Regression Models

Provides an introduction to the construction and validation of regression models from a practical point of view. Scientific and engineering examples are used to show the steps in the model-building process and to give an intuitive understanding of regression algorithms and the associated hypothesis tests and statistical intervals. Both linear and nonlinear regression are covered, with special attention to calibration and outlier resistant regression. A computer-based approach is used for calculations to minimize the number of equations and formulas used. Matrix algebra is not emphasized in this course.

Check the SED Calendar for the current schedule of upcoming courses. If the course is not currently scheduled in the SED Calendar, please contact Will Guthrie for more information or to register scheduling requests.

Class Notes from FY 2000 Regression Models Course (in pdf)

Section 1

Section 2

Some of the data sets used in the notes can be found here.