287668 0 86,02741157 95,7008067 90,00187193 0 0 0 100,04328 0 84,85287668 8 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] = + 42.6772231284869 -7.03032756296926X[t] + 0.238951819573219Y1[t] + 0.0273389519798325Y2[t] + 0.402193805311858Y3[t] + 35.1639106297578O1[t] + 46.4502697020821O2[t] + 48.3685051126588O3[t] -11.0549275547205M1[t] -13.5843855756605M2[t] -8.58775754021385M3[t] -20.1079767133796M4[t] -20.0203484270343M5[t] -19.5705285141657M6[t] -11.6321889599801M7[t] -26.7128084146589M8[t] -13.1819891149909M9[t] -20.6649646317261M10[t] -9.85178558791026M11[t] + 0.183754648718384t + 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) 42.6772231284869 8.797585 4.851 6e-06 3e-06 X -7.03032756296926 3.552399 -1.979 0.051049 0.025525 Y1 0.238951819573219 0.082788 2.8863 0.00494 0.00247 Y2 0.0273389519798325 0.083131 0.3289 0.743066 0.371533 Y3 0.402193805311858 0.080734 4.9817 3e-06 2e-06 O1 35.1639106297578 10.177873 3.4549 0.000861 0.000431 O2 46.4502697020821 10.606483 4.3794 3.4e-05 1.7e-05 O3 48.3685051126588 10.129672 4.7749 7e-06 4e-06 M1 -11.0549275547205 4.699848 -2.3522 0.020975 0.010487 M2 -13.5843855756605 4.758527 -2.8547 0.00541 0.002705 M3 -8.58775754021385 4.701252 -1.8267 0.071256 0.035628 M4 -20.1079767133796 4.662171 -4.313 4.3e-05 2.2e-05 M5 -20.0203484270343 4.654534 -4.3013 4.5e-05 2.3e-05 M6 -19.5705285141657 4.751112 -4.1191 8.8e-05 4.4e-05 M7 -11.6321889599801 4.72272 -2.463 0.015794 0.007897 M8 -26.7128084146589 4.617594 -5.785 0 0 M9 -13.1819891149909 4.73033 -2.7867 0.006566 0.003283 M10 -20.6649646317261 5.198466 -3.9752 0.000147 7.4e-05 M11 -9.85178558791026 4.740023 -2.0784 0.040686 0.020343 t 0.183754648718384 0.061673 2.9795 0.003763 0.001882 Multiple Linear Regression - Regression Statistics Multiple R 0.8889895636866 R-squared 0.79030244434369 Adjusted R-squared 0.743428873079338 F-TEST (value) 16.8602993760949 F-TEST (DF numerator) 19 F-TEST (DF denominator) 85 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 9.36884861575549 Sum Squared Residuals 7460.9025727202 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 102.1880309 102.226401502267 -0.0383706022670232 2 110.3672031 100.495098444817 9.87210465518332 3 96.8602511 109.663025019250 -12.8027739192496 4 94.1944583 90.5659149253409 3.62854337465911 5 99.51621961 93.1206482995364 6.39557131046357 6 94.06333487 89.5205750050903 4.54275986490975 7 97.5541476 95.4130185039455 2.14112909605448 8 78.15062422 83.3415930297799 -5.19096880977987 9 81.2434643 90.3219784584722 -9.0785141584722 10 92.36262465 84.6353086168048 7.72731603319523 11 96.06324371 90.5697640088665 5.49347970113348 12 114.0523777 103.037481215290 11.0148964847095 13 110.6616666 101.038073068561 9.62359353143855 14 104.9171949 99.86232324141 5.05487165858988 15 90.00187193 110.812473726136 -20.8106017961359 16 95.7008067 94.3911948025069 1.30961189749312 17 86.02741157 93.3061883395213 -7.27877676952126 18 84.85287668 85.7852399345976 -0.932363254597588 19 100.04328 95.6542926648133 4.38898733518674 20 80.91713823 80.4645122216417 0.452626008358317 21 74.06539709 89.5517588427103 -15.4863617527103 22 77.30281369 86.201899406129 -8.89908571612898 23 97.23043249 90.0926845245347 7.13774796546531 24 90.75515676 102.222745268043 -11.4675885080425 25 100.5614455 91.651162559298 8.91028294070206 26 92.01293267 99.4864273084972 -7.47349463849718 27 99.24012138 100.287905167960 -1.04778378795975 28 105.8672755 94.388711738735 11.478563761265 29 90.9920463 93.0030900696017 -2.01104376960173 30 93.30624423 93.1701115245115 0.136132705488542 31 91.17419413 104.103914690765 -12.9297205607647 32 77.33295039 82.7781353432341 -5.44518495323413 33 91.1277721 94.0577869712 -2.93001487120001 34 85.01249943 88.8189614106923 -3.8064619806923 35 83.90390242 93.1649130498042 -9.26101062980418 36 104.8626302 108.316558704995 -3.45392850499472 37 110.9039108 99.963679271875 10.9402315281250 38 95.43714373 99.1886696939024 -3.75152596390237 39 111.6238727 109.267873004329 2.35599969567055 40 108.8925403 103.806177251463 5.08636304853713 41 96.17511682 97.6467936419532 -1.47167682195316 42 101.9740205 