Multiple regression 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: Mon, 20 Dec 2010 19:46:10 +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/Dec/20/t1292874259dgdvd210n2rz1mk.htm/, Retrieved Mon, 20 Dec 2010 20:44:19 +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/Dec/20/t1292874259dgdvd210n2rz1mk.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 «

27951 29781 32914 33488 35652 36488 35387 35676 34844 32447 31068 29010 29812 30951 32974 32936 34012 32946 31948 30599 27691 25073 23406 22248 22896 25317 26558 26471 27543 26198 24725 25005 23462 20780 19815 19761 21454 23899 24939 23580 24562 24696 23785 23812 21917 19713 19282 18788 21453 24482 27474 27264 27349 30632 29429 30084 26290 24379 23335 21346 21106 24514 28353 30805 31348 34556 33855 34787 32529 29998 29257 28155 30466 35704 39327 39351 42234 43630 43722 43121 37985 37135 34646 33026 35087 38846 42013 43908 42868 44423 44167 43636 44382 42142 43452 36912 42413 45344 44873 47510 49554 47369 45998 48140 48441 44928 40454 38661 37246 36843 36424 37594 38144 38737 34560 36080 33508 35462 33374 32110 35533 35532 37903 36763 40399 44164 44496 43110 43880 43930 44327

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 Vacatures[t] = + 19092.2638297872 + 2256.62092198582M1[t] + 4466.72037395229M2[t] + 6380.72891682785M3[t] + 6783.73745970342M4[t] + 7921.0187298517M5[t] + 8710.93636363636M6[t] + 7506.21763378466M7[t] + 7551.0443584784M8[t] + 5677.78017408124M9[t] + 3820.7887169568M10[t] + 2452.06998710509M11[t] + 134.991457124436t + 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) 19092.2638297872 2053.491033 9.2975 0 0 M1 2256.62092198582 2555.016899 0.8832 0.378918 0.189459 M2 4466.72037395229 2554.693522 1.7484 0.082989 0.041495 M3 6380.72891682785 2554.441979 2.4979 0.013871 0.006935 M4 6783.73745970342 2554.26229 2.6558 0.009004 0.004502 M5 7921.0187298517 2554.15447 3.1012 0.002411 0.001206 M6 8710.93636363636 2554.118529 3.4105 0.000888 0.000444 M7 7506.21763378466 2554.15447 2.9388 0.003964 0.001982 M8 7551.0443584784 2554.26229 2.9563 0.003762 0.001881 M9 5677.78017408124 2554.441979 2.2227 0.028141 0.01407 M10 3820.7887169568 2554.693522 1.4956 0.137429 0.068714 M11 2452.06998710509 2555.016899 0.9597 0.339165 0.169582 t 134.991457124436 13.549748 9.9627 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.716794881754803 R-squared 0.513794902509882 Adjusted R-squared 0.464350316324447 F-TEST (value) 10.3913277903259 F-TEST (DF numerator) 12 F-TEST (DF denominator) 118 p-value 9.84767822842514e-14 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 5845.58181029365 Sum Squared Residuals 4032157550.69865 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 27951 21483.8762088975 6467.12379110252 2 29781 23828.9671179884 5952.03288201161 3 32914 25877.9671179884 7036.0328820116 4 33488 26415.9671179884 7072.0328820116 5 35652 27688.2398452611 7963.76015473888 6 36488 28613.1489361702 7874.8510638298 7 35387 27543.4216634429 7843.57833655706 8 35676 27723.2398452611 7952.76015473888 9 34844 25984.9671179884 8859.0328820116 10 32447 24262.9671179884 8184.0328820116 11 31068 23029.2398452611 8038.76015473888 12 29010 20712.1613152805 8297.83868471954 13 29812 23103.7736943907 6708.22630560929 14 30951 25448.8646034816 5502.13539651837 15 32974 27497.8646034816 5476.13539651837 16 32936 28035.8646034816 4900.13539651838 17 34012 29308.1373307544 4703.86266924565 18 32946 30233.0464216634 2712.95357833656 19 31948 29163.3191489362 2784.68085106383 20 30599 29343.1373307544 1255.86266924565 21 27691 27604.8646034816 86.1353965183763 22 25073 25882.8646034816 -809.864603481627 23 23406 24649.1373307544 -1243.13733075435 24 22248 22332.0588007737 -84.0588007736974 25 22896 24723.6711798839 -1827.67117988395 26 25317 27068.7620889749 -1751.76208897486 27 26558 29117.7620889749 -2559.76208897486 28 26471 29655.7620889749 -3184.76208897485 29 27543 30928.0348162476 -3385.03481624758 30 26198 31852.9439071567 -5654.94390715667 31 24725 30783.2166344294 -6058.2166344294 32 25005 30963.0348162476 -5958.03481624758 33 23462 29224.7620889749 -5762.76208897486 34 20780 27502.7620889749 -6722.76208897486 35 19815 26269.0348162476 -6454.03481624758 36 19761 23951.9562862669 -4190.95628626692 37 21454 26343.5686653772 -4889.56866537718 38 23899 28688.6595744681 -4789.65957446808 39 24939 30737.6595744681 -5798.65957446809 40 23580 31275.6595744681 -7695.65957446809 41 24562 32547.9323017408 -7985.93230174082 42 24696 33472.8413926499 -8776.8413926499 43 23785 32403.1141199226 -8618.11411992263 44 23812 32582.9323017408 -8770.93230174082 45 21917 30844.6595744681 -8927.65957446809 46 19713 29122.6595744681 -9409.65957446809 47 19282 27888.9323017408 -8606.93230174082 48 18788 25571.8537717602 -6783.85377176015 49 21453 27963.4661508704 -6510.46615087041 50 24482 30308.5570599613 -5826.55705996132 51 27474 32357.5570599613 -4883.55705996131 52 27264 32895.5570599613 -5631.55705996131 53 27349 34167.8297872340 -6818.82978723404 54 30632 35092.7388781431 -4460.73887814313 55 29429 34023.0116054159 -4594.01160541586 56 30084 34202.8297872340 -4118.82978723404 57 26290 32464.5570599613 -6174.55705996131 58 24379 30742.5570599613 -6363.55705996131 59 23335 29508.8297872340 -6173.82978723404 60 21346 27191.7512572534 -5845.75125725339 61 21106 29583.3636363636 -8477.36363636364 62 24514 31928.4545454545 -7414.45454545455 63 28353 33977.4545454545 -5624.45454545455 64 30805 34515.4545454545 -3710.45454545454 65 31348 35787.7272727273 -4439.72727272727 66 34556 36712.6363636364 -2156.63636363636 67 33855 35642.9090909091 -1787.90909090909 68 34787 35822.7272727273 -1035.72727272727 69 32529 34084.4545454545 -1555.45454545454 70 29998 32362.4545454545 -2364.45454545455 71 29257 31128.7272727273 -1871.72727272727 72 28155 28811.6487427466 -656.648742746616 73 30466 31203.2611218569 -737.261121856867 74 35704 33548.3520309478 2155.64796905222 75 39327 35597.3520309478 3729.64796905222 76 39351 36135.3520309478 3215.64796905223 77 42234 37407.6247582205 4826.3752417795 78 43630 38332.5338491296 5297.4661508704 79 43722 37262.8065764023 6459.19342359768 80 43121 37442.6247582205 5678.3752417795 81 37985 35704.3520309478 2280.64796905223 82 37135 33982.3520309478 3152.64796905222 83 34646 32748.6247582205 1897.37524177950 84 33026 30431.5462282398 2594.45377176016 85 35087 32823.1586073501 2263.84139264990 86 38846 35168.249516441 3677.75048355899 87 42013 37217.249516441 4795.75048355899 88 43908 37755.249516441 6152.75048355899 89 42868 39027.5222437137 3840.47775628627 90 44423 39952.4313346228 4470.56866537717 91 44167 38882.7040618956 5284.29593810445 92 43636 39062.5222437137 4573.47775628626 93 44382 37324.249516441 7057.750483559 94 42142 35602.249516441 6539.750483559 95 43452 34368.5222437137 9083.47775628627 96 36912 32051.4437137331 4860.55628626693 97 42413 34443.0560928433 7969.94390715667 98 45344 36788.1470019342 8555.85299806576 99 44873 38837.1470019342 6035.85299806576 100 47510 39375.1470019342 8134.85299806577 101 49554 40647.4197292070 8906.58027079304 102 47369 41572.3288201161 5796.67117988395 103 45998 40502.6015473888 5495.39845261122 104 48140 40682.419729207 7457.58027079304 105 48441 38944.1470019342 9496.85299806576 106 44928 37222.1470019342 7705.85299806576 107 40454 35988.419729207 4465.58027079304 