Data Analysis and Probability Models

Course #OS3080

Est.imated Completion Time: 3 months

POC: NPS Online Support

Overview

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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.

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