olieprijs en iraq

*Unverified author*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 20 Nov 2008 12:05:41 -0700

Cite this page as follows:

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d.htm/, Retrieved Thu, 20 Nov 2008 19:06:40 +0000

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/2008/Nov/20/t1227208000lll7qp2pr17u44d.htm/}, year = {2008}, } @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 = {2008}, note = {{ISBN} 3-900051-07-0}, url = {http://www.R-project.org}, }

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

Feedback Forum:

Post a new message

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords:

Dataseries X:

» Textbox « » Textfile « » CSV «

31.54 0 32.43 0 26.54 0 25.85 0 27.6 0 25.71 0 25.38 0 28.57 0 27.64 0 25.36 0 25.9 0 26.29 0 21.74 0 19.2 0 19.32 0 19.82 0 20.36 0 24.31 0 25.97 0 25.61 0 24.67 0 25.59 0 26.09 0 28.37 0 27.34 0 24.46 0 27.46 0 30.23 0 32.33 0 29.87 1 24.87 1 25.48 1 27.28 1 28.24 1 29.58 1 26.95 1 29.08 1 28.76 1 29.59 1 30.7 1 30.52 1 32.67 1 33.19 1 37.13 1 35.54 1 37.75 1 41.84 1 42.94 1 49.14 1 44.61 1 40.22 1 44.23 1 45.85 1 53.38 1 53.26 1 51.8 1 55.3 1 57.81 1 63.96 1 63.77 1 59.15 1 56.12 1 57.42 1 63.52 1 61.71 1 63.01 1 68.18 1 72.03 1 69.75 1 74.41 1 74.33 1 64.24 1 60.03 1 59.44 1 62.5 1 55.04 1 58.34 1 61.92 1 67.65 1 67.68 1 70.3 1 75.26 1 71.44 1 76.36 1 81.71 1 92.6 1 90.6 1 92.23 1 94.09 1 102.79 1 109.65 1 124.05 1 132.69 1 135.81 1 116.07 1 101.42 1 75.73 1

