1 / 11

Warm-up

Warm-up. Explain the difference between a discrete and a continuous variable. Write a probability distribution for the outcomes of the sum of two dice. Chapter 5 Overview. Introduction 5-1 Probability Distributions 5-2 Mean, Variance, Standard Deviation, and Expectation

kurt
Download Presentation

Warm-up

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Warm-up Explain the difference between a discrete and a continuous variable. Write a probability distribution for the outcomes of the sum of two dice.

  2. Chapter 5 Overview Introduction 5-1 Probability Distributions 5-2 Mean, Variance, Standard Deviation, and Expectation 5-3 The Binomial Distribution 5-4 Other Types of Distributions Bluman, Chapter 5 2

  3. Chapter 5 Objectives Construct a probability distribution for a random variable. Find the mean, variance, standard deviation, and expected value for a discrete random variable. Find the exact probability for X successes in n trials of a binomial experiment. Find the mean, variance, and standard deviation for the variable of a binomial distribution. Find probabilities for outcomes of variables, using the Poisson, hypergeometric, and multinomial distributions. Bluman, Chapter 5 3

  4. 5.1 Probability Distributions A random variable is a variable whose values are determined by chance. A discrete probability distribution consists of the values a random variable can assume and the corresponding probabilities of the values. The sum of the probabilities of all events in a sample space add up to 1. Each probability is between 0 and 1, inclusively. Bluman, Chapter 5 4

  5. Example 5-1: Rolling a Die Construct a probability distribution for rolling a single die. Bluman, Chapter 5 5

  6. Example 5-2: Tossing Coins Represent graphically the probability distribution for the sample space for tossing three coins. . Bluman, Chapter 5 6

  7. Baseball World Series Find P(X) for each X, construct a probability distribution, and draw a graph for the data. Bluman, Chapter 5 7

  8. Number of games X 4 5 6 7 Probability P(X)

  9. Graph of the probability distribution Number of games played in World Series Probability Number of games • Two requirements for a probability distribution: • The sum of the probabilities of all events in the sample space must equal 1, that is • The probability of each event in the sample space must be between or equal to 0 or 1. That is, 0 < P(X) < 1.

  10. Applying the Concepts 5-1(bottom of pg 257 and top of pg 258) Bluman, Chapter 5 10

  11. Classwork and Assignment • CW: Exercises 5-1: pg 258: 1-6, 19, 30 • Assignment: pg 258: 7-11 odd, 12-18 all, 20-26 even, 27, 29

More Related