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(106.2
+ ,101.6
+ ,99
+ ,96.4
+ ,94
+ ,93.2
+ ,103
+ ,103.6
+ ,103.2
+ ,102.2
+ ,100
+ ,99.6
+ ,98.8
+ ,95.2
+ ,91.6
+ ,88.6
+ ,86
+ ,84.8
+ ,95.2
+ ,96.2
+ ,94
+ ,92
+ ,90.2
+ ,90
+ ,88.8
+ ,85.8
+ ,84.2
+ ,80
+ ,77.8
+ ,76.8
+ ,86.4
+ ,89.2
+ ,86.2
+ ,84.6
+ ,83.2
+ ,83.2
+ ,82.6
+ ,79.8
+ ,77.2
+ ,74.8
+ ,73
+ ,73
+ ,83.6
+ ,85.6
+ ,84.8
+ ,84.2
+ ,83.4
+ ,84.6
+ ,84.6
+ ,83.8
+ ,81.2
+ ,79.6
+ ,78
+ ,78.2
+ ,88.8
+ ,92
+ ,91
+ ,91.2
+ ,90.4
+ ,91.8
+ ,92.2
+ ,90.2
+ ,88.6
+ ,87.8
+ ,86
+ ,87.2
+ ,97.6
+ ,101.2
+ ,100.4
+ ,100.2
+ ,100.2
+ ,103
+ ,104.2
+ ,104
+ ,102.4
+ ,101.8
+ ,101
+ ,102.2
+ ,114
+ ,118.4
+ ,118.8
+ ,117.2
+ ,117.2
+ ,118.4
+ ,118.8
+ ,117.2
+ ,114.4
+ ,112.6
+ ,111
+ ,110.8
+ ,120.2
+ ,124.4
+ ,123.4
+ ,121.2
+ ,119
+ ,119.8
+ ,120
+ ,118.4
+ ,115
+ ,113.4
+ ,111
+ ,111
+ ,121.6
+ ,126.2
+ ,125.8
+ ,124.8
+ ,122
+ ,123.2
+ ,124.2
+ ,120.8
+ ,116.8
+ ,114.8
+ ,111
+ ,109
+ ,119.8
+ ,124
+ ,121.6
+ ,118
+ ,115.8
+ ,116
+ ,115.8
+ ,114.4
+ ,112
+ ,110.2
+ ,107.4
+ ,108.2
+ ,117.6
+ ,121.4
+ ,119.8
+ ,115.6
+ ,112.6
+ ,113.2
+ ,112.2
+ ,110.8
+ ,108
+ ,105.2
+ ,102.4
+ ,101
+ ,110.8
+ ,116.8
+ ,113.8
+ ,108
+ ,104.4
+ ,105.2
+ ,105.4
+ ,103.2
+ ,100.6
+ ,97.8
+ ,95.8
+ ,95
+ ,104.8
+ ,110.4
+ ,106.4
+ ,102.2
+ ,98.4
+ ,98.4
+ ,98.6
+ ,96.2
+ ,92.4
+ ,91.4
+ ,88.4
+ ,87.8
+ ,97.6
+ ,104.2
+ ,100.2
+ ,97
+ ,92.8
+ ,92
+ ,93.4
+ ,92
+ ,89.6
+ ,88.6
+ ,87.2
+ ,86.2
+ ,96.8
+ ,102
+ ,102.6
+ ,100.6
+ ,94.2
+ ,94.2
+ ,95.2
+ ,95
+ ,94
+ ,92.2
+ ,91
+ ,91.2
+ ,103.4
+ ,105
+ ,104.6
+ ,103.8
+ ,101.8
+ ,102.4
+ ,103.8
+ ,103.4
+ ,102
+ ,101.8
+ ,100.2
+ ,101.4
+ ,113.8
+ ,116
+ ,115.6
+ ,113
+ ,109.4
+ ,111
+ ,112.4
+ ,112.2
+ ,111
+ ,108.8
+ ,107.4
+ ,108.6
+ ,118.8
+ ,122.2
+ ,122.6
+ ,122.2
+ ,118.8
+ ,119
+ ,118.2
+ ,117.8
+ ,116.8
+ ,114.6
+ ,113.4
+ ,113.8
+ ,124.2
+ ,125.8
+ ,125.6
+ ,122.4
+ ,119
+ ,119.4
+ ,118.6
+ ,118
+ ,116
+ ,114.8
+ ,114.6
+ ,114.6
+ ,124
+ ,125.2
+ ,124
+ ,117.6
+ ,113.2
+ ,111.4
+ ,112.2
+ ,109.8
+ ,106.4
+ ,105.2
+ ,102.2
+ ,99.8
+ ,111
+ ,113
+ ,108.4
+ ,105.4
+ ,102
+ ,102.8)
> #'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] 0.4694821 0.4694881 0.4694938 0.4694991 0.4695041 0.4695087 0.4695130
  [8] 0.4695170 0.4695206 0.4695239 0.4695268 0.4695294 0.4695316 0.4695335
 [15] 0.4695351 0.4695363 0.4695372 0.4695378 0.4695380 0.4695378 0.4695374
 [22] 0.4695365 0.4695354 0.4695339 0.4695321 0.4695299 0.4695274 0.4695245
