OS3080 Data Analysis and Probability Models

Additional topics in probability and statistics for systems analysis, including data analysis, simple and multiple regression, conditional probability, conditioning, conditional expectation, and basic probabilistic process models. This course is a follow-on to OS2080 for Master of Systems Analysis students.

Prerequisite

OS2080

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.