OS3080 Data Analysis and Probability Models

Lecture Hours

3

Lab Hours

0

Course Learning Outcomes

·       Learn hypothesis testing for contingency tables, ANOVA, and nonparametric tests.

·       Discuss and design experiments for two-factor, three factor and larger. Methods to screen experiments when number of factors are large.

·       Effectively use simple and multiple regression to create models for data.

·       Learn how to effectively work with time series, including use of lagging variables, autoregression techniques and smoothing models.

·       Review basic probability concepts and Bayes’ theorem. Learn about conditioning to compute expectation and probability.

·       Introduce reliability for systems in series and/or parallel. Define failure rate and hazard rate. Fit parametric models to failure data including censored data.

·       Review Poisson and exponential distributions. Define Poisson Processes.

·       Introduce stochastic models. Learn terminology for Markov models, one-step of n-step transition matrices, steady state probabilities and mean first passage time.