Course Number
One semester of Statistics and familiarity with running programs in own machine.

The process of finding a mathematical model (an equation) that best fits the data is part of a statistical technique known as regression analysis. Regression analysis techniques are widely used, and applications of such techniques are widespread in almost every industry—including healthcare, finance, marketing, and engineering to name a few. Receive an overview of regression analysis, with a focus on building a greater understanding, theoretical underpinning, and statistical software (R) for applying the regression models and its generalizations. Using the bigger umbrella of generalized linear models, study topics such as the design of experiments, logistic and Poisson regression. Examine all aspects of the model-building process using real-life data and statistical software.

Course Objectives
  1. Develop a deeper understanding of the regression models and their applications and limitations.
  2. Know how to build a regression model.
  3. Know how to diagnose a model and update it.
  4. Learn how to validate the final model.
  5. Develop greater familiarity with a range of related regression techniques and methods through a diverse set of theoretical and applied tools.
  6. Learn how to carry out a task from start to finish using statistical software.