Data Analysis and Probability Models

Course #OS3080

Start Starts: not available

Clock Est. completion in 3 months

Location pin Offered through Distance Learning

Avg. tuition cost per course: See tuition Info For specific tuition costs of each program or contact information, please contact the NPS Tuition office at tuition@nps.edu .

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Overview

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.

Included in Degrees & Certificates

  • 363
  • 379

Prerequisites

  • OS2080

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.