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

INTRODUCTION TO THEORY AND METHODS OF MATHEMATICAL STATISTICS I

STAT 311
Course Description

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.

Prerequisties

MATH 234 , MATH 376 , or concurrent enrollment or graduate/professsional standing. Not open to students with credit for MATH/STAT 309 or MATH/STAT 431

Satisfies
Credits

3

Offered

Fall, Spring

Grade Point Average
3.55

10.68% from Historical

Completion Rate
99.48%

3.52% from Historical

A Rate
53.13%

45.81% from Historical

Class Size
192

29.96% from Historical

2025 Fall Grade Distribution

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

Similar Courses

AN INTRODUCTION TO SAMPLE SURVEY THEORY AND METHODS

STAT 411

An introduction to the methods used to design sample surveys and analyze the results. Topics covered include: basic tools, simple random sampling, ratio and regression estimation, stratification, systematic sampling, cluster (area) sampling, two-stage sampling, unequal probability sampling, non-sampling errors, and missing data. For illustration and clarification, examples are drawn from diverse areas of application.

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 METHODS I

STAT 601

Provides a thorough grounding in modern statistical methods. The specific learning outcomes for the course are to understand data collection in context (how/why data were collected, key questions under study); explore data by effective graphical and numerical summaries; understand probability concepts and models as tools for studying random phenomena and for statistical inference; analyze data using appropriate, modern statistical models, methods, and software; understand the statistical concepts underlying methods; develop the ability to interpret results and critically evaluate the methods used; communicate data analysis and key findings in context.

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.