ws3

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Tue, 17 Nov 2009 08:40:23 -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/17/t1258472553he7xlzplxx9kwad.htm/, Retrieved Tue, 17 Nov 2009 16:42:45 +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/17/t1258472553he7xlzplxx9kwad.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 «

405.7 0 403.3 403.5 395.1 395.3 406.7 0 405.7 403.3 403.5 395.1 407.2 0 406.7 405.7 403.3 403.5 412.4 0 407.2 406.7 405.7 403.3 415.9 0 412.4 407.2 406.7 405.7 414.0 0 415.9 412.4 407.2 406.7 411.8 0 414.0 415.9 412.4 407.2 409.9 0 411.8 414.0 415.9 412.4 412.4 0 409.9 411.8 414.0 415.9 415.9 0 412.4 409.9 411.8 414.0 416.3 0 415.9 412.4 409.9 411.8 417.2 0 416.3 415.9 412.4 409.9 421.8 0 417.2 416.3 415.9 412.4 421.4 0 421.8 417.2 416.3 415.9 415.1 0 421.4 421.8 417.2 416.3 412.4 0 415.1 421.4 421.8 417.2 411.8 0 412.4 415.1 421.4 421.8 408.8 0 411.8 412.4 415.1 421.4 404.5 0 408.8 411.8 412.4 415.1 402.5 0 404.5 408.8 411.8 412.4 409.4 0 402.5 404.5 408.8 411.8 410.7 0 409.4 402.5 404.5 408.8 413.4 0 410.7 409.4 402.5 404.5 415.2 0 413.4 410.7 409.4 402.5 417.7 0 415.2 413.4 410.7 409.4 417.8 0 417.7 415.2 413.4 410.7 417.9 0 417.8 417.7 415.2 413.4 418.4 0 417.9 417.8 417.7 415.2 418.2 0 418.4 417.9 417.8 417.7 416.6 0 418.2 418.4 417.9 417.8 418.9 0 416.6 418.2 418.4 417.9 4 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 9 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 17.0254941272264 -2.97333759398567X[t] + 1.41303661442087Y1[t] -0.370434738549315Y2[t] -0.0226275979163081Y3[t] -0.0678756241040444Y4[t] + 0.163198807114589t + 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) 17.0254941272264 6.601448 2.5791 0.011278 0.005639 X -2.97333759398567 3.315291 -0.8969 0.371828 0.185914 Y1 1.41303661442087 0.096648 14.6204 0 0 Y2 -0.370434738549315 0.168032 -2.2046 0.029647 0.014824 Y3 -0.0226275979163081 0.168536 -0.1343 0.893452 0.446726 Y4 -0.0678756241040444 0.097449 -0.6965 0.487624 0.243812 t 0.163198807114589 0.069208 2.3581 0.020203 0.010101 Multiple Linear Regression - Regression Statistics Multiple R 0.997970467716217 R-squared 0.995945054433724 Adjusted R-squared 0.995715529212992 F-TEST (value) 4339.153018805 F-TEST (DF numerator) 6 F-TEST (DF denominator) 106 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 7.27639098080917 Sum Squared Residuals 5612.2617647937 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 405.7 401.824544380568 3.87545561943187 2 406.7 405.276621312325 1.42337868767535 3 407.2 405.398183638451 1.80181636154907 4 412.4 405.856734904048 6.54326509595179 5 415.9 412.996977641111 2.90302235888937 6 414 416.10035453518 -2.1003545351796 7 411.8 412.130660868755 -0.330660868755126 8 409.9 409.456855289339 0.443144710660566 9 412.4 407.55566870554 4.84433129446036 10 415.9 412.134029453164 3.76597054683632 11 416.3 416.509088373448 -0.209088373447860 12 417.2 416.013374932415 1.18662506758483 13 421.8 417.051247144122 4.74875285587844 14 421.4 423.134407389347 -1.73440738934716 15 415.1 420.9808766656 -5.88087666560015 16 412.4 412.224943685174 0.175056314825548 17 411.8 410.603505654501 1.19649434549879 18 408.8 411.088760403561 -2.28876040356085 19 404.5 407.723821156772 -3.22382115677194 20 402.5 403.119107481355 -0.619107481355438 21 409.4 402.157710603602 7.24228939639828 22 410.7 413.112657070671 -2.41265707067113 23 413.4 412.893924160023 0.506075839977374 24 415.2 416.370377488545 -1.17037748854498 25 417.7 417.579110723925 0.120889276075128 26 417.8 420.458785711994 -2.65878571199355 27 417.9 419.613207472847 -1.71320747284674 28 418.4 419.70192134937 -1.30192134937036 29 418.2 420.362643169789 -2.16264316978871 30 416.6 420.048966962542 -3.44896696254241 31 418.9 418.007292772925 0.892707227074969 32 421 421.983759082418 -0.983759082417645 33 423.5 424.31211416264 -0.812114162639592 34 432.3 427.286549078212 5.01345092178829 35 432.3 438.754751354793 -6.45475135479311 36 428.6 435.459016657264 -6.85901665726445 37 426.7 430.025168069098 -3.32516806909827 38 427.3 428.27690034933 -0.976900349330005 39 428.5 430.075469240631 -1.57546924063121 40 437 432.006183387147 4.99381661285283 41 442 443.851058857628 -1.85105885762786 42 444.9 447.862866967216 -2.96286696721563 43 441.4 449.997912932191 -8.59791293219065 44 440.3 443.451142052573 -3.15114205257325 45 447.1 442.95152401427 4.14847598572999 46 455.3 453.013207294656 2.28679270534389 47 478.6 462.506805159959 16.0931948400415 48 486.5 492.476987747659 -5.97698774765852 49 487.8 494.524945853678 -6.72494585367764 50 485.9 492.514854675897 -6.61485467589668 51 483.8 487.751458690334 -3.95145869033436 52 488.4 485.085473302696 3.31452669730423 53 494 492.481307611806 1.51869238819444 54 493.6 499.029993303772 -5.42999330377211 55 487.3 496.591994789446 -9.29199478944569 56 482.1 487.562294401919 -5.46229440191858 57 484.2 482.340389211089 1.85961078891073 58 496.8 487.566929665458 9.23307033454159 59 501.1 505.301756804343 -4.20175680434267 60 499.8 507.178970637462 -7.37897063746242 61 495.5 503.484705925704 -7.98470592570384 62 498.1 497.100880916172 0.99911908382831 63 503.8 502.268394990186 1.53160500981355 64 516.2 509.708309161647 6.49169083835291 65 526.1 525.514717406914 0.585282593085601 66 527.1 534.76813400799 -7.66813400799054 67 525.1 532.009592246333 -6.90959224633251 68 528.9 527.910612127794 0.989387872205504 69 540.1 533.489623270261 6.61037672973944 70 549 548.04855972413 0.95144027586986 71 556 556.688681703964 -0.688681703964186 72 568.9 562.934911170678 5.96508882932204 73 589.1 577.771646522556 11.328353477444 74 590.3 600.937090573746 -10.6370905737459 75 603.3 594.54612621762 8.7538737823794 76 638.8 611.301606297096 27.4983937029043 77 643 655.413712590609 -12.4137125906087 78 656.7 647.985622437953 8.71437756204665 79 656.1 664.265934121345 -8.16593412134524 80 654.1 656.00173447474 -1.90173447473958 81 659.9 652.966045183452 6.93395481654834 82 662.1 661.14940633983 0.950593660169812 83 669.2 662.35874478538 6.84125521462021 84 673.1 671.744058310368 1.35594168963245 85 678.3 674.344553934804 3.95544606519593 86 677.4 680.10086533833 -2.70086533833003 87 678.5 676.495905988997 2.00409401100289 88 672.4 678.164457893499 -5.76445789349856 89 665.3 668.968066733025 -3.66806673302521 90 667.9 661.394555186888 6.5054448131119 91 672.1 667.925100995972 4.17489900402786 92 662.5 673.634620515667 -11.1346205156667 93 682.3 659.09992709899 23.2000729010099 94 692.1 690.525911827792 1.57408817220781 95 702.7 697.134408951715 5.5655910482855 96 721.4 708.849114986563 12.5508850134370 97 733.2 729.943802437885 3.25619756211501 98 747.7 738.948670030161 8.7513299698388 99 737.6 754.087152134959 -16.4871521349586 100 729.3 733.0710976013 -3.77109760129934 101 706.1 724.118450833854 -18.0184508338545 102 674.3 693.81815070581 -19.5181507058105 103 659 658.514223974842 0.485776025158301 104 645.7 649.926115218907 -4.2261152189072 105 646.1 639.257850646981 6.8421493530187 106 633 644.444355623212 -11.4443556232125 107 622.3 627.288044987073 -4.98804498707281 108 628.2 618.078141856297 10.1218581437027 109 637.3 630.811179674035 6.48882032596509 110 639.6 642.778732688406 -3.17873268840567 111 638.5 643.413725938097 -4.9137259380967 112 650.5 640.564207247433 9.93579275256742 113 655.4 657.421611985447 -2.02161198544754 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 10 0.0532720146394707 0.106544029278941 0.94672798536053 11 0.0165236760293821 0.0330473520587643 0.983476323970618 12 0.00564820605724697 0.0112964121144939 0.994351793942753 13 0.0043117864823602 0.0086235729647204 0.99568821351764 14 0.00145624428416534 0.00291248856833068 0.998543755715835 15 0.00149652385760621 0.00299304771521243 0.998503476142394 16 0.000547234217799026 0.00109446843559805 0.9994527657822 17 0.000425035311243594 0.000850070622487188 0.999574964688756 18 0.000417972654252295 0.00083594530850459 0.999582027345748 19 0.000190746788129147 0.000381493576258295 0.99980925321187 20 6.38627785698852e-05 0.000127725557139770 0.99993613722143 21 0.000149419619871325 0.00029883923974265 0.999850580380129 22 7.50041848527208e-05 0.000150008369705442 0.999924995815147 23 5.13559479874637e-05 0.000102711895974927 0.999948644052013 24 1.86608872214456e-05 3.73217744428912e-05 0.999981339112779 25 8.67637480874332e-06 1.73527496174866e-05 0.999991323625191 26 3.00552095746989e-06 6.01104191493977e-06 0.999996994479043 27 1.07778191072874e-06 2.15556382145748e-06 0.99999892221809 28 3.75309826427624e-07 7.50619652855249e-07 0.999999624690174 29 1.2125394178203e-07 2.4250788356406e-07 0.999999878746058 30 3.86348245074370e-08 7.72696490148741e-08 0.999999961365176 31 2.28301951754785e-08 4.56603903509570e-08 0.999999977169805 32 7.9097420867311e-09 1.58194841734622e-08 0.999999992090258 33 3.79559636741921e-09 7.59119273483842e-09 0.999999996204404 34 4.68148204914326e-08 9.36296409828652e-08 0.99999995318518 35 2.11885750837688e-08 4.23771501675376e-08 0.999999978811425 36 7.72114613779455e-09 1.54422922755891e-08 0.999999992278854 37 2.52651635098144e-09 5.05303270196289e-09 0.999999997473484 38 1.03685776461038e-09 2.07371552922076e-09 0.999999998963142 39 3.99284355974862e-10 7.98568711949725e-10 0.999999999600716 40 4.37612120623795e-09 8.75224241247591e-09 0.999999995623879 41 1.81180819039469e-09 3.62361638078937e-09 0.999999998188192 42 8.26637666212131e-10 1.65327533242426e-09 0.999999999173362 43 4.88249334042997e-10 9.76498668085993e-10 0.99999999951175 44 1.93863335889733e-10 3.87726671779465e-10 0.999999999806137 45 5.03063835514618e-10 1.00612767102924e-09 0.999999999496936 46 3.88639550646753e-10 7.77279101293506e-10 0.99999999961136 47 1.83966241515281e-06 3.67932483030561e-06 0.999998160337585 48 1.50603034866150e-06 3.01206069732299e-06 0.999998493969651 49 8.28950826127074e-07 1.65790165225415e-06 0.999999171049174 50 4.27765254061355e-07 8.5553050812271e-07 0.999999572234746 51 2.38473642953617e-07 4.76947285907233e-07 0.999999761526357 52 1.27741584388530e-07 2.55483168777060e-07 0.999999872258416 53 6.22207638783779e-08 1.24441527756756e-07 