The aim of this course is to provide students with the skills necessary to tell interesting and useful stories in real-world encounters with data. Students will learn fundamental concepts and tools relevant to the practice of summarizing, visualizing, modeling, and analyzing data. Students will learn how to build statistical models that can be used to describe multidimensional relationships that exist in the real world. Specific methods covered will include linear, logistic, and Poisson regression. This course will introduce students to the R statistical computing language and by the end of the course will require substantial independent programming. To the extent possible, the course will draw on real datasets from biological and biomedical applications. This course is designed for students who are looking for a second course in applied statistics/biostatistics (e.g. beyond BIOSTATS 391B or STAT 240), or an accelerated introduction to statistics and modern statistical computing.
Course number: PUBHLTH 490ST
Instructor: Nicholas Reich
TA: Chu-Yuan Luo
Office hours: Wednesdays 11:30-12:30 (Instructor), or Mondays 12-1pm (TA, in 211 Arnold House), or by appointment
One of any of the following introductory stats courses taught at UMass: BIOSTAT 391B, STAT 240, STAT 501, ResEcon 212, PSYCH 240. If you have not taken an intro stats course at UMass but still want to enroll in this course, you are encouraged to petition the instructor for permission, especially if any of the following apply: (a) you have taken AP Stats in high school, (b) you have taken a college-level intro stats course just not one of the ones listed above, or (c) you are confident in your quantitative skills and your ability to succeed in a fast-paced, advanced introductory course.
Lectures: Tu/Th, 2:30pm–3:45pm
Kaplan D. 2012. Statistical Modeling: A Fresh Approach.
Recommended book (free download)
Diez D, Barr C, and Çetinkaya-Rundel M. 2012. OpenIntro Statistics, 3rd Ed.
Simple and multiple linear regression
Least squares estimation, interpretation and inference about linear regression
Goodness of fit, model diagnostics
Inference using bootstrapping
Logistic regression (introduction)
Longitudinal data analysis (introduction)
Poisson regression (introduction, time permitting)