MATH 36B: Mathematical StatisticsInstructor: Shahriar Mirzadeh Prerequisites: A solid foundation in calculus and introductory probability is recommended. Course Description: In this course, we will explore the fundamental principles of probability and statistical inference, bridging rigorous mathematical theory with hands-on data analysis. Students will engage with essential topics such as probability distributions, estimators, and hypothesis testing, learning how these concepts shape real-world decision-making in fields ranging from finance to healthcare. </br></br> We will prove and apply key theorems to real datasets, gaining both a theoretical and practical understanding of statistical methods. Core topics include: </br></br> Maximum Likelihood Estimators (MLE): A powerful tool for parameter estimation.</br> The Information Inequality: Understanding the limits of statistical estimation.</br> Chi-Square Tests: Hypothesis testing for categorical data.</br> Analysis of Variance (ANOVA): Comparing means across multiple groups to uncover significant differences.</br></br> Through a combination of lectures, problem-solving, and computational exercises, students will develop the skills needed to interpret data and make informed conclusions. This course is ideal for students looking to deepen their understanding of probability and statistics while applying these techniques to real-world scenarios. </br></br> Prerequisites: A solid foundation in calculus and introductory probability is recommended. 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 Enrollment Limit: Course Classification: Undergraduate Level Course Course Tuition: $3,700 Course Fees: None Open to High School Students: No |