108 38661 33671.3411992263 4989.65880077369 109 37246 36062.9535783366 1183.04642166344 110 36843 38408.0444874275 -1565.04448742746 111 36424 40457.0444874275 -4033.04448742746 112 37594 40995.0444874275 -3401.04448742746 113 38144 42267.3172147002 -4123.31721470019 114 38737 43192.2263056093 -4455.22630560928 115 34560 42122.499032882 -7562.49903288201 116 36080 42302.3172147002 -6222.31721470019 117 33508 40564.0444874275 -7056.04448742747 118 35462 38842.0444874275 -3380.04448742746 119 33374 37608.3172147002 -4234.31721470019 120 32110 35291.2386847195 -3181.23868471954 121 35533 37682.8510638298 -2149.85106382979 122 35532 40027.9419729207 -4495.94197292069 123 37903 42076.9419729207 -4173.94197292069 124 36763 42614.9419729207 -5851.94197292069 125 40399 43887.2147001934 -3488.21470019342 126 44164 44812.1237911025 -648.123791102516 127 44496 43742.3965183752 753.603481624758 128 43110 43922.2147001934 -812.214700193427 129 43880 42183.9419729207 1696.05802707930 130 43930 40461.9419729207 3468.05802707930 131 44327 39228.2147001934 5098.78529980657 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.00469531525251381 0.00939063050502761 0.995304684747486 17 0.00280325949891354 0.00560651899782709 0.997196740501086 18 0.00442510215152767 0.00885020430305534 0.995574897848472 19 0.0029384297745197 0.0058768595490394 0.99706157022548 20 0.00355527074316022 0.00711054148632045 0.99644472925684 21 0.00745539473496915 0.0149107894699383 0.99254460526503 22 0.00934211823241927 0.0186842364648385 0.99065788176758 23 0.00977927081496115 0.0195585416299223 0.990220729185039 24 0.00715194397352382 0.0143038879470476 0.992848056026476 25 0.00348326627336475 0.0069665325467295 0.996516733726635 26 0.00158832407356870 0.00317664814713741 0.998411675926431 27 0.000742309765745696 0.00148461953149139 0.999257690234254 28 0.000344816784254909 0.000689633568509817 0.999655183215745 29 0.000167916413059483 0.000335832826118967 0.99983208358694 30 0.000103594138622039 0.000207188277244078 0.999896405861378 31 6.69557557392975e-05 0.000133911511478595 0.99993304424426 32 3.23421093144455e-05 6.4684218628891e-05 0.999967657890686 33 1.40805592008771e-05 2.81611184017542e-05 0.9999859194408 34 6.16445084039198e-06 1.23289016807840e-05 0.99999383554916 35 2.40847501663378e-06 4.81695003326756e-06 0.999997591524983 36 9.17030948123391e-07 1.83406189624678e-06 0.999999082969052 37 7.76748682885126e-07 1.55349736577025e-06 0.999999223251317 38 7.37546973762453e-07 1.47509394752491e-06 0.999999262453026 39 3.84767832148699e-07 7.69535664297397e-07 0.999999615232168 40 1.49890267202104e-07 2.99780534404208e-07 0.999999850109733 41 5.69099199198927e-08 1.13819839839785e-07 0.99999994309008 42 2.36774710957820e-08 4.73549421915639e-08 0.999999976322529 43 1.01060159686704e-08 2.02120319373408e-08 0.999999989893984 44 4.62217639580785e-09 9.2443527916157e-09 0.999999995377824 45 2.10113403164324e-09 4.20226806328648e-09 0.999999997898866 46 1.12157557881826e-09 2.24315115763651e-09 0.999999998878424 47 7.422095125092e-10 1.4844190250184e-09 0.99999999925779 48 5.21957580190219e-10 1.04391516038044e-09 0.999999999478042 49 1.83818552444286e-09 3.67637104888572e-09 0.999999998161815 50 8.50716871561104e-09 1.70143374312221e-08 0.999999991492831 51 4.73569876256364e-08 9.47139752512727e-08 0.999999952643012 52 1.56694745934738e-07 3.13389491869475e-07 0.999999843305254 53 2.23862112344181e-07 4.47724224688362e-07 0.999999776137888 54 1.50585961547965e-06 3.01171923095929e-06 0.999998494140385 55 5.4507034690268e-06 1.09014069380536e-05 0.99999454929653 56 1.91872657578320e-05 3.83745315156639e-05 0.999980812734242 57 