Explores how data shape public understanding of social issues while building statistical literacy. Students will learn to identify bias, consider ethical and structural influences, and apply descriptive and inferential statistics to interpret, visualize, and evaluate data across varied communities and perspectives. Credit not awarded after STAT 2000 or greater. Prerequisites: Two years High School algebra; no credit for Sophomores, Juniors, or Seniors in the mathematical and engineering sciences; credit for only one of STAT 051 and STAT 1050.
Basic statistical concepts, methods, and applications, including correlation, regression, confidence intervals, and hypothesis tests. Prerequisites: Two years of high school algebra.
Foundational course for students taking further quantitative courses. Exploratory data analysis, probability distributions, estimation, hypothesis testing. Introductory regression, experimentation, contingency tables, and nonparametrics. Computer software used. Credit not awarded for more than one of STAT 1410 or STAT 2430.
Basic techniques of data harvesting and cleaning; association rules, classification, clustering; analyze, manipulate, visualize data using programming languages. Basic principles of probability and statistical modeling/inference to make meaning out of large datasets. Credit not awarded after STAT 3000 or greater. Cross-listed with: CS 1870.
Data analysis, probability models, parameter estimation, hypothesis testing. Multi- factor experimental design and regression analysis. Quality control, SPC, reliability. Engineering cases and project. Statistical analysis software. Credit not awarded for both STAT 1410 and STAT 2430. Prerequisites: MATH 1212 or MATH 1234.
Foundations of probability, conditioning, and independence. Business, computing, biological, engineering reliability, and quality control applications. Classical discrete and continuous models. Pseudo-random number generation. Prerequisites: MATH 1224 or MATH 1248 or MATH 1242.
Quantitative statistical methodologies useful across disciplines. Analysis of variance, multiple and logistic regression, time series analysis, non-parametric methods, Bayesian statistics and decision analysis. Prerequisite: A grade of C or better in STAT 1410, STAT 2430, or STAT 3210.
Learn data science techniques, from import to cleaning to visualizing and modeling, using the Python language. Machine learning methods include regression, classification and clustering algorithms. Programming methods include user-defined functions. Prerequisites: CS 1210; STAT 1110, STAT 1410, or STAT 2430. Cross-listed with: CS 2870.
Introductory design and analysis of medical studies. Epidemiological concepts, case-control and cohort studies. Clinical trials. Students evaluate statistical aspects of published health science studies. Credit not awarded for both STAT 3000 and STAT 5000. Prerequisite: STAT 1110, STAT 1410, STAT 2430, or STAT 3210.
Statistical process control; Shewhart, cusum and other control charts; process capability studies. Total Quality Management. Acceptance, continuous, sequential sampling. Process design and improvement. Case studies. Prerequisite: STAT 1410, STAT 2430, or STAT 3210.
Data harvesting, cleaning, and summarizing; working with non-traditional, non-numeric data (social network, natural language textual data, etc.); scientific visualization; advanced data pipelines; Project-based. Credit not awarded for both STAT 3870 and STAT 5870. Prerequisites: CS 1210; STAT 3880 or CS 3540; STAT 2870 or CS 2870. Cross-listed with: CS 3870.
A program of reading, research, design, and analysis culminating in a written thesis and oral defense. Honors notation appears on transcript and Commencement Program. Contact Statistics Program Director for procedures.
Introductory design and analysis of medical studies. Epidemiological concepts, case-control and cohort studies. Clinical trials. Students evaluate statistical aspects of published health science studies. Understand the relevance of published public health research to clinical practice. Credit not awarded for both STAT 5000 and STAT 3000. Prerequisite: Graduate student or Instructor permission.
Foundational statistics, conducting data analysis using statistical software, collaborating as part of an interdisciplinary team, and communicating and presenting research findings. Practical issues and meaningful, real-world impacts of data projects with an emphasis on data equity, data processing, visualization, basic statistical procedures and concepts, and interpretation and communication of results. Focuses on the responsible application of basic statistical methods, concentrating on concepts rather than mathematical theory. Background in calculus or linear algebra is not required. Prerequisites: Graduate student or Instructor permission.
Parametric and non-parametric two-sample tests. Multiple regression and correlation. Matrix representations. Basic experimental design. Analysis of variance (fixed, random, and mixed models). Statistical Software usage. Credit not awarded for both STAT 5210 and STAT 3210. Prerequisites: Graduate student or Instructor permission; content knowledge of STAT 2830 assumed.
Distributions of random variables and functions of random variables. Expectations, stochastic independence, sampling and limiting distributions (central limit theorems). Concepts of random number generation. Prerequisites: Graduate student or Instructor permission; content knowledge of MATH 2248, STAT 2510 assumed.
Introduction to Bayesian inference. Posterior inference, predictive distributions, prior distribution selection. MCMC algorithms. Hierarchical models. Model checking and selection. Use of computer software. Prerequisite: Content knowledge of STAT 5510 assumed.
Advanced data analysis, collection, and filtering; statistical modeling, monte carlo statistical methods, and in particular Bayesian data analysis, including necessary probabilistic background material; a practical focus on real datasets and developing good habits for rigorous and reproducible computational science. Prerequisites: STAT 5870, CS 5870, CSYS 5870, or Instructor permission. Cross-listed with: CS 6870, CSYS 6870.