This course is an applied statistics course for a mathematically sophisticated audience. We use real data in examples and exercises, and, to inform our analysis, we study the context in which the data were collected. The practice of statistics may involve many lengthy calculations, and to ensure accuracy and to make it feasible to carry out these calculations we use statistical software, Minitab, for many of the assignments. Minitab is user-friendly and can be mastered quickly.
The course material includes four main topic areas: graphical and numeric descriptive statistics, experimental design, probability, and statistical inference. The material on descriptive statistics focuses on contrasting graphical methods that display salient features of the data and graphical methods that obscure or distort the message in the data, and on numeric summaries that are appropriate for the data we have. Experimental design introduces students to the science of data collection, types of studies, both surveys and experiments, and the question of when causal inference may be concluded from study results. Students are introduced to the concepts of confounding and interacting variables. The probability methods in this course focus on modeling variation in data. Inference introduces students to standard methods for testing hypotheses and constructing confidence intervals. We emphasize interpreting the results of a data analysis in the context of the original research question.
The course meets for three lecture/discussion periods each week and has an additional laboratory period once each week. In the labs we design experiments, carry out the data collections, and analyze the data. We generally have a separate lab for Biology students, and an Open lab for all other students. Students should pick a lab based on their interests and schedule.
15pt