This preview shows pages 1–5. Sign up to view the full content.
This preview has intentionally blurred sections. Sign up to view the full version.
View Full DocumentThis preview has intentionally blurred sections. Sign up to view the full version.
View Full Document
Unformatted text preview: LECTURE III Ou Zhao ouzhao@stat.sc.edu University of South Carolina Lecture 3 p.1 Expected value The mean (or expected value) of a discrete random variable X is = E [ X ] = xp ( x ) . As you can see, the mean comes out of a summation, so it may not be a possible value for X at all; but it certainly tells roughly where X would very much like to take values. Example: Consider the mass function shown below: x 1 2 4 10 p(x) .2 .4 .2 .2 what is the mean? Lecture 3 p.2 The variance of a random variable X is 2 = E [( X ) 2 ] = summationdisplay ( x ) 2 p ( x ) , does that equal summationdisplay x 2 p ( x ) 2 ? Again, the standard deviation is defined to be 2 Can you compute 2 , or for the previous example? Lecture 3 p.3 Exercise about the mean Suppose you work for an insurance company and you sell a $ 10,000 oneyear term insurance policy at an annual premium of $ 290. This premium is targeted on those customers (with certain age, sex, health, etc), for whom...
View Full
Document
 Spring '10
 Zhao

Click to edit the document details