Twin Cities campus
 
Twin Cities Campus

Statistics M.S.

Statistics, School of
College of Liberal Arts
Link to a list of faculty for this program.
Contact Information
School of Statistics, 313 Ford Hall, 224 Church Street SE, Minneapolis, MN 55455 (612-624-8046; fax: 612-624-8868)
  • Program Type: Master's
  • Requirements for this program are current for Spring 2022
  • Length of program in credits: 30
  • This program does not require summer semesters for timely completion.
  • Degree: Master of Science
Along with the program-specific requirements listed below, please read the General Information section of this website for requirements that apply to all major fields.
The School of Statistics is the primary venue at the University for research, teaching, and dissemination of the theory, methodology, and applications of statistical procedures. Students may specialize in any area of statistics. The core program for all students has strong components of both theoretical and applied statistics.
Program Delivery
  • via classroom (the majority of instruction is face-to-face)
Prerequisites for Admission
International applicants must submit score(s) from one of the following tests:
  • TOEFL
    • Internet Based - Total Score: 79
    • Internet Based - Writing Score: 21
    • Internet Based - Reading Score: 19
    • Paper Based - Total Score: 550
  • IELTS
    • Total Score: 6.5
  • MELAB
    • Final score: 80
Key to test abbreviations (TOEFL, IELTS, MELAB).
For an online application or for more information about graduate education admissions, see the General Information section of this website.
Program Requirements
Plan B: Plan B requires 24 major credits and 6 credits outside the major. The final exam is oral.
This program may be completed with a minor.
Use of 4xxx courses towards program requirements is not permitted.
A minimum GPA of 3.00 is required for students to remain in good standing.
At least 1 semesters must be completed before filing a Degree Program Form.
A maximum of 9.0 S/N credits can be applied to degree requirements.
Required Coursework
Core Courses (18 credits)
Take the following courses:
STAT 5701 - Statistical Computing (3.0 cr)
STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods (3.0 cr)
STAT 8052 - Applied Statistical Methods 2: Design of Experiments and Mixed -Effects Modeling (3.0 cr)
STAT 8101 - Theory of Statistics 1 (3.0 cr)
STAT 8102 - Theory of Statistics 2 (3.0 cr)
STAT 8801 - Statistical Consulting (3.0 cr)
Statistics Electives (6 credits)
Select 6 credits from the following in consultation with the advisor. Other courses may be selected with advisor approval.
PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis (3.0 cr)
PUBH 7450 - Survival Analysis (3.0 cr)
PUBH 8442 - Bayesian Decision Theory and Data Analysis (3.0 cr)
PUBH 8472 - Spatial Biostatistics (3.0 cr)
STAT 5201 - Sampling Methodology in Finite Populations (3.0 cr)
STAT 5401 - Applied Multivariate Methods (3.0 cr)
STAT 5421 - Analysis of Categorical Data (3.0 cr)
STAT 5511 - Time Series Analysis (3.0 cr)
STAT 5601 - Nonparametric Methods (3.0 cr)
STAT 8053 - Applied Statistical Methods 3: Multivariate Analysis and Advanced Regression (3.0 cr)
STAT 8054 - Statistical Methods 4: Advanced Statistical Computing (3.0 cr)
STAT 8111 - Mathematical Statistics I (3.0 cr)
STAT 8112 - Mathematical Statistics II (3.0 cr)
STAT 8931 - Advanced Topics in Statistics (3.0 cr)
STAT 8932 - Advanced Topics in Statistics (3.0 cr)
Outside Coursework (6 credits)
Select 6 credits outside the major in consultation with the advisor. Other courses may be selected with advisor approval.
