Multiple Regression Paper

*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: Fri, 17 Dec 2010 13:46:43 +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/17/t1292593565r4n81vc9fe4m8pv.htm/, Retrieved Fri, 17 Dec 2010 14:46:08 +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/17/t1292593565r4n81vc9fe4m8pv.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 «

15 10 12 16 6 12 9 7 12 6 9 12 11 11 4 10 12 11 12 6 13 9 14 14 6 16 11 16 16 7 14 12 13 13 6 16 11 13 14 7 10 12 5 13 6 8 12 8 13 4 12 11 14 13 5 15 11 15 15 8 14 12 8 14 4 14 6 13 12 6 12 13 12 12 6 12 11 11 12 5 10 12 8 11 4 4 10 4 10 2 14 11 15 15 8 15 12 12 16 7 16 12 14 14 6 12 12 9 13 4 12 11 16 13 4 12 12 10 13 4 12 12 8 13 5 12 12 14 14 4 11 6 6 9 4 11 5 16 14 6 11 12 11 12 6 11 14 7 13 6 11 12 13 11 4 11 9 7 13 2 15 11 14 15 7 15 11 17 16 6 9 11 15 15 7 16 12 8 14 4 13 10 8 8 4 9 12 11 11 4 16 11 16 15 6 12 12 10 15 6 15 9 5 11 3 5 15 8 12 3 11 11 8 12 6 17 11 15 14 5 9 15 6 8 4 13 12 16 16 6 16 9 16 16 6 16 12 16 14 6 14 9 19 12 6 16 11 14 15 6 11 12 15 12 6 11 11 11 14 5 11 6 14 17 6 12 10 12 13 6 12 12 15 13 6 12 13 14 12 5 14 11 13 16 6 10 10 11 12 5 9 11 8 10 4 12 7 11 15 5 10 11 9 12 4 14 11 10 16 6 8 7 4 13 6 16 12 15 15 7 14 14 17 18 6 14 11 12 12 4 12 12 12 13 4 14 11 15 14 6 7 12 13 12 3 19 12 15 15 6 15 12 14 16 4 8 12 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 KnowingPeople[t] = + 0.704773125332251 + 0.388112646909159Popularity[t] -0.072086130850391FindingFriends[t] + 0.267672517283153Liked[t] + 0.6695223593835Celebrity[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) 0.704773125332251 1.797372 0.3921 0.695528 0.347764 Popularity 0.388112646909159 0.097694 3.9727 0.00011 5.5e-05 FindingFriends -0.072086130850391 0.121313 -0.5942 0.553257 0.276628 Liked 0.267672517283153 0.125002 2.1413 0.033851 0.016926 Celebrity 0.6695223593835 0.199827 3.3505 0.001019 0.00051 Multiple Linear Regression - Regression Statistics Multiple R 0.652918056368862 R-squared 0.426301988332492 Adjusted R-squared 0.411104690010174 F-TEST (value) 28.0511693125379 F-TEST (DF numerator) 4 F-TEST (DF denominator) 151 p-value 0 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 2.65644680094332 Sum Squared Residuals 1065.56315054255 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals Interpolation Forecast Residuals Prediction Error 1 12 14.1054959532972 -2.10549595329718 2 7 11.9425540742875 -4.94255407428748 3 11 8.95524050495868 2.04475949504132 4 11 10.950070387918 0.0499296120820082 5 14 12.8660117557629 1.13398824423705 6 16 15.0910448287395 0.908955171260549 7 13 12.7701934928378 0.229806507162218 8 13 14.5556997941731 -1.55569979417314 9 5 11.2177429052011 -6.21774290520114 10 8 9.10247289261583 -1.10247289261583 11 14 11.3965319704864 2.60346802951365 12 15 15.1047820239306 -0.104782023930638 13 8 11.6988212913539 -3.69882129135394 14 13 12.935037760657 0.0649622393430255 15 12 11.6542095508859 0.345790449114081 16 11 11.1288594532032 -0.128859453203201 17 8 9.34335315186784 -1.34335315186784 18 4 5.55213229606351 -1.55213229606351 19 15 14.7166693770215 0.283330622978522 20 12 14.6308460509799 -2.6308460509799 21 14 13.8140913039393 0.185908696060746 22 9 10.6549234802525 -1.65492348025246 23 16 10.7270096111029 5.27299038889714 24 10 10.6549234802525 -0.654923480252464 25 8 11.324445839636 -3.32444583963596 26 14 10.9225959975356 3.07740400246438 27 6 9.62863754931304 -3.62863754931304 28 16 12.3781309853462 3.62186901465381 29 11 11.3381830348271 -0.33818303482715 30 7 11.4616832904095 -4.46168329040952 31 13 9.731465798777 3.268534201223 32 7 9.14402450712748 -2.14402450712748 33 14 14.4352596645471 -0.435259664547138 34 17 14.0334098224468 2.96659017755321 35 15 12.1065837830922 2.89341621690782 36 8 12.4750465851723 -4.47504658517226 37 8 9.84884580244664 -1.84884580244664 38 11 8.95524050495868 2.04475949504132 39 16 14.1538499520728 1.8461500479272 40 10 12.5293132335858 -2.52931323358577 41 5 10.8306524195813 -5.83065241958131 42 8 6.78468168267052 1.21531831732948 43 8 11.4102691656775 -3.41026916567754 44 15 13.6047677223153 1.3952322776847 45 6 7.93596456055805 -1.93596456055805 46 16 13.1850983977781 2.81490160222192 47 16 14.5656947310567 1.43430526894327 