R version 2.7.0 (2008-04-22)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(98,97,96,96,96,96,96,96,96,97,97,98,99,101,102,103,104,105,106,107,106,105,105,105,105,105,105,105,105,106,106,106,105,105,107,108,110,111,111,112,112,112,112,113,115,117,118,118,118,118,118,118,119,121,121,122,123,123,124,124)
> #'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(qnorm(ppoints(x), mean=0, sd=1),x1)
+ if (mx < c[i])
+ {
+ mx <- c[i]
+ mxli <- l[i]
+ }
+ }
> c
  [1] 0.9551761 0.9552009 0.9552255 0.9552501 0.9552745 0.9552988 0.9553229
  [8] 0.9553470 0.9553709 0.9553947 0.9554183 0.9554419 0.9554653 0.9554886
 [15] 0.9555118 0.9555348 0.9555577 0.9555805 0.9556032 0.9556258 0.9556482
 [22] 0.9556705 0.9556926 0.9557147 0.9557366 0.9557584 0.9557801 0.9558016
 [29] 0.9558231 0.9558444 0.9558655 0.9558866 0.9559075 0.9559283 0.9559490
 [36] 0.9559695 0.9559900 0.9560103 0.9560304 0.9560505 0.9560704 0.9560902
 [43] 0.9561099 0.9561294 0.9561489 0.9561682 0.9561873 0.9562064 0.9562253
 [50] 0.9562441 0.9562628 0.9562813 0.9562997 0.9563180 0.9563362 0.9563542
 [57] 0.9563722 0.9563900 0.9564076 0.9564252 0.9564426 0.9564599 0.9564770
 [64] 0.9564941 0.9565110 0.9565278 0.9565444 0.9565610 0.9565774 0.9565937
 [71] 0.9566098 0.9566258 0.9566418 0.9566575 0.9566732 0.9566887 0.9567041
 [78] 0.9567194 0.9567345 0.9567496 0.9567645 0.9567792 0.9567939 0.9568084
 [85] 0.9568228 0.9568371 0.9568512 0.9568652 0.9568791 0.9568929 0.9569065
 [92] 0.9569200 0.9569334 0.9569467 0.9569598 0.9569728 0.9569857 0.9569984
 [99] 0.9570111 0.9570236 0.9570359 0.9570482 0.9570603 0.9570723 0.9570842
[106] 0.9570959 0.9571075 0.9571190 0.9571304 0.9571416 0.9571527 0.9571637
[113] 0.9571746 0.9571853 0.9571959 0.9572064 0.9572168 0.9572270 0.9572371
[120] 0.9572471 0.9572569 0.9572667 0.9572762 0.9572857 0.9572951 0.9573043
[127] 0.9573134 0.9573223 0.9573312 0.9573399 0.9573485 0.9573569 0.9573653
[134] 0.9573735 0.9573816 0.9573895 0.9573973 0.9574050 0.9574126 0.9574201
[141] 0.9574274 0.9574346 0.9574417 0.9574486 0.9574554 0.9574621 0.9574687
[148] 0.9574751 0.9574814 0.9574876 0.9574937 0.9574996 0.9575054 0.9575111
[155] 0.9575167 0.9575221 0.9575274 0.9575326 0.9575376 0.9575426 0.9575474
[162] 0.9575520 0.9575566 0.9575610 0.9575653 0.9575695 0.9575735 0.9575774
[169] 0.9575812 0.9575849 0.9575884 0.9575919 0.9575951 0.9575983 0.9576013
[176] 0.9576043 0.9576070 0.9576097 0.9576122 0.9576146 0.9576169 0.9576191
[183] 0.9576211 0.9576230 0.9576248 0.9576264 0.9576280 0.9576294 0.9576306
[190] 0.9576318 0.9576328 0.9576337 0.9576345 0.9576351 0.9576357 0.9576361
[197] 0.9576363 0.9576365 0.9576365 0.9576364 0.9576362 0.9576358 0.9576353
[204] 0.9576347 0.9576340 0.9576331 0.9576322 0.9576311 0.9576298 0.9576285
[211] 0.9576270 0.9576254 0.9576237 0.9576218 0.9576198 0.9576177 0.9576155
[218] 0.9576131 0.9576106 0.9576080 0.9576053 0.9576024 0.9575995 0.9575964
[225] 0.9575931 0.9575898 0.9575863 0.9575827 0.9575790 0.9575751 0.9575711
[232] 0.9575670 0.9575628 0.9575585 0.9575540 0.9575494 0.9575447 0.9575398
[239] 0.9575349 0.9575298 0.9575245 0.9575192 0.9575137 0.9575081 0.9575024
[246] 0.9574966 0.9574906 0.9574845 0.9574783 0.9574720 0.9574655 0.9574589
[253] 0.9574522 0.9574454 0.9574385 0.9574314 0.9574242 0.9574169 0.9574094
