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(78.4,114.6,113.3,117.0,99.6,99.4,101.9,115.2,108.5,113.8,121.0,92.2,90.2,101.5,126.6,93.9,89.8,93.4,101.5,110.4,105.9,108.4,113.9,86.1,69.4,101.2,100.5,98.0,106.6,90.1,96.9,125.9,112.0,100.0,123.9,79.8,83.4,113.6,112.9,104.0,109.9,99.0,106.3,128.9,111.1,102.9,130.0,87.0,87.5,117.6,103.4,110.8,112.6,102.5,112.4,135.6,105.1,127.7,137.0,91.0,90.5,122.4,123.3,124.3,120.0,118.1,119.0,142.7,123.6,129.6,151.6,110.4,99.2,130.5,136.2,129.7,128.0,121.6,135.8,143.8,147.5,136.2,156.6,123.3,100.4)
> #'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] 0.4424287 0.4427571 0.4430851 0.4434128 0.4437401 0.4440670 0.4443935
  [8] 0.4447196 0.4450454 0.4453707 0.4456957 0.4460203 0.4463445 0.4466683
 [15] 0.4469918 0.4473148 0.4476374 0.4479597 0.4482815 0.4486030 0.4489240
 [22] 0.4492447 0.4495649 0.4498847 0.4502042 0.4505232 0.4508418 0.4511601
 [29] 0.4514779 0.4517953 0.4521123 0.4524289 0.4527450 0.4530608 0.4533761
 [36] 0.4536910 0.4540055 0.4543196 0.4546333 0.4549465 0.4552593 0.4555717
 [43] 0.4558837 0.4561952 0.4565063 0.4568170 0.4571273 0.4574371 0.4577465
 [50] 0.4580554 0.4583640 0.4586721 0.4589797 0.4592869 0.4595937 0.4599001
 [57] 0.4602060 0.4605114 0.4608164 0.4611210 0.4614251 0.4617288 0.4620320
 [64] 0.4623348 0.4626371 0.4629390 0.4632405 0.4635414 0.4638420 0.4641421
 [71] 0.4644417 0.4647408 0.4650395 0.4653378 0.4656356 0.4659329 0.4662298
 [78] 0.4665262 0.4668221 0.4671176 0.4674126 0.4677071 0.4680012 0.4682948
 [85] 0.4685879 0.4688806 0.4691728 0.4694645 0.4697558 0.4700466 0.4703369
 [92] 0.4706267 0.4709160 0.4712049 0.4714933 0.4717812 0.4720687 0.4723556
 [99] 0.4726421 0.4729281 0.4732136 0.4734986 0.4737831 0.4740672 0.4743507
[106] 0.4746338 0.4749164 0.4751984 0.4754800 0.4757611 0.4760418 0.4763219
[113] 0.4766015 0.4768806 0.4771593 0.4774374 0.4777150 0.4779922 0.4782688
[120] 0.4785449 0.4788206 0.4790957 0.4793703 0.4796445 0.4799181 0.4801912
[127] 0.4804638 0.4807359 0.4810075 0.4812786 0.4815492 0.4818192 0.4820888
[134] 0.4823578 0.4826264 0.4828944 0.4831619 0.4834289 0.4836954 0.4839613
[141] 0.4842268 0.4844917 0.4847561 0.4850200 0.4852833 0.4855462 0.4858085
[148] 0.4860703 0.4863316 0.4865924 0.4868526 0.4871123 0.4873715 0.4876301
[155] 0.4878883 0.4881459 0.4884029 0.4886595 0.4889155 0.4891710 0.4894259
[162] 0.4896804 0.4899343 0.4901876 0.4904404 0.4906927 0.4909445 0.4911957
[169] 0.4914464 0.4916965 0.4919462 0.4921952 0.4924438 0.4926917 0.4929392
[176] 0.4931861 0.4934325 0.4936783 0.4939236 0.4941684 0.4944126 0.4946562
[183] 0.4948993 0.4951419 0.4953839 0.4956254 0.4958663 0.4961067 0.4963465
[190] 0.4965858 0.4968246 0.4970627 0.4973004 0.4975375 0.4977740 0.4980100
[197] 0.4982454 0.4984803 0.4987146 0.4989484 0.4991816 0.4994143 0.4996464
[204] 0.4998779 0.5001089 0.5003393 0.5005692 0.5007985 0.5010273 0.5012555
[211] 0.5014831 0.5017102 0.5019368 0.5021627 0.5023881 0.5026129 0.5028372
