STA3106 Linear Models

Statistics is all about using probability models to make decisions from data in the face of uncertainty. This course gives an introduction to the process of building statistical models using an important class of models (linear models). In a linear we try to predict or explain variation in a response variable in terms of related quantities. The relationship between the expected response and predictors is linear in unknown model parameters. Topics covered include how to estimate parameters in linear models, how to compare models using hypothesis testing, how to select a good model or models when prediction of the response is the goal, how to use statistical packages like R, SPSS, MatLab and how to detect violation of model assumptions and observations which have undue influence on decisions of interest. Concepts are illustrated from finance, economics, medicine, environmental science and engineering.

General Course Objectives
By the end of the course, the student should be able to:

• Show understanding of the principles of linear models; ANOVA, ANCOVA and random effects linear models.
• Produce estimates and fit models for data obtained and test for goodness of fit of the models.
• Develop competence in identifying the appropriate model to be fitted, do relevant data transformations and be able to analyse messy (unbalanced and un-replicated) data.

Expected Learning Outcomes

• Learners will understand how to use regression analysis to analyse different type of data.
• Learners will understand the concept of regression analysis in the context of experimental and sampling designs and also be able to interpret regression analysis results in a meaningful context for application by practitioners in the field.
• Learners will demonstrate competency in oral and written communication skills.