Multiple Regression (A)

*The author of this computation has been verified*

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

Title produced by software: Multiple Regression

Date of computation: Thu, 11 Dec 2008 07:26:18 -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/Dec/11/t1229006187dxr7jzyh7bw5i2j.htm/, Retrieved Thu, 11 Dec 2008 14:36:39 +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/Dec/11/t1229006187dxr7jzyh7bw5i2j.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}, }

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User-defined keywords:

Dataseries X:

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15044,5 1 14944,2 1 16754,8 1 14254 1 15454,9 1 15644,8 1 14568,3 1 12520,2 1 14803 1 15873,2 1 14755,3 1 12875,1 1 14291,1 1 14205,3 1 15859,4 1 15258,9 1 15498,6 1 15106,5 1 15023,6 1 12083 1 15761,3 1 16943 1 15070,3 1 13659,6 1 14768,9 0 14725,1 0 15998,1 0 15370,6 0 14956,9 0 15469,7 0 15101,8 0 11703,7 0 16283,6 0 16726,5 0 14968,9 0 14861 0 14583,3 0 15305,8 0 17903,9 0 16379,4 0 15420,3 0 17870,5 0 15912,8 0 13866,5 0 17823,2 0 17872 0 17420,4 0 16704,4 0 15991,2 0 16583,6 0 19123,5 0 17838,7 0 17209,4 0 18586,5 0 16258,1 0 15141,6 0 19202,1 0 17746,5 0 19090,1 0 18040,3 0 17515,5 0 17751,8 0 21072,4 0 17170 0 19439,5 0 19795,4 0 17574,9 0 16165,4 0 19464,6 0 19932,1 0 19961,2 0 17343,4 0 18924,2 0 18574,1 0 21350,6 0 18594,6 0 19823,1 0 20844,4 0 19640,2 0 17735,4 0 19813,6 0 22160 0 20664,3 0 17877,4 0 21211,2 0 21423,1 0 21688,7 0 23243,2 0 21490,2 0 22925,8 0 23184,8 0 18562,2 0

Output produced by software:

