R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0

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> x <- c(101,101,98.7,96.4,95.6,95.4,98.7,98.7,98.7,99.3,99.5,99.6,100.8,100.8,100.8,101,101.6,102.3,102.3,102.3,102.3,102.4,102.4,102.4,102.4,102.4,102.4,102.4,101.6,102.2,102.2,102.2,102.2,102.2,102.2,102.2,102.2,102.2,102.2,102.2,102.2,102.2,102.2,102.2,102.2,102.2,102.2,102.2,102.1,102.1,102.1,102.1,100.7,100.7,100.7,100.7,100.7,100.7,100.7,100.7)
> #'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] 0.4431034 0.4430950 0.4430865 0.4430781 0.4430696 0.4430612 0.4430527
  [8] 0.4430442 0.4430357 0.4430272 0.4430187 0.4430102 0.4430016 0.4429931
 [15] 0.4429845 0.4429760 0.4429674 0.4429588 0.4429503 0.4429417 0.4429331
 [22] 0.4429245 0.4429158 0.4429072 0.4428986 0.4428899 0.4428813 0.4428726
 [29] 0.4428640 0.4428553 0.4428466 0.4428379 0.4428292 0.4428205 0.4428118
 [36] 0.4428031 0.4427943 0.4427856 0.4427768 0.4427681 0.4427593 0.4427505
 [43] 0.4427417 0.4427329 0.4427241 0.4427153 0.4427065 0.4426977 0.4426888
 [50] 0.4426800 0.4426711 0.4426623 0.4426534 0.4426445 0.4426357 0.4426268
 [57] 0.4426179 0.4426089 0.4426000 0.4425911 0.4425822 0.4425732 0.4425643
 [64] 0.4425553 0.4425463 0.4425373 0.4425283 0.4425194 0.4425103 0.4425013
 [71] 0.4424923 0.4424833 0.4424742 0.4424652 0.4424561 0.4424471 0.4424380
 [78] 0.4424289 0.4424198 0.4424107 0.4424016 0.4423925 0.4423834 0.4423743
 [85] 0.4423651 0.4423560 0.4423468 0.4423376 0.4423285 0.4423193 0.4423101
 [92] 0.4423009 0.4422917 0.4422825 0.4422733 0.4422640 0.4422548 0.4422455
 [99] 0.4422363 0.4422270 0.4422177 0.4422085 0.4421992 0.4421899 0.4421806
[106] 0.4421712 0.4421619 0.4421526 0.4421432 0.4421339 0.4421245 0.4421152
[113] 0.4421058 0.4420964 0.4420870 0.4420776 0.4420682 0.4420588 0.4420494
[120] 0.4420400 0.4420305 0.4420211 0.4420116 0.4420021 0.4419927 0.4419832
[127] 0.4419737 0.4419642 0.4419547 0.4419452 0.4419356 0.4419261 0.4419166
[134] 0.4419070 0.4418975 0.4418879 0.4418783 0.4418687 0.4418592 0.4418496
[141] 0.4418399 0.4418303 0.4418207 0.4418111 0.4418014 0.4417918 0.4417821
[148] 0.4417725 0.4417628 0.4417531 0.4417434 0.4417337 0.4417240 0.4417143
[155] 0.4417046 0.4416949 0.4416851 0.4416754 0.4416656 0.4416558 0.4416461
[162] 0.4416363 0.4416265 0.4416167 0.4416069 0.4415971 0.4415873 0.4415774
[169] 0.4415676 0.4415577 0.4415479 0.4415380 0.4415282 0.4415183 0.4415084
[176] 0.4414985 0.4414886 0.4414787 0.4414688 0.4414588 0.4414489 0.4414389
[183] 0.4414290 0.4414190 0.4414091 0.4413991 0.4413891 0.4413791 0.4413691
[190] 0.4413591 0.4413491 0.4413390 0.4413290 0.4413190 0.4413089 0.4412989
[197] 0.4412888 0.4412787 0.4412686 0.4412585 0.4412484 0.4412383 0.4412282
[204] 0.4412181 0.4412079 0.4411978 0.4411877 0.4411775 0.4411673 0.4411572
[211] 0.4411470 0.4411368 0.4411266 0.4411164 0.4411062 0.4410959 0.4410857
