stats191 - intro to applied stats

Instructor:	Prof. J. Taylor Sequoia Hall #137 Email 723-9230
Schedule:	MW 11:00-12:15
Location:	200-305
Office Hours:	M 3:00-4:00, W 9:00-10:00 or by appointment
Textbook:	*Regression Analysis by Example. Chaterjee, Hadi & Price*
Optional reference:	*Practical ANOVA and Regression in R. Faraway*
TAs & Office Hours:	Austen Head Office hours: W 3:15-5:15, Sequoia Hall #208 Jun Li Office hours: Th 3:00-5:00, Sequoia Hall #211
Computing environment:	R, read the New York Times article.
General Outline:	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.
Prerequisites:	An introductory statistics course, such as STATS 60.
Evaluation:	6 (short) assignments: 60% take home final exam: 40%
Assignments:	Assignment 1, Solution Assignment 2, Solution Assignment 3, Solution Assignment 4, Solution Assignment 5 Take home final
Notes:	Review. Examples: heights, right-to-work, review, simulation. Simple linear regression. Examples: simple, loss, T density, confidence intervals, confidence intervals for lm, dummy variables. Diagnostics for simple linear regression. Examples: R squared, F statistic, diagnostic, non-constant variance, Multiple linear regression. Examples: multiple linear regression, partial regression coefficients, matrix representation, confidence intervals, F tests. constraints on coefficients. Diagnostics. Examples: diagnostics. Interactions. Examples: IT salaries job performance rehab time kidney random effects simulated blood pressure Transformations. Examples: bacterial growth education expenditure weighted least squares Correlated errors. Examples: bond data time series simulation correlated errors Model selection. Examples: best subset model selection stepwise model selection bias variance tradeoff Logistic regression. Examples: flu shots iterative least squares Poisson regression. Examples: afterlife example lumber example iterative least squares marijuana use Penalized regression. Examples: bias variance tradeoff Ridge regression LASSO cross-validation Nonparametric regression. Examples: GAG urine smoothers