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. We will focus on linear regression and generalized linear regression models using a variety of examples and exercises from medical and public health research.

Course Details

Course number: PUBHLTH 690NR

Instructor: Nicholas Reich

Office hours: Wed 9:30-10:30 or by appointment

    A first course in statistics or biostatistics.
    Familiarity with the R statistical programming language.
    Working knowledge of basic matrix methods and calculus (optional, but recommended).

Lectures: Tu/Th, 11:15am–12:30pm, LGRC 204

Required books (all freely available online)
    Faraway JJ. 2002. Practical Regression and Anova using R.
    James G, Witten D, Hastie T, and Tibshirani R. 2014. An Introduction to Statistical Learning.
    Diez D, Barr C, and Çetinkaya-Rundel M. 2012. OpenIntro Statistics, 2nd Ed.

Recommended books
    Hefferon J. 2014. Linear Algebra (free textboox)
    Weisberg S. 2005. Applied Linear Regression, 3rd Edition.
    Kutner M, Nachtsheim C, Neter J, and Li W. 2004. Applied Linear Regression Models, 4th Edition.
    Hosmer DW and Lemeshow S. 2000. Applied Logistic Regression, 2nd Edition.

Topics covered
    Simple and multiple linear regression
    Least squares estimation, interpretation and inference about linear regression
    Goodness of fit, model diagnostics
    Model selection
    Inference using bootstrapping
    Smooth splines
    Logistic regression (introduction)
    Longitudinal data analysis (introduction)
    Poisson regression (introduction, time permitting)

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