In [1]:

    

x <- c(1, 10, 20, 30, 40, 52, 63, 70, 80, 90, 100, 102, 130, 140, 190, 210, 266, 310, 530, 590, 640, 1340)
n <- length(x)
n


    

    
Out[1]:




22

In [2]:

    

alpha <- 0.05
epsn <- sqrt(1/(2*n) * log(2/alpha))


    

In [3]:

    

Fn <- (1:n)/n
plot(x, Fn, type="s", lwd=1, ylab="", xlab="x", main="95% DKW-Konfidenzband")
lower <- pmax(Fn - epsn, 0)
upper <- pmin(Fn + epsn, 1)
conf.b <- cbind(lower, upper)
matplot(x, conf.b, type="s", col=2, add=TRUE)


    

In [4]:

    

dist.exp <- function(DATA) {
 n <- length(DATA) 
 mu <- mean(DATA)
 d.s <- sort(DATA)
 e.1 <- (1:n)/n
 e.2 <- (0:(n-1))/n
 Fx <- pexp(d.s,1/mu)
 ABS <- c(abs(Fx-e.1),abs(Fx-e.2))
 MAX <- max(ABS)
 ind <- which.max(ABS)
 c(MAX, ind)
}


    

In [5]:

    

dist.exp(x)


    

    
Out[5]:





	
0.184077376897692
	
12

In [6]:

    

dist.norm <- function(DATA) {
 n <- length(DATA) 
 mu <- mean(DATA)
 sig <- sd(DATA)
 d.s <- sort(DATA)
 e.1 <- (1:n)/n
 e.2 <- (0:(n-1))/n
 Fx <- pnorm(d.s,mu,sig)
 ABS <- c(abs(Fx-e.1),abs(Fx-e.2))
 MAX <- max(ABS)
 ind <- which.max(ABS)
 c(MAX, ind)
}


    

In [7]:

    

dist.norm(x)


    

    
Out[7]:





	
0.249662698866083
	
16

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