101.677047077382 0.29697342261759 43 99.11953031 109.738593864798 -10.6190635547978 44 86.78158147 89.2033104400426 -2.42172897004263 45 118.4195003 102.223953430774 16.1955468692255 46 118.7441447 100.999305972088 17.7448387279119 47 106.5296192 107.976514984649 -1.44689578464920 48 134.7772694 127.826822531877 6.95044686812313 49 104.6778714 123.502114680341 -18.8242432803408 50 105.2954304 109.823760049398 -4.52832964939781 51 139.4139849 125.689853508819 13.7241313911809 52 103.6060491 110.417171659984 -6.81112255998436 53 99.78182974 103.313327107788 -3.53149736778837 54 103.4610301 115.776417323618 -12.3153872236181 55 120.0594945 110.271383037911 9.78811146208948 56 96.71377168 97.903259647672 -1.18948796767198 57 107.1308929 107.972866863431 -0.841973963430958 58 105.3608372 109.200388029907 -3.83955082990689 59 111.6942359 110.669651770250 1.02458412974980 60 132.0519998 126.359879306409 5.69212049359073 61 126.8037879 119.814474172086 6.9893137279144 62 154.4824253 154.4824253 -1.10458517332823e-15 63 141.5570984 139.157044041912 2.40005435808808 64 109.9506882 123.377955759733 -13.4272675597327 65 127.904198 126.875741077639 1.02845692236082 66 133.0888617 125.736766932138 7.35209476786163 67 120.0796299 122.876673711739 -2.79704381173873 68 117.5557142 112.233762993010 5.32195120698979 69 143.0362309 127.074823551723 15.9614073482774 70 159.982927 159.982927 5.22368606703516e-15 71 128.5991124 128.260542222115 0.338570177885233 72 149.7373327 141.508273738705 8.2290589612951 73 126.8169313 141.645972627557 -14.8290413275566 74 140.9639674 121.778918606001 19.1850487939986 75 137.6691981 138.214802814574 -0.545604714573759 76 117.9402337 117.259368853071 0.680864846928982 77 122.3095247 118.416254589725 3.89327011027469 78 127.7804207 118.229374173272 9.55104652672794 79 136.1677176 119.843333500009 16.3243840999913 80 116.2405856 108.857498886440 7.38308671356037 81 123.1576893 120.240108772445 2.91758052755468 82 116.3400234 117.422274371368 -1.08225097136772 83 108.6119282 118.964651709717 -10.3527235097168 84 125.8982264 129.749177954655 -3.85095155465549 85 112.8003105 120.055296441994 -7.25498594199434 86 107.5182447 111.944219492949 -4.42597479294927 87 135.0955413 122.456801702086 12.6387395979136 88 115.5096488 112.297675598383 3.21197320161680 89 115.8640759 106.518494126078 9.34558177392226 90 104.5883906 117.792719773344 -13.2043291733439 91 163.7213386 163.7213386 3.38704758684472e-16 92 113.4482275 114.420177167808 -0.971949667807623 93 98.0428844 113.203521790734 -15.1606373907343 94 116.7868521 124.631657363011 -7.84480526301127 95 126.5330444 119.466796450064 7.06624794993638 96 113.0336597 126.147713940026 -13.1140542400258 97 124.3392163 119.855996876021 4.48321942397876 98 109.8298759 123.762575963025 -13.9327000630252 99 124.4434777 120.355638524934 4.08783917506596 100 111.5039454 116.661475410783 -5.15753001078309 101 102.0350019 108.404887288157 -6.36988538815682 102 116.8726598 112.299587436046 4.57307236395417 103 112.2073122 118.504096266021 -6.29678406602085 104 101.1513902 99.0897337603722 2.06165643962777 105 124.4255108 116.002543408510 8.42296739149013 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 23 0.352910328754100 0.705820657508201 0.6470896712459 24 0.686145372693966 0.627709254612067 0.313854627306034 25 0.562389608012211 0.875220783975577 0.437610391987789 26 0.506451236685197 0.987097526629606 0.493548763314803 27 0.470061648376634 0.940123296753268 0.529938351623366 28 0.639983564304086 0.720032871391829 0.360016435695914 29 0.541639717934672 0.916720564130655 0.458360282065328 30 0.488962226713076 0.977924453426153 0.511037773286924 31 0.428222933765779 0.856445867531557 0.571777066234221 32 0.34683084474597 0.69366168949194 0.65316915525403 33 0.438910315073898 0.877820630147797 0.561089684926102 34 0.361822667780859 0.723645335561717 0.638177332219141 35 0.355066487413616 0.710132974827231 0.644933512586384 36 0.302794800506555 0.605589601013111 0.697205199493445 37 0.289849965293229 0.579699930586458 0.710150034706771 38 0.232850637577868 0.465701275155737 0.767149362422131 39 