R version 2.8.0 (2008-10-20)
Copyright (C) 2008 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(107.06,109.98,112.64,112.89,112.9,112.9,112.9,112.99,113.01,113,113.07,113.07,113.21,114.13,114.59,114.88,115.3,115.33,115.36,115.41,115.43,115.43,115.43,115.43,115.56,115.88,116.02,116.09,116.28,116.28,116.28,116.25,116.07,116.08,116.07,115.92,116.07,117.22,117.75,117.78,117.78,117.81,117.81,117.74,117.75,117.76,117.76,117.75,117.8,118.09,118.95,119.03,118.9,118.9,118.9,118.87,118.88,119.36,119.39,119.47)
> #'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] 0.9438654 0.9439097 0.9439540 0.9439983 0.9440425 0.9440867 0.9441308
  [8] 0.9441749 0.9442190 0.9442630 0.9443070 0.9443510 0.9443949 0.9444388
 [15] 0.9444826 0.9445264 0.9445702 0.9446140 0.9446577 0.9447013 0.9447450
 [22] 0.9447886 0.9448321 0.9448756 0.9449191 0.9449626 0.9450060 0.9450494
 [29] 0.9450927 0.9451360 0.9451793 0.9452226 0.9452658 0.9453089 0.9453521
 [36] 0.9453951 0.9454382 0.9454812 0.9455242 0.9455672 0.9456101 0.9456530
 [43] 0.9456958 0.9457386 0.9457814 0.9458241 0.9458668 0.9459095 0.9459521
 [50] 0.9459947 0.9460372 0.9460798 0.9461223 0.9461647 0.9462071 0.9462495
 [57] 0.9462918 0.9463341 0.9463764 0.9464186 0.9464608 0.9465030 0.9465451
 [64] 0.9465872 0.9466293 0.9466713 0.9467133 0.9467552 0.9467972 0.9468390
 [71] 0.9468809 0.9469227 0.9469645 0.9470062 0.9470479 0.9470896 0.9471312
 [78] 0.9471728 0.9472143 0.9472559 0.9472974 0.9473388 0.9473802 0.9474216
 [85] 0.9474629 0.9475043 0.9475455 0.9475868 0.9476280 0.9476692 0.9477103
 [92] 0.9477514 0.9477924 0.9478335 0.9478745 0.9479154 0.9479564 0.9479972
 [99] 0.9480381 0.9480789 0.9481197 0.9481604 0.9482011 0.9482418 0.9482825
[106] 0.9483231 0.9483636 0.9484042 0.9484447 0.9484851 0.9485256 0.9485660
[113] 0.9486063 0.9486467 0.9486869 0.9487272 0.9487674 0.9488076 0.9488478
[120] 0.9488879 0.9489279 0.9489680 0.9490080 0.9490480 0.9490879 0.9491278
[127] 0.9491677 0.9492075 0.9492473 0.9492871 0.9493268 0.9493665 0.9494062
[134] 0.9494458 0.9494854 0.9495250 0.9495645 0.9496040 0.9496434 0.9496828
[141] 0.9497222 0.9497616 0.9498009 0.9498402 0.9498794 0.9499186 0.9499578
[148] 0.9499969 0.9500360 0.9500751 0.9501141 0.9501531 0.9501921 0.9502310
[155] 0.9502699 0.9503088 0.9503476 0.9503864 0.9504252 0.9504639 0.9505026
[162] 0.9505413 0.9505799 0.9506185 0.9506570 0.9506955 0.9507340 0.9507725
[169] 0.9508109 0.9508493 0.9508876 0.9509259 0.9509642 0.9510024 0.9510406
[176] 0.9510788 0.9511170 0.9511551 0.9511931 0.9512312 0.9512692 0.9513071
[183] 0.9513451 0.9513830 0.9514208 0.9514587 0.9514965 0.9515342 0.9515720
[190] 0.9516097 0.9516473 0.9516849 0.9517225 0.9517601 0.9517976 0.9518351
[197] 0.9518726 0.9519100 0.9519474 0.9519847 0.9520221 0.9520593 0.9520966
[204] 0.9521338 0.9521710 0.9522081 0.9522453 0.9522823 0.9523194 0.9523564
[211] 0.9523934 0.9524303 0.9524672 0.9525041 0.9525410 0.9525778 0.9526146
