Statistics Courses | Troy University

Statistics Courses (STAT)

STAT 5551

Mathematical Statistics I (3)

 

A study of probability theory, sample spaces, random variables, mutual exclusion, independence, conditional probability, permutations and combinations, common discrete and continuous distributions, expected value, mean, variance, multivariate distributions, covariance, Central Limit Theorem. This course contains additional graduate-level content equivalent to a one-hour recitation with the instructor which will further investigate the theoretical aspects of or applications of the topics discussed in the course.

STAT 5552

Mathematical Statistics II (3)

 

A study of the fundamentals of the theory of statistics, the Central Limit Theorem, point estimation, sufficiency, consistency, hypothesis testing, sampling distributions, confidence intervals, linear regression models, interpretation of experimental results, Bayesian Estimation. This course contains additional graduate-level content equivalent to a one-hour recitation with the instructor which will further investigate the theoretical aspects of or applications of the topics discussed in the course. Prerequisite: STAT 4451 or STAT 5551.

STAT 5556

Mathematics of Finance (3)

 

The fundamental concepts of financial mathematics and how these concepts are applied in calculating present and accumulated values for various streams of cash flows as a basis for future use in the following; reserving, valuation, pricing, asset/liability management, investment income, capital budgeting, and valuing contingent flows. This course contains additional graduate-level content equivalent to a one-hour recitation with the instructor which will further investigate the theoretical aspects of or applications of the topics discussed in the course. Prerequisites: STAT 4451 or STAT 5551

STAT 5559

Regression Analysis (3)

 

Topics include simple linear regression, inferences in regression analysis, techniques of multiple regression and model building, ANOVA as regression analysis, analysis of covariance, model selection and diagnostic checking techniques, nonlinear regression, and logistic regression. Computations are an integral part of the course and will involve the use of SAS and R statistical software. This course contains additional graduate-level content equivalent to a one-hour recitation with the instructor which will further investigate the theoretical aspects of or applications of the topics discussed in the course. Prerequisites: STAT 4451 or STAT 5551

STAT 5564

Multivariate Analysis (3)

 

Possible topics to be covered include multivariate descriptive statistics, multivariate normal distribution, analysis of covariance, MANOVA, multivariate regression, principal components, discriminate analysis, cluster analysis, factor analysis. This course contains additional graduate-level content equivalent to a one-hour recitation with the instructor which will further investigate the theoretical aspects of or applications of the topics discussed in the course. Some familiarity with R and SAS is expected. Prerequisites: STAT 4451 or STAT 5551, and STAT 3350

STAT 5565

Categorical Data Analysis (3)

 

In recent years, the use of specialized statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. The course gives an introduction to analyzing categorical data. Principal topics include: contingency tables, generalized Linear Models,log-linear models, logistic regression, and models for matched pairs. Two statistical software packages SAS and R will be used appropriately throughout the course. This course contains additional graduate-level content equivalent to a one-hour recitation with the instructor which will further investigate the theoretical aspects of or applications of the topics discussed in the course. Prerequisites: STAT 4451 or STAT 5551

STAT 5566

Data Mining (3)

 

Data mining is a fast growing interdisciplinary field between Statistics and Computer Science due to the emergence of massive data sets. This course covers an information extraction activity whose goal is to discover hidden facts from large data warehouses. A number of data mining task including description, classification, selection, estimation, prediction, and affinity grouping and clustering will be discussed. Also students will learn how to use data mining software to perform data mining functionalities. This course contains additional graduate-level content equivalent to a one-hour recitation with the instructor which will further investigate the theoretical aspects of or applications of the topics discussed in the course. Prerequisites: STAT 4459.

STAT 5567

Experimental Design (3)

 

Fundamentals principles of designing and analyzing experiments with application are considered. The concepts of experimental unit, randomization, blocking, replication, error reduction and structure are introduced. The design and analysis of completely randomized, randomized complete block, incomplete block, Latin square, split-plot, repeated measures, factorial and fractional designs will be covered. This course contains additional graduate-level content equivalent to a one-hour recitation with the instructor which will further investigate the theoretical aspects of or applications of the topics discussed in the course. Prerequisites: STAT 4451 or STAT 5551