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 olie[t] = + 7.71877900232017 -21.5680607115236iraq[t] -1.44152667676377M1[t] -0.258273046102948M2[t] -1.90729650038669M3[t] -2.06381995467044M4[t] -2.06909340895419M5[t] + 2.33164072570251M6[t] + 2.99011727141876M7[t] + 4.86234381713501M8[t] + 5.06207036285125M9[t] + 6.0417969085675M10[t] + 3.51152345428376M11[t] + 1.15277345428375t + 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) 7.71877900232017 4.52253 1.7067 0.091609 0.045805 iraq -21.5680607115236 4.094795 -5.2672 1e-06 1e-06 M1 -1.44152667676377 5.4283 -0.2656 0.791238 0.395619 M2 -0.258273046102948 5.596049 -0.0462 0.963299 0.48165 M3 -1.90729650038669 5.593317 -0.341 0.733969 0.366984 M4 -2.06381995467044 5.591383 -0.3691 0.712988 0.356494 M5 -2.06909340895419 5.590246 -0.3701 0.712232 0.356116 M6 2.33164072570251 5.59556 0.4167 0.677977 0.338989 M7 2.99011727141876 5.591173 0.5348 0.594223 0.297112 M8 4.86234381713501 5.587582 0.8702 0.386698 0.193349 M9 5.06207036285125 5.584786 0.9064 0.367346 0.183673 M10 6.0417969085675 5.582789 1.0822 0.28229 0.141145 M11 3.51152345428376 5.58159 0.6291 0.530994 0.265497 t 1.15277345428375 0.066793 17.2589 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.926707616339391 R-squared 0.858787006181437 Adjusted R-squared 0.836669308354433 F-TEST (value) 38.8280467930495 F-TEST (DF numerator) 13 F-TEST (DF denominator) 83 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 11.1623811237660 Sum Squared Residuals 10341.6964452331 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 31.54 7.43002577984017 24.1099742201598 2 32.43 9.76605286478476 22.6639471352152 3 26.54 9.26980286478474 17.2701971352153 4 25.85 10.2660528647848 15.5839471352152 5 27.6 11.4135528647847 16.1864471352153 6 25.71 16.9670604537252 8.74293954627481 7 25.38 18.7783104537252 6.60168954627481 8 28.57 21.8033104537252 6.76668954627482 9 27.64 23.1558104537252 4.48418954627483 10 25.36 25.2883104537252 0.0716895462748208 11 25.9 23.9108104537252 1.98918954627481 12 26.29 21.5520604537252 4.73793954627483 13 21.74 21.2633072312452 0.47669276875484 14 19.2 23.5993343161897 -4.39933431618974 15 19.32 23.1030843161897 -3.78308431618974 16 19.82 24.0993343161897 -4.27933431618974 17 20.36 25.2468343161897 -4.88683431618974 18 24.31 30.8003419051302 -6.4903419051302 19 25.97 32.6115919051302 -6.6415919051302 20 25.61 35.6365919051302 -10.0265919051302 21 24.67 36.9890919051302 -12.3190919051302 22 25.59 39.1215919051302 -13.5315919051302 23 26.09 37.7440919051302 -11.6540919051302 24 28.37 35.3853419051302 -7.01534190513018 25 27.34 35.0965886826502 -7.75658868265017 26 24.46 37.4326157675947 -12.9726157675947 27 27.46 36.9363657675947 -9.47636576759474 28 30.23 37.9326157675947 -7.70261576759474 29 32.33 39.0801157675947 -6.75011576759474 30 29.87 23.0655626450116 6.8044373549884 31 24.87 24.8768126450116 -0.00681264501159733 32 25.48 27.9018126450116 -2.42181264501160 33 27.28 29.2543126450116 -1.97431264501158 34 28.24 31.3868126450116 -3.14681264501159 35 29.58 30.0093126450116 -0.429312645011599 36 26.95 27.6505626450116 -0.700562645011587 37 29.08 27.3618094225316 1.71819057746843 38 28.76 29.6978365074762 -0.937836507476153 39 29.59 29.2015865074762 0.388413492523847 40 30.7 30.1978365074761 0.502163492523851 41 30.52 31.3453365074762 -0.825336507476161 42 32.67 36.8988440964166 -4.2288440964166 43 33.19 38.7100940964166 -5.5200940964166 44 37.13 41.7350940964166 -4.6050940964166 45 35.54 43.0875940964166 -7.5475940964166 46 37.75 45.2200940964166 -7.4700940964166 47 41.84 43.8425940964166 -2.00259409641659 48 42.94 41.4838440964166 1.45615590358341 49 49.14 41.1950908739366 7.94490912606341 50 44.61 43.5311179588811 1.07888204111886 51 40.22 43.0348679588812 -2.81486795888116 52 44.23 44.0311179588812 0.19888204111884 53 45.85 45.1786179588812 0.671382041118846 54 53.38 50.7321255478216 2.64787445217839 55 53.26 52.5433755478216 0.71662445217839 56 51.8 55.5683755478216 -3.76837554782161 57 55.3 56.9208755478216 -1.62087554782160 58 57.81 59.0533755478216 -1.24337554782160 59 63.96 57.6758755478216 6.2841244521784 60 63.77 55.3171255478216 8.4528744521784 61 59.15 55.0283723253416 4.12162767465842 62 56.12 57.3643994102861 -1.24439941028615 63 57.42 56.8681494102862 0.551850589713845 64 63.52 57.8643994102862 5.65560058971384 65 61.71 59.0118994102862 2.69810058971384 66 63.01 64.5654069992266 -1.55540699922662 67 68.18 66.3766569992266 1.80334300077340 68 72.03 69.4016569992266 2.62834300077339 69 69.75 70.7541569992266 -1.0041569992266 70 74.41 72.8866569992266 1.52334300077339 71 74.33 71.5091569992266 2.82084300077340 72 64.24 69.1504069992266 -4.9104069992266 73 60.03 68.8616537767466 -8.83165377674658 74 59.44 71.1976808616911 -11.7576808616911 75 62.5 70.7014308616912 -8.20143086169116 76 55.04 71.6976808616912 -16.6576808616912 77 58.34 72.8451808616912 -14.5051808616912 78 61.92 78.3986884506316 -16.4786884506316 79 67.65 80.2099384506316 -12.5599384506316 80 67.68 83.2349384506316 -15.5549384506316 81 70.3 84.5874384506316 -14.2874384506316 82 75.26 86.7199384506316 -11.4599384506316 83 71.44 85.3424384506316 -13.9024384506316 84 76.36 82.9836884506316 -6.6236884506316 85 81.71 82.6949352281516 -0.98493522815159 86 92.6 85.0309623130962 7.56903768690384 87 90.6 84.5347123130962 6.06528768690383 88 92.23 85.5309623130962 6.69903768690385 89 94.09 86.6784623130961 7.41153768690385 90 102.79 92.2319699020366 10.5580300979634 91 109.65 94.0432199020366 15.6067800979634 92 124.05 97.0682199020366 26.9817800979634 93 132.69 98.4207199020366 34.2692800979634 94 135.81 100.553219902037 35.2567800979634 95 116.07 99.1757199020366 16.8942800979634 96 101.42 96.8169699020366 