*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Sat, 18 Dec 2010 17:25:25 +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/18/t1292693086vdea7gmpvrycyp9.htm/, Retrieved Sat, 18 Dec 2010 18:24:46 +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/18/t1292693086vdea7gmpvrycyp9.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 «

54.64 4606.07 14.36 52.39 4176.60 14.62 52.51 4331.53 13.51 52.92 3987.30 14.95 55.22 4205.86 16.72 55.41 4331.37 16.33 57.02 4106.30 15.21 58.55 4009.33 16.69 57.49 3857.64 15.81 55.52 3929.00 16.02 57.84 4210.47 16.7 58.69 4445.82 15.99 59.74 4497.07 17.68 60.7 4443.71 18.89 60.74 4529.25 18.72 64.32 4634.22 21.14 66.9 4772.67 20.97 70.93 4881.52 23.75 75.89 5153.13 23.05 80.6 5324.19 23.45 81.39 5209.36 21.74 81.33 5108.92 19.37 77.04 5130.88 21.1 79.54 5195.68 21.2 81.93 5050.42 22.67 80.79 5101.03 22.24 81.98 5139.84 23.78 85.94 5234.31 23.27 86.6 5435.73 25.74 87.42 5633.57 26.1 93.14 5498.28 27.49 95.76 5668.13 31.41 99.75 5537.64 28.79 97.71 5442.78 26.76 94.99 5491.50 26.41 96.41 5501.20 27.05 96.28 5658.08 29.43 100.14 5686.16 32.1 99.9 5801.24 36.84 102.87 5678.40 34.22 107.37 5793.68 36.53 115.68 5866.10 40.99 124.33 6087.27 45.97 128.44 6058.70 43.6 130.19 6171.63 47.84 148.4 6385.55 51.47 169.14 6180.05 51.31 1 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 6 seconds R Server 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 R Framework error message Warning: there are blank lines in the 'Data X' field. Please, use NA for missing data - blank lines are simply deleted and are NOT treated as missing values. Multiple Linear Regression - Estimated Regression Equation RioTinto[t] = -32.5248999044313 + 0.377868320188452Commodity[t] + 0.00535216266541196WorldLeaders[t] + 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) -32.5248999044313 7.551553 -4.307 3.9e-05 2e-05 Commodity 0.377868320188452 0.041498 9.1056 0 0 WorldLeaders 0.00535216266541196 0.001911 2.8002 0.006153 0.003077 Multiple Linear Regression - Regression Statistics Multiple R 0.865918352119919 R-squared 0.749814592538075 Adjusted R-squared 0.744708767895995 F-TEST (value) 146.854748272867 F-TEST (DF numerator) 2 F-TEST (DF denominator) 98 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13.1885858119561 Sum Squared Residuals 17046.0019804943 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 14.36 12.7742609989398 1.58573900106016 2 14.62 9.62546397860132 4.99453602139868 3 13.51 10.5000187387762 3.00998126122381 4 14.95 8.8125697957387 6.1374302042613 5 16.72 10.8514356043246 5.86856439567542 6 16.33 11.5949805212962 4.73501947870376 7 15.21 10.9987372656954 4.21126273430462 8 16.69 11.0578765819187 5.63212341808129 9 15.81 9.84546660780262 5.96453339219738 10 16.02 9.48299634483516 6.53700365516484 11 16.7 11.8661240731059 4.83387592689412 12 15.99 13.4469436285708 2.54305637142924 13 17.68 14.118003701371 3.561996298629 14 18.89 14.1951658889255 4.69483411107447 15 18.72 14.6681046161324 4.05189538386759 16 21.14 16.5826897173954 4.55731028260464 17 20.97 18.2985969045079 2.67140309549215 18 23.75 20.4039891409974 3.34601085900259 19 23.05 23.7319169106847 -0.681916910684664 20 23.45 26.4272176443176 -2.97721764431764 21 21.74 26.1111447783973 -4.37114477839726 22 19.37 25.550901461072 -6.18090146107198 23 21.1 24.0473798595960 -2.94737985959597 24 21.2 25.3388708007858 -4.13887080078580 25 22.67 25.4645209372585 -2.79452093725845 26 22.24 25.3046240047401 -3.06462400474012 27 23.78 25.962004738809 -2.18200473880901 28 23.27 27.9639820937567 -4.69398209375675 29 25.74 29.2914077891484 -3.55140778914839 30 26.1 30.660131673428 -4.56013167342803 31 27.49 32.0974443779024 -4.60744437790239 32 31.41 33.9965242055164 -2.58652420551636 33 28.79 34.8058150968587 -6.01581509685867 34 26.76 33.5272575732332 -6.76725757323325 35 26.41 32.7602131073795 -6.35021310737953 36 27.05 33.3487020999016 -6.29870209990163 37 29.43 34.1392264972270 -4.70922649722696 38 32.1 35.7480869407991 -3.64808694079915 39 36.84 36.2733254234895 0.566674576510471 40 34.22 36.73813467263 -2.51813467263003 41 36.53 39.0555394255468 -2.52553942554675 42 40.99 42.5832287865419 -1.59322878654192 43 45.97 47.0355275728812 -1.06552757288119 44 43.6 48.4356550815049 -4.8356550815049 45 47.84 49.7013443716397 -1.86134437163967 46 51.47 57.7272611196563 -6.2572611196563 47 51.31 64.4643806526226 -13.1543806526226 48 48.47 58.6267128001913 -10.1567128001913 49 48.28 62.6833290186818 -14.4033290186818 50 46.56 64.0441933960584 -17.4841933960584 51 43.83 64.6984471144882 -20.8684471144882 52 51.17 68.6014891060444 -17.4314891060444 53 49.59 69.3410537611562 -19.7510537611562 54 49.11 71.2193361554943 -22.1093361554943 55 49.97 69.0832206413275 -19.1132206413275 56 50.07 69.6022273549807 -19.5322273549807 57 53.3 73.8618544849765 -20.5618544849765 58 57.08 82.2647667540992 -25.1847667540992 59 68.54 84.8754532862241 -16.3354532862241 60 71.62 81.5500987170752 -9.93009871707516 61 67.64 80.1382577540029 -12.4982577540029 62 64.79 74.1833801840295 -9.39338018402952 63 80.97 74.2050812207982 6.76491877920178 64 88.42 77.1320574147294 11.2879425852706 65 110.22 72.7227773997456 37.4972226002544 66 99 68.2421782893808 30.7578217106192 67 95.95 71.5525480593663 24.3974519406337 68 107.94 75.5351516499829 32.4048483500171 69 97.82 78.780246995419 19.0397530045810 70 111.64 80.0078498933172 31.6321501066828 71 114.73 76.7562347724474 37.9737652275526 72 117.58 72.1759159831071 45.4040840168929 73 99.19 72.0894559679422 27.1005440320578 74 90.19 66.7311615553386 23.4588384446614 75 59.74 59.5551666768839 0.184833323116091 76 44.51 41.7504142815143 2.75958571848568 77 23.94 34.3074819706895 -10.3674819706894 78 21.29 30.0291269974246 -8.73912699742464 79 20.77 25.0972005809235 -4.32720058092348 80 25.07 21.581111451984 3.48888854801599 81 32.95 24.0460979177046 8.9039020822954 82 40.05 29.0538338920158 10.9961661079842 83 44.59 33.0350355377972 11.5549644622028 84 40.28 35.7716220170184 4.50837798298164 85 41.19 39.144993137967 2.04500686203296 86 38.14 45.6404098944419 -7.50040989444192 87 41.85 45.5567842065367 -3.70678420653673 88 43.76 46.4222272832796 -2.66222728327957 89 50.16 49.2012726277698 0.958727372230181 90 52.94 51.9985797285435 0.941420271456505 91 47.69 52.7046022777469 -5.01460227774689 92 51.52 49.8005768254575 1.71942317454254 93 58.69 55.2259083655863 3.46409163441374 94 50.44 62.4457671098739 -12.0057671098739 95 45.72 53.1945640465843 -7.47456404658432 96 43.24 49.1131743599659 -5.87317435996587 97 51.49 55.5945473152185 -4.10454731521849 98 50.43 58.863777705844 -8.43377770584405 99 58.73 63.287782476395 -4.55778247639503 100 65.12 65.8922254038609 -0.7722254038609 101 64.13 65.6460423743079 -1.51604237430787 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 6 3.31381938265085e-05 6.6276387653017e-05 0.999966861806173 7 7.02952933105933e-05 0.000140590586621187 0.99992970470669 8 4.76784671658683e-06 9.53569343317365e-06 0.999995232153283 9 3.88713026898309e-07 7.77426053796619e-07 0.999999611286973 10 2.47258176414310e-08 4.94516352828621e-08 0.999999975274182 11 1.59296280506289e-09 3.18592561012579e-09 0.999999998407037 12 9.42289907103886e-11 1.88457981420777e-10 0.999999999905771 13 9.7760762149047e-12 1.95521524298094e-11 0.999999999990224 14 1.86088356200232e-12 3.72176712400463e-12 0.99999999999814 15 1.61534637052527e-13 3.23069274105054e-13 0.999999999999838 16 2.16052357460769e-14 4.32104714921539e-14 0.999999999999978 17 1.54165685175535e-15 3.08331370351069e-15 0.999999999999998 18 9.19988990409818e-17 1.83997798081964e-16 1 19 1.50590929199138e-16 3.01181858398276e-16 1 20 2.29021230924293e-16 4.58042461848586e-16 1 21 6.74564162733754e-16 1.34912832546751e-15 1 22 4.28279104554939e-15 8.56558209109877e-15 0.999999999999996 23 5.59616966905066e-16 1.11923393381013e-15 1 24 8.43545883698301e-17 1.68709176739660e-16 1 25 8.82290863193964e-18 1.76458172638793e-17 1 26 9.1394007142471e-19 1.82788014284942e-18 1 27 1.06805553909613e-19 2.13611107819225e-19 1 28 1.12027714543572e-20 2.24055429087144e-20 1 29 1.64756908776565e-21 3.29513817553130e-21 1 30 2.04304986214598e-22 4.08609972429196e-22 1 31 2.81360799279083e-23 5.62721598558166e-23 1 32 6.50254789892538e-23 1.30050957978508e-22 1 33 6.58390652917612e-24 1.31678130583522e-23 1 34 8.53181591963037e-25 1.70636318392607e-24 1 35 9.40036337795445e-26 1.88007267559089e-25 1 36 9.3725139525155e-27 1.8745027905031e-26 1 37 1.59692236202637e-27 3.19384472405275e-27 1 38 1.08731425365316e-27 2.17462850730631e-27 1 39 3.35483432306142e-25 6.70966864612283e-25 1 40 1.78420348179167e-25 3.56840696358333e-25 1 41 1.22682147172868e-25 2.45364294345736e-25 1 42 1.69280011668667e-25 3.38560023337333e-25 1 43 3.3586792612263e-25 6.7173585224526e-25 1 44 4.78103628253307e-26 9.56207256506614e-26 1 45 2.16092550494429e-26 4.32185100988857e-26 1 46 3.26562760759716e-27 6.53125521519432e-27 1 47 5.16339847283092e-26 1.03267969456618e-25 1 48 1.33311267215861e-26 2.66622534431722e-26 1 49 1.44159321712410e-26 2.88318643424820e-26 1 50 4.68057333124504e-26 9.36114666249008e-26 1 51 7.44532981811592e-25 1.48906596362318e-24 1 52 3.6852839626275e-25 7.370567925255e-25 1 53 4.37451816554986e-25 8.74903633109972e-25 1 54 1.40287411566083e-24 2.80574823132165e-24 1 55 1.68425801158026e-24 3.36851602316053e-24 1 56 2.6198167811415e-24 5.239633562283e-24 1 57 5.49724690675642e-24 1.09944938135128e-23 1 58 6.6651580483602e-23 1.33303160967204e-22 1 59 2.63835896471265e-21 5.27671792942529e-21 1 60 1.29529755685769e-18 2.59059511371538e-18 1 61 1.00621868174587e-16 2.01243736349174e-16 1 62 3.25494767882872e-14 6.50989535765743e-14 0.999999999999967 63 7.38021355238883e-09 1.47604271047777e-08 0.999999992619786 64 8.43026651245552e-05 0.000168605330249110 0.999915697334875 65 0.0981042865769474 0.196208573153895 0.901895713423053 66 0.344749811638908 0.689499623277817 0.655250188361092 67 0.621166043940163 0.757667912119673 0.378833956059837 68 0.868656740454813 0.262686519090374 0.131343259545187 69 0.902376951566535 0.195246096866930 0.0976230484334652 70 