STATS305B: Methods for Applied Statistics¶
Syllabus¶
Piazza¶
Here is the piazza page
Gradescope¶
Here is the gradescope page
Schedule¶
MW 11:30-1:00, Building 200, Rm 303
Textbook¶
Categorical Data Analysis Agresti.
Lectures will be a mix between chalk talk and computing examples.
Additional comments¶
Videos: Lectures will be recorded on Zoom for first two weeks of quarter.
Email policy: For administrative issues that only concern you, email the course staff mailing list: stats305b-win2122-staff@lists.stanford.edu
Website: stats305b.stanford.edu
Computing environment¶
We will use R for most calculations. Examples will typically be in the form of jupyter notebooks.
Prerequisites¶
Stats 305A or similar.
Course description¶
The first half of the course will be from the textbook, focusing on logistic regression and log-linear models.
The second half of the course will consist of different topics, focusing on exponential families:
Regularized regression: LASSO / group LASSO for logistic and log-linear model
General properties of exponential families
Bayesian analysis / conjugate priors
EM algorithm
Pseudo-likelihood
Gibbs sampling
Sampling of topics in survival analysis: censoring / log-rank test / Cox proportional hazards model.
Evaluation¶
5 assignments (70%)
final exam (30%) (according to Stanford calendar: T 03/15 @ 8:30AM)
Slides¶
Notes on these pages are available as HTML slides:
Course introduction as HTML or notability
Contigency tables notability
Inference in two-way tables notability
More inference in two-way tables notability
Logistic regression HTML or notability
Generalized linear models HTML or notability
Bayesian GLMs HTML
Multinomial models HTML
Regularized GLMs HTML
Loglinear GLMs HTML
Matched paris HTML
Survival analysis HTML
Course wrapup HTML