math215 Lecture Notes - Lecture 3: Prediction Interval, Probability Mass Function, Normal Distribution
Document Summary
Assume x bin(n, : domain of variation : , n: probability mass function (pmf ) : x(cid:19) x(1 )n x, p(x) =(cid:18)n, cumulative distribution function (cdf ) : for x sx. Xk=0(cid:18)n k(cid:19) k(1 )n k (where x denotes the integer part of x): expectation , variance : Assume x p( : domain of variation : Sx = {0, 1, 2, : probability mass function (pmf ) : p(x) = e x x! for x sx, cumulative distribution function (cdf ) : Xk=0 k! (where x denotes the integer part of x). Assume x u[ , : domain of variation : Sx = [ , : probability density function (pdf ) : f (x) = 1 for x sx: cumulative distribution function (cdf ) : F (x) = x for x sx: expectation , variance : Assume x exp( : domain of variation :
