MATH 270 - Probability and Statistical Models

5 CR

Provides a rigorous introduction to the fundamental principles of probability with emphasis on applications to data-driven problem solving. Starting from an axiomatic definition of probability, students learn how to work with both discrete and continuous random variables and apply these concepts to practical situations.

Topics include: conditional probability, Bayes' theorem; Bernoulli, binomial, geometric, Poisson, uniform (discrete and continuous), normal, and exponential distributions; the law of large numbers; the central limit theorem and its applications; confidence intervals; and the Z-test.

A portion of coursework will include techniques and examples in the Python programming language.

Recommended: MATH 153 , CS 310 or familiarity with Python
Prerequisite(s): MATH 152 with a B- or better or placement by assessment.

Course Outcomes

Model real-world problems by an appropriate probability distribution and use these models to derive insights about these problems.
Apply Bayes' Theorem to update a probabilistic model based on new observations.
Calculate probabilities using appropriate distributions, theorems, diagrams, or software tools.
Choose appropriate calculations for a confidence interval based on theory and simulation (including bootstrapping).
Compute p-values for a Z-test or discrete hypothesis test and interpret the results.
Perform probabilistic and statistical calculations in simulated and real-world data using the Python programming language.

GenEd Outcomes: Creative and Critical Thinking

Critical Thinking/Problem Solving
Quantitative/Symbolic Reasoning
Research/Information Literacy

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