We're still actively developing this site. If you encounter any issues, please report them! - Report an issue

MATHEMATICAL STATISTICS I

MATH/STAT 709
Course Description

An introduction to measure theoretic probability, random variables, and high-dimensional statistics; conditional expectation, sufficiency, and unbiased estimation; methods of large sample theory including laws of large numbers and central limit theorems.

Prerequisites

Declared in Statistics MS, Statistics PhD, Biomedical Data Science PhD, Biomedical Data Science MS, or Statistics Doctoral Minor

Satisfies
Credits

Not Reported

Offered

Not Reported

Grade Point Average
3.67

4.69% from Historical

Completion Rate
100%

0.25% from Historical

A Rate
50%

23.78% from Historical

Class Size
26

21.67% from Historical

Cumulative Grade Distribution

Instructors (2026 Summr)

Sorted by ratings from Rate My Professors

Similar Courses

INTRODUCTION TO PROBABILITY AND MATHEMATICAL STATISTICS I

MATH/STAT 309

Probability and combinatorial methods, discrete and continuous, univariate and multivariate distributions, expected values, moments, normal distribution and derived distributions, estimation.

INTRODUCTION TO THE THEORY OF PROBABILITY

MATH/STAT 431

Topics covered include axioms of probability, random variables, the most important discrete and continuous probability distributions, expectation and variance, moment generating functions, conditional probability and conditional expectations, multivariate distributions, Markov's and Chebyshev's inequalities, laws of large numbers, and the central limit theorem.

STATISTICAL MODELS FOR DATA SCIENCE

STAT 611

Probability, random variables and their distributions, joint and conditional distributions, moments and inequalities, generating functions, transformations of random variables, sampling and distribution theory, convergence concepts and limit theorems for sequences of random variables.

MATHEMATICAL STATISTICS I

STAT 609

Review of probability, random variables and vectors and their distributions, moments and inequalities, generating functions, transformations of random variables, sampling and distribution theory, convergence concepts for sequences of random variables, laws of large numbers, central limit and other limit theorems.

THEORY OF PROBABILITY I

MATH/STAT 733

An introduction to measure theoretic probability and stochastic processes.Topics include foundations, independence, zero-one laws, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, random walks, conditional expectations. Familiarity with basic measure theory (e.g.MATH 629or721) or concurrent registration inMATH 721is strongly recommended.