Biological Statistics I

Prerequisite: one semester of calculus. Instructor: Cecilia Earls In this course, students develop statistical methods and apply them to problems encountered in the biological and environmental sciences. Methods include data visualization, population parameter estimation, sampling, bootstrap resampling, hypothesis testing, the Normal and other probability distributions, and an introduction to linear modeling. Applied analysis is carried out in the R statistical computing environment.  Outcome 1: Students will be able to discuss and explain hypothesis testing and the basic principles of probability and statistics. Outcome 2: Students will be able visualize trends in complex data sets and develop simple models for analysis. Outcome 3: Students will be able to estimate population means, variances, standard deviations, and standard errors through a variety of methods. Outcome 4: Students will be able to critically evaluate the assumptions upon which statistical estimation is based. Outcome 5: Students will be able to conduct a single-sample, two-sample, and paired t-tests. Outcome 6: Students will be able to conduct goodness-of-fit tests, contingency tables, simple linear regression and one-way analysis of variance.