Course summary

The aim of this course is to provide fundamental statistical concepts and tools relevant to the practice of summarizing, analyzing, and visualizing data. This course will build your knowledge of the fundamental principles of biostatistical inference. The course will focus on linear regression and generalized linear regression models. We will use a variety of examples and exercises from scientific, medical, and public health research.

Course Details

Course number: PUBHLTH 690NR

Instructor: Nicholas Reich

Office hours: Wed 9–10am

    Biostatistics Methods 1, or equivalent. Otherwise, permission of instructor required.
    Working knowledge of basic matrix methods and calculus will be helpful.
    Familiarity with the R statistical programming language is expected.

Lectures: Tu/Th, 11:15am–12:30pm, LGRC A203

Required books
    M Kutner, C Nachtsheim, J Neter. (2004). Applied Linear Regression Models 4th Edition.

Recommended books
    D Diez, C Barr, and M Cetinkaya-Rundel. (2012) OpenIntro Statistics 2nd Edition.
    Y Xie. (2013) Dynamic documents with R and knitr
    H Wickham. (2009) ggplot2: Elegant Graphics for Data Analysis

Links to course resources

    PDF Syllabus

    Detailed class schedules

    Lecture Notes



    Problem sets
        Problem Set 1 due 2/25/2014
        Problem Set 2 due 3/7/2014
        Problem Set 3 due 4/4/2014


        Some datasets may be found in the datsets folder of the Google Drive folder for this course.
        Datasets from the KNN textbook exercises can be downloaded here.
        Other datasets are available from the Vanderbilt data website or UCI Machine Learning Repository.

Source on github
    The source for the website
    The source for other material