Discover the power of data with a master's or doctoral degree in Statistics at Michigan Technological University. Build a strong foundation in statistical theory while gaining expertise in predictive modeling, computational methods, time series analysis, and statistical genetics through hands-on research and real-world applications. Work alongside faculty tackling challenges in healthcare, engineering, economics, and other data-intensive fields, using advanced statistical methods to solve complex problems. Graduates leave prepared for impactful careers in industry, government, research, and academia, where data-driven decision-making shapes the future.
Degree Options
To complete a doctoral degree, students must complete the following milestones:
- Complete all coursework and research credits (see credit requirements below)
- Pass Qualifying Examination
- Pass Research Proposal Examination
- Prepare and Submit Approved Dissertation
- Pass Final Oral Defense
The minimum credit requirements are as follows:
| Degrees | Credits |
|---|---|
| MS-PhD (minimum) | 30 Credits |
| BS-PhD (minimum) | 60 Credits |
Individual programs may have higher standards and students are expected to know their program's requirements. See the Doctor of Philosophy Requirements website for more information about PhD milestones and related timelines.
This option requires a research thesis prepared under the supervision of the advisor. The thesis describes a research investigation and its results. The scope of the research topic for the thesis should be defined in such a way that a full-time student could complete the requirements for a master’s degree in 12 months or three semesters following the completion of coursework by regularly scheduling graduate research credits.
The minimum requirements are as follows:
| Option Parts | Credits |
|---|---|
| Coursework (minimum) | 20 Credits |
| Thesis research | 6-10 Credits |
| Total (minimum) | 30 Credits |
| Distribution | Credits |
|---|---|
| 5000-6000 series (minimum) | 12 Credits |
| 3000-4000 (maximum) | 12 Credits |
Programs may have stricter requirements and may require more than the minimum number of credits listed here.
This option requires a report describing the results of an independent study project. The scope of the research topic should be defined in such a way that a full-time student could complete the requirements for a master’s degree in 12 months or three semesters following the completion of coursework by regularly scheduling graduate research credits.
Of the minimum total of 30 credits, at least 24 must be earned in coursework other than the project:
| Option Parts | Credits |
|---|---|
| Coursework (minimum) | 24 Credits |
| Report | 2-6 Credits |
| Total (minimum) | 30 Credits |
| Distribution | Credits |
|---|---|
| 5000-6000 series (minimum) | 12 Credits |
| 3000-4000 (maximum) | 12 Credits |
Programs may have stricter requirements and may require more than the minimum number of credits listed here.
This option requires a minimum of 30 credits be earned through coursework. A limited number of research credits may be used with the approval of the advisor, department, and Graduate School. See degree requirements for more information.
A graduate program may require an oral or written examination before conferring the degree and may require more than the minimum credits listed here:
| Distribution | Credits |
|---|---|
| 5000-6000 series (minimum) | 18 Credits |
| 3000-4000 (maximum) | 12 Credits |
Bachelor's + 1 Year = Master's Degree
Our accelerated master's degree program is a faster, easier way for Michigan Tech students to earn a master's degree. Up to nine approved credits from your bachelor's degree can be applied towards your accelerated master's degree. Consult your graduate program director for your individualized plan. If you're thinking about pursuing a master's following your bachelor's this option may be the right choice for you.
Coursework Option Curriculum
Core courses
Review of distribution theory and transformation theory of random variables. Topics include sufficiency; exponential and Bayesian models; estimation methods, including optimality theory; basics of confidence procedures and hypothesis testing, including the Neyman-Pearson framework.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Fall
- Pre-Requisite(s): MA 4450 and (MA 4770 or MA 4705)
Optimal tests and decision theory. Other topics may include regression and analysis of variance, discrete data analysis, nonparametric models.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Spring
- Pre-Requisite(s): MA 5711
A unified development of linear statistical models that includes the following topics: matrices and quadratic forms, normal and chi-square distribution theory, ordinary and generalized least squares modeling, estimability, estimation and tests of hypothesis.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Fall
- Pre-Requisite(s): MA 4710 and MA 4720 and MA 4760 and MA 4330
Random vectors and matrix algebra. Multivariate Normal distribution. Theory and application of multivariate techniques including discrimination and classification, clustering, principal components, canonical correlation, and factor analysis.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Spring
- Pre-Requisite(s): (MA 4710 or MA 4720) and MA 2320
Elective courses
Choose three courses.
Introduces nonparametric techniques that require less restrictive assumptions on the data. Topics include statistical inference concerning location and dispersion parameters as well as the general distributions. Goodness-of-fit tests for count and ordinal data are also discussed.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Fall, in odd years
- Pre-Requisite(s): MA 2710 or MA 2720 or MA 3710 or MA 3715
The focus of this course is on generalized linear models (GLM), including the structure of GLM, statistical theory for GLM (maximum-likelihood estimation of GLM and hypothesis tests), and their applications. Also covers generalized linear mixed and random effects models.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Spring, in odd years
- Pre-Requisite(s): (MA 4710 or MA 5731) and (MA 4770 or MA 5712)
Application of statistical methods to solve problems in genetics such as locating genes. Topics include basic concepts of genetics, linkage analysis and association studies of family data, association tests based on population samples (for both qualitative and quantitative traits), gene mapping methods based on family data and population samples.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Spring, in odd years
Introduction to computationally intensive statistical methods. Topics include resampling methods, Monte Carlo simulation methods, smoothing technique to estimate functions, and methods to explore data structure. This course will use the statistical software R.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Fall
- Pre-Requisite(s): MA 4770(C) or (MA 4700 and MA 5701)
The theory of Bayesian inference. Topics include prior specifications, basics of decision theory, Markov chain, Monte Carlo, Bayes factor, linear regression, linear random effects model, hierarchical models, Bayesian hypothesis testing, Bayesian model selection.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Fall, in even years
- Pre-Requisite(s): MA 4330 and MA 4710 and MA 4760
Application, construction, and evaluation of statistical models used for prediction and classification. Topics include data pre-processing, over-fitting and model tuning, linear and nonlinear regression models and linear and nonlinear classification models.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Fall, Spring
- Pre-Requisite(s): MA 3740 or MA 4710 or MA 4720 or MA 4780 or (MA 4700 and MA 5701)
Structure of 2-way contingency tables. Goodness-of-fit tests and Fisher's exact test for categorical data. Fitting models, including logistic regression, logit models, probit and extreme value models for binary response variables. Building and applying log linear models for contingency tables.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Spring, in odd years
Topics may include but are not limited to experimental designs, methods of quality improvement, discrete data analysis, regression analysis, sampling theory, multivariate methods, resampling methods, statistical computing, integral and measure theory, stochastic processes, asymptotic methods, optimization, modeling, nonparametric and parametric statistics.
- Credits: variable to 12.0; Repeatable to a Max of 18
- Semesters Offered: Spring, in even years
- Restrictions: Must be enrolled in one of the following Level(s): Graduate
Review of discrete probability, probability measures, random variables, distribution functions, expectation as a Lebesgue-Stieltjes integral, independence, modes of convergence, laws of large numbers and iterated logarithms, characteristic functions, central limit theorems, conditional expectation, martingales, introduction to stochastic processes.
- Credits: 3.0
- Lec-Rec-Lab: (0-3-0)
- Semesters Offered: Fall, in odd years
- Pre-Requisite(s): MA 3720 and MA 5401