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(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
+ ,103.4
+ ,101.6
+ ,98.6
+ ,98
+ ,93.8
+ ,95.6
+ ,105.6
+ ,106.8
+ ,103.6
+ ,101.2
+ ,100.4
+ ,103.2
+ ,105.6
+ ,106.6
+ ,107.2
+ ,107.4
+ ,104.8
+ ,107.2
+ ,117.4
+ ,119.4
+ ,116.2
+ ,112.8
+ ,111.6)
> #'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] 0.5137554 0.5135973 0.5134389 0.5132800 0.5131207 0.5129610 0.5128008
  [8] 0.5126402 0.5124791 0.5123177 0.5121558 0.5119935 0.5118307 0.5116675
 [15] 0.5115039 0.5113399 0.5111755 0.5110106 0.5108453 0.5106796 0.5105134
 [22] 0.5103469 0.5101799 0.5100125 0.5098447 0.5096765 0.5095078 0.5093387
 [29] 0.5091692 0.5089993 0.5088290 0.5086583 0.5084871 0.5083156 0.5081436
 [36] 0.5079712 0.5077984 0.5076252 0.5074516 0.5072776 0.5071032 0.5069284
 [43] 0.5067531 0.5065775 0.5064014 0.5062250 0.5060481 0.5058708 0.5056932
 [50] 0.5055151 0.5053367 0.5051578 0.5049785 0.5047989 0.5046188 0.5044383
 [57] 0.5042575 0.5040762 0.5038946 0.5037126 0.5035301 0.5033473 0.5031641
 [64] 0.5029805 0.5027965 0.5026121 0.5024273 0.5022422 0.5020566 0.5018707
 [71] 0.5016844 0.5014977 0.5013106 0.5011231 0.5009353 0.5007470 0.5005584
 [78] 0.5003694 0.5001800 0.4999903 0.4998002 0.4996096 0.4994188 0.4992275
 [85] 0.4990359 0.4988439 0.4986515 0.4984587 0.4982656 0.4980721 0.4978782
 [92] 0.4976840 0.4974894 0.4972944 0.4970991 0.4969034 0.4967073 0.4965109
 [99] 0.4963141 0.4961169 0.4959194 0.4957215 0.4955233 0.4953247 0.4951257
[106] 0.4949264 0.4947267 0.4945267 0.4943263 0.4941256 0.4939245 0.4937231
[113] 0.4935213 0.4933191 0.4931166 0.4929138 0.4927106 0.4925070 0.4923031
[120] 0.4920989 0.4918943 0.4916894 0.4914841 0.4912785 0.4910726 0.4908663
[127] 0.4906597 0.4904527 0.4902454 0.4900377 0.4898297 0.4896214 0.4894128
[134] 0.4892038 0.4889945 0.4887848 0.4885748 0.4883645 0.4881539 0.4879429
[141] 0.4877316 0.4875200 0.4873080 0.4870957 0.4868831 0.4866702 0.4864569
[148] 0.4862434 0.4860295 0.4858153 0.4856007 0.4853859 0.4851707 0.4849552
[155] 0.4847394 0.4845233 0.4843069 0.4840902 0.4838731 0.4836557 0.4834381
[162] 0.4832201 0.4830018 0.4827832 0.4825643 0.4823451 0.4821255 0.4819057
[169] 0.4816856 0.4814652 0.4812444 0.4810234 0.4808021 0.4805805 0.4803585
[176] 0.4801363 0.4799138 0.4796910 0.4794679 0.4792445 0.4790208 0.4787968
[183] 0.4785725 0.4783480 0.4781231 0.4778980 0.4776725 0.4774468 0.4772208
[190] 0.4769945 0.4767680 0.4765411 0.4763140 0.4760866 0.4758589 0.4756309
[197] 0.4754027 0.4751741 0.4749453 0.4747163 0.4744869 0.4742573 0.4740274
[204] 0.4737972 0.4735668 0.4733361 0.4731051 0.4728739 0.4726424 0.4724106
[211] 0.4721786 0.4719463 0.4717137 0.4714809 0.4712478 0.4710144 0.4707808
[218] 0.4705469 0.4703128 0.4700784 0.4698438 0.4696089 0.4693738 0.4691384
[225] 0.4689027 0.4686668 0.4684307 0.4681943 0.4679576 0.4677207 0.4674836