[218] 0.5030609 0.5032841 0.5035067 0.5037287 0.5039502 0.5041710 0.5043914
[225] 0.5046111 0.5048303 0.5050490 0.5052670 0.5054845 0.5057014 0.5059178
[232] 0.5061336 0.5063488 0.5065635 0.5067775 0.5069910 0.5072040 0.5074163
[239] 0.5076281 0.5078394 0.5080500 0.5082601 0.5084696 0.5086785 0.5088869
[246] 0.5090947 0.5093019 0.5095085 0.5097146 0.5099201 0.5101250 0.5103293
[253] 0.5105330 0.5107362 0.5109388 0.5111408 0.5113423 0.5115431 0.5117434
[260] 0.5119431 0.5121423 0.5123408 0.5125388 0.5127362 0.5129330 0.5131292
[267] 0.5133249 0.5135199 0.5137144 0.5139083 0.5141016 0.5142944 0.5144865
[274] 0.5146781 0.5148691 0.5150595 0.5152493 0.5154385 0.5156272 0.5158153
[281] 0.5160027 0.5161896 0.5163759 0.5165617 0.5167468 0.5169314 0.5171153
[288] 0.5172987 0.5174815 0.5176637 0.5178453 0.5180264 0.5182068 0.5183867
[295] 0.5185659 0.5187446 0.5189227 0.5191002 0.5192771 0.5194535 0.5196292
[302] 0.5198043 0.5199789 0.5201529 0.5203262 0.5204990 0.5206712 0.5208428
[309] 0.5210138 0.5211843 0.5213541 0.5215233 0.5216920 0.5218601 0.5220275
[316] 0.5221944 0.5223607 0.5225264 0.5226915 0.5228560 0.5230199 0.5231832
[323] 0.5233460 0.5235081 0.5236697 0.5238306 0.5239910 0.5241507 0.5243099
[330] 0.5244685 0.5246265 0.5247839 0.5249407 0.5250969 0.5252525 0.5254075
[337] 0.5255620 0.5257158 0.5258690 0.5260217 0.5261737 0.5263252 0.5264760
[344] 0.5266263 0.5267760 0.5269251 0.5270735 0.5272214 0.5273687 0.5275154
[351] 0.5276615 0.5278071 0.5279520 0.5280963 0.5282400 0.5283832 0.5285257
[358] 0.5286676 0.5288090 0.5289497 0.5290899 0.5292295 0.5293684 0.5295068
[365] 0.5296446 0.5297818 0.5299184 0.5300544 0.5301898 0.5303246 0.5304588
[372] 0.5305924 0.5307254 0.5308579 0.5309897 0.5311209 0.5312516 0.5313816
[379] 0.5315111 0.5316399 0.5317682 0.5318959 0.5320230 0.5321495 0.5322753
[386] 0.5324006 0.5325253 0.5326495 0.5327730 0.5328959 0.5330182 0.5331400
[393] 0.5332611 0.5333817 0.5335016 0.5336210 0.5337398 0.5338579 0.5339755
[400] 0.5340925 0.5342089
> mx
[1] 0.5342089
> mxli
[1] 2
> if (mxli != 0)
+ {
+ x1 <- (x^mxli - 1) / mxli
+ } else {
+ x1 <- log(x)
+ }
> postscript(file="/var/www/html/rcomp/tmp/192iy1226509359.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/21oi71226509359.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/3g7q31226509359.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/403v41226509359.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/5j6en1226509359.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/6x00d1226509359.tab") 
> 
> system("convert tmp/192iy1226509359.ps tmp/192iy1226509359.png")
> system("convert tmp/21oi71226509359.ps tmp/21oi71226509359.png")
> system("convert tmp/3g7q31226509359.ps tmp/3g7q31226509359.png")
> system("convert tmp/403v41226509359.ps tmp/403v41226509359.png")
> system("convert tmp/5j6en1226509359.ps tmp/5j6en1226509359.png")
> 
> 
> proc.time()
   user  system elapsed 
  2.397   1.293   2.569