Course Descriptions

Bachelor of Science in Statistics

For a full list of all courses offered by the Department of Mathematics, visit the course catalogue.
This is a three-hour course, which includes functions, limits, derivatives, indeterminate forms, and integrals, exponential and logarithmic functions; inverse trigonometric functions, and applications.
This course includes techniques of integration, applications of integration, improper integrals, infinite series and calculus using polar and parametric curves.
This course covers vector spaces, linear transformations and matrices.

This course covers vectors, differential calculus of functions of several variables, multiple integrals, and applications.

Prerequisite: MATH 2313

This course is a rigorous development of ideas prerequisite to the study of abstract mathematics with emphasis on learning mathematical fundamentals and the techniques of proof while proving some basic theorems involving logic, set theory, relations, and functions.

Prerequisite: MATH 2313

This three hour course covers probability, fundamentals of statistics, functions of random variables, discrete and continuous distributions, moments and moment-generating functions. It is part one of a two-course sequence with MATH 3332, Foundations of Statistical Inference. 

Prerequisite: Students should have either completed MATH 2313, Calculus II, or be enrolled in MATH 2313 in the same semester with this course.

This three-hour course covers techniques of statistical inference including sampling theory, estimation procedures, hypothesis testing, and method of maximum likelihood. It is part two of a two-course sequence with MATH 3331, Foundations of Probability and Statistics.

Prerequisite: MATH 3331

This three-hour course covers statistical theory of planning, designing, and conducting experiments. The course covers topics such as introduction to experiments, randomized design, blocked design, factorial design, and fractional design. Data will be analyzed using statistical software packages such as R, SAS, and/or SPSS.

This three-hour course covers the theory and basic applications of regression analysis. Topics covered include simple linear regression, multiple regression, model selection processes, and basic nonlinear models, such as logistic and probit regression. The course also covers the use of software packages R, Minitab, and SAS.