CSCI 5523 - Introduction to Data Mining (3.0 cr)
CSCI 5525 - Machine Learning: Analysis and Methods (3.0 cr)
IE 8521 - Optimization (4.0 cr)
MATH 5075 - Mathematics of Options, Futures, and Derivative Securities I (4.0 cr)
MATH 5076 - Mathematics of Options, Futures, and Derivative Securities II (4.0 cr)
MATH 5652 - Introduction to Stochastic Processes (4.0 cr)
POL 8124 - Game Theory (3.0 cr)
POL 8125 - Dynamic Analysis (3.0 cr)
PSY 5862 - Psychological Measurement: Theory and Methods (3.0 cr)
 
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STAT 5701 - Statistical Computing
Credits: 3.0 [max 3.0]
Prerequisites: (Stat 5102 or Stat 8102) and (Stat 5302 or STAT 8051) or consent
Grading Basis: A-F or Aud
Typically offered: Every Fall
Statistical programming, function writing, graphics using high-level statistical computing languages. Data management, parallel computing, version control, simulation studies, power calculations. Using optimization to fit statistical models. Monte Carlo methods, reproducible research. prereq: (Stat 5102 or Stat 8102) and (Stat 5302 or STAT 8051) or consent
STAT 8051 - Advanced Regression Techniques: linear, nonlinear and nonparametric methods
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Fall
Linear/generalized linear models, modern regression methods including nonparametric regression, generalized additive models, splines/basis function methods, regularization, bootstrap/other resampling-based inference. prereq: Statistics grad or instr consent
STAT 8052 - Applied Statistical Methods 2: Design of Experiments and Mixed -Effects Modeling
Credits: 3.0 [max 3.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Design experiments/analyze data with fixed effects, random/mixed effects models. ANOVA for factorial designs. Contrasts, multiple comparisons, power/sample size, confounding, fractional factorials. Computer-generated designs. Response surfaces. Multi-level models. Generalized estimating equations (GEE) for longitudinal data with non-normal errors. prereq: 8051 or instr consent
STAT 8101 - Theory of Statistics 1
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Review of linear algebra. Introduction to probability theory. Random variables, their transformations/expectations. Standard distributions, including multivariate Normal distribution. Probability inequalities. Convergence concepts, including laws of large numbers, Central Limit Theorem. delta method. Sampling distributions. prereq: Statistics grad major or instr consent
STAT 8102 - Theory of Statistics 2
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Statistical inference. Sufficiency. Likelihood-based methods. Point estimation. Confidence intervals. Neyman Pearson hypothesis testing theory. Introduction to theory of linear models. prereq: 8101, Statistics graduate major or instr consent
STAT 8801 - Statistical Consulting
Credits: 3.0 [max 3.0]
Prerequisites: STAT 8051 and STAT Grad Student or Instructor Consent
Grading Basis: S-N or Aud
Typically offered: Every Spring
Principles of effective consulting/problem-solving, meeting skills, reporting. Aspects of professional practice/behavior, ethics, continuing education. prereq: STAT 8051 and STAT Grad Student or Instructor Consent
PUBH 7420 - Clinical Trials: Design, Implementation, and Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Introduction to and methodology of randomized clinical trials. Design issues, sample size, operational details, interim monitoring, data analysis issues, overviews. prereq: 6451 or concurrent registration is required (or allowed) in 6451 or 7406 or instr consent
PUBH 7450 - Survival Analysis
Credits: 3.0 [max 3.0]
Prerequisites: 7406, [STAT 5102 or STAT 8102]
Typically offered: Every Fall
Statistical methodologies in analysis of survival data. Kaplan-Meier estimator, Cox's proportional hazards multiple regression model, time-dependent covariates, analysis of residuals, multiple failure outcomes. Typical biomedical applications, including clinical trials and person-years data. prereq: 7406, [STAT 5102 or STAT 8102]
PUBH 8442 - Bayesian Decision Theory and Data Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Theory/application of Bayesian methods. Bayesian methods compared with traditional, frequentist methods. prereq: [[7460 or experience with FORTRAN or with [C, S+]], Stat 5101, Stat 5102, Stat 8311, grad student in [biostatistics or statistics]] or instr consent
PUBH 8472 - Spatial Biostatistics
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Spatial data, spatial statistical models, and spatial inference on unknown parameters or unobserved spatial data. Nature of spatial data. Special analysis tools that help to analyze such data. Theory/applications. prereq: [[STAT 5101, STAT 5102] or [STAT 8101, STAT 8102]], some experience with S-plus; STAT 8311 recommended
STAT 5201 - Sampling Methodology in Finite Populations
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Simple random, systematic, stratified, unequal probability sampling. Ratio, model based estimation. Single stage, multistage, adaptive cluster sampling. Spatial sampling. prereq: 3022 or 3032 or 3301 or 4102 or 5021 or 5102 or instr consent
STAT 5401 - Applied Multivariate Methods
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall
Bivariate and multivariate distributions. Multivariate normal distributions. Analysis of multivariate linear models. Repeated measures, growth curve, and profile analysis. Canonical correlation analysis. Principal components and factor analysis. Discrimination, classification, and clustering. pre-req: STAT 3032 or 3301 or 3022 or 4102 or 5021 or 5102 or instr consent Although not a formal prerequisite of this course, students are encouraged to have familiarity with linear algebra prior to enrolling. Please consult with a department advisor with questions.
