1. Introductory Statistics

2. Probability Heads or Tails Parents Getting a Girl Blood Type Getting Brown Eyes Rolling a Seven in a game of monopoly Probability of Getting Rained Upon in a Picnic Winning in Roulette Winning a Million Dollars in the Lottery Rare or Unusual Events (Term to Know in Inferential Statistics)

3. Three Probability Rules The probability of any event must be greater than or equal to 0 –and- less than or equal to 1. Sum of all Probabilities of all possible outcomes must equal 1. If E represents an event, P(E) is the probability of that event, then the probability of that event not happening is 1-P(E) – Complement Rule.

4. Three Probability Rules The probability of any event must be greater than or equal to 0 –and- less than or equal to 1. Sum of all Probabilities of all possible outcomes must equal 1. If E represents an event, P(E) is the probability of that event, then the probability of that event not happening is 1-P(E) – Complement Rule. Example Two True Probability Model? Yes – First two rules apply Probability of only getting one head in two flips? 50% Probability of not getting one head in two flips (0 or 2 heads)? 1-50%=50% Example One True Probability Model? No – Second Rule Doesn’t apply

5. Three Probability Rules –Page 2 Example Three – Empirical Data What is the probability of drawing a brown M&M? What is the probability of not drawing a brown M&M? What is the probability of not drawing a yellow M&M? Example Four – # of girls with three children (assuming the same likelihood for boy/girl) What is the probability of getting exactly two girls? What is the probability of not getting exactly two girls? What is the probability of getting no more than one girl? 0.2 0.8 3/8 5/8 4/8 = 1/2

6. Mean of Discrete Random Variable Mean = (0 * 0.25) + (1 *0.50) + (2 *.25) = 0 + 0.5 +0.5 = 1 Mean and Variance of Discrete Random Variable