An understanding of probability and uncertainty in real-world
situations. Case studies of the role of uncertainty in the
life sciences. Analyzing chance phenomena to identify the
underlying probabilistic principles and translating them into
probability distributions or simulations. Introduction to
random variables, expected values, and variance in applied
settings to understand decision making in the face of
variability. Introduction to common discrete and continuous
random variables and their applications to the life sciences.
Extensive use of computer simulations to aid in conceptual
understanding.
Athena Title
Intro to Prob for Life Science
Prerequisite
MATH 2250 or MATH 2250E or MATH 2300H or MATH 2400 or MATH 2400H
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Student Learning Outcomes
Students will explain the role that randomness plays in the world around us, with special emphasis on biological processes.
Students will formulate a computer-based simulation to estimate the probability of a complex event, conduct the simulation and interpret the results in practical terms.
Students will calculate the probability of an event using the laws of probability including conditional probability for dependent events and compare related probabilities to determine how one event affects another.
Students will compute the expected value and standard deviation of a random variable as well as a linear combination of random variables and describe the implications of these values within the original context.
Students will use computational software to compute the probabilities for events that follow a specified discrete or continuous probability distribution and identify when events would be expected to follow these probability distributions.
Students will calculate the probability of an event based on certain known information using Bayes' theorem, use Bayes' theorem to solve problems in the health and related sciences such as the probability of obtaining a false negative or false positive result, and describe how Bayes' theorem enables scientists to update the probability of an event by combining previously known information with new evidence.
Topical Outline
This course will cover case studies of randomness in biology; identifying principles underlying random processes; translating these principles into simple simulations; recognizing when those rules imply common probability distributions; properties of common distributions, including binomial, Poisson, normal, and gamma distributions; expectation, variance, and other properties of random variables; joint and conditional probability; simulations involving multiple random variables.