0.999999937779236 54 5.75404165003672e-08 1.15080833000734e-07 0.999999942459584 55 7.54332259129016e-08 1.50866451825803e-07 0.999999924566774 56 4.23991443181684e-08 8.47982886363368e-08 0.999999957600856 57 2.04788544157639e-08 4.09577088315279e-08 0.999999979521146 58 3.70346482775592e-08 7.40692965551185e-08 0.999999962965352 59 2.65806501537123e-08 5.31613003074245e-08 0.99999997341935 60 1.89134759333784e-08 3.78269518667568e-08 0.999999981086524 61 1.6962144823663e-08 3.3924289647326e-08 0.999999983037855 62 9.25130273445294e-09 1.85026054689059e-08 0.999999990748697 63 4.02602988655916e-09 8.05205977311832e-09 0.99999999597397 64 4.24061109482202e-09 8.48122218964405e-09 0.999999995759389 65 2.14749453614351e-09 4.29498907228703e-09 0.999999997852505 66 2.47084276148594e-09 4.94168552297187e-09 0.999999997529157 67 4.01096077768932e-09 8.02192155537864e-09 0.99999999598904 68 3.93597895746357e-09 7.87195791492715e-09 0.999999996064021 69 3.0156687393205e-09 6.031337478641e-09 0.999999996984331 70 1.71008198864545e-09 3.4201639772909e-09 0.999999998289918 71 2.12223151849986e-09 4.24446303699972e-09 0.999999997877768 72 7.62566033566779e-09 1.52513206713356e-08 0.99999999237434 73 2.243017201485e-08 4.48603440297e-08 0.999999977569828 74 2.01837987634177e-06 4.03675975268355e-06 0.999997981620124 75 1.91228249219319e-05 3.82456498438637e-05 0.999980877175078 76 0.000775905684035355 0.00155181136807071 0.999224094315965 77 0.0130035678894402 0.0260071357788804 0.98699643211056 78 0.0179346434775202 0.0358692869550404 0.98206535652248 79 0.113384522604281 0.226769045208561 0.886615477395719 80 0.153407108986637 0.306814217973274 0.846592891013363 81 0.120538603474843 0.241077206949686 0.879461396525157 82 0.100555165861474 0.201110331722948 0.899444834138526 83 0.0792671815588644 0.158534363117729 0.920732818441136 84 0.0606848241573943 0.121369648314789 0.939315175842606 85 0.043784974962598 0.087569949925196 0.956215025037402 86 0.0392679268634444 0.0785358537268889 0.960732073136556 87 0.0273297845383606 0.0546595690767213 0.97267021546164 88 0.0336525715592372 0.0673051431184743 0.966347428440763 89 0.0392717118778147 0.0785434237556295 0.960728288122185 90 0.0268839861963212 0.0537679723926425 0.973116013803679 91 0.0173133651188421 0.0346267302376843 0.982686634881158 92 0.207206492359526 0.414412984719051 0.792793507640474 93 0.241196162812895 0.48239232562579 0.758803837187105 94 0.284970322750422 0.569940645500845 0.715029677249578 95 0.264363254336483 0.528726508672965 0.735636745663517 96 0.274584924626957 0.549169849253914 0.725415075373043 97 0.220956402087038 0.441912804174076 0.779043597912962 98 0.347863299296102 0.695726598592203 0.652136700703898 99 0.361057225051362 0.722114450102724 0.638942774948638 100 0.730022143570548 0.539955712858905 0.269977856429452 101 0.7151800305288 0.569639938942402 0.284819969471201 102 0.687501704669693 0.624996590660614 0.312498295330307 103 0.821732439162005 0.35653512167599 0.178267560837995 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 64 0.680851063829787 NOK 5% type I error level 69 0.73404255319149 NOK 10% type I error level 75 0.797872340425532 NOK

Charts produced by software:

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal 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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