STAT 5421 - Analysis of Categorical Data
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Varieties of categorical data, cross-classifications, contingency tables. Tests for independence. Combining 2x2 tables. Multidimensional tables/loglinear models. Maximum-likelihood estimation. Tests for goodness of fit. Logistic regression. Generalized linear/multinomial-response models. prereq: STAT 3022 or 3032 or 3301 or 5302 or 4051 or 8051 or 5102 or 4102
STAT 5511 - Time Series Analysis
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Characteristics of time series. Stationarity. Second-order descriptions, time-domain representation, ARIMA/GARCH models. Frequency domain representation. Univariate/multivariate time series analysis. Periodograms, non parametric spectral estimation. State-space models. prereq: STAT 4102 or STAT 5102
STAT 5601 - Nonparametric Methods
Credits: 3.0 [max 3.0]
Typically offered: Every Fall & Spring
Order statistics. Classical rank-based procedures (e.g., Wilcoxon, Kruskal-Wallis). Goodness of fit. Topics may include smoothing, bootstrap, and generalized linear models. prereq: Stat classes 3032 or 3022 or 4102 or 5021 or 5102 or instr consent
STAT 8053 - Applied Statistical Methods 3: Multivariate Analysis and Advanced Regression
Credits: 3.0 [max 3.0]
Prerequisites: PhD student in stat or DGS permission and 8052
Grading Basis: A-F or Aud
Typically offered: Every Fall
Standard multivariate analysis. Multivariate linear model, classification, clustering, principal components, factor analysis, canonical correlation. Topics in advanced regression. prereq: PhD student in stat or DGS permission and 8052
STAT 8054 - Statistical Methods 4: Advanced Statistical Computing
Credits: 3.0 [max 3.0]
Prerequisites: STAT 8053 or #
Grading Basis: A-F or Aud
Typically offered: Every Spring
Optimization, numerical integration, Markov chain Monte Carlo, related topics. prereq: STAT 8053 or instr consent
STAT 8111 - Mathematical Statistics I
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Probability theory, basic inequalities, characteristic functions, and exchangeability. Multivariate normal distribution. Exponential family. Decision theory, admissibility, and Bayes rules. prereq: [5102 or 8102 or instr consent], [[Math 5615, Math 5616] or real analysis], matrix algebra
STAT 8112 - Mathematical Statistics II
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Statistical inference, estimation, and hypothesis testing. Convergence and relationship between convergence modes. Asymptotics of maximum likelihood estimators, distribution functions, quantiles. Delta method. prereq: 8111
STAT 8931 - Advanced Topics in Statistics
Credits: 3.0 [max 12.0]
Typically offered: Periodic Fall & Spring
Topics vary according to student needs/available staff.
STAT 8932 - Advanced Topics in Statistics
Credits: 3.0 [max 12.0]
Typically offered: Periodic Fall & Spring
Topics vary according to student needs/available staff.
CSCI 5523 - Introduction to Data Mining
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Data pre-processing techniques, data types, similarity measures, data visualization/exploration. Predictive models (e.g., decision trees, SVM, Bayes, K-nearest neighbors, bagging, boosting). Model evaluation techniques, Clustering (hierarchical, partitional, density-based), association analysis, anomaly detection. Case studies from areas such as earth science, the Web, network intrusion, and genomics. Hands-on projects. prereq: 4041 or equiv or instr consent
CSCI 5525 - Machine Learning: Analysis and Methods
Credits: 3.0 [max 3.0]
Typically offered: Fall Even Year
Models of learning. Supervised algorithms such as perceptrons, logistic regression, and large margin methods (SVMs, boosting). Hypothesis evaluation. Learning theory. Online algorithms such as winnow and weighted majority. Unsupervised algorithms, dimensionality reduction, spectral methods. Graphical models. prereq: Grad student or instr consent
IE 8521 - Optimization
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Theory and applications of linear and nonlinear optimization. Linear optimization: simplex method, convex analysis, interior point method, duality theory. Nonlinear optimization: interior point methods and first-order methods, convergence and complexity analysis. Applications in engineering, economics, and business problems. prereq: Familiarity with linear algebra and calculus.
MATH 5075 - Mathematics of Options, Futures, and Derivative Securities I
Credits: 4.0 [max 4.0]
Typically offered: Every Fall
Mathematical background (e.g., partial differential equations, Fourier series, computational methods, Black-Scholes theory, numerical methods--including Monte Carlo simulation). Interest-rate derivative securities, exotic options, risk theory. First course of two-course sequence. prereq: Two yrs calculus, basic computer skills
MATH 5076 - Mathematics of Options, Futures, and Derivative Securities II
Credits: 4.0 [max 4.0]
Grading Basis: A-F or Aud
Typically offered: Every Spring
Mathematical background such as partial differential equations, Fourier series, computational methods, Black-Scholes theory, numerical methods (including Monte Carlo simulation), interest-rate derivative securities, exotic options, risk theory. prereq: 5075
MATH 5652 - Introduction to Stochastic Processes
Credits: 4.0 [max 4.0]
Typically offered: Every Fall & Spring
Random walks, Markov chains, branching processes, martingales, queuing theory, Brownian motion. prereq: 5651 or Stat 5101
POL 8124 - Game Theory
Credits: 3.0 [max 3.0]
Typically offered: Every Spring
Application of noncooperative game theory in political science. Equilibrium concepts, bargaining, repeated games, games of incomplete information, signaling games, reputation, learning in games. prereq: [8122, grad pol sci major] or instr consent
POL 8125 - Dynamic Analysis
Credits: 3.0 [max 3.0]
Typically offered: Periodic Fall & Spring
Time series method, its application in political science. prereq: Pol sci grad student or instr consent
PSY 5862 - Psychological Measurement: Theory and Methods
Credits: 3.0 [max 3.0]
Typically offered: Every Fall
Types of measurements (tests, scales, inventories) and their construction. Theory/measurement of reliability/validity. prereq: 3801H or MATH 1271 or grad student