INTRODUCTORY APPLIED STATISTICS FOR THE LIFE SCIENCES
Introduction to modern statistical practice in the life sciences, using the R programming language. Topics include: exploratory data analysis, probability and random variables; one-sample testing and confidence intervals, role of assumptions, sample size determination, two-sample inference; basic ideas in experimental design, analysis of variance, linear regression, goodness-of fit; biological applications.
Topics
- exploratory data analysis
- probability and random variables
- one-sample testing and confidence intervals
- role of assumptions
- sample size determination
- two-sample inference
- basic ideas in experimental design
- analysis of variance
- linear regression
- goodness-of fit
- biological applications
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: ( MATH 112 and placed out of MATH 113 ), ( MATH 113 and placed out of MATH 112 ), ( MATH 112 and 113 ), MATH 114 , 171, 211 , 221 , or placement in MATH 221 . Not open to students with credit for STAT 302 or 324 Course Designation: Core GenEd - Mathematics & Quantitative Reasoning Comm QR - Quantitative Reasoning B Breadth - Natural Science Level - Intermediate L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Articulate the basics of probability and statistics Audience: Undergraduate 2. Make numeric and graphical summaries of simple data Audience: Undergraduate 3. Produce appropriate statistical analyses of simple data sets Audience: Undergraduate 4. Design simple experiments whose data will suit basic statistical analysis Audience: Undergraduate 5. Use RStudio, a free statistical software package, for statistical computations and graphs Audience: Undergraduate 6. Study and learn additional statistical methods Audience: Undergraduate Learning Outcomes: 1. Articulate the basics of probability and statistics Audience: Undergraduate 2. Make numeric and graphical summaries of simple data Audience: Undergraduate 3. Produce appropriate statistical analyses of simple data sets Audience: Undergraduate 4. Design simple experiments whose data will suit basic statistical analysis Audience: Undergraduate 5. Use RStudio, a free statistical software package, for statistical computations and graphs Audience: Undergraduate 6. Study and learn additional statistical methods Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027