[232] 0.4672462 0.4670085 0.4667707 0.4665325 0.4662942 0.4660556 0.4658167
[239] 0.4655777 0.4653383 0.4650988 0.4648590 0.4646190 0.4643787 0.4641382
[246] 0.4638975 0.4636565 0.4634153 0.4631739 0.4629323 0.4626904 0.4624483
[253] 0.4622059 0.4619634 0.4617206 0.4614776 0.4612344 0.4609909 0.4607473
[260] 0.4605034 0.4602593 0.4600150 0.4597704 0.4595257 0.4592807 0.4590355
[267] 0.4587901 0.4585445 0.4582987 0.4580527 0.4578064 0.4575600 0.4573133
[274] 0.4570665 0.4568194 0.4565721 0.4563247 0.4560770 0.4558291 0.4555810
[281] 0.4553327 0.4550843 0.4548356 0.4545867 0.4543376 0.4540884 0.4538389
[288] 0.4535893 0.4533394 0.4530894 0.4528392 0.4525887 0.4523381 0.4520873
[295] 0.4518364 0.4515852 0.4513338 0.4510823 0.4508306 0.4505787 0.4503266
[302] 0.4500743 0.4498219 0.4495693 0.4493165 0.4490635 0.4488103 0.4485570
[309] 0.4483035 0.4480498 0.4477960 0.4475420 0.4472878 0.4470334 0.4467789
[316] 0.4465242 0.4462693 0.4460143 0.4457591 0.4455038 0.4452482 0.4449926
[323] 0.4447367 0.4444807 0.4442246 0.4439682 0.4437118 0.4434551 0.4431983
[330] 0.4429414 0.4426843 0.4424270 0.4421696 0.4419121 0.4416544 0.4413965
[337] 0.4411385 0.4408803 0.4406220 0.4403636 0.4401050 0.4398463 0.4395874
[344] 0.4393284 0.4390692 0.4388099 0.4385505 0.4382909 0.4380312 0.4377713
[351] 0.4375113 0.4372512 0.4369909 0.4367306 0.4364700 0.4362094 0.4359486
[358] 0.4356877 0.4354266 0.4351654 0.4349041 0.4346427 0.4343812 0.4341195
[365] 0.4338577 0.4335958 0.4333337 0.4330716 0.4328093 0.4325469 0.4322843
[372] 0.4320217 0.4317589 0.4314961 0.4312331 0.4309700 0.4307068 0.4304434
[379] 0.4301800 0.4299164 0.4296528 0.4293890 0.4291251 0.4288612 0.4285971
[386] 0.4283329 0.4280686 0.4278042 0.4275397 0.4272751 0.4270104 0.4267456
[393] 0.4264807 0.4262157 0.4259506 0.4256854 0.4254201 0.4251547 0.4248892
[400] 0.4246236 0.4243580
> mx
[1] 0.5137554
> mxli
[1] -2
> if (mxli != 0)
+ {
+ x1 <- (x^mxli - 1) / mxli
+ } else {
+ x1 <- log(x)
+ }
> postscript(file="/var/www/html/rcomp/tmp/1tmpo1261298642.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/2lrfo1261298642.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/3ly3t1261298642.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/4u1b41261298642.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/5feqe1261298642.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/6l07r1261298642.tab") 
> 
> try(system("convert tmp/1tmpo1261298642.ps tmp/1tmpo1261298642.png",intern=TRUE))
character(0)
> try(system("convert tmp/2lrfo1261298642.ps tmp/2lrfo1261298642.png",intern=TRUE))
character(0)
> try(system("convert tmp/3ly3t1261298642.ps tmp/3ly3t1261298642.png",intern=TRUE))
character(0)
> try(system("convert tmp/4u1b41261298642.ps tmp/4u1b41261298642.png",intern=TRUE))
character(0)
> try(system("convert tmp/5feqe1261298642.ps tmp/5feqe1261298642.png",intern=TRUE))
character(0)
> 
> 
> proc.time()
   user  system elapsed 
  1.157   0.831   1.550