Chapter 4.1（课件）- 《统计学基础（英文版）》同步教学（人民大学版）

Chapter Discrete Probability Distributions 4 1 Copyright 2019, 2015, 2012, Pearson Education, Inc. 1 Larson/Farber 6th ed Chapter 1 Chapter Outline 4.1 Probability Distributions 4.2 Binomial Distributions 4.3 More Discrete Probability Distributions . 1 Copyright 2019, 2015, 2012, Pearson Education, Inc. Section 4.1 Probability Distributions . 1 Copyright 2019, 2015, 2012, Pearson Education, Inc. Section 4.1 Objectives How to distinguish between discrete random variables and continuous random variables How to construct a discrete probability distribution and its graph and how to determine if a distribution is a probability distribution How to find the mean, variance, and standard deviation of a discrete probability distribution How to find the expected value of a discrete probability distribution . 1 Copyright 2019, 2015, 2012, Pearson Education, Inc. Random Variables Random Variable Represents a numerical value associated with each outcome of a probability distribution. Denoted by x Examples x = Number of sales calls a salesperson makes in one day. x = Hours spent on sales calls in one day. . 1 Copyright 2019, 2015, 2012, Pearson Education, Inc. 5 Random Variables Discrete Random Variable Has a finite or countable number of possible outcomes that can be listed. Example x = Number of sales calls a salesperson makes in one day. x 1 5 3 0 2 4 . 1 Copyright 2019, 2015, 2012, Pearson Education, Inc. 6 Random Variables Continuous Random Variable Has an uncountable number of possible outcomes, represented by an interval on the number line. Example x = Hours spent on sales calls in one day. x 1 24 3 0 2 … . 1 Copyright 2019, 2015, 2012, Pearson Education, Inc. 7 Example: Discrete and Continuous Variables Determine whether each random variable x is discrete or continuous. Explain your reasoning. Solution: Discrete random variable (The number of companies that lost money in the previous year can be counted.) x 1 500 3 0 2 … Let x represent the number of Fortune 500 compani