*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 15:03:03 +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/t12926849268ps5qod9f32h70x.htm/, Retrieved Sat, 18 Dec 2010 16:08:48 +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/t12926849268ps5qod9f32h70x.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 «

13.193 0.651 3.063 5.951 15.234 0.736 3.547 6.789 14.718 0.878 3.240 6.302 16.961 0.916 3.708 6.961 13.945 0.724 3.337 6.162 15.876 0.841 4.104 7.534 16.226 1.028 4.846 7.462 18.316 0.994 4.590 8.894 16.748 0.855 3.917 7.734 17.904 0.889 4.376 8.968 17.209 1.117 4.312 8.383 18.950 1.132 4.941 9.790 17.225 0.899 4.659 9.656 18.710 0.944 5.227 10.440 17.236 1.167 4.933 9.820 18.687 1.089 5.381 10.947 17.580 0.970 5.472 10.439 19.568 1.151 6.405 12.289 17.381 1.246 5.622 11.303 19.580 1.583 6.229 12.240 17.260 1.120 5.671 11.392 18.661 1.063 5.606 11.120 15.658 1.015 4.516 9.597 18.674 1.175 5.483 10.692 15.908 0.882 4.985 9.217 17.475 0.911 5.332 9.371 17.725 1.076 5.377 9.526 19.562 1.147 5.948 10.837 16.368 0.946 5.308 9.749 19.555 1.032 6.721 9.939 17.743 1.090 5.840 9.309 19.867 1.131 6.152 10.316 15.703 0.870 5.184 8.546 19.324 1.113 6.610 9.885 18.162 1.172 6.417 9.266 19.074 1.147 6.529 9.978 15.323 0.891 5.412 8.685 19.704 1.036 6.807 10.066 18.375 1.204 6.817 9.668 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 7 seconds R Server 'RServer@AstonUniversity' @ vre.aston.ac.uk Multiple Linear Regression - Estimated Regression Equation huis[t] = + 7.45966125213115 -0.533844764417756villa[t] + 0.826992844213572app[t] + 0.668882973188407grond[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) 7.45966125213115 0.771344 9.671 0 0 villa -0.533844764417756 0.163834 -3.2584 0.001536 0.000768 app 0.826992844213572 0.09477 8.7263 0 0 grond 0.668882973188407 0.063262 10.5732 0 0 Multiple Linear Regression - Regression Statistics Multiple R 0.810470701148701 R-squared 0.656862757420467 Adjusted R-squared 0.646464659160481 F-TEST (value) 63.171432025048 F-TEST (DF numerator) 3 F-TEST (DF denominator) 99 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 0.94576717537569 Sum Squared Residuals 88.5530794517931 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 13.193 13.6257299657656 -0.432729965765566 2 15.234 14.5411416289213 0.692858371078686 3 14.718 13.8857028612577 0.83229713874233 4 16.961 14.6932432906329 2.26775670936709 5 13.945 13.9544896446203 -0.00948964462034433 6 15.876 15.4440407579098 0.431959242090229 7 16.226 15.9096809033006 0.316319096699443 8 18.316 16.6739618747779 1.64203812522212 9 16.748 15.4156958639777 1.33230413602234 10 17.904 16.602536446396 1.30146355360401 11 17.209 16.0365957587639 1.17240424123615 12 18.95 17.489884929584 1.46011507041599 13 17.225 17.2914284592179 -0.0664284592178717 14 18.71 18.2615416313121 0.448458368687907 15 17.236 17.4846509092713 -0.248650909271331 16 18.687 18.6506147058869 0.0363852941130694 17 17.58 18.4496060312964 -0.869606031296371 18 19.568 20.3619979529866 -0.79399795298657 19 17.381 19.0042286917839 -1.62322869178389 20 19.58 19.9530510084903 -0.373051008490281 21 17.26 19.1715463660808 -1.91154636608076 22 18.661 18.9662848140714 -0.30528481407144 23 15.658 17.0717783944048 -1.41377839440476 24 18.674 18.5184921680937 0.155507831906254 25 15.908 17.2764638621969 -1.36846386219689 26 17.475 17.6509568588419 -0.175956858841897 27 17.725 17.7037640115468 0.0212359884532193 28 19.562 19.0149795251691 0.547020474830928 