[260] 0.9574018 0.9573942 0.9573863 0.9573784 0.9573703 0.9573621 0.9573538
[267] 0.9573454 0.9573369 0.9573282 0.9573194 0.9573104 0.9573014 0.9572922
[274] 0.9572829 0.9572735 0.9572640 0.9572543 0.9572445 0.9572346 0.9572246
[281] 0.9572145 0.9572042 0.9571938 0.9571833 0.9571726 0.9571619 0.9571510
[288] 0.9571400 0.9571289 0.9571176 0.9571062 0.9570948 0.9570831 0.9570714
[295] 0.9570595 0.9570476 0.9570355 0.9570232 0.9570109 0.9569984 0.9569858
[302] 0.9569731 0.9569603 0.9569474 0.9569343 0.9569211 0.9569078 0.9568943
[309] 0.9568808 0.9568671 0.9568533 0.9568394 0.9568254 0.9568112 0.9567969
[316] 0.9567825 0.9567680 0.9567534 0.9567386 0.9567237 0.9567087 0.9566936
[323] 0.9566784 0.9566630 0.9566475 0.9566319 0.9566162 0.9566003 0.9565844
[330] 0.9565683 0.9565521 0.9565358 0.9565193 0.9565028 0.9564861 0.9564693
[337] 0.9564524 0.9564354 0.9564182 0.9564009 0.9563835 0.9563660 0.9563484
[344] 0.9563306 0.9563128 0.9562948 0.9562767 0.9562585 0.9562401 0.9562217
[351] 0.9562031 0.9561844 0.9561656 0.9561466 0.9561276 0.9561084 0.9560891
[358] 0.9560697 0.9560502 0.9560306 0.9560108 0.9559909 0.9559709 0.9559508
[365] 0.9559306 0.9559102 0.9558898 0.9558692 0.9558485 0.9558277 0.9558068
[372] 0.9557857 0.9557646 0.9557433 0.9557219 0.9557004 0.9556787 0.9556570
[379] 0.9556351 0.9556131 0.9555911 0.9555688 0.9555465 0.9555241 0.9555015
[386] 0.9554788 0.9554561 0.9554331 0.9554101 0.9553870 0.9553637 0.9553404
[393] 0.9553169 0.9552933 0.9552696 0.9552458 0.9552218 0.9551978 0.9551736
[400] 0.9551493 0.9551249
> mx
[1] 0.9576365
> mxli
[1] -0.02
> if (mxli != 0)
+ {
+ x1 <- (x^mxli - 1) / mxli
+ } else {
+ x1 <- log(x)
+ }
> postscript(file="/var/www/html/rcomp/tmp/15ojd1226320138.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> plot(l,c,main='Box-Cox Normality Plot',xlab='Lambda',ylab='correlation')
> mtext(paste('Optimal Lambda =',mxli))
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/2aq8f1226320138.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> hist(x,main='Histogram of Original Data',xlab='X',ylab='frequency')
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/39y821226320138.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> hist(x1,main='Histogram of Transformed Data',xlab='X',ylab='frequency')
> grid()
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/44k491226320138.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> qqnorm(x)
> qqline(x)
> grid()
> mtext('Original Data')
> dev.off()
null device 
          1 
> postscript(file="/var/www/html/rcomp/tmp/5crdt1226320138.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556) 
> qqnorm(x1)
> qqline(x1)
> grid()
> mtext('Transformed Data')
> 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 Normality 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',header=TRUE)
> a<-table.element(a,mxli)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/6729t1226320138.tab") 
> 
> system("convert tmp/15ojd1226320138.ps tmp/15ojd1226320138.png")
> system("convert tmp/2aq8f1226320138.ps tmp/2aq8f1226320138.png")
> system("convert tmp/39y821226320138.ps tmp/39y821226320138.png")
> system("convert tmp/44k491226320138.ps tmp/44k491226320138.png")
> system("convert tmp/5crdt1226320138.ps tmp/5crdt1226320138.png")
> 
> 
> proc.time()
   user  system elapsed 
  2.371   1.303   2.571