Central Michigan University's statistics program will help you build problem-solving skills that prepare you for a wide range of in-demand and versatile careers. As a statistics major at CMU, you will work with top faculty supported by National Science Foundation grants. You'll have the chance to apply equations and theories learned in class and to conduct research and present your results at CMU’s Student Research and Creative Endeavors Exhibition. Professional contacts through student organizations such as Kappa Mu Epsilon, Gamma Iota Sigma Nu and the Statistics Club also prepare you well for life beyond CMU.
Points of Pride
- CMU's statistics program is designed to balance the foundation and applications of techniques for quantitative problem solving.
- CMU's statistics program gives you the opportunity to take courses not only in statistics, but also in actuarial and data sciences. You will be well prepared for a broad range of different types of jobs in many different disciplines and industries.
- Statistics is considered a high-salary profession with great job security and vast opportunities.
Put Your Statistics Degree to Work
Graduates of CMU's statistics program will find a variety of career options in business, industry, government and more. Although statisticians work mostly in offices, they may travel to oversee a survey design, plan implementation or to collect data.
U.S. Bureau of Labor Statistics sample data
Below is a list of potential careers, median salary over the course of the career and projected job growth.
|Job||Median Pay||Job Growth through 2026|
|Statistician||$81,950 per year||33% (13,300 more jobs)|
|Auditor||$68,150 per year||10% (140,300 more jobs)|
|Survey researcher||$54,470 per year||1% (100 more jobs)|
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 107, 109; or MTH 130. (University Program Group II-B: Quantitative and Mathematical Sciences)
Techniques of integration, applications of definite integrals, improper integrals, elementary differential equations, infinite series, Taylor series, and polar coordinates. Prerequisite: MTH 132 or placement.
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. Quantitative Reasoning. Prerequisite: MTH 130 or 132 or 133. (University Program Group II-B: Quantitative and Mathematical Sciences)
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. Prerequisites: STA 282 or 382 or 392; or graduate standing.
Applied Statistical Methods I
Applications of statistical analysis methods including the usage of computer software packages. Topics include simple and multiple regression, diagnostics, forecasting, and analysis of variance. This course may be offered in an online or hybrid format. Prerequisites: STA 282 or 382 or 392; 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
Linear models with autocorrelated errors, non-linear regression, logistic regression, multiway ANOVA, simultaneous comparison procedures, ANOVA diagnostics, analysis of covariance, unbalanced data and missing data analysis. Prerequisites: MTH 223; STA 580; or graduate standing.
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-Smirnov tests and contingency tables. Prerequisites: STA 282 or 382 or 392; or graduate standing.
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. Prerequisite: STA 282 or 382 or 392; or graduate standing.
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 282 or 382 or 392; or graduate standing.
Time Series Forecasting
Introduction to basic time series forecasting techniques. Topics include forecasting, basic stochastic models, time series regression, stationary and nonstationary models. Prerequisite: STA 580.
Data Mining Techniques I
Data mining techniques for analyzing large and high dimensional data. Topics include data mining strategy, exploratory analysis, predictive modeling techniques, model assessment and comparison. Prerequisite: STA 580 or graduate standing.
Six Sigma: Foundations and Techniques for Green Belts
Six Sigma problem solving strategy for continuous improvement. Topics include DMAIC and PDSA strategies and applications, tools and statistical techniques used in the strategies. Prerequisites: STA 282 or 382 or 392; 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.