29 16.368 17.8652622276914 -1.49726222769137 30 19.555 19.114980231731 0.440019768268983 31 17.743 17.9340402665339 -0.191040266533934 32 19.867 18.8437395525882 1.02326044741183 33 15.703 16.998621100359 -1.29562110035898 34 19.324 18.9438229195533 0.380177080446703 35 18.162 18.3386778991158 -0.176677899115808 36 19.074 18.9208918936883 0.153108106311685 37 15.323 17.2689394620601 -1.94593946206009 38 19.704 19.2689143748706 0.435085625129359 39 18.375 18.9212829595616 -0.546282959561607 40 18.352 18.7355809159117 -0.383580915911692 41 13.927 16.4789062142223 -2.55190621422226 42 17.795 17.1840681473895 0.610931852610513 43 16.761 16.5961393096251 0.16486069037486 44 18.902 17.6850461041725 1.2169538958275 45 16.239 17.2721467115394 -1.03314671153936 46 19.158 18.909594170154 0.248405829845987 47 18.279 17.7169485818671 0.562051418132896 48 15.698 16.7566832984825 -1.05868329848249 49 16.239 17.2721467115394 -1.03314671153936 50 18.431 18.2410266205724 0.189973379427608 51 18.414 17.802040486515 0.611959513485017 52 19.801 19.2439907174873 0.557009282512736 53 14.995 16.702757735928 -1.70775773592795 54 18.706 17.8516589161876 0.854341083812365 55 18.232 17.2920613586115 0.939938641388543 56 19.409 18.2233948262368 1.18560517376317 57 16.263 16.9722744234301 -0.709274423430069 58 19.017 18.2874268869566 0.729573113043416 59 20.298 18.9691562976812 1.32884370231881 60 19.891 18.8371785003657 1.05382149963428 61 15.203 16.2689718620543 -1.06597186205429 62 17.845 17.4863710088744 0.358628991125568 63 17.502 17.2314724807984 0.270527519201555 64 18.532 17.6188987575171 0.913101242482854 65 15.737 17.0043727190716 -1.26737271907164 66 17.77 17.5286596759928 0.241340324007248 67 17.224 17.0502624230857 0.173737576914326 68 17.601 16.771713184238 0.829286815762012 69 14.94 15.9560564909747 -1.0160564909747 70 18.507 17.5142241987886 0.992775801211414 71 17.635 16.8632992908959 0.771700709104063 72 19.392 17.3547216294277 2.03727837057227 73 15.699 16.21586446236 -0.516864462359963 74 17.661 17.1312074294671 0.529792570532931 75 18.243 17.2359254955674 1.00707450443255 76 19.643 18.3688282765119 1.27417172348812 77 15.77 16.9650291893289 -1.19502918932895 78 17.344 17.6068093404419 -0.262809340441922 79 17.229 17.4683389525122 -0.239338952512231 80 17.322 17.9583112500517 -0.636311250051686 81 16.152 15.5774735432029 0.574526456797066 82 17.919 16.9146838321008 1.00431616789922 83 16.918 15.9272983539468 0.99070164605317 84 18.114 17.7727838293958 0.34121617060419 85 16.308 16.9766418910719 -0.668641891071944 86 17.759 17.3882657836246 0.370734216375354 87 16.021 16.3385010321415 -0.317501032141531 88 17.952 17.6074327075461 0.344567292453876 89 15.954 16.8371941408161 -0.883194140816053 90 17.762 17.9194924235511 -0.157492423551087 91 16.61 16.7726300786081 -0.162630078608061 92 17.751 17.6806186712588 0.0703813287411989 93 15.458 16.6872729416993 -1.22927294169931 94 18.106 18.3899238390765 -0.283923839076458 95 15.99 16.314014000869 -0.324014000869013 96 15.349 16.1052292397915 -0.756229239791487 97 13.185 14.7712719053561 -1.58627190535615 98 15.409 15.7595405299778 -0.350540529977769 99 16.007 15.7318134555146 0.275186544485449 100 16.633 16.9440342299498 -0.311034229949756 101 14.8 15.9324060355567 -1.13240603555671 102 15.974 16.5847259933666 -0.6107259933666 103 15.693 15.7119242844302 -0.0189242844302001 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 7 0.294811574784571 0.589623149569142 