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Data:
124.06 124.58 122.00 124.02 124.16 124.29 123.93 124.62 121.81 124.14 124.31 125.15 125.35 125.48 124.17 125.33 124.46 123.39 123.14 122.24 119.31 120.87 120.43 119.41 118.85 119.08 117.25 118.51 118.42 118.56 117.97 117.98 115.25 117.23 117.08 116.83 117.17 117.73 115.74 116.99 116.90 116.49 115.84 115.92 113.32 114.84 114.75 114.84 115.03 115.03 112.99 114.15 113.77 113.57 113.38 112.71 110.27 111.73 112.12 112.31 111.73 111.83 109.99 111.15 111.25 110.87 110.27 110.18 108.15 109.60 109.60 109.41 109.80 109.60 107.76 109.02 108.62 109.02 109.22 108.92 106.69 107.76 107.66 107.85 107.95 107.85 106.30 107.37 107.66 107.46 107.37 107.18 105.43 106.39 106.50 106.50 106.69 106.50 105.14 106.50 106.20 105.72 104.76 104.55 102.71 104.36 104.65 104.46 104.65 103.88 102.32 103.39 103.00 102.71 102.51 102.04 100.00
Type of transformation
Full Box-Cox transform
Simple Box-Cox transform
Minimum lambda
-2
-8
-7
-6
-5
-4
-3
-2
-1
Maximum lambda
2
1
2
3
4
5
6
7
8
Constant term to be added before analysis is performed
(?)
Display table with original and transformed data?
No
Yes
Chart options
R Code
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 mx mxli if (mxli != 0) { x1 <- (x^mxli - 1) / mxli } else { x1 <- log(x) } bitmap(file='test1.png') plot(l,c,main='Box-Cox Normality Plot',xlab='Lambda',ylab='correlation') mtext(paste('Optimal Lambda =',mxli)) grid() dev.off() bitmap(file='test2.png') hist(x,main='Histogram of Original Data',xlab='X',ylab='frequency') grid() dev.off() bitmap(file='test3.png') hist(x1,main='Histogram of Transformed Data',xlab='X',ylab='frequency') grid() dev.off() bitmap(file='test4.png') qqnorm(x) qqline(x) grid() mtext('Original Data') dev.off() bitmap(file='test5.png') qqnorm(x1) qqline(x1) grid() mtext('Transformed Data') dev.off() load(file='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='mytable.tab')
Compute
Summary of computational transaction
Raw Input
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Raw Output
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Computing time
1 seconds
R Server
Big Analytics Cloud Computing Center
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