Course Search
Courses may be offered in one of the following modalities:
- Traditional in-person courses (0–29 percent of coursework is delivered online, the majority being offered in person.)
- Hybrid/blended courses (30–79 percent of coursework is delivered online.)
- Online courses (100 percent of coursework is delivered online, either synchronously on a designated day and time or asynchronously as a deadline-driven course.)
- Hyflex (Students will be assigned to attend in-person or live streamed sessions as a reduced-size cohort on a rotating basis; live sessions are also recorded, offering students the option to participate synchronously or view asynchronously as needed.)
If you are enrolled in courses delivered in traditional or hybrid modalities, you will be expected to attend face-to-face instruction as scheduled.
MTH 560: Regression Analysis
3 credits
Students will explore simple linear regression, multiple regression, non-linear regression and logistic regression models. Students will study random and mixed effects models and penalized regression. Finally, students will learn analysis of variance models including: within subject designs, mixed models, blocking, Latin Square, path analysis, and models with categorical dependent variables.
Learning Goals
Students will:• Explain the context for simple linear regression. This will be assessed by Quiz 1 and the mid-term examination. • Evaluate simple linear regression models. This will be assessed by Quiz 1 and the mid-term examination.• Explain the assumptions that need to be met for a simple linear regression model to be valid. This will be assessed by Quiz 1 and the mid-term examination.• Explain how multiple predictors can be included into a regression model. This will be assessed by the mid-term examination and Quiz 4.• Explain the assumptions that need to be met when multiple predictors are included in the regression model for the model to be valid. This will be assessed by Quiz 2 and the mid-term examination.• Use multiple linear regression model is used to estimate and predict likely values. This will be assessed by Quiz 3 and the mid-term examination.• Apply categorical predictors into regression models. This will be assessed by Quiz 10 and the final examination.• Transform data in order to deal with problems identified in the regression model. This will be assessed by Quiz 5 and the final examination.• Explore strategies for building regression models. This will be assessed by all course assignments.• Distinguish between outliers and influential data points and how to deal with these. This will be assessed by Quiz 6 and the final examination.• Solve problems typically encountered in regression contexts. This will be assessed by Quiz 7 and the final examination.• Apply alternative methods for estimating a regression line besides using ordinary least squares. This will be assessed by Quiz 8 and the final examination.• Apply regression models in time dependent contexts. This will be assessed by Quiz 9 and the final examination.• Apply regression models in non-linear contexts. This will be assessed by the final examination.
*The learning goals displayed here are those for one section of this
course as offered in a recent semester, and are provided for the purpose of
information only. The exact learning goals for each course section in a
specific semester will be stated on the syllabus distributed at the start
of the semester, and may differ in wording and emphasis from those shown
here.