4.6030300979634 97 75.73 96.5282166795566 -20.7982166795566 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.0165268571031771 0.0330537142063542 0.983473142896823 18 0.0156225362894539 0.0312450725789078 0.984377463710546 19 0.0139889272608463 0.0279778545216926 0.986011072739154 20 0.0051706424068735 0.010341284813747 0.994829357593127 21 0.00179104837519575 0.00358209675039151 0.998208951624804 22 0.000999947259208325 0.00199989451841665 0.999000052740792 23 0.000490223879483535 0.00098044775896707 0.999509776120517 24 0.000317846711671776 0.000635693423343552 0.999682153288328 25 0.000229371603812607 0.000458743207625214 0.999770628396187 26 8.96087116544657e-05 0.000179217423308931 0.999910391288346 27 0.000111916833336344 0.000223833666672688 0.999888083166664 28 0.000190933336595519 0.000381866673191038 0.999809066663405 29 0.000248911738017586 0.000497823476035172 0.999751088261982 30 0.000107842954718556 0.000215685909437111 0.999892157045281 31 5.15133329063019e-05 0.000103026665812604 0.999948486667094 32 2.15120617433772e-05 4.30241234867543e-05 0.999978487938257 33 7.65219582406712e-06 1.53043916481342e-05 0.999992347804176 34 2.77437121941398e-06 5.54874243882797e-06 0.99999722562878 35 1.00367940181109e-06 2.00735880362218e-06 0.999998996320598 36 3.50512210392094e-07 7.01024420784187e-07 0.99999964948779 37 1.27361812778842e-07 2.54723625557685e-07 0.999999872638187 38 4.351699363322e-08 8.703398726644e-08 0.999999956483006 39 1.76789114860782e-08 3.53578229721564e-08 0.999999982321089 40 7.01122208994256e-09 1.40224441798851e-08 0.999999992988778 41 2.19617192552535e-09 4.39234385105069e-09 0.999999997803828 42 9.8698898778921e-10 1.97397797557842e-09 0.999999999013011 43 6.11418011641845e-10 1.22283602328369e-09 0.999999999388582 44 7.58897021114038e-10 1.51779404222808e-09 0.999999999241103 45 5.21135019224649e-10 1.04227003844930e-09 0.999999999478865 46 6.40169987060484e-10 1.28033997412097e-09 0.99999999935983 47 1.56936845323731e-09 3.13873690647461e-09 0.999999998430632 48 3.79257529155798e-09 7.58515058311596e-09 0.999999996207425 49 6.07375948046692e-08 1.21475189609338e-07 0.999999939262405 50 9.46169185543327e-08 1.89233837108665e-07 0.999999905383081 51 5.47283110899984e-08 1.09456622179997e-07 0.999999945271689 52 5.0457368838646e-08 1.00914737677292e-07 0.999999949542631 53 4.61944994679795e-08 9.2388998935959e-08 0.9999999538055 54 1.78769752054029e-07 3.57539504108057e-07 0.999999821230248 55 3.98734401136553e-07 7.97468802273107e-07 0.999999601265599 56 3.45703577470531e-07 6.91407154941063e-07 0.999999654296423 57 4.69790862746804e-07 9.39581725493609e-07 0.999999530209137 58 7.18232001162769e-07 1.43646400232554e-06 0.999999281767999 59 2.23807048575532e-06 4.47614097151065e-06 0.999997761929514 60 6.98283755565129e-06 1.39656751113026e-05 0.999993017162444 61 1.35333962900084e-05 2.70667925800167e-05 0.99998646660371 62 8.58098659166907e-06 1.71619731833381e-05 0.999991419013408 63 6.49084508285144e-06 1.29816901657029e-05 0.999993509154917 64 1.34468697031036e-05 2.68937394062073e-05 0.999986553130297 65 1.70740561622846e-05 3.41481123245692e-05 0.999982925943838 66 1.49539937372988e-05 2.99079874745976e-05 0.999985046006263 67 1.86571573505963e-05 3.73143147011926e-05 0.99998134284265 68 2.37087502276701e-05 4.74175004553402e-05 0.999976291249772 69 1.60451264731337e-05 3.20902529462674e-05 0.999983954873527 70 1.43122432367276e-05 2.86244864734552e-05 0.999985687756763 71 3.39795034150049e-05 6.79590068300098e-05 0.999966020496585 72 6.08052632073345e-05 0.000121610526414669 0.999939194736793 73 0.00118884586491329 0.00237769172982658 0.998811154135087 74 0.000670175972266855 0.00134035194453371 0.999329824027733 75 0.000417318167055737 0.000834636334111474 0.999582681832944 76 0.000252146092883323 0.000504292185766645 0.999747853907117 77 0.000125324686155957 0.000250649372311913 0.999874675313844 78 5.24488293851956e-05 0.000104897658770391 0.999947551170615 79 1.67948404919189e-05 3.35896809838377e-05 0.999983205159508 80 1.18176353966586e-05 2.36352707933173e-05 0.999988182364603 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 60 0.9375 NOK 5% type I error level 64 1 NOK 10% type I error level 64 1 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/10b66e1227207929.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/10b66e1227207929.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/1rbbg1227207929.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/1rbbg1227207929.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/2q0q91227207929.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/2q0q91227207929.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/3y46r1227207929.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/3y46r1227207929.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/4w7vc1227207929.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/4w7vc1227207929.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/5raym1227207929.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/5raym1227207929.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/6bbkl1227207929.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/6bbkl1227207929.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/7lpry1227207929.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/7lpry1227207929.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/8h2yi1227207929.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/8h2yi1227207929.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/9p0fx1227207929.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Nov/20/t1227208000lll7qp2pr17u44d/9p0fx1227207929.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') }

