PPT-Discrete Probability Chapter 7

Chapter Summary Introduction to Discrete Probability Probability Theory Bayes Theorem An Introduction to Discrete Probability Section 71 Section Summary Finite Probability Probabilities of Complements and Unions of Events. Discrete Probability Distributions. 1. Larson/Farber 4th ed. Chapter Outline. 5.1 Probability Distributions. 5.2 Binomial Distributions. 5.3 More Discrete Probability Distributions. Larson/Farber 4th ed. QSCI 381 – Lecture 12. (Larson and Farber, Sect 4.1). Learning objectives. Become comfortable with variable definitions. Create and use probability distributions. Random Variables-I. A . 1. http://www.landers.co.uk/statistics-cartoons/. 5.1-5.2: Random Variables - Goals. Be able to define what a random variable is.. Be able to differentiate between discrete and continuous random variables.. Sections 4.7, 4.8: Poisson and . Hypergeometric. Distributions. Jiaping. Wang. Department of Mathematical Science . 03/04/2013, Monday. Outline. Poisson: Probability Function. . Poisson: Mean and Variance. 13. . Tuesday, October 4, . 2016. Textbook: Sections 7.3, 7.4, . 8.1. , 8.2, 8.3. • . Identify, and resist the temptation to fall for, the “gambler’s fallacy. ”. • Define “random variable” and identify the difference between discrete and continuous.. St. . Edward’s. University. .. .. .. .. .. .. .. .. .. .. .. Chapter 5. Discrete Probability Distributions. .10. .20. .30. .40. 0 . . 1 . . 2 3 4. Random Variables. Fall 2010. Sukumar Ghosh. Sample Space. DEFINITION. . The . sample space S . of an experiment is the set . of possible outcomes. An . event. . E. is a . subset. of the sample space.. What is probability?. Chapter 4: Probability: The Study of Randomness Lecture Presentation Slides Macmillan Learning © 2017 Chapter 4 Probability: The Study of Randomness 4.1 Randomness 4.2 Probability Models 4.3 Random Variables Chapter 4: Probability: The Study of Randomness Lecture Presentation Slides Macmillan Learning © 2017 Chapter 4 Probability: The Study of Randomness 4.1 Randomness 4.2 Probability Models 4.3 Random Variables . 3.1 - Random Variables. 3.2 - Probability Distributions for Discrete. Random Variables . 3.3 - Expected Values. 3.4 - . The Binomial Probability Distribution. 3.5 - Hypergeometric and Negative. Random variable: A variable whose value is determined by the outcome of a random experiment is called a random variable. Random variable is usually denoted by X. A random variable may be discrete or Nisheeth. Random Variables. 2. Informally, a random variable (. r.v.. ) . denotes possible outcomes of an event. Can be discrete (i.e., finite many possible outcomes) or continuous. Some examples of discrete . Section 6.1. Discrete and Continuous. Random Variables. Discrete and Continuous Random Variables. USE the probability distribution of a discrete random variable to CALCULATE the probability of an event.. 1. http://www.landers.co.uk/statistics-cartoons/. 5.1-5.2: Random Variables - Goals. Be able to define what a random variable is.. Be able to differentiate between discrete and continuous random variables..