``````

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

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``````

Out[1]:

22

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``````

In [2]:

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

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``````

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)

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``````

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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)
}

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In [5]:

dist.exp(x)

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Out[5]:

0.184077376897692
12

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Tabelle 4: 0.95 / n=20 -> 0.234 => nicht verwerfen

``````

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)
}

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``````

In [7]:

dist.norm(x)

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Out[7]:

0.249662698866083
16

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0.190 => verwerfen

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In [ ]:

``````