APST 705 - Linear and Nonlinear Regression Models

(3 units)

Applications of simple, multiple, linear and nonlinear regression models, and time series analysis in the fields of biology; engineering; physical, life and environmental sciences; and economics. Emphasis is given to computer applications.

Grading Basis: Graded
Units of Lecture: 3
Offered: Every Fall

Student Learning Outcomes
Upon completion of this course, students will be able to:
1. design and create cutting-edge-quality graphics/visualizations (including simulations) and tables for linear and non-linear regression models using spreadsheets, statistical packages and special purpose programming.
2. report and interpret linear and non-linear regression models in writing and in presentation format at a graduate level.
3. translate a research problem into regression terms; formulate hypotheses; evaluate alternative regression estimators with respect to their precision, their robustness, and their compliance with assumptions, especially concerning distributions of residuals. Distinguish serious and trivial consequences of assumption violation. Defend their choice. Explain and deploy basic strategies for exploratory analysis and model building.
4. compute statistics in the regression family (OLS, WLS, GLS, and multi-level analysis, including analysis using complex sampling designs) using statistical packages; identify relevant estimates in output of a statistical package; identify implications of the results for the hypotheses; estimate and interpret the magnitudes of associations using both estimates, first differences and confidence intervals.

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