Copyright

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 License.

Software written by Ed van Stee & Patrick Wessa

Disclaimer

Information provided on this web site is provided "AS IS" without warranty of any kind, either express or implied, including, without limitation, warranties of merchantability, fitness for a particular purpose, and noninfringement. We use reasonable efforts to include accurate and timely information and periodically update the information, and software without notice. However, we make no warranties or representations as to the accuracy or completeness of such information (or software), and we assume no liability or responsibility for errors or omissions in the content of this web site, or any software bugs in online applications. Your use of this web site is AT YOUR OWN RISK. Under no circumstances and under no legal theory shall we be liable to you or any other person for any direct, indirect, special, incidental, exemplary, or consequential damages arising from your access to, or use of, this web site.

Privacy Policy

We may request personal information to be submitted to our servers in order to be able to:

• personalize online software applications according to your needs
• enforce strict security rules with respect to the data that you upload (e.g. statistical data)
• manage user sessions of online applications
• alert you about important changes or upgrades in resources or applications

We NEVER allow other companies to directly offer registered users information about their products and services. Banner references and hyperlinks of third parties NEVER contain any personal data of the visitor.

We do NOT sell, nor transmit by any means, personal information, nor statistical data series uploaded by you to third parties.

We carefully protect your data from loss, misuse, alteration, and destruction. However, at any time, and under any circumstance you are solely responsible for managing your passwords, and keeping them secret.

We store a unique ANONYMOUS USER ID in the form of a small 'Cookie' on your computer. This allows us to track your progress when using this website which is necessary to create state-dependent features. The cookie is used for NO OTHER PURPOSE. At any time you may opt to disallow cookies from this website - this will not affect other features of this website.

We examine cookies that are used by third-parties (banner and online ads) very closely: abuse from third-parties automatically results in termination of the advertising contract without refund. We have very good reason to believe that the cookies that are produced by third parties (banner ads) do NOT cause any privacy or security risk.

FreeStatistics.org is safe. There is no need to download any software to use the applications and services contained in this website. Hence, your system's security is not compromised by their use, and your personal data - other than data you submit in the account application form, and the user-agent information that is transmitted by your browser - is never transmitted to our servers.

As a general rule, we do not log on-line behavior of individuals (other than normal logging of webserver 'hits'). However, in cases of abuse, hacking, unauthorized access, Denial of Service attacks, illegal copying, hotlinking, non-compliance with international webstandards (such as robots.txt), or any other harmful behavior, our system engineers are empowered to log, track, identify, publish, and ban misbehaving individuals - even if this leads to ban entire blocks of IP addresses, or disclosing user's identity.

FreeStatistics.org is powered by