This course applies statistical theory to problems in ecology and experimental biology to illustrate some of the more common techniques of experimental design and data analysis. Students will learn how to plan an experiment and consider the observations, measurements, and potential statistical tests before data are collected and analyzed. The course will enable students to work with complex datasets and distill them into meaningful information from which they can draw reasoned conclusions and communicate their findings. Specific topics include best practices in data visualization, probability distributions and their applications, one- and two-way ANOVA and t-tests, regression and correlation, goodness-of-fit tests, and nonparametric alternatives. The course will be run as a studio with combined lecture and hands-on data analysis using the open-source computing software R.
Max Enrollment: 25
Prerequisites: Fulfillment of the Quantitative Reasoning (QR) component of the Quantitative Reasoning & Data Literacy requirement and one course in biology, chemistry, ES 100 or ES 101.
Distribution Requirements: NPS - Natural and Physical Sciences
Degree Requirements: DL - Data Literacy (Formerly QRF); DL - Data Literacy (Formerly QRDL)
Typical Periods Offered: Spring
Semesters Offered this Academic Year: Spring