3.09771607813495e-05 6.1954321562699e-05 0.999969022839219 58 5.96530114247195e-05 0.000119306022849439 0.999940346988575 59 0.000108445743805157 0.000216891487610313 0.999891554256195 60 0.000136714925019997 0.000273429850039994 0.99986328507498 61 0.000203994582957987 0.000407989165915974 0.999796005417042 62 0.000333455899499251 0.000666911798998503 0.9996665441005 63 0.000576197003364851 0.00115239400672970 0.999423802996635 64 0.00146507077635991 0.00293014155271981 0.99853492922364 65 0.00322713916820044 0.00645427833640089 0.9967728608318 66 0.00911874248962302 0.0182374849792460 0.990881257510377 67 0.0213269835876654 0.0426539671753309 0.978673016412335 68 0.0453090369046411 0.0906180738092822 0.954690963095359 69 0.0873398587302891 0.174679717460578 0.91266014126971 70 0.169897691084843 0.339795382169687 0.830102308915157 71 0.290958102849960 0.581916205699921 0.70904189715004 72 0.378786662282697 0.757573324565395 0.621213337717303 73 0.490650142708189 0.981300285416377 0.509349857291811 74 0.592059386409939 0.815881227180122 0.407940613590061 75 0.671665177225944 0.656669645548113 0.328334822774056 76 0.729970004247863 0.540059991504275 0.270029995752137 77 0.786803112708075 0.42639377458385 0.213196887291925 78 0.827805121808176 0.344389756383649 0.172194878191824 79 0.861817916501631 0.276364166996737 0.138182083498369 80 0.877948419897602 0.244103160204797 0.122051580102398 81 0.896639135431296 0.206721729137408 0.103360864568704 82 0.920137769402561 0.159724461194878 0.079862230597439 83 0.946700761830404 0.106598476339193 0.0532992381695965 84 0.949616181280732 0.100767637438537 0.0503838187192684 85 0.955085710128038 0.0898285797439245 0.0449142898719622 86 0.951772017893277 0.0964559642134465 0.0482279821067232 87 0.943362085182401 0.113275829635197 0.0566379148175987 88 0.936016459901916 0.127967080196168 0.0639835400980839 89 0.927103854809047 0.145792290381906 0.0728961451909529 90 0.915483736531509 0.169032526936982 0.0845162634684912 91 0.899255468238788 0.201489063522424 0.100744531761212 92 0.882781741842266 0.234436516315468 0.117218258157734 93 0.870108489033867 0.259783021932266 0.129891510966133 94 0.863548913869966 0.272902172260069 0.136451086130034 95 0.851751090344524 0.296497819310952 0.148248909655476 96 0.82025774164353 0.35948451671294 0.17974225835647 97 0.793710875463387 0.412578249073225 0.206289124536612 98 0.78813139802725 0.423737203945499 0.211868601972749 99 0.75973268561837 0.480534628763259 0.240267314381630 100 0.77585310377493 0.44829379245014 0.22414689622507 101 0.806262675642326 0.387474648715349 0.193737324357674 102 0.7768148964333 0.4463702071334 0.2231851035667 103 0.757092776612014 0.485814446775971 0.242907223387986 104 0.795344223752347 0.409311552495307 0.204655776247653 105 0.89667840728065 0.2066431854387 0.10332159271935 106 0.928929528249322 0.142140943501356 0.071070471750678 107 0.931949506573689 0.136100986852622 0.068050493426311 108 0.970308056523485 0.0593838869530308 0.0296919434765154 109 0.972213498717203 0.0555730025655934 0.0277865012827967 110 0.978070512110099 0.043858975779803 0.0219294878899015 111 0.972841585774507 0.0543168284509869 0.0271584142254934 112 0.991373311419847 0.017253377160306 0.008626688580153 113 0.99719313015231 0.00561373969537923 0.00280686984768962 114 0.997606491735448 0.00478701652910367 0.00239350826455184 115 0.98754272276539 0.0249145544692191 0.0124572772346095 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 48 0.48 NOK 5% type I error level 57 0.57 NOK 10% type I error level 63 0.63 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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