Course Requirements

The Master of Arts program in Statistics requires 34 credit hours.
 
Required courses:

MATH 522 - Theory of Sampling and Surveys  (3)

MATH 528 - Regression and Time Series Models  (3)

MATH 529 - Analysis of Variance in Experimental Design Models  (3)

MATH 620 - Mathematical Theory Statistics 1  (4)

MATH 621 - Mathematical Theory Statistics 2  (4)

MATH 625 - Probability Theory and Applications  (3)

MATH 626 - Probability and Stochastic Processes  (3)

MATH 627 - Generalized Linear Models with Applications  (4)

MATH 628 - Computational Methods in Statistics (4)

MATH 689 - Research Methods in Mathematics and Statistics (3)

The Master of Science program in Statistics requires 34 credit hours.

Required Courses:

MATH 529 - Analysis of Variance in Experimental Design Models (3)

MATH 620 - Mathematical Theory of Statistics 1 (4)

MATH 621 - Mathematical Theory of Statistics 2 (4)

MATH 625 - Probability Theory and Applications (3)

MATH 626 - Probability and Stochastic Processes (3)

MATH 627 - Generalized Linear Models with Applications (4)

MATH 628 - Computational Methods in Statistics (4)

THES 698 - Thesis (1-6 credit hours)

An additional 3 credit hours from:

MATH 522 - Theory of Sampling and Surveys (3)

MATH 528 - Regression and Time Series Models (3)

Related Links 
Graduate Course Offering Schedule (PDF)
                                                                                                                                                                                                 

522 Theory of Sampling and Surveys (3)

Survey designs; simple random, stratified, cluster, and systematic sampling; ratio estimates; regression estimates; cost and variance functions.

Prerequisite:  MATH 321 or the equivalent.

528 Regression and Time Series Models (3)

Addresses regression topics that include simple and multiple linear regression, polynomial regression, regression diagnostics, and forecasting. Also introduces time series topics that include exponential smoothing, auto-regressive, integrated, moving average (ARIMA) models, and forecasting.

Prerequisite:  MATH 321 or the equivalent.

Not open to students who have credit in MATH 428.

529 Analysis of Variance in Experimental Design Models (3)

Multivariate normal distribution; quadratic forms; linear models; simple random, randomized block, Latin squares, factorial, split-plot, balanced incomplete block designs; analysis of covariance; confounding; and multiple comparison tests.

Prerequisite:  MATH 321 or equivalent.

Not open to students who have credit in MATH 429.

620 Mathematical Theory of Statistics 1 (4)

Probability set functions, random variables, density and distribution functions, mathematical expectations, marginal and conditional distributions, sampling distributions, and limiting distributions.  The mathematical rigor requires a strong background in calculus.

Prerequisite:  MATH 166 and MATH 215.

621 Mathematical Theory of Statistics 2 (4)

Estimation theory and statistical tests of hypothesis.  Topics include: classical and Bayesian estimation, sufficiency, completeness, uniqueness, likelihood function, exponential families, Rao-Blackwell Theorem, Rao-Cramer inequality, hypothesis testing, Neyman-Pearson Lemma, likelihood ratio tests, goodness-of-fit, contingency tables, nonparametric tests, distribution of quadratic forms, and correlation and regression, bootstrapping.

Prerequisite:  MATH 620.

625 Probability Theory and Applications (3)

Basic probability theory, random variables, conditional probability and conditional expectation, Poisson process, interarrival time, and waiting time distributions.

Prerequisite:  MATH 166 or equivalent.

626 Probability and Stochastic Processes (3)

Discrete and continuous time Markov chains, queuing theory, renewal theory.

Prerequisite:  MATH 625 or equivalent.

627 Generalized Linear Models with Applications (4)

Methods needed to analyze non-normal data.  Topics include exponential family of distributions, an overview of generalized linear models.  Models for:  continuous data with constant variance, binary data, polytomous data, count data, time to events or survival data.

Prerequisite:  MATH 621 or permission of the department chairperson.

628 Computational Methods in Statistics (4)

Theory and application of simulation techniques used in statistics.  The use of statistical software such as SAS and R in statistical analysis.

Prerequisite:  MATH 620 or permission of the department chairperson.

689 Research Methods in Mathematics and Statistics (3)

The scientific method in mathematical research.  Location of relevant journal articles, reference books, and reviews.  Development of research and problem-solving techniques.  Each student will write a mathematical paper.  The instructor will assist students whose work is of exceptional quality in submitting their results for publication.

Thesis (THES)

698 Thesis

This plan requires the candidate to present a thesis embodying the results of a study of some subject directly related to the area of specialization.  The thesis must show that the candidate possesses the abilities to pursue a research problem successfully and to draw valid and significant conclusions from the data.  The student must have a committee of three faculty members seleced in consultation with the department chairperson.

 

Mathematical Sciences
Robert Bell Building (RB), room 465
Ball State University
Muncie, IN 47306

Hours: 8:00 a.m. - 5:00 p.m. Eastern Time, Monday-Friday (Summer Hours 7:30 a.m. - 4:00 p.m. Eastern Time, Monday - Friday)
Phone: 765-285-8640
Fax: 765-285-1721
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