R version 2.7.2 (2008-08-25)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> y <- c(10165,269,708,1362,2271,3516,4775,6334,6150,7794,8851,9721,9676,402,1046,1743,2711,3817,4128,5505,4921,6091,7263,8035,7828,296,500,1134,2061,2737,2959,4113,3494,4518,5470,5664,4717,606,615,1062,983,340,467,1580,804,1709,2335,2832,2582)
> x <- c(10165,269,708,1362,2271,3516,4775,6334,6150,7794,8851,9721,9676,402,1046,1743,2711,3817,4128,5505,4921,6091,7263,8035,7828,296,500,1134,2061,2737,2959,4113,3494,4518,5470,5664,4717,606,615,1062,983,340,467,1580,804,1709,2335,2832,2582)
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> n <- length(x)
> c <- array(NA,dim=c(401))
> l <- array(NA,dim=c(401))
> mx <- 0
> mxli <- -999
> for (i in 1:401)
+ {
+ l[i] <- (i-201)/100
+ if (l[i] != 0)
+ {
+ x1 <- (x^l[i] - 1) / l[i]
+ } else {
+ x1 <- log(x)
+ }
+ c[i] <- cor(x1,y)
+ if (mx < abs(c[i]))
+ {
+ mx <- abs(c[i])
+ mxli <- l[i]
+ }
+ }
> c
  [1] 0.4952795 0.4965698 0.4978687 0.4991761 0.5004922 0.5018170 0.5031506
  [8] 0.5044930 0.5058443 0.5072045 0.5085737 0.5099520 0.5113394 0.5127360
 [15] 0.5141417 0.5155568 0.5169812 0.5184150 0.5198582 0.5213109 0.5227732
 [22] 0.5242451 0.5257267 0.5272179 0.5287190 0.5302298 0.5317505 0.5332812
 [29] 0.5348218 0.5363725 0.5379332 0.5395041 0.5410851 0.5426763 0.5442778
 [36] 0.5458896 0.5475118 0.5491443 0.5507873 0.5524408 0.5541048 0.5557793
 [43] 0.5574644 0.5591602 0.5608667 0.5625838 0.5643117 0.5660504 0.5677999
 [50] 0.5695602 0.5713314 0.5731134 0.5749064 0.5767103 0.5785251 0.5803509
 [57] 0.5821877 0.5840355 0.5858943 0.5877642 0.5896451 0.5915370 0.5934400
 [64] 0.5953541 0.5972792 0.5992154 0.6011626 0.6031208 0.6050901 0.6070704
 [71] 0.6090618 0.6110641 0.6130774 0.6151016 0.6171368 0.6191828 0.6212397
 [78] 0.6233075 0.6253860 0.6274753 0.6295753 0.6316860 0.6338073 0.6359391
 [85] 0.6380814 0.6402341 0.6423973 0.6445707 0.6467543 0.6489481 0.6511520
 [92] 0.6533658 0.6555895 0.6578231 0.6600663 0.6623192 0.6645815 0.6668533
 [99] 0.6691343 0.6714245 0.6737237 0.6760318 0.6783487 0.6806743 0.6830083
[106] 0.6853507 0.6877013 0.6900600 0.6924265 0.6948008 0.6971827 0.6995719
[113] 0.7019683 0.7043718 0.7067821 0.7091991 0.7116225 0.7140522 0.7164879
[120] 0.7189294 0.7213765 0.7238291 0.7262868 0.7287494 0.7312168 0.7336886
[127] 0.7361647 0.7386447 0.7411284 0.7436156 0.7461060 0.7485993 0.7510953
[134] 0.7535937 0.7560942 0.7585965 0.7611004 0.7636055 0.7661116 0.7686183
[141] 0.7711254 0.7736326 0.7761394 0.7786458 0.7811512 0.7836555 0.7861582
[148] 0.7886591 0.7911578 0.7936540 0.7961475 0.7986377 0.8011245 0.8036074
[155] 0.8060862 0.8085604 0.8110298 0.8134941 0.8159528 0.8184056 0.8208522
[162] 0.8232922 0.8257254 0.8281512 0.8305695 0.8329799 0.8353819 0.8377753
[169] 0.8401598 0.8425349 0.8449004 0.8472559 0.8496010 0.8519355 0.8542590
[176] 0.8565711 0.8588716 0.8611601 0.8634363 0.8656999 0.8679505 0.8701878
[183] 0.8724116 0.8746215 0.8768172 0.8789984 0.8811648 0.8833162 0.8854522
[190] 0.8875726 0.8896770 0.8917653 0.8938371 0.8958922 0.8979304 0.8999513
[197] 0.9019548 0.9039406 0.9059084 0.9078581 0.9097894 0.9117021 0.9135960
[204] 0.9154709 0.9173265 0.9191629 0.9209796 0.9227766 0.9245536 0.9263106
[211] 0.9280474 0.9297638 0.9314596 0.9331348 0.9347892 0.9364228 0.9380352
[218] 0.9396266 0.9411968 0.9427456 0.9442730 0.9457789 0.9472633 0.9487261
[225] 0.9501672 0.9515865 0.9529841 0.9543599 0.9557138 0.9570458 0.9583560
[232] 0.9596443 0.9609106 0.9621550 0.9633776 0.9645782 0.9657570 0.9669140
[239] 0.9680491 0.9691624 0.9702540 0.9713239 0.9723722 0.9733989 0.9744041
[246] 0.9753878 0.9763501 0.9772912 0.9782110 0.9791097 0.9799874 0.9808441