 [29] 0.4695213 0.4695178 0.4695140 0.4695098 0.4695052 0.4695004 0.4694951
 [36] 0.4694896 0.4694837 0.4694775 0.4694709 0.4694640 0.4694568 0.4694492
 [43] 0.4694413 0.4694331 0.4694245 0.4694156 0.4694064 0.4693968 0.4693869
 [50] 0.4693767 0.4693661 0.4693552 0.4693439 0.4693324 0.4693204 0.4693082
 [57] 0.4692956 0.4692827 0.4692695 0.4692559 0.4692420 0.4692278 0.4692132
 [64] 0.4691983 0.4691831 0.4691676 0.4691517 0.4691355 0.4691189 0.4691020
 [71] 0.4690848 0.4690673 0.4690494 0.4690313 0.4690127 0.4689939 0.4689747
 [78] 0.4689552 0.4689354 0.4689153 0.4688948 0.4688740 0.4688529 0.4688314
 [85] 0.4688096 0.4687875 0.4687651 0.4687423 0.4687193 0.4686959 0.4686721
 [92] 0.4686481 0.4686237 0.4685990 0.4685740 0.4685487 0.4685230 0.4684971
 [99] 0.4684708 0.4684441 0.4684172 0.4683899 0.4683623 0.4683344 0.4683062
[106] 0.4682777 0.4682488 0.4682197 0.4681902 0.4681604 0.4681302 0.4680998
[113] 0.4680690 0.4680379 0.4680065 0.4679748 0.4679428 0.4679105 0.4678778
[120] 0.4678448 0.4678116 0.4677780 0.4677440 0.4677098 0.4676753 0.4676404
[127] 0.4676053 0.4675698 0.4675340 0.4674979 0.4674615 0.4674248 0.4673878
[134] 0.4673504 0.4673128 0.4672748 0.4672366 0.4671980 0.4671591 0.4671199
[141] 0.4670804 0.4670406 0.4670005 0.4669601 0.4669194 0.4668784 0.4668370
[148] 0.4667954 0.4667535 0.4667112 0.4666687 0.4666258 0.4665827 0.4665392
[155] 0.4664955 0.4664514 0.4664071 0.4663624 0.4663175 0.4662722 0.4662266
[162] 0.4661808 0.4661346 0.4660882 0.4660414 0.4659944 0.4659470 0.4658994
[169] 0.4658514 0.4658032 0.4657547 0.4657058 0.4656567 0.4656073 0.4655576
[176] 0.4655076 0.4654573 0.4654067 0.4653558 0.4653046 0.4652531 0.4652014
[183] 0.4651493 0.4650970 0.4650444 0.4649914 0.4649382 0.4648847 0.4648309
[190] 0.4647769 0.4647225 0.4646678 0.4646129 0.4645577 0.4645022 0.4644464
[197] 0.4643903 0.4643339 0.4642773 0.4642203 0.4641631 0.4641056 0.4640478
[204] 0.4639898 0.4639314 0.4638728 0.4638139 0.4637547 0.4636952 0.4636355
[211] 0.4635755 0.4635152 0.4634546 0.4633937 0.4633326 0.4632712 0.4632095
[218] 0.4631475 0.4630853 0.4630227 0.4629600 0.4628969 0.4628335 0.4627699
[225] 0.4627060 0.4626419 0.4625775 0.4625128 0.4624478 0.4623825 0.4623170
[232] 0.4622512 0.4621852 0.4621189 0.4620523 0.4619854 0.4619183 0.4618509
[239] 0.4617832 0.4617153 0.4616471 0.4615787 0.4615099 0.4614410 0.4613717
[246] 0.4613022 0.4612324 0.4611624 0.4610921 0.4610215 0.4609507 0.4608796
[253] 0.4608083 0.4607367 0.4606648 0.4605927 0.4605203 0.4604477 0.4603748
