PPT-Discrete Probability Distributions

Unit 4 Introduction Many decisions in business insurance and other reallife situations are made by assigning probabilities to all possible outcomes pertaining to the situation and then evaluating the results For example a saleswomen can compute the probability that she will make 012 or 3 or more sales in a single day An insurance company might be able to assign probabilities to the number of vehicles a family owns Once these probabilities are assigned statistics such as mean variance and standard deviations can be computed for these events With these statistics various decisions can be made. AS91586 Apply probability distributions in solving problems. NZC level 8. Investigate situations that involve elements of chance. calculating and interpreting expected values and standard deviations of discrete random variables. 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 . Continuous distributions. Sample size 24. Guess the mean and standard deviation. Dot plot sample size 49. Draw the population distribution you expect. Sample size 93. Sample size 476. Sample size 948. 1. 3. Discrete Random Variables and Probability Distributions. 3-1 Discrete Random Variables. 3-2 Probability Distributions and Probability Mass Functions. 3-3 Cumulative Distribution Functions. 3-4 Mean and Variance of a Discrete Random Variable. A Brief Introduction. Random Variables. Random Variable (RV): A numeric outcome that results from an experiment. For each element of an experiment’s sample space, the random variable can take on exactly one value. 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. Random Variables. Definition:. A rule that assigns one (and only one) numerical value to each simple event of an experiment; or. A function that assigns numerical values to the possible outcomes of an experiment.. Probability Terminology. Classical Interpretation. : Notion of probability based on equal likelihood of individual possibilities (coin toss has 1/2 chance of Heads, card draw has 4/52 chance of an Ace). Origins in games of chance.. St. . Edward’s. University. .. .. .. .. .. .. .. .. .. .. .. Chapter 5. Discrete Probability Distributions. .10. .20. .30. .40. 0 . . 1 . . 2 3 4. Random Variables. Random Variables. Definition:. A rule that assigns one (and only one) numerical value to each simple event of an experiment; or. A function that assigns numerical values to the possible outcomes of an experiment.. http://www.answers.com/topic/binomial-distribution. Chapter 13: Bernoulli Random Variables. http://www.boost.org/doc/libs/1_42_0/libs/math/doc/sf_and_dist/html. /. math_toolkit. /. dist. /. dist_ref. . 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. Probability Space of Two Die. σ-. Algebra (. ℱ. ). Sample Space (Ω). [...]. E5={(1,4),(2,3),(3,2),(4,1)}. [...]. Probability Measure Function (P). P. E5. 0.11. Probability Measure Function (P). . 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..