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

INTRODUCTION TO PROBABILITY AND MATHEMATICAL STATISTICS I

MATH/STAT 309
Course Description

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

Prerequisties

MATH 234 , MATH 376 , or concurrent enrollment. Not open to students with credit for MATH/STAT 431 or STAT 311

Satisfies
Credits

3

Offered

Fall

Grade Point Average
3.01

-4.66% from Historical

Completion Rate
91.73%

-1.51% from Historical

A Rate
26.32%

-31.8% from Historical

Class Size
133

56.66% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

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.

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.

MATHEMATICAL STATISTICS

MATH/STAT 709

Introduction to measure theoretic probability; derivation and transformation of probability distributions; generating functions and characteristic functions; conditional expectation, sufficiency, and unbiased estimation; methods of large sample theory including laws of large numbers and central limit theorems; order statistics.

INTRODUCTION TO THEORY AND METHODS OF MATHEMATICAL STATISTICS I

STAT 311

Elements of probability, important discrete distributions, acceptance sampling by attributes, sample characteristics, probability distributions and population characteristics, the normal distribution, acceptance sampling plans based on sample means and variances, sampling from the normal, the central limit theorem, point and interval estimation.

INTRODUCTORY PROBABILITY

MATH 331

Topics covered include axioms of probability, random variables, the most important discrete and continuous probability distributions, expectation and variance, conditional probability and conditional expectations, Markov's and Chebyshev's inequalities, laws of large numbers, and the central limit theorem. Includes a brief introduction to techniques of multivariate integration.