Why study statistics at CMU?
These programs offer students a comprehensive training in thinking, reasoning, and problem solving, all of which will strengthen skills for many careers in business, industry, government, and more. Consider some key features of these programs:
- Flexible course work in mathematics, education, statistics, actuarial science, and computer science/mathematics
- Commitment to the latest technology
- Professional development, resources, and contacts through student organizations such as the Kappa Mu Epsilon mathematics honors society
- Free tutoring in mathematics and statistics at the Mathematics Assistance Center
According to the Bureau of Labor Statistics Occupational Outlook Handbook, for all occupations through the year 2014:
- Employment of actuaries is expected to grow 18 to 26 percent.
- Computer scientists and database administrators are expected to be among the fastest growing occupations.
- Employment among scientists and database administrators is expected to grow 27 percent or more.
- The most common fields mathematicians find work in are computer science and software development, physics, engineering, and operations research.
Graduates of the mathematics and statistics programs will find a variety of career opportunities. Some of these may require additional education.
- Applied Mathematician
- Business Administrator
- Computer Scientist
- Database Manager
- Elementary, Middle, or High School Teacher
- Information Specialist
- University or College Professor
The course listings below are a representation of what this academic program requires. For a full review of this program in detail please see our official online academic bulletin AND consult with an academic advisor. This listing does not include the General Education courses required for all majors and may not include some program specific information, such as admissions, retention, and termination standards.
(Click on the course name or number for a complete course description.)
Total: 45 semester hours
Note to students with Mathematics major and Statistics minor or Statistics major and Mathematics minor: these combinations are permitted only if another major or minor is also obtained.
Note to students with Actuarial Science major and Mathematics major or Mathematics minor: For this combination, student must take 6 hours of MTH or STA courses numbered 300 or above which are not counted toward the Actuarial Science major.
Note to student with Mathematics major and Statistics major with Mathematics track: on the Mathematics major, at least 9 hours at the 300 level or above must not be counted on the Statistics major. Also, student must have an outside major or minor.
Note to students with Statistics major with Application track: Student must have a minor in an area other than Mathematics or another major.
Limits, continuity, interpretations of the derivative, differentiation of elementary functions, applications of derivatives, antiderivatives, Riemann sums, definite integrals, fundamental theorem of calculus. This course may be offered in an online or hybrid format. Recommended: MTH 106, 107; or MTH 130. (University Program Group II-B)
Techniques of integration, applications of definite integrals, improper integrals, elementary differential equations, infinite series, Taylor series, and polar coordinates. Prerequisite: MTH 132.
Linear Algebra and Matrix Theory
Systems of linear equations, matrices, determinants, vectors, vector spaces, eigenvalues, linear transformations, applications and numerical methods. Prerequisite: MTH 132.
Vectors and surfaces in R3, vector-valued functions, functions of several variables, partial differentiation and some applications, multiple integrals, vector calculus. Prerequisites: MTH 133. Pre/Co-Requisites: MTH 223 or 232.
Elementary Statistical Analysis
An introduction to statistical analysis. Topics will include descriptive statistics, probability, sampling distributions, statistical inference, and regression. Credit may not be earned in more than one of these courses: STA 282, STA 382, STA 392. Prerequisite: MTH 130 or 132 or 133.
Statistical Programming for Data Management and Analysis
Introduction to statistical programming for managing and analyzing data, including programming logic, data manipulation, missing data handling, basic techniques for analyzing data and creating reports. This course is approved for offering in a distance learning format. Prerequisites: STA 282 or STA 382; or graduate standing.
Applied Statistical Methods I
Applications of statistical methods including the usage of computer packages. Topics include forecasting, simple and multiple regression, and analysis of variance. This course is approved for offering in a distance learning format. Prerequisites: STA 282 or STA 382; or graduate standing.
Mathematical Statistics I
Probability defined on finite and infinite samples spaces, conditional probability and independence, random variables, expectations, moment-generating functions, probability models, limit theorems. Prerequisite: MTH 233.
Mathematical Statistics II
Introductory topics from mathematical theory of statistics: population distributions, sampling distributions, point and interval estimation, tests of hypotheses. Prerequisite: STA 584.
Applied Statistical Methods II
Multiway ANOVA, multiple comparison procedures, analysis of covariance, repeated measures analysis, unbalanced data and missing data analysis. Prerequisites: STA 580 and MTH 223.
Select one of the following options:
Select from the following:
Randomized block designs, Latin square designs, factorial designs, fractional factorial designs, response surface methods, robust designs. Prerequisite: STA 580.
Theory and applications of nonparametric methods. Topics include one-, two-, and several-sample problems, rank correlation and regression, Kolmogorov-Simirnov tests, and contingency tables. Prerequisite: STA 382.
Clinical Trials and Survival Analysis
Simple and advanced statistical techniques used in the analysis and interpretation of clinical research data. Emphasis on statistical techniques commonly used in chronic disease analysis. Prerequisites: STA 382.
Statistical Theory and Methods for Quality Improvement
Statistical theory and methods for optimizing quality and minimizing costs: classical and recently developed on-line methods and Taguchi's off-line quality and robust designs. Prerequisites: STA 580.
Principles of sampling; simple random sampling; stratified random sampling; systematic sampling; cluster sampling; sample size determination; ratio and regression estimates; comparisons among the designs. Prerequisites: STA 382.
Time Series Forecasting
Introduction to basis timer series forecasting techniques. Topics include forecasting, Box-Jenkins models, time series regression, exponential smoothing, and transfer function models Prerequisites: STA 580.
Data Mining Techniques I
Supervised data mining techniques for analyzing large and high dimensional data. Topics include data mining strategy, data processing, predictive modeling techniques, model assessment and comparison. This course has been approved for offering in a distance learning format. Prerequisites: STA 580 or graduate standing.
Six Sigma: Foundations and Techniques for Green Belts
Six Sigma problem solving strategy for continuous improvements. Topics include DMAIC and PDSA strategies and applications, tools and statistical techniques used in the strategies. Prerequisites: STA 282 or 382; or graduate standing.
Special Topics In Statistics
Subject matter not included in regular courses. May be taken for credit more than once, total credit not to exceed 6 hours. Prerequisite: permission of the instructor.
The in-depth study of a topic in statistics under the direction of a faculty member. May be taken for credit more than once, total credit not to exceed six hours. Prerequisite: Permission of instructor.