COURSE OBJECTIVES
Upon successful completion of the course, each participant should be able to:
1. Learn how to describe and explore sets of data both numerically and graphically.
2. Learn about the normal, binomial, and other basic models for the distribution of a single
variable and the linear regression model for the relationship between two variables.
3. Learn the basic ideas of good experimental design and good sampling design.
4. Understand some basic probability theory, and the importance of the normal
distribution and Central Limit Theorem to statistical inference.
5. Learn the fundamental ideas of statistical inference for means and proportions including
both hypothesis testing and confidence intervals.
6. Understand multiple linear regression, model building, and associated normal-based
inference procedures.
7. Understand analysis of variance and to carry out analyses of variance for a variety of
experimental designs, including completely randomized and randomized block designs.
8. Understand the assumptions behind standard statistical inference procedures for linear
regression and analysis of variance.
9. Carry out analyses of real data sets using R and communicate the results in written form.
10. Learn how to critically evaluate scientific journal articles with respect to the material
learned in this class.