MATH 11 Lecture Notes - Lecture 12: Probability Distribution, Random Variable
Document Summary
Note: the (cid:271)ook does(cid:374)"t (cid:272)o(cid:448)er this (cid:373)aterial (cid:449)ell, so a supple(cid:373)e(cid:374)t on continuous random variables can be found on triton ed. X = number of trials needed to get first success probability. A discrete random variable takes on finitely many values or infinitely many values which are discrete (spaces in between) A continuous random variable is a random quantity that can take on any value on a continuous scale (a smooth interval of possibilities). Examples: the amount of water you drink in a day, how long you wait for a bus, how far you live from the nearest grocery store. For continuous random variables, we have to change how we present the probability model (no more tables) alter how we seek questions, generalize our definition of probability and rethink what a probability of 0 means. Suppose we flip a coin 16 times and record how many heads we get. That is let x = binom (n=16, p=0. 5)
