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For students in all disciplines. Provides the background needed to evaluate statistical arguments found in newspapers, magazines, reports, and journals and the logic and techniques necessary to perform responsible elementary statistical analysis. Topics include basic data analysis, one-variable regression, experimental and sampling design, random variables, sampling distributions, and inference (confidence intervals and significance testing). This course may not be taken on a Satisfactory/Unsatisfactory basis. Students with credit for MATH 140 or equivalent, or BIOL 170, or AP Statistics should not take this course and should instead consider STAT 255.

An introduction to programming with emphasis on learning from data in order to gain useful insights. Topics focus on elementary programming concepts in the R language and the necessary tools to handle, analyze and interpret data. This course will be taught in a workshop format, and students will complete regular assignments and a final project that provide hands-on programming/analysis experiences. PREREQUISITES: One prior course MATH, STAT, or CMSC course, or BIOL 170, or BIOL 280, or consent of instructor

An overview of techniques used to discover structural patterns and make predictions using complex datasets that are prevalent in today's world. The central machine learning tasks of classification, clustering, and regression will be explored, along with methods for training models and evaluating predictions. This course will be taught in a workshop format. Assignments will involve the use of statistical software. PREREQUISITES: CMSC 150, or CMSC 205, or CMSC 210, or consent of instructor

This course introduces modern statistical techniques in the context of predictive inference and modeling. Topics will include data analysis techniques such as linear and logistic regression, ANOVA, nonparametric methods, and computational approaches such as cross-validation and bootstrapping. Statistical software will be used frequently. This class will involve regular in-class and out-of-class assignments as well as exams and quizzes. PREREQUISITES: STAT 107, BIOL 170, BIOL 280, or MATH 140, or instructor permission

An introduