140.651.01
Methods in Biostatistics I

Prerequisite:

Working knowledge of calculus and linear algebra

Description:

Presents fundamental concepts in applied probability, exploratory data analysis, and statistical inference, focusing on probability and analysis of one and two samples. Topics include discrete and continuous probability models; expectation and variance; central limit theorem; inference, including hypothesis testing and confidence for means, proportions, and counts; maximum likelihood estimation; sample size determinations; elementary non-parametric methods; graphical displays; and data transformations.

Learning Objectives:

Upon successfully completing this course, students will be able to:

1. Discuss core applied statistical concepts and methods
2. Discuss the display and communication of statistical data
3. List the distinctions between the fundamental paradigms underlying statistical methodology
4. Identify the basics of maximum likelihood
5. Identify the basics of frequentist methods: hypothesis testing, confidence intervals
6. Identify basic Bayesian techniques, interpretation and prior specification
7. Discuss the creation and interpretation of P values
8. Describe estimation, testing and interpretation for single group summaries such as means, medians, variances, correlations and rates
9. Describe estimation, testing and interpretation for two group comparisons such as odds ratios, relative risks and risk differences
10. Describe the basic concepts of ANOVA

Methods of Assessment:

Student evaluation based on several problem sets and one exam each term.

Instructor Consent:

No consent required

Special Comments:

Students will choose one lab time: Tuesday OR Wednesday.