Q SCI 483
Analysis of linear regression models and introduction to nonlinear models. Model selection using generalized F-tests; residual analysis. Application to categorical, count, binomial, transformed variables. Introduction to matrix formation of regression models and applications. Prerequisite: Q SCI 381; Q SCI 482. Offered: Sp.
This course introduces regression analysis with applications in natural sciences and resource management. Regression analysis provides an analytical framework for determining and using models that explain how a response variable (aka dependent variable) relates to one or more explanatory variable/s (aka predictor variables).
Student learning goals
The course goal is to develop critical thinking for statistical modeling in natural sciences and natural resource management contexts and to develop relevant data analysis skills.
Topics covered in the course include:
simple linear regression
multiple and non-linear regression
Implementation of regression methods in the R statistical software
General method of instruction
Two 2-hour lectures per week, along with a 2-hour computer lab using R. Implementation of statistical methods in the R statistical software will be covered for all topics. Examples will form an important part of the lectures. Problem sets will focus on conducting data analyses and reporting the results.
Students enrolling for this course should have taken at least one other statistics course (equivalent to QSCI 482 or QSCI 381) and should be conversant with the basic fundamentals of statistical testing and estimation. Prior knowledge of the statistical software R is NOT required.
Class assignments and grading
Weekly homework assignments covering aspects introduced in the lectures and the computer labs. One midterm and one final examination.
Homework: 50% Class Participation: 10% Midterm: 15% Final: 25%