## Biostat Methods 2 Spring, 2014

#### 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

**Prerequisites**:

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**

*Problem sets*

Problem Set 1 due 2/25/2014

Problem Set 2 due 3/7/2014

Problem Set 3 due 4/4/2014

*Datasets*

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