0.705188425215429 8 0.236672103018581 0.473344206037161 0.763327896981419 9 0.146164145686274 0.292328291372548 0.853835854313726 10 0.0953533833898682 0.190706766779736 0.904646616610132 11 0.217622495964786 0.435244991929573 0.782377504035214 12 0.203179853186638 0.406359706373275 0.796820146813362 13 0.283379370634794 0.566758741269587 0.716620629365206 14 0.209237829067821 0.418475658135642 0.790762170932179 15 0.506099079239296 0.987801841521407 0.493900920760704 16 0.466594640157871 0.933189280315742 0.533405359842129 17 0.458092074362353 0.916184148724707 0.541907925637647 18 0.417778244525167 0.835556489050333 0.582221755474833 19 0.763776367738894 0.472447264522212 0.236223632261106 20 0.736493123874476 0.527013752251048 0.263506876125524 21 0.875443184203033 0.249113631593935 0.124556815796967 22 0.839708207782774 0.320583584434451 0.160291792217226 23 0.903731158304879 0.192537683390242 0.0962688416951211 24 0.874347925512265 0.251304148975471 0.125652074487735 25 0.892756482856475 0.21448703428705 0.107243517143525 26 0.859777616051425 0.280444767897149 0.140222383948575 27 0.820229900833983 0.359540198332034 0.179770099166017 28 0.813415846129456 0.373168307741089 0.186584153870544 29 0.852739328273642 0.294521343452716 0.147260671726358 30 0.834830989771669 0.330338020456662 0.165169010228331 31 0.800473899205872 0.399052201588256 0.199526100794128 32 0.80953450794381 0.380930984112382 0.190465492056191 33 0.844779523583939 0.310440952832123 0.155220476416061 34 0.808289748145565 0.383420503708871 0.191710251854435 35 0.781232374212432 0.437535251575136 0.218767625787568 36 0.736405871927488 0.527188256145025 0.263594128072512 37 0.86738928984142 0.265221420317161 0.132610710158581 38 0.85445245425736 0.291095091485281 0.14554754574264 39 0.862633738140137 0.274732523719726 0.137366261859863 40 0.852424763632465 0.29515047273507 0.147575236367535 41 0.9724507063866 0.0550985872267987 0.0275492936133993 42 0.967146777458124 0.0657064450837527 0.0328532225418763 43 0.956761946194086 0.0864761076118284 0.0432380538059142 44 0.959754595225826 0.0804908095483482 0.0402454047741741 45 0.972133276409655 0.0557334471806895 0.0278667235903447 46 0.96517491091217 0.069650178175658 0.034825089087829 47 0.956954591941317 0.0860908161173667 0.0430454080586834 48 0.970317585293272 0.059364829413455 0.0296824147067275 49 0.97850097936406 0.0429980412718788 0.0214990206359394 50 0.973264335428007 0.0534713291439855 0.0267356645719928 51 0.962896711112876 0.0742065777742486 0.0371032888871243 52 0.955589206001273 0.0888215879974533 0.0444107939987267 53 0.99090301169419 0.0181939766116223 0.00909698830581116 54 0.987075743209109 0.0258485135817825 0.0129242567908913 55 0.982240714816661 0.0355185703666772 0.0177592851833386 56 0.977561077362785 0.0448778452744307 0.0224389226372153 57 0.979697497109676 0.0406050057806489 0.0203025028903244 58 0.972192080641148 0.0556158387177046 0.0278079193588523 59 0.963004687163194 0.0739906256736111 0.0369953128368056 60 0.94916994253889 0.101660114922218 0.050830057461109 61 0.97127399275297 0.0574520144940601 0.0287260072470301 62 0.960958025016493 0.078083949967015 0.0390419749835075 63 0.955885885181623 0.0882282296367535 0.0441141148183768 64 0.940385751879454 0.119228496241091 0.0596142481205457 65 0.981763658098387 0.036472683803226 0.018236341901613 66 0.975922077100592 0.0481558457988159 0.0240779228994079 67 0.968496376884753 0.0630072462304936 0.0315036231152468 