datamap Courses Projects Support

Regression models for continuous outcome data - least squares estimates and their properties, interpreting coefficients, prediction, comparing models, checking model assumptions, transformations, outliers, and influential points. Categorical explanatory variables - interaction and analysis of covariance, correlation and partial correlation. Appropriate graphical methods and statistical computing. Analysis of variance for one- and two-factor models - F tests, assumption checking, multiple comparisons. Random effects models and variance components. Introduction to repeated measures models.

Details

  • Department: Public Health (PB HLTH)
  • Units: 4
  • Prerequisites: PB HLTH 142
  • Tools:
  • Cluster(s): Mathematics/Statistics Biological Science
  • Tags: Applied