This course provides students with an introduction to probability models including sample spaces, mutually exclusive and independent events, and conditional probability and Bayes’ Theorem. The named distributions (Discrete Uniform, Hypergeometric, Binomial, Negative Binomial, Geometric, Poisson, Continuous Uniform, Exponential, Normal (Gaussian), and Multinomial) are used to model real phenomena. Discrete and continuous univariate random variables and their distributions are discussed. Joint probability functions, marginal probability functions, and conditional probability functions of two or more discrete random variables and functions of random variables are also discussed. Students learn how to calculate and interpret means, variances, and covariances, particularly for the named distributions. The central limit theorem is used to approximate probabilities.
Note: Each week you have several sections of the notes to read carefully and 2-4 corresponding presentations. You should continually attempt problems at the end of the corresponding chapters on your own to make sure you understand the concepts since these are similar to problems on the final exams. Some of these problems are easy; others are very difficult.