68 0.958911138275274 0.0821777234494513 0.0410888617247257 69 0.972803728365267 0.0543925432694664 0.0271962716347332 70 0.966651814142785 0.0666963717144303 0.0333481858572152 71 0.959143248969123 0.0817135020617547 0.0408567510308773 72 0.993817689253359 0.0123646214932821 0.00618231074664106 73 0.991075932080846 0.0178481358383078 0.00892406791915391 74 0.99175216242191 0.0164956751561825 0.00824783757809126 75 0.998232930665185 0.00353413866962971 0.00176706933481485 76 0.999939159367434 0.000121681265131472 6.08406325657358e-05 77 0.999912725589208 0.000174548821583699 8.72744107918497e-05 78 0.99996396841461 7.20631707817129e-05 3.60315853908565e-05 79 0.999978210951188 4.35780976234401e-05 2.178904881172e-05 80 0.999998355115665 3.28976866944537e-06 1.64488433472268e-06 81 0.99999777755349 4.44489301924421e-06 2.2224465096221e-06 82 0.99999706559194 5.86881612033e-06 2.934408060165e-06 83 0.999999212477607 1.57504478631371e-06 7.87522393156856e-07 84 0.999997530374386 4.93925122770396e-06 2.46962561385198e-06 85 0.9999960425128 7.9149743994462e-06 3.9574871997231e-06 86 0.99999563185061 8.73629878156748e-06 4.36814939078374e-06 87 0.999984505504666 3.09889906676765e-05 1.54944953338382e-05 88 0.999947804246783 0.000104391506434163 5.21957532170816e-05 89 0.999840135714957 0.000319728570085108 0.000159864285042554 90 0.999578150189345 0.000843699621309804 0.000421849810654902 91 0.998759138270776 0.00248172345844884 0.00124086172922442 92 0.996488961194979 0.00702207761004221 0.0035110388050211 93 0.99067919745978 0.0186416050804391 0.00932080254021955 94 0.992358110295081 0.0152837794098374 0.00764188970491869 95 0.977628446651872 0.0447431066962551 0.0223715533481276 96 0.929007889952943 0.141984220094114 0.070992110047057 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 18 0.2 NOK 5% type I error level 32 0.355555555555556 NOK 10% type I error level 53 0.588888888888889 NOK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/10hiv21292684573.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/10hiv21292684573.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/1tzg81292684573.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/1tzg81292684573.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/2tzg81292684573.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/2tzg81292684573.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/3lqfb1292684573.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/3lqfb1292684573.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/4lqfb1292684573.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/4lqfb1292684573.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/5lqfb1292684573.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/5lqfb1292684573.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/6ezee1292684573.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/6ezee1292684573.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/779eh1292684573.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/779eh1292684573.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/879eh1292684573.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/879eh1292684573.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/979eh1292684573.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/18/t12926849268ps5qod9f32h70x/979eh1292684573.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') }

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