[218] 0.9526513 0.9526880 0.9527247 0.9527613 0.9527979 0.9528345 0.9528710
[225] 0.9529076 0.9529440 0.9529805 0.9530169 0.9530532 0.9530896 0.9531259
[232] 0.9531622 0.9531984 0.9532346 0.9532708 0.9533069 0.9533430 0.9533791
[239] 0.9534151 0.9534511 0.9534871 0.9535230 0.9535589 0.9535948 0.9536306
[246] 0.9536664 0.9537022 0.9537379 0.9537736 0.9538093 0.9538449 0.9538805
[253] 0.9539161 0.9539516 0.9539871 0.9540226 0.9540580 0.9540934 0.9541288
[260] 0.9541641 0.9541995 0.9542347 0.9542700 0.9543052 0.9543403 0.9543755
[267] 0.9544106 0.9544456 0.9544807 0.9545157 0.9545506 0.9545856 0.9546205
[274] 0.9546553 0.9546902 0.9547250 0.9547598 0.9547945 0.9548292 0.9548639
[281] 0.9548985 0.9549331 0.9549677 0.9550022 0.9550367 0.9550712 0.9551056
[288] 0.9551400 0.9551744 0.9552088 0.9552431 0.9552774 0.9553116 0.9553458
[295] 0.9553800 0.9554141 0.9554482 0.9554823 0.9555164 0.9555504 0.9555844
[302] 0.9556183 0.9556522 0.9556861 0.9557199 0.9557538 0.9557876 0.9558213
[309] 0.9558550 0.9558887 0.9559224 0.9559560 0.9559896 0.9560231 0.9560566
[316] 0.9560901 0.9561236 0.9561570 0.9561904 0.9562238 0.9562571 0.9562904
[323] 0.9563237 0.9563569 0.9563901 0.9564233 0.9564564 0.9564895 0.9565226
[330] 0.9565556 0.9565886 0.9566216 0.9566545 0.9566874 0.9567203 0.9567531
[337] 0.9567859 0.9568187 0.9568515 0.9568842 0.9569169 0.9569495 0.9569821
[344] 0.9570147 0.9570473 0.9570798 0.9571123 0.9571447 0.9571771 0.9572095
[351] 0.9572419 0.9572742 0.9573065 0.9573388 0.9573710 0.9574032 0.9574354
[358] 0.9574675 0.9574996 0.9575317 0.9575637 0.9575957 0.9576277 0.9576596
[365] 0.9576915 0.9577234 0.9577553 0.9577871 0.9578189 0.9578506 0.9578823
[372] 0.9579140 0.9579457 0.9579773 0.9580089 0.9580404 0.9580720 0.9581035
[379] 0.9581349 0.9581664 0.9581978 0.9582291 0.9582605 0.9582918 0.9583231
[386] 0.9583543 0.9583855 0.9584167 0.9584478 0.9584790 0.9585100 0.9585411
[393] 0.9585721 0.9586031 0.9586341 0.9586650 0.9586959 0.9587267 0.9587576
[400] 0.9587884 0.9588191
> mx
[1] 0.9588191
> mxli
[1] 2
> if (mxli != 0)
+ {
+ x1 <- (x^mxli - 1) / mxli
+ } else {
+ x1 <- log(x)
+ }
> postscript(file="/var/www/html/rcomp/tmp/1ci1h1227106437.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/2ozuu1227106437.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/3tjz11227106437.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/4z9aq1227106437.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/5vpum1227106437.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/63hpg1227106437.tab") 
> 
> system("convert tmp/1ci1h1227106437.ps tmp/1ci1h1227106437.png")
> system("convert tmp/2ozuu1227106437.ps tmp/2ozuu1227106437.png")
> system("convert tmp/3tjz11227106437.ps tmp/3tjz11227106437.png")
> system("convert tmp/4z9aq1227106437.ps tmp/4z9aq1227106437.png")
> system("convert tmp/5vpum1227106437.ps tmp/5vpum1227106437.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.086   0.780   1.333