STATS 191 (Autumn 2019, Stanford)
Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Student assignments require use of the software package R.
By the end of the course, students should be able to:
- Enter tabular data using R.
- Plot data using R, to help in exploratory data analysis.
- Formulate regression models for the data, while understanding some of the limitations and assumptions implicit in using these models.
- Fit models using R and interpret the output.
- Test for associations in a given model.
- Use diagnostic plots and tests to assess the adequacy of a particular model.
- Find confidence intervals for the effects of different explanatory variables in the model.
- Use some basic model selection procedures, as found in R, to find a best model in a class of models.
- Fit simple ANOVA models in R, treating them as special cases of multiple regression models.
- Fit simple logistic and Poisson regression models.
- Term: Autumn 2019
- Units: 3
- In this course, we will use R for computing and R Markdown for producing lecture slides, solutions for homework assignments. R Markdown is highly recommended to write the solutions for homework assignments. Install the following software:
- R (required): https://www.r-project.org/.
- R Studio is highly recommended for syntax highlighting, package management, document generation, and more: https://www.rstudio.com/.
- The newest version of R Studio is highly recommended.
- LaTeX, which will enable you to create PDFs directly from the R Markdown in RStudio.
The final letter grade for this course will be determined by each method of assessment weighted as follows:
- 7 weekly homework assignments (55%)
- Midterm examination (15%, Wednesday, 10/23/2019)
- Final examination (30%, according to Stanford calendar: Wednesday, 12/11/2019 @ 3:30 PM, location TBD)
|09/23/2019||Week 1 Lecture 1||Course introduction and review||Syllabus|
|09/25/2019||Week 1 Lecture 2||Review||CH: 1|
|09/27/2019||Week 1 Lecture 3||Some tips on R||Homework 1 posted|
|09/30/2019||Week 2 Lecture 4||Simple linear regression 1 (introduction, correlation, model, estimation)||CH: 2.1-2.4||–|
|10/02/2019||Week 2 Lecture 5||Simple linear regression 2 (inference and prediction)||CH: Chapter 2.5-2.8||–|
|10/04/2019||Week 2 Lecture 6||Diagnostics for simple linear regression||CH: 2.9||Homework 2 posted, Homework 1 Due|
|10/07/2019||Week 3 Lecture 7||Multiple linear regression 1 (introduction, model, estimation, geometry of least squares)||CH: 3.1-3.5||–|
|10/09/2019||Week 3 Lecture 8||Multiple linear regression 2 (interpretation, matrix formulation, estimation, inference)||CH: 3.6-3.9||–|
|10/11/2019||Week 3 Lecture 9||Multiple linear regression 3 (prediction, contrasts, testing)||CH: 3.10-3.11||Homework 3 posted, Homework 2 Due|
|10/14/2019||Week 4 Lecture 10||Diagnostics in multiple linear regression (types of residuals, influence)||CH: 4||–|
|10/16/2019||Week 4 Lecture 11||Diagnostics in multiple linear regression (outlier detection, residual plots)||CH: 4||–|
|10/18/2019||Week 4 Lecture 12||Interactions and qualitative variables (interactions)||CH: 5||Homework 4 posted, Homework 3 Due|
|10/21/2019||Week 5 Lecture 13||Interactions and qualitative variables (visualization, ANOVA)||CH: 5||–|
|10/25/2019||Week 5 Lecture 14||ANOVA models (one-way ANOVA, testing, contrasts)||CH: 5||–|
|10/28/2019||Week 6 Lecture 15||ANOVA models (two-way ANOVA, testing, contrasts, mixed effects model)||CH: 5||–|
|10/30/2019||Week 6 Lecture 16||Transformations and Weighted Least Squares||CH: 6,7||–|
|11/01/2019||Week 6 Lecture 17||Correlated errors||CH: Chapter 8,9||Homework 5 posted, Homework 4 Due|
|11/04/2019||Week 7 Lecture 18||Correlated errors||CH: Chapter 8,9||–|
|11/06/2019||Week 7 Lecture 19||Bootstrapping regression||An Introduction to the Bootstrap by Bradley Efron, Robert Tibshirani, Chapter 9||–|
|11/08/2019||Week 7 Lecture 20||Model selection||CH: 11||Homework 6 posted, Homework 5 Due|
|11/11/2019||Week 8 Lecture 21||Selection||CH: 11||–|
|11/13/2019||Week 8 Lecture 22||Selection||CH: 11||–|
|11/15/2019||Week 8 Lecture 23||Penalized regression||CH: 10||Homework 7 posted, Homework 6 Due|
|11/18/2019||Week 9 Lecture 24||Penalized regression||CH: 10||–|
|11/20/2019||Week 9 Lecture 25||Penalized regression||CH: 10||–|
|11/22/2019||Week 9 Lecture 26||Logistic regression||CH: 12||Homework 7 Due|
|11/25/2019||–||–||–||Thanksgiving Recess (no classes)|
|11/27/2019||–||–||–||Thanksgiving Recess (no classes)|
|11/29/2019||–||–||–||Thanksgiving Recess (no classes)|
|12/02/2019||Week 10 Lecture 27||Logistic regression||CH: 12||–|
|12/04/2019||Week 10 Lecture 28||Poisson regression||CH: Chapter 13.3||–|
|12/06/2019||Week 10 Lecture 29||Final Review||Review will be posted||–|
R Markdown files
R Markdown files to create the lecture slides and PDFs are available in https://github.com/PratheepaJ/STATS191.