STAT231 Lecture 2: Week 2 --Lecture 1--Continuous Distributions and Likelihood.pdf

• School: University of Waterloo
• Department: Statistics
• Course: STAT231
• Professor: Matthias Schonlau

Document Summary

Continuous random variables and likelihood. Sections in the notes: 2. 2, 2. 3, 3. 3, 3. 5, 3. 6. We specify the probability structure for a continuous r. v. Y using a pdf f(y), y s, where f (y)dy b a. , y>0 where the exp{ y / } 2 2 }, - < y< (y )2. Follows g(0,1) f (y) = Y has pdf parameter >0. We say that y exp( ) If y g( , ), then. If c, d, b1, b2, b3, . bn are constants, Yi g( i, i) i=1,2, n and y1, y2, . yn are independent, then c + dyi g(c+d i, |d| i) and b1y1+b2y2+ bnyn. Suppose w1, w2, w3, wn are independent random variables each with mean and standard deviation . We let be the average so. W] = , and s. d. b1 1 + b2 2 +bn n, b1. The standardized form of the average. The c. l. t. says that limn p(zn z)=p(z z)