0.940671351510441 0.118657296979117 0.0593286484895586 71 0.969341724440544 0.0613165511189112 0.0306582755594556 72 0.999356460705918 0.00128707858816329 0.000643539294081643 73 0.999926231784928 0.000147536430143438 7.37682150717189e-05 74 0.999997985225683 4.02954863346795e-06 2.01477431673398e-06 75 0.999995314234204 9.37153159121791e-06 4.68576579560896e-06 76 0.999989757595779 2.04848084427841e-05 1.02424042213920e-05 77 0.999995127068816 9.74586236878838e-06 4.87293118439419e-06 78 0.999998827678088 2.34464382442220e-06 1.17232191221110e-06 79 0.999999447520482 1.10495903563028e-06 5.5247951781514e-07 80 0.999998669724075 2.66055184990029e-06 1.33027592495014e-06 81 0.999995985644298 8.02871140425528e-06 4.01435570212764e-06 82 0.999994701964944 1.05960701120903e-05 5.29803505604516e-06 83 0.999998100631626 3.79873674718766e-06 1.89936837359383e-06 84 0.999998812105567 2.37578886555616e-06 1.18789443277808e-06 85 0.99999790917275 4.18165450091320e-06 2.09082725045660e-06 86 0.99999600997608 7.9800478382341e-06 3.99002391911705e-06 87 0.99998959633328 2.08073334406710e-05 1.04036667203355e-05 88 0.9999620439512 7.59120976014086e-05 3.79560488007043e-05 89 0.999854172632031 0.00029165473593799 0.000145827367968995 90 0.999456939692716 0.00108612061456733 0.000543060307283663 91 0.998385547848512 0.00322890430297575 0.00161445215148788 92 0.995131024303805 0.0097379513923898 0.0048689756961949 93 0.993422888977734 0.0131542220445313 0.00657711102226566 94 0.999350700572076 0.00129859885584776 0.000649299427923881 95 0.996602399680105 0.00679520063978986 0.00339760031989493 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 82 0.911111111111111 NOK 5% type I error level 83 0.922222222222222 NOK 10% type I error level 84 0.933333333333333 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/10fc091292693115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/10fc091292693115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/1ik2i1292693115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/1ik2i1292693115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/2ik2i1292693115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/2ik2i1292693115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/3ik2i1292693115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/3ik2i1292693115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/4tt1l1292693115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/4tt1l1292693115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/5tt1l1292693115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/5tt1l1292693115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/6tt1l1292693115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/6tt1l1292693115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/7ml161292693115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/7ml161292693115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/8ml161292693115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/8ml161292693115.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/9fc091292693115.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t1292693086vdea7gmpvrycyp9/9fc091292693115.ps (open in new window)

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No 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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