Gretchen Martinet headshot

Gretchen Falk Martinet

Associate Professor
Unit: College of Arts and Sciences
Department: Department of Statistics
Office location and address
Halsey 105
148 Amphitheater Way
Charlottesville, Virginia 22904
B.A. - Vanderbilt University
M.S. and Ph.D. - University of Virginia
STAT 1559: New Course in Statistics
Credits: 1–4
This course provides the opportunity to offer a new topic in the subject area of statistics.
STAT 2120: Introduction to Statistical Analysis
Credits: 4
Introduction to the probability and statistical theory underlying the estimation of parameters and testing of statistical hypotheses, including those arising in the context of simple and multiple regression models. Students will use computers and statistical programs to analyze data. Examples and applications are drawn from economics, business, and other fields. Students will not receive credit for both STAT 2120 and ECON 3710. Prerequisite: MATH 1210 or equivalent; co-requisite: Concurrent enrollment in a discussion section of STAT 2120.
STAT 2125: Statistics Workshop
Credits: 1
This course is a workshop to support deeper understanding of concepts introduced in STAT 2120.
STAT 2559: New Course in Statistics
Credits: 1–4
This course provides the opportunity to offer a new topic in the subject area of statistics.
STAT 3080: From Data to Knowledge
Credits: 3
Most elementary statistics courses start with a technique & present various surface level examples. This course will use relatively complicated data sets and approach them from multiple angles with elementary statistical techniques. Simulation techniques such as the bootstrap will also be used. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using R statistical software.
STAT 4220: Applied Analytics for Business
Credits: 3
This course focuses on applying data analytic techniques to business, including customer analytics, business analytics, and web analytics through mining of social media and other online data. Several projects are incorporated into the course.
STAT 4996: Capstone
Credits: 3
Students will work in teams on a capstone project. The project will involve significant data preparation and analysis of data, preparation of a comprehensive project report, and presentation of results. Many projects will come from external clients who have data analysis challenges.
STAT 6021: Linear Models for Data Science
Credits: 3
An introduction to linear statistical models in the context of data science. Topics include simple and multiple linear regression, generalized linear models, time series, analysis of covariance, tree-based classification, and principal components. The primary software is R. Prerequisite: A previous statistics course, a previous linear algebra course, and permission of instructor.
STAT 6430: Statistical Computing for Data Science
Credits: 3
An introduction to statistical programming, including data manipulation and cleaning, importing and exporting data, managing missing values, data frames, functions, lists, matrices, writing functions, and the use of packages. Efficient programming practices and methods of summarizing and visualizing data are emphasized throughout. SAS and R are the primary computational tools. Prerequisite: A previous statistics course and permission of instructor.