About Stat 151a¶
Statistics 151A is an upper-division undergraduate course on linear modeling. Topics include the geometry and statistical theory of Gaussian linear models, model selection and shrinkage, diagnostics, logistic regression, and regression splines; students are also required to conduct a substantial amount of data analysis and visualization in R or other scientific programming language, and to give coherent verbal interpretations of linear modeling results.
Goals¶
By the end of the semester you should be able to:
Understand the purposes and benefits of linear modeling in common applied contexts.
Design and conduct regression analyses in R for common data settings.
Interpret statistical models, estimates of model parameters, and inferences correctly.
Evaluate the quality of a regression analysis and suggest improvements.
Communicate the process and results of a data analysis simply and clearly for a broad audience, using well-organized prose and code and effective data visualizations.
Prerequisites¶
While we are working to make this class widely accessible, we currently require the following (or equivalent) prerequisites. Prerequisites will be enforced in Stat 151a. It is your responsibility to know the material in the prerequisites.
Stat 135 and its other prequisite courses (including Stat 134).
Linear algebra including matrix operations, vector subspaces and projections, rank and invertibility of matrices, and quadratic forms.
Familiarity with R (Stat 133 is recommended).