48 16 13.8140913039393 2.18590869606075 49 19 12.7187793681058 6.2812206318942 50 14 14.1538499520728 -0.153849952072798 51 15 11.3381830348271 3.66181696517285 52 11 11.2760918408603 -0.276091840860348 53 14 13.1090624063453 0.890937593654738 54 12 12.1381404607202 -0.138140460720245 55 15 11.9939681990195 3.00603180098054 56 14 10.9846871915024 3.01531280849758 57 13 13.6452971755376 -0.645297175537633 58 11 10.4247202902353 0.575279709764726 59 8 8.75965411852592 -0.759654118525917 60 11 12.2202215284542 -1.22022152845423 61 9 9.68311180000138 -0.683111800001383 62 10 13.6452971755376 -3.64529717553763 63 4 10.8019482656348 -6.80194826563478 64 15 14.7512861806059 0.248713819394093 65 17 13.9643838175528 3.03561618244723 66 12 11.235562387638 0.76443761236198 67 12 10.6549234802525 1.34507651974754 68 15 13.1099521409713 1.89004785902867 69 13 7.77716536904001 5.22283463095999 70 15 15.2461017619499 -0.246101761949886 71 14 12.6222789728294 1.3777210271706 72 8 10.0396677692825 -2.03966776928248 73 15 11.5368295472163 3.46317045278372 74 12 13.1236893361625 -1.12368933616251 75 14 10.3014276369277 3.69857236307227 76 10 10.1570477729521 -0.15704777295212 77 7 9.71772860358581 -2.71772860358581 78 16 15.7636273140793 0.236372685920685 79 12 8.30251546672273 3.69748453327727 80 15 13.6452971755376 1.35470282446237 81 7 8.88009424815192 -1.88009424815192 82 9 8.15133321848316 0.848666781516844 83 15 10.2054017717277 4.79459822827226 84 7 10.1195784456862 -3.11957844568616 85 15 12.7701934928378 2.22980650716222 86 14 12.8076628201037 1.19233717989626 87 14 13.9750608867876 0.0249391132124122 88 8 10.5551553567449 -2.55515535674487 89 8 9.83796113093699 -1.83796113093699 90 14 12.454166976779 1.54583302322099 91 10 8.90688650618307 1.09311349381694 92 12 10.2321940297589 1.76780597024112 93 15 9.34620567554937 5.65379432445063 94 12 11.5921183569191 0.407881643080883 95 13 12.0285850026039 0.971414997396109 96 12 10.2153967086113 1.78460329138868 97 10 8.37085933926553 1.62914066073447 98 8 7.77716536904001 0.222834630959986 99 6 10.1086937741765 -4.1086937741765 100 13 12.1006711334543 0.899328866545718 101 7 11.444885969262 -4.44488596926197 102 13 13.1583061397469 -0.158306139746941 103 4 4.31353783315534 -0.31353783315534 104 14 12.5608699112138 1.43913008878617 105 13 11.7125584865451 1.28744151345488 106 13 11.6127903630375 1.38720963696247 107 6 6.4518578454642 -0.451857845464202 108 7 8.44668772842351 -1.44668772842351 109 5 10.105841250495 -5.10584125049497 110 14 15.4928946708398 -1.4928946708398 111 13 11.8251740706178 1.17482592938216 112 16 15.0189586978891 0.98104130211094 113 16 13.3024784014477 2.69752159855228 114 7 7.55784685053248 -0.557846850532476 115 14 11.7600227506947 2.23997724930533 116 11 12.3820808459286 -1.38208084592862 117 17 15.0910448287395 1.90895517126055 118 5 9.74146073566059 -4.74146073566059 119 10 14.8401696326038 -4.84016963260385 120 11 8.96523544184227 2.03476455815773 121 10 8.84479531221626 1.15520468778374 122 9 9.9982485814341 -0.998248581434088 123 12 11.3481779717107 0.651822028289261 124 15 12.9518350818045 2.04816491819547 125 7 11.6058555521103 -4.6058555521103 126 13 13.3055385274041 -0.305538527404088 127 8 12.4088731039598 -4.40887310395976 128 16 11.9050847470215 4.09491525297848 129 15 12.5776672323614 2.42233276763862 130 6 11.5921183569191 -5.59211835691912 131 6 5.42931417283237 0.570685827167627 132 12 13.2303922705973 -1.23039227059733 133 8 10.6817157382836 -2.6817157382836 134 11 12.8453397496445 -1.84533974964454 135 13 13.4259786570301 -0.425978657030095 136 14 11.7362906186199 2.2637093813801 137 14 12.6497533632118 1.35024663678822 138 10 7.37293753333454 2.62706246666546 139 4 11.1456567743508 -7.14565677435075 140 16 13.4980647878805 2.50193521211951 141 12 15.4208085399894 -3.42080853998941 142 15 15.0189586978891 -0.01895869788906 143 12 13.4497107891049 -1.44971078910487 144 14 12.574607106405 1.42539289359498 145 11 9.65563740961901 1.34436259038099 146 16 12.0255248766475 3.97447512335247 147 14 12.2685755272298 1.73142447277016 148 14 13.8861774347896 0.113822565210355 149 15 13.0378660101209 1.96213398987907 150 9 8.90688650618306 0.093113493816936 151 15 13.8140913039393 1.18590869606075 