Instructor: Shahriar Mirzadeh

Prerequisites: A solid foundation in calculus and introductory statistics is recommended. Students who have taken MATH 8a or a similar course and are comfortable with single-variable calculus may contact the instructor for permission to enroll.

Course Description: This course explores the fundamental principles of probability and statistical inference, bridging rigorous mathematical theory with practical data analysis. Students will engage with core topics such as probability distributions, estimators, and hypothesis testing, gaining insight into how these tools inform real-world decisions in areas ranging from finance to healthcare.</br>
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The course emphasizes both proof-based understanding and application. Students will learn to derive and apply key theorems using real datasets, building fluency in both theoretical and computational methods. Topics include: </br>
• <b>Maximum Likelihood Estimators (MLE):</b> A foundational technique for estimating unknown parameters. </br>
• <b> The Information Inequality: </b> Insight into the theoretical limits of estimation accuracy. </br>
• <b> Chi-Square Tests: </b> Methods for hypothesis testing with categorical data. </br>
• <b> Analysis of Variance (ANOVA): </b> Statistical techniques for comparing group means. </br>
</br>Through a combination of lectures, problem-solving, and hands-on computation, students will develop the analytical and statistical skills necessary to interpret data and draw meaningful conclusions. This course is ideal for students seeking a deeper mathematical understanding of probability and statistics and how these tools are applied across disciplines.

Session: Session II

Day: M, T, W, Th

Time: 11:20am - 1:40pm

Credit Hours: 4 Credits

Course Format: Remote Learning Course for Summer 2025

Brandeis Graduation Requirement Fulfilled: QR, SN

Course Classification: Undergraduate Level Course

Course Tuition: $3,700

Course Fees: None

Open to High School Students: No