The course provides an introduction to applied statistics and data analysis. The course will cover the following topics: collecting and exploring data, estimation and hypothesis testing, linear regression, analysis of variance and elementary methods in non-parametric statistics. Applications and examples based on engineering systems data will be given.

Learning Objectives

At the end of the term, students will be able to:

  • Examine data and use tools to visualize the data to uncover relationships
  • Develop estimates and confidence intervals from a data sample; perform hypotheses testing and goodness-of-fit tests
  • Build a regression model and estimate the parameters, and perform a diagnosis on the quality and validity of the model
  • Design an experiment and conduct analysis of variance for a multi-treatment sample

Measurable Outcomes

  • Demonstrate working knowledge of statistical methods and tools
  • Perform various types of data analysis: estimation, hypothesis testing, goodness-of-fit; regression; ANOVA
  • Know what methods to use when for statistical inference and estimation

12 Credits

Prerequisites: 40.001 Probability (or 30.3003/50.034 Introduction to Probability and Statistics)

Image Credit