Enter (or paste) a matrix (table) containing all data (time) series. Every column represents a different variable and must be delimited by a space or Tab. Every row represents a period in time (or category) and must be delimited by hard returns. The easiest way to enter data is to copy and paste a block of spreadsheet cells. Please, do not use commas or spaces to seperate groups of digits! Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'George Udny Yule' @ 72.249.76.132 Multiple Linear Regression - Estimated Regression Equation Y[t] = + 17966.5617647059 -3122.69093137255X[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) 17966.5617647059 267.024066 67.2844 0 0 X -3122.69093137255 522.803754 -5.973 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.532799118305665 R-squared 0.283874900467294 Adjusted R-squared 0.275917954916931 F-TEST (value) 35.6763658454757 F-TEST (DF numerator) 1 F-TEST (DF denominator) 90 p-value 4.57455602287382e-08 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2201.93685818789 Sum Squared Residuals 436367333.470172 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 15044.5 14843.8708333333 200.629166666710 2 14944.2 14843.8708333333 100.329166666654 3 16754.8 14843.8708333333 1910.92916666666 4 14254 14843.8708333333 -589.870833333335 5 15454.9 14843.8708333333 611.029166666665 6 15644.8 14843.8708333333 800.929166666665 7 14568.3 14843.8708333333 -275.570833333336 8 12520.2 14843.8708333333 -2323.67083333333 9 14803 14843.8708333333 -40.8708333333348 10 15873.2 14843.8708333333 1029.32916666667 11 14755.3 14843.8708333333 -88.5708333333355 12 12875.1 14843.8708333333 -1968.77083333333 13 14291.1 14843.8708333333 -552.770833333334 14 14205.3 14843.8708333333 -638.570833333335 15 15859.4 14843.8708333333 1015.52916666666 16 15258.9 14843.8708333333 415.029166666665 17 15498.6 14843.8708333333 654.729166666666 18 15106.5 14843.8708333333 262.629166666665 19 15023.6 14843.8708333333 179.729166666666 20 12083 14843.8708333333 -2760.87083333333 21 15761.3 14843.8708333333 917.429166666665 22 16943 14843.8708333333 2099.12916666667 23 15070.3 14843.8708333333 226.429166666665 24 13659.6 14843.8708333333 -1184.27083333333 25 14768.9 17966.5617647059 -3197.66176470588 26 14725.1 17966.5617647059 -3241.46176470588 27 15998.1 17966.5617647059 -1968.46176470588 28 15370.6 17966.5617647059 -2595.96176470588 29 14956.9 17966.5617647059 -3009.66176470588 30 15469.7 17966.5617647059 -2496.86176470588 31 15101.8 17966.5617647059 -2864.76176470588 32 11703.7 17966.5617647059 -6262.86176470588 33 16283.6 17966.5617647059 -1682.96176470588 34 16726.5 17966.5617647059 -1240.06176470588 35 14968.9 17966.5617647059 -2997.66176470588 36 14861 17966.5617647059 -3105.56176470588 37 14583.3 17966.5617647059 -3383.26176470588 38 15305.8 17966.5617647059 -2660.76176470588 39 17903.9 17966.5617647059 -62.6617647058813 40 16379.4 17966.5617647059 -1587.16176470588 41 15420.3 17966.5617647059 -2546.26176470588 42 17870.5 17966.5617647059 -96.0617647058827 43 15912.8 17966.5617647059 -2053.76176470588 44 13866.5 17966.5617647059 -4100.06176470588 45 17823.2 17966.5617647059 -143.361764705882 46 17872 17966.5617647059 -94.5617647058827 47 17420.4 17966.5617647059 -546.161764705881 48 16704.4 17966.5617647059 -1262.16176470588 49 15991.2 17966.5617647059 -1975.36176470588 50 16583.6 17966.5617647059 -1382.96176470588 51 19123.5 17966.5617647059 1156.93823529412 52 17838.7 17966.5617647059 -127.861764705882 53 17209.4 17966.5617647059 -757.161764705881 54 18586.5 17966.5617647059 619.938235294117 55 16258.1 17966.5617647059 -1708.46176470588 56 15141.6 17966.5617647059 -2824.96176470588 57 19202.1 17966.5617647059 1235.53823529412 58 17746.5 17966.5617647059 -220.061764705883 59 19090.1 17966.5617647059 1123.53823529412 60 18040.3 17966.5617647059 73.7382352941165 61 17515.5 17966.5617647059 -451.061764705883 62 17751.8 17966.5617647059 -214.761764705883 63 21072.4 17966.5617647059 3105.83823529412 64 17170 17966.5617647059 -796.561764705883 65 19439.5 17966.5617647059 1472.93823529412 66 19795.4 17966.5617647059 1828.83823529412 67 17574.9 17966.5617647059 -391.661764705881 68 16165.4 17966.5617647059 -1801.16176470588 69 19464.6 17966.5617647059 1498.03823529412 70 19932.1 17966.5617647059 1965.53823529412 71 19961.2 17966.5617647059 1994.63823529412 72 17343.4 17966.5617647059 -623.161764705881 73 18924.2 17966.5617647059 957.638235294118 74 18574.1 17966.5617647059 607.538235294116 75 21350.6 17966.5617647059 3384.03823529412 76 18594.6 17966.5617647059 628.038235294116 77 19823.1 17966.5617647059 1856.53823529412 78 20844.4 17966.5617647059 2877.83823529412 79 19640.2 17966.5617647059 1673.63823529412 80 17735.4 17966.5617647059 -231.161764705881 81 19813.6 17966.5617647059 1847.03823529412 82 22160 17966.5617647059 4193.43823529412 83 20664.3 17966.5617647059 2697.73823529412 84 17877.4 17966.5617647059 -89.1617647058813 85 21211.2 17966.5617647059 3244.63823529412 86 21423.1 17966.5617647059 3456.53823529412 87 21688.7 17966.5617647059 3722.13823529412 88 23243.2 17966.5617647059 5276.63823529412 89 21490.2 17966.5617647059 3523.63823529412 90 22925.8 17966.5617647059 4959.23823529412 91 23184.8 17966.5617647059 5218.23823529412 92 18562.2 17966.5617647059 595.638235294118 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 5 0.121132625427479 0.242265250854957 0.878867374572522 6 0.0479005321542789 0.0958010643085579 0.952099467845721 7 0.0224174729942988 0.0448349459885976 0.977582527005701 8 0.0848521827544706 0.169704365508941 0.91514781724553 9 0.0422954503268738 0.0845909006537477 0.957704549673126 10 0.0256586794532652 0.0513173589065304 0.974341320546735 11 0.0119731671023316 0.0239463342046631 0.988026832897668 12 0.0175261968092396 0.0350523936184792 0.98247380319076 13 0.0090367421902134 0.0180734843804268 0.990963257809787 14 0.00459125118132575 0.0091825023626515 0.995408748818674 15 0.0030366137783477 0.0060732275566954 0.996963386221652 16 0.00145541577875453 0.00291083155750905 0.998544584221245 17 0.000736050355332496 