Applied Statistics MS Degree Program

Covers joint probability distributions, functions of random variables, sampling and limiting distributions, introduction to parameter estimation.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): (MA 3720 or EE 3180) and MA 3160

Continuation of MA4760. Theory of point and interval estimation; properties of estimators, theory of hypothesis testing, analysis of variance, analysis of categorical data and other topics as time allows

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): MA 4760

Introduction to design, conduct, and analysis of statistical studies, with an introduction to statistical computing and preparation of statistical reports. Topics include design, descriptive, and graphical methods, probability models, parameter estimation and hypothesis testing.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring, Summer
• Restrictions: Must be enrolled in one of the following Level(s): Graduate

Introduction to computationally intensive statistical methods. Topics include resampling methods, Monte Carlo simulation methods, smoothing technique to estimate functions, and methods to explore data structure. This course will use the statistical software R.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): MA 4770(C) or (MA 4700 and MA 5701)

Application, construction, and evaluation of statistical models used for prediction and classification. Topics include data pre-processing, over-fitting and model tuning, linear and nonlinear regression models and linear and nonlinear classification models.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, Spring
• Pre-Requisite(s): MA 3740 or MA 4710 or MA 4720 or MA 4780 or (MA 4700 and MA 5701)

Project-based course enabling students to identify statistical methods and analysis using R. Topics include exploratory data analysis, classical statistical tests, sample size and power considerations, correlation, regression, and design experiments using advanced programming techniques.

• Credits: 3.0
• Lec-Rec-Lab: (0-2-2)
• Semesters Offered: Fall, Spring
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715

Covers simple, multiple, and polynomial regression; estimation, testing, and prediction; weighted least squares, matrix approach, dummy variables, multicollinearity, model diagnostics and variable selection. A statistical computing package is an integral part of the course. Some prior experience with R is expected.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701

Covers construction and analysis of completely randomized, randomized block, incomplete block, Latin squares, factorial, fractional factorial, nested and split-plot designs. Also examines fixed, random and mixed effects models and multiple comparisons and contrasts. The R programming language is an integral part of the course.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701

Review of distribution theory and transformation theory of random variables. Topics include sufficiency; exponential and Bayesian models; estimation methods, including optimality theory; basics of confidence procedures and hypothesis testing, including the Neyman-Pearson framework.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): MA 4450 and (MA 4770 or MA 4705)

Optimal tests and decision theory. Other topics may include regression and analysis of variance, discrete data analysis, nonparametric models.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): MA 5711

Introduces nonparametric techniques that require less restrictive assumptions on the data. Topics include statistical inference concerning location and dispersion parameters as well as the general distributions. Goodness-of-fit tests for count and ordinal data are also discussed.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, in odd years
• Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715

A unified development of linear statistical models that includes the following topics: matrices and quadratic forms, normal and chi-square distribution theory, ordinary and generalized least squares modeling, estimability, estimation and tests of hypothesis.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall
• Pre-Requisite(s): MA 4710 and MA 4720 and MA 4760 and MA 4330

The focus of this course is on generalized linear models (GLM), including the structure of GLM, statistical theory for GLM (maximum-likelihood estimation of GLM and hypothesis tests), and their applications. Also covers generalized linear mixed and random effects models.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring, in odd years
• Pre-Requisite(s): (MA 4710 or MA 5731) and (MA 4770 or MA 5712)

Random vectors and matrix algebra. Multivariate Normal distribution. Theory and application of multivariate techniques including discrimination and classification, clustering, principal components, canonical correlation, and factor analysis.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): (MA 4710 or MA 4720) and MA 2320

Application of statistical methods to solve problems in genetics such as locating genes. Topics include basic concepts of genetics, linkage analysis and association studies of family data, association tests based on population samples (for both qualitative and quantitative traits), gene mapping methods based on family data and population samples.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring, in odd years

The theory of Bayesian inference. Topics include prior specifications, basics of decision theory, Markov chain, Monte Carlo, Bayes factor, linear regression, linear random effects model, hierarchical models, Bayesian hypothesis testing, Bayesian model selection.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Fall, in even years
• Pre-Requisite(s): MA 4330 and MA 4710 and MA 4760

Statistical modeling and inference for analyzing experimental data that have been observed at different points in time. Topics include models for stationary and non stationary time series, model specification, parametric estimation, model diagnostics and forecasting, seasonal models and time series regression models.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring
• Pre-Requisite(s): (MA 2710 or MA 2720 or MA 3710 or MA 3715 or MA 5701) and (MA 3720 or EE 3180 or MA 4700)

Structure of 2-way contingency tables. Goodness-of-fit tests and Fisher's exact test for categorical data. Fitting models, including logistic regression, logit models, probit and extreme value models for binary response variables. Building and applying log linear models for contingency tables.

• Credits: 3.0
• Lec-Rec-Lab: (0-3-0)
• Semesters Offered: Spring, in odd years