[253] 0.9816800 0.9824951 0.9832896 0.9840636 0.9848172 0.9855505 0.9862637
[260] 0.9869568 0.9876300 0.9882834 0.9889172 0.9895315 0.9901265 0.9907022
[267] 0.9912589 0.9917966 0.9923156 0.9928160 0.9932979 0.9937615 0.9942070
[274] 0.9946345 0.9950442 0.9954362 0.9958108 0.9961680 0.9965080 0.9968311
[281] 0.9971374 0.9974271 0.9977002 0.9979571 0.9981978 0.9984227 0.9986317
[288] 0.9988251 0.9990031 0.9991659 0.9993135 0.9994463 0.9995644 0.9996679
[295] 0.9997571 0.9998320 0.9998930 0.9999401 0.9999735 0.9999934 1.0000000
[302] 0.9999935 0.9999739 0.9999416 0.9998966 0.9998392 0.9997695 0.9996877
[309] 0.9995939 0.9994883 0.9993711 0.9992424 0.9991024 0.9989514 0.9987893
[316] 0.9986164 0.9984329 0.9982389 0.9980346 0.9978201 0.9975956 0.9973612
[323] 0.9971171 0.9968634 0.9966003 0.9963280 0.9960466 0.9957561 0.9954569
[330] 0.9951490 0.9948325 0.9945077 0.9941746 0.9938333 0.9934841 0.9931271
[337] 0.9927623 0.9923899 0.9920101 0.9916230 0.9912287 0.9908273 0.9904189
[344] 0.9900038 0.9895819 0.9891535 0.9887186 0.9882774 0.9878300 0.9873765
[351] 0.9869169 0.9864515 0.9859804 0.9855035 0.9850212 0.9845334 0.9840402
[358] 0.9835418 0.9830383 0.9825298 0.9820163 0.9814980 0.9809750 0.9804474
[365] 0.9799152 0.9793786 0.9788376 0.9782923 0.9777429 0.9771894 0.9766320
[372] 0.9760706 0.9755054 0.9749364 0.9743638 0.9737876 0.9732080 0.9726249
[379] 0.9720385 0.9714488 0.9708560 0.9702600 0.9696610 0.9690591 0.9684543
[386] 0.9678466 0.9672362 0.9666231 0.9660074 0.9653892 0.9647685 0.9641454
[393] 0.9635199 0.9628921 0.9622621 0.9616299 0.9609957 0.9603594 0.9597210
[400] 0.9590808 0.9584387
> mx
[1] 1
> mxli
[1] 1
> if (mxli != 0)
+ {
+ x1 <- (x^mxli - 1) / mxli
+ } else {
+ x1 <- log(x)
+ }
> r<-lm(y~x)
> se <- sqrt(var(r$residuals))
> r1 <- lm(y~x1)
> se1 <- sqrt(var(r1$residuals))
> postscript(file="/var/www/html/rcomp/tmp/1trmd1226616071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(l,c,main='Box-Cox Linearity Plot',xlab='Lambda',ylab='correlation')
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/2qqwj1226616071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(x,y,main='Linear Fit of Original Data',xlab='x',ylab='y')
> abline(r)
> grid()
> mtext(paste('Residual Standard Deviation = ',se))
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/3ioek1226616071.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(x1,y,main='Linear Fit of Transformed Data',xlab='x',ylab='y')
> abline(r1)
> grid()
> mtext(paste('Residual Standard Deviation = ',se1))
> dev.off()
null device 
          1 
> 
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
> 
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,'Box-Cox Linearity Plot',2,TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'# observations x',header=TRUE)
> a<-table.element(a,n)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'maximum correlation',header=TRUE)
> a<-table.element(a,mx)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'optimal lambda(x)',header=TRUE)
> a<-table.element(a,mxli)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Residual SD (orginial)',header=TRUE)
> a<-table.element(a,se)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Residual SD (transformed)',header=TRUE)
> a<-table.element(a,se1)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/4zuzz1226616071.tab") 
> 
> system("convert tmp/1trmd1226616071.ps tmp/1trmd1226616071.png")
> system("convert tmp/2qqwj1226616071.ps tmp/2qqwj1226616071.png")
> system("convert tmp/3ioek1226616071.ps tmp/3ioek1226616071.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.185   0.572   3.049