0.00147210071066499 0.999263949644668 18 0.000318892477421381 0.000637784954842763 0.999681107522579 19 0.00013195975550557 0.00026391951101114 0.999868040244494 20 0.000710819735471258 0.00142163947094252 0.999289180264529 21 0.000431582102867742 0.000863164205735484 0.999568417897132 22 0.000674131510794607 0.00134826302158921 0.999325868489205 23 0.000330398302731585 0.000660796605463171 0.999669601697268 24 0.000223795640122431 0.000447591280244863 0.999776204359878 25 0.000128957518438023 0.000257915036876046 0.999871042481562 26 7.5537950286437e-05 0.000151075900572874 0.999924462049714 27 5.07273132767167e-05 0.000101454626553433 0.999949272686723 28 2.78002713480649e-05 5.56005426961298e-05 0.999972199728652 29 1.65121101992951e-05 3.30242203985902e-05 0.9999834878898 30 9.18558383228257e-06 1.83711676645651e-05 0.999990814416168 31 5.40853541510919e-06 1.08170708302184e-05 0.999994591464585 32 0.000187487782711361 0.000374975565422722 0.999812512217289 33 0.000176001458445073 0.000352002916890146 0.999823998541555 34 0.000186454311201559 0.000372908622403118 0.999813545688798 35 0.0001522899115111 0.0003045798230222 0.999847710088489 36 0.000135686342543124 0.000271372685086249 0.999864313657457 37 0.000146853205692379 0.000293706411384758 0.999853146794308 38 0.000130624514103181 0.000261249028206362 0.999869375485897 39 0.000335075633119926 0.000670151266239851 0.99966492436688 40 0.000298771788285328 0.000597543576570655 0.999701228211715 41 0.000287479106486455 0.00057495821297291 0.999712520893514 42 0.000496936759068901 0.000993873518137801 0.999503063240931 43 0.000455751733023767 0.000911503466047534 0.999544248266976 44 0.00158760426485067 0.00317520852970134 0.99841239573515 45 0.00230726782510060 0.00461453565020121 0.9976927321749 46 0.00304920220796257 0.00609840441592514 0.996950797792037 47 0.00322260789290372 0.00644521578580745 0.996777392107096 48 0.00314802471006628 0.00629604942013256 0.996851975289934 49 0.00363997693149866 0.00727995386299732 0.996360023068501 50 0.00387399685671491 0.00774799371342983 0.996126003143285 51 0.00776604412119025 0.0155320882423805 0.99223395587881 52 0.0081476590638272 0.0162953181276544 0.991852340936173 53 0.00812217481520875 0.0162443496304175 0.991877825184791 54 0.0097462457434222 0.0194924914868444 0.990253754256578 55 0.0122651311658833 0.0245302623317667 0.987734868834117 56 0.0313055816606616 0.0626111633213231 0.968694418339338 57 0.0418036633567337 0.0836073267134673 0.958196336643266 58 0.0435054890172191 0.0870109780344382 0.95649451098278 59 0.0503706481385521 0.100741296277104 0.949629351861448 60 0.0507898550768629 0.101579710153726 0.949210144923137 61 0.054591879468344 0.109183758936688 0.945408120531656 62 0.0579747556467113 0.115949511293423 0.942025244353289 63 0.112394032169091 0.224788064338182 0.887605967830909 64 0.132492564563030 0.264985129126061 0.86750743543697 65 0.134019555270824 0.268039110541648 0.865980444729176 66 0.137891739888216 0.275783479776431 0.862108260111784 67 0.151557268279894 0.303114536559788 0.848442731720106 68 0.287517500974609 0.575035001949219 0.71248249902539 69 0.279664015983422 0.559328031966845 0.720335984016578 70 0.273210071609545 0.54642014321909 0.726789928390455 71 0.261409080632516 0.522818161265033 0.738590919367484 72 0.343185814615532 0.686371629231063 0.656814185384468 73 0.338040550091122 0.676081100182245 0.661959449908878 74 0.355539331276477 0.711078662552954 0.644460668723523 75 0.373401951665348 0.746803903330696 0.626598048334652 76 0.392020455418196 0.784040910836392 0.607979544581804 77 0.359988467195663 0.719976934391326 0.640011532804337 78 0.328372622987464 0.656745245974928 0.671627377012536 79 0.295755862861221 0.591511725722442 0.704244137138779 80 0.445909087600886 0.891818175201772 0.554090912399114 81 0.420182565958817 0.840365131917633 0.579817434041183 82 0.403095713132083 0.806191426264166 0.596904286867917 83 0.333242215458917 0.666484430917835 0.666757784541083 84 0.602653114717295 0.79469377056541 0.397346885282705 85 0.50699868443584 0.98600263112832 0.49300131556416 86 0.39383063532197 0.78766127064394 0.60616936467803 87 0.271224737741298 0.542449475482596 0.728775262258702 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 37 0.445783132530120 NOK 5% type I error level 46 0.554216867469880 NOK 10% type I error level 52 0.626506024096386 NOK

Charts produced by software:

http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/10n1cw1229005571.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/10n1cw1229005571.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/1zpl91229005571.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/1zpl91229005571.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/2qy031229005571.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/2qy031229005571.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/39mpg1229005571.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/39mpg1229005571.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/4qrdu1229005571.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/4qrdu1229005571.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/5nir61229005571.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/5nir61229005571.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/6ss881229005571.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/6ss881229005571.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/7y0dz1229005571.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/7y0dz1229005571.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/8012n1229005571.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/8012n1229005571.ps (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/9ibig1229005571.png (open in new window) http://127.0.0.1/wessadotnet/public_html/freestatisticsdotorg/blog/date/2008/Dec/11/t1229006187dxr7jzyh7bw5i2j/9ibig1229005571.ps (open in new window)

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; 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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