Let χ be rv, counting of events , 0, 1, 2 … per interval. χ might be
counts per unit time, distance.
typos per page.
cars passing per unit time.
ATM customers per hour.
Motivating Poisson:
Suppose, on average, receive 9 letters in mail each day. And suppose can model post office as poisson. This addresses question: how many letters will I received today? Then expect sd to be sqrt(9) and so expect actual number of letters to vary between 3 and 12 (2 sd).
Pr(X=x)=e−λλx/x!
lambda=9# rate of 1 per unitx<-0:20# how many counts?z<-dpois(x =x, lambda =lambda)w<-dpois(x =x, lambda =1)plot(z, main ="Poisson Distribution: upto n=20 counts, compare lambda= c(1,9)", ylab ="dpois", xlab ="counts, 1:n", pch =19, col ="plum")points(w, pch =20, col="red")# colors()