Academic Catalog > Courses > OS > 3000 > OS3080
· 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.