There is a growing need for qualified statistical analysts of the ever-increasing amounts of data collected in business, industry, and government. The Certificates in Applied Statistics program is designed to give students a strong background in statistical methodology and data analysis in preparation for opportunities in the work force or for graduate studies.

Students will gain competence in such topics as descriptive statistics, estimation, confidence intervals, probability and inferential techniques, simple and multiple regression, analysis of variance, and more-advanced topics. Students can focus on a particular application area such as economics, psychology, sociology, geology or environmental science through the choice of an elective course and the data analysis project.​

Admission Requirements

All undergraduate students in good standing (including non-degree seeking students) will be admitted to the program. For students in the College of Liberal Arts and Sciences, this requires a cumulative grade point average of 2.0 or above. Students must also have some mathematical background, including completion of calculus 1, 2 and 3, as well as linear algebra with a grade of B- (3.0) or better. Students enrolled in the certificate program will be expected to utilize concepts from calculus and linear algebra without the use of technology, e.g., evaluation of limits, derivatives and integrals.

Learning Outcomes

Students completing this certificate will have essential competencies in several areas related to analysis of data:  

1.    Understand probability and statistics in order to quantify uncertainty
2.    Build complex models for finding patterns and explaining data
3.    Communicate statistical findings both orally and in writing

Degree Requirements:

The Statistics Certificate program requires four courses and an independent study (13 total credit hours). 
One Course in Probability (3 credit hours)

It is strongly advised that MATH 4810 be taken rather than MATH 3800.

MATH 3800- Probability and Statistics for Engineers
Basic probability theory, discrete and continuous random variables, point and interval estimation, test of hypotheses, one-way analysis of variance, and simple linear regression. Note: no co-credit with MATH 4810. Prereq: MATH 2411; coreq: MATH 2421. Semester Hours: 3 to 3


​MATH 4810 – Probability
Examines elementary theory of probability, including independence, conditional probability, and Bayes’ theorem; random variables, expectations and probability distributions; joint and conditional distributions; functions of random variables; limit theorems, including the central limit theorem. Note: No co-credit with MATH 3800. Prereq: MATH 3191; Coreq: MATH 2421. Cross-listed with MATH 5310. Semester Hours: 3 to 3

One Course in Mathematical Statistics (3 credit hours)

MATH 3382 – Statistical Theory
Probability, random variables, properties of distributions, bootstrap methods, maximum likelihood and method of moments estimation, properties of estimators, classical methods for confidence intervals and hypothesis testing. This course assumes students have passed Math 2421 with a C- or better.  Students who have a grade a B- or better in Math 2421 pass this course at a much higher rate. Semester Hours: 3 to 3

One Advanced Applications Course (3 credit hours)

MATH 4387 - Applied Regression Analysis

Topics include simple and multiple linear regression, model diagnostics and remediation, and model selection. Emphasis is on practical aspects and applications of linear models to the analysis of data in business, engineering and behavioral, biological and physical sciences. Prereq: Grade of C- (1.7) or better in MATH 3191 and in MATH 3800 or 4820 or 3382. Note: Students who have a grade of B- or better in MATH 3191, an A in MATH 3800 or a B- or better in MATH 4820 pass this course at a much higher rate. Cross-listed with MATH 5387. Term offered: fall, spring, summer. Max hours: 3 Credits. Semester Hours: 3 to 3.

One Elective (3 credit hours)

Choose any statistics course in the Department of Mathematical and Statistical Sciences at the 4000 level or higher. Course must be pre-approved by the Certificate Coordinator. MATH 4830 cannot apply towards the certificate. Representative courses include:

  • ECON 4030- Data Analysis with SAS
  • ECON 4150- Economic Forecasting
  • ECON 4811- Introduction to Econometrics
  • GEOG 4770- Applied Statistics for the Natural Sciences
  • Cross-listed with GEOL 4770
  • Or an equivalent course pre-approved by the Certificate Coordinator

Project, Independent Study (1 credit hour)

An independent data analysis project with a report and presentation to demonstrate proficiency with data analysis techniques and a statistical computing software package. Enroll for one hour of MATH 4840, Independent Study, or in an equivalent course pre-approved by the Certificate Coordinator.

For more information:

Dr. Joshua French, Director of Statistical Programs