PADM 5310

PADM 5310

Course information provided by the Courses of Study 2018-2019.

This class is an applied introduction to multivariate statistical inference that is aimed at graduate students with little prior statistical experience, and satisfies the Quantitative Methods and Analytics requirement in CIPA. We will begin with a brief introduction to basic statistical concepts and probability theory before introducing the linear regression model. We then review several tools for diagnosing violations of statistical assumptions, including how to deal with outliers, missing data, omitted variables, and weighting. We will next consider situations in which linear regression will yield biased estimates of the population parameters of interest, with particular attention paid to measurement error, selection on unobservables, and omitted variables. The course will end with an introduction to extensions of the linear regression model, including models for binary and categorical outcomes. While statistical modeling is the focus of the course, we proceed with the assumption that models are only as good as the theoretical and substantive knowledge behind them. Thus, in covering the technical material, we will spend considerable time discussing the link between substantive knowledge and statistical practice. The course is designed primarily for professional masters students.

When Offered Spring.

  • Conduct statistical analysis using the multiple regression tool
  • Diagnosis and understand the limitations to the multiple regression tool
  • Interpret results from a linear model
  • Critically assess statistical models displayed in scholarly research articles
  • Produce a data and research project using multivariate tools

View Enrollment Information

Syllabi: none
  •   Regular Academic Session.  Choose one lecture and one laboratory.

  • 3 Credits Stdnt Opt

  • 17380 PADM 5310   LEC 001

  • 18371 PADM 5310   LAB 401

  • 18372 PADM 5310   LAB 402