152 14 15.6884810572726 -1.68848105727256 153 15 11.9939681990195 3.00603180098054 154 10 11.4923502334115 -1.49235023341152 155 14 15.6884810572726 -1.68848105727256 156 9 9.29499915309222 -0.294999153092224 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 8 0.531517602314718 0.936964795370565 0.468482397685282 9 0.813912439370541 0.372175121258917 0.186087560629459 10 0.720282442022533 0.559435115954934 0.279717557977467 11 0.657904630900664 0.684190738198672 0.342095369099336 12 0.682046198411008 0.635907603177984 0.317953801588992 13 0.839697007354928 0.320605985290144 0.160302992645072 14 0.777653251711927 0.444693496576146 0.222346748288073 15 0.708630852715897 0.582738294568205 0.291369147284103 16 0.626709851704733 0.746580296590534 0.373290148295267 17 0.551477162208686 0.897045675582628 0.448522837791314 18 0.468531778426622 0.937063556853245 0.531468221573378 19 0.403363486957646 0.806726973915293 0.596636513042354 20 0.358130058318999 0.716260116637998 0.641869941681001 21 0.28966382044265 0.579327640885301 0.71033617955735 22 0.233659622251139 0.467319244502279 0.76634037774886 23 0.499896904590234 0.999793809180468 0.500103095409766 24 0.430998638418712 0.861997276837425 0.569001361581288 25 0.439681655794539 0.879363311589077 0.560318344205461 26 0.481729327812443 0.963458655624887 0.518270672187557 27 0.498678804679855 0.99735760935971 0.501321195320146 28 0.569867922415226 0.860264155169548 0.430132077584774 29 0.515370753626273 0.969258492747453 0.484629246373727 30 0.548234400854444 0.903531198291111 0.451765599145556 31 0.624874159201382 0.750251681597237 0.375125840798618 32 0.631707968350881 0.736584063298238 0.368292031649119 33 0.574397600474495 0.85120479905101 0.425602399525505 34 0.580294796736032 0.839410406527936 0.419705203263968 35 0.586781163401302 0.826437673197396 0.413218836598698 36 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0.0302121011333485 0.0151060505666742 141 0.994031928664284 0.0119361426714318 0.00596807133571591 142 0.988255436741953 0.0234891265160943 0.0117445632580471 143 0.978153409662698 0.0436931806746039 0.021846590337302 144 0.96058750577567 0.0788249884486611 0.0394124942243306 145 0.93049243234045 0.139015135319098 0.0695075676595491 146 0.918651611480295 0.16269677703941 0.0813483885197051 147 0.929817199673893 0.140365600652214 0.070182800326107 148 0.90869868548233 0.182602629035338 0.0913013145176692 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 5 0.0354609929078014 OK 10% type I error level 9 0.0638297872340425 OK

Charts produced by software:

http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/1099mu1292593591.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/1099mu1292593591.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/1kqpi1292593591.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/1kqpi1292593591.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/2kqpi1292593591.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/2kqpi1292593591.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/3vho31292593591.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/3vho31292593591.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/4vho31292593591.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/4vho31292593591.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/5vho31292593591.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/5vho31292593591.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/6nqn61292593591.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/6nqn61292593591.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/7yznr1292593591.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/7yznr1292593591.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/8yznr1292593591.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/8yznr1292593591.ps (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/9yznr1292593591.png (open in new window) http://www.freestatistics.org/blog/date/2010/Dec/17/t1292593565r4n81vc9fe4m8pv/9yznr1292593591.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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Software written by Ed van Stee & Patrick Wessa

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