0.334464160575603 0.668928321151205 0.665535839424397 40 0.298717157764362 0.597434315528724 0.701282842235638 41 0.246777356327045 0.49355471265409 0.753222643672955 42 0.201029649968424 0.402059299936848 0.798970350031576 43 0.206737433754852 0.413474867509704 0.793262566245148 44 0.185594443911517 0.371188887823034 0.814405556088483 45 0.437420918861137 0.874841837722275 0.562579081138863 46 0.577569167898136 0.844861664203729 0.422430832101864 47 0.517614378722756 0.964771242554488 0.482385621277244 48 0.471220686898609 0.942441373797218 0.528779313101391 49 0.69095976513152 0.61808046973696 0.30904023486848 50 0.646010069828044 0.707979860343912 0.353989930171956 51 0.727131035166796 0.545737929666408 0.272868964833204 52 0.695249079425826 0.609501841148347 0.304750920574174 53 0.650015791507066 0.699968416985869 0.349984208492934 54 0.740478134428546 0.519043731142909 0.259521865571454 55 0.725535759603412 0.548928480793177 0.274464240396589 56 0.71769513802178 0.56460972395644 0.28230486197822 57 0.73362594580363 0.532748108392741 0.266374054196371 58 0.708418276993747 0.583163446012507 0.291581723006253 59 0.693270950673751 0.613458098652498 0.306729049326249 60 0.647225462096205 0.70554907580759 0.352774537903795 61 0.58699732351 0.82600535298 0.41300267649 62 0.513609709925897 0.972780580148205 0.486390290074103 63 0.456797677383785 0.913595354767569 0.543202322616215 64 0.457423715585032 0.914847431170064 0.542576284414968 65 0.397219721977236 0.794439443954473 0.602780278022764 66 0.349194215907692 0.698388431815383 0.650805784092308 67 0.318088780265585 0.63617756053117 0.681911219734415 68 0.330369049099256 0.660738098198512 0.669630950900744 69 0.307394839508877 0.614789679017755 0.692605160491123 70 0.239086230889119 0.478172461778238 0.760913769110881 71 0.191503422906962 0.383006845813924 0.808496577093038 72 0.234582423346692 0.469164846693385 0.765417576653308 73 0.227193407706159 0.454386815412319 0.77280659229384 74 0.556421121478639 0.887157757042723 0.443578878521362 75 0.462007717163515 0.924015434327029 0.537992282836485 76 0.375952852898251 0.751905705796502 0.624047147101749 77 0.281564430619134 0.563128861238268 0.718435569380866 78 0.235372552365092 0.470745104730185 0.764627447634908 79 0.349861949899768 0.699723899799536 0.650138050100232 80 0.285311847111056 0.570623694222113 0.714688152888943 81 0.200749929484737 0.401499858969474 0.799250070515263 82 0.11977533085352 0.23955066170704 0.88022466914648 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 0 0 OK 10% type I error level 0 0 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/106vqr1258654568.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/106vqr1258654568.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/1xaj41258654568.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/1xaj41258654568.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/21dhn1258654568.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/21dhn1258654568.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/3344r1258654568.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/3344r1258654568.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/4wvdr1258654568.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/4wvdr1258654568.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/50nd91258654568.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/50nd91258654568.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/6nq2f1258654568.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/6nq2f1258654568.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/73paw1258654568.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/73paw1258654568.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/8gpqj1258654568.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/8gpqj1258654568.ps (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/9flh41258654568.png (open in new window) http://www.freestatistics.org/blog/date/2009/Nov/19/t125865463536q62p1wlj07k3u/9flh41258654568.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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