[218] 0.4410755 0.4410652 0.4410550 0.4410447 0.4410344 0.4410241 0.4410139
[225] 0.4410036 0.4409933 0.4409829 0.4409726 0.4409623 0.4409520 0.4409416
[232] 0.4409313 0.4409209 0.4409105 0.4409001 0.4408898 0.4408794 0.4408690
[239] 0.4408586 0.4408481 0.4408377 0.4408273 0.4408168 0.4408064 0.4407959
[246] 0.4407855 0.4407750 0.4407645 0.4407540 0.4407435 0.4407330 0.4407225
[253] 0.4407120 0.4407014 0.4406909 0.4406803 0.4406698 0.4406592 0.4406487
[260] 0.4406381 0.4406275 0.4406169 0.4406063 0.4405957 0.4405851 0.4405744
[267] 0.4405638 0.4405531 0.4405425 0.4405318 0.4405212 0.4405105 0.4404998
[274] 0.4404891 0.4404784 0.4404677 0.4404570 0.4404463 0.4404355 0.4404248
[281] 0.4404140 0.4404033 0.4403925 0.4403818 0.4403710 0.4403602 0.4403494
[288] 0.4403386 0.4403278 0.4403169 0.4403061 0.4402953 0.4402844 0.4402736
[295] 0.4402627 0.4402519 0.4402410 0.4402301 0.4402192 0.4402083 0.4401974
[302] 0.4401865 0.4401756 0.4401646 0.4401537 0.4401427 0.4401318 0.4401208
[309] 0.4401098 0.4400989 0.4400879 0.4400769 0.4400659 0.4400549 0.4400438
[316] 0.4400328 0.4400218 0.4400107 0.4399997 0.4399886 0.4399776 0.4399665
[323] 0.4399554 0.4399443 0.4399332 0.4399221 0.4399110 0.4398999 0.4398887
[330] 0.4398776 0.4398665 0.4398553 0.4398441 0.4398330 0.4398218 0.4398106
[337] 0.4397994 0.4397882 0.4397770 0.4397658 0.4397546 0.4397433 0.4397321
[344] 0.4397208 0.4397096 0.4396983 0.4396870 0.4396758 0.4396645 0.4396532
[351] 0.4396419 0.4396306 0.4396192 0.4396079 0.4395966 0.4395852 0.4395739
[358] 0.4395625 0.4395511 0.4395398 0.4395284 0.4395170 0.4395056 0.4394942
[365] 0.4394828 0.4394714 0.4394599 0.4394485 0.4394370 0.4394256 0.4394141
[372] 0.4394027 0.4393912 0.4393797 0.4393682 0.4393567 0.4393452 0.4393337
[379] 0.4393222 0.4393106 0.4392991 0.4392875 0.4392760 0.4392644 0.4392529
[386] 0.4392413 0.4392297 0.4392181 0.4392065 0.4391949 0.4391833 0.4391717
[393] 0.4391600 0.4391484 0.4391367 0.4391251 0.4391134 0.4391017 0.4390901
[400] 0.4390784 0.4390667
> mx
[1] 0.4431034
> mxli
[1] -2
> if (mxli != 0)
+ {
+ x1 <- (x^mxli - 1) / mxli
+ } else {
+ x1 <- log(x)
+ }
> postscript(file="/var/www/html/rcomp/tmp/1kr881257940796.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/2bgi31257940796.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/36h3u1257940796.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/42syv1257940796.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/5zfzd1257940796.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/6f3ef1257940796.tab") 
> 
> system("convert tmp/1kr881257940796.ps tmp/1kr881257940796.png")
> system("convert tmp/2bgi31257940796.ps tmp/2bgi31257940796.png")
> system("convert tmp/36h3u1257940796.ps tmp/36h3u1257940796.png")
> system("convert tmp/42syv1257940796.ps tmp/42syv1257940796.png")
> system("convert tmp/5zfzd1257940796.ps tmp/5zfzd1257940796.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.064   0.783   1.332