[260] 0.4603017 0.4602283 0.4601546 0.4600807 0.4600065 0.4599321 0.4598574
[267] 0.4597825 0.4597073 0.4596319 0.4595562 0.4594802 0.4594040 0.4593276
[274] 0.4592509 0.4591740 0.4590968 0.4590193 0.4589417 0.4588637 0.4587855
[281] 0.4587071 0.4586284 0.4585495 0.4584704 0.4583910 0.4583113 0.4582314
[288] 0.4581513 0.4580709 0.4579903 0.4579094 0.4578283 0.4577469 0.4576654
[295] 0.4575835 0.4575015 0.4574192 0.4573366 0.4572538 0.4571708 0.4570876
[302] 0.4570041 0.4569203 0.4568364 0.4567522 0.4566678 0.4565831 0.4564982
[309] 0.4564131 0.4563277 0.4562421 0.4561563 0.4560702 0.4559839 0.4558974
[316] 0.4558107 0.4557237 0.4556365 0.4555491 0.4554614 0.4553735 0.4552854
[323] 0.4551971 0.4551085 0.4550197 0.4549307 0.4548415 0.4547520 0.4546623
[330] 0.4545724 0.4544823 0.4543920 0.4543014 0.4542106 0.4541196 0.4540284
[337] 0.4539369 0.4538453 0.4537534 0.4536613 0.4535690 0.4534765 0.4533837
[344] 0.4532908 0.4531976 0.4531042 0.4530106 0.4529168 0.4528228 0.4527285
[351] 0.4526341 0.4525394 0.4524445 0.4523494 0.4522542 0.4521586 0.4520629
[358] 0.4519670 0.4518709 0.4517746 0.4516780 0.4515813 0.4514843 0.4513872
[365] 0.4512898 0.4511922 0.4510945 0.4509965 0.4508983 0.4508000 0.4507014
[372] 0.4506026 0.4505036 0.4504045 0.4503051 0.4502055 0.4501057 0.4500058
[379] 0.4499056 0.4498052 0.4497047 0.4496039 0.4495030 0.4494018 0.4493005
[386] 0.4491990 0.4490972 0.4489953 0.4488932 0.4487909 0.4486884 0.4485857
[393] 0.4484828 0.4483798 0.4482765 0.4481731 0.4480695 0.4479656 0.4478616
[400] 0.4477574 0.4476531
> mx
[1] 0.469538
> mxli
[1] -1.82
> if (mxli != 0)
+ {
+ x1 <- (x^mxli - 1) / mxli
+ } else {
+ x1 <- log(x)
+ }
> postscript(file="/var/www/html/rcomp/tmp/19je61229608274.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/2gwya1229608274.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/3ki9q1229608274.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/4394e1229608274.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/5hk3g1229608274.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/6767m1229608274.tab") 
> 
> system("convert tmp/19je61229608274.ps tmp/19je61229608274.png")
> system("convert tmp/2gwya1229608274.ps tmp/2gwya1229608274.png")
> system("convert tmp/3ki9q1229608274.ps tmp/3ki9q1229608274.png")
> system("convert tmp/4394e1229608274.ps tmp/4394e1229608274.png")
> system("convert tmp/5hk3g1229608274.ps tmp/5hk3g1229608274.png")
> 
> 
> proc.time()
   user  system elapsed 
  1.276   0.912   1.792