Applied Statistics - Doctor of Philosophy

A distinguishing feature of the Applied Statistics doctoral program will be the close integration of research and teaching. All program faculty conduct applied collaborative research, applying a broad range of statistical methodologies to problems in economics, public health, medicine, biology, chemistry, engineering, agriculture, climatology, and other fields. Each faculty member has examples of research they have been engaged in that can be presented to students in nearly all of the program's courses.

To expand the reach of the program, all required courses in the proposed program will be offered through synchronous delivery in which students meet at a regularly scheduled time and material is presented live over the internet. Classes will be scheduled at times that are accessible to the vast majority of our target audience, including late afternoon and evening when necessary. The department and college have invested in technologies that facilitate synchronous delivery.

Students entering the program with a bachelor's degree, or a master's degree in a field other than statistics, will need to complete a total of 70 credit hours – 52 credit hours of coursework and 18 credit hours of dissertation research. 

As part of the required 52 credit hours of coursework, students must complete 12 credit hours of A ST electives at the 500 level or higher. Students may concentrate on a substantive area in economics, marketing, finance, or information systems by choosing quantitative electives from that area. The student’s committee will determine whether these courses are acceptable substitutes for A ST electives.

Students must complete at least 18 credit hours of dissertation research. The dissertation is expected to consist of three chapters that are standalone manuscripts that may be submitted to applied journals. Additional chapters in the dissertation may provide background of the problem, literature review, and simulations to support the main topic of research. Deviation from this format will be allowed at the discretion of the student’s advisor.

Students entering the program with a master’s degree in statistics or biostatistics will need to complete a minimum of 36 credit hours – 18 credit hours of coursework (including 12 credits at the 600 level or higher) and 18 credit hours of dissertation research. Additional coursework may be necessary to make up for deficiencies in the student’s prior master’s degree.

Students will be required to pass a written qualifying exam after completing at least 12 credit hours of A ST courses at the 500 level or above, typically at the end of their first year in the doctoral program. Students who enter with a master's degree in statistics or biostatistics may elect to take the qualifying exam earlier. The exam will cover the first year of required theory and methods coursework. The exam will be assigned one of three grades: PhD pass, which enables the student to continue in the second year of the doctoral program; Master's pass, indicating that the student has the requisite knowledge for the master's degree in Applied Statistics but has deficiencies that prevent them from continuing in the doctoral program; and fail. Doctoral students who fail the exam or receive a Master's pass on their first attempt will be allowed one opportunity to re-take the exam. Upon completion of their coursework (typically at the end of their third year), students in the doctoral program will be required to pass a comprehensive exam that has both oral and written components. Students who do not pass the comprehensive exam on their first attempt will be allowed a second opportunity to take the exam after a lapse of at least one semester.

Prefix Title Credits
A ST 565Statistical Analysis I3
A ST 566Statistical Analysis II3
A ST 609Linear Model Theory3
A ST 665Bayesian Theory3
A ST 503SAS Basics3
or A ST 515 Statistical Analysis with R
A ST 616Computational Statistics3
A ST 505Statistical Inference I4
A ST 506Statistical Inference II3
A ST 507Advanced Regression3
A ST 509Statistical Models for Complex Data Structures3
A ST 540Predictive Analytics3
A ST 645Time Series Methods3
A ST 554Practicum in Statistics3
Electives -- Additional A ST courses at the 500 level or higher or in other areas as determined by student's committee12
A ST 700Doctoral Dissertation (Dissertation)18
Total Credits70

A Suggested Plan of Study

Plan of Study Grid
First Year
A ST 565 Statistical Analysis I 3
A ST 503
SAS Basics
or Statistical Analysis with R
A ST 505 Statistical Inference I 4
A ST 566 Statistical Analysis II 3
A ST 507 Advanced Regression 3
Elective 3
Second Year
A ST 509 Statistical Models for Complex Data Structures 3
A ST 506 Statistical Inference II 3
Elective 3
A ST 616 Computational Statistics 3
A ST 540 Predictive Analytics 3
A ST 554 Practicum in Statistics 3
Third Year
A ST 665 Bayesian Theory 3
A ST 609 Linear Model Theory 3
Elective 3
A ST 645 Time Series Methods 3
Elective 3
Dissertation research 3
Fourth Year
A ST 700 Doctoral Dissertation (Dissertation) 9
Dissertation research 6
 Total Credits70