This is a calculus-based introductory statistics course. Topics covered include data collection, data visualization, descriptive statistics, linear regression, sampling schemes, design of experiment, probability, random variables (both discrete and continuous cases), Normal model, statistical tests and inference (e.g. one-sample and two-sample z-tests and t-tests, chi-square test, etc). Statistical language R will be used throughout the course to realize data visualization, linear regression, simulations, and statistical tests and inference.
Max Enrollment: 25
Instructor: J. Lauer
Distribution Requirements: MM - Mathematical Modeling and Problem Solving
Degree Requirements: QRF - Quantitative Reasoning - Overlay
Typical Periods Offered: Spring; Fall
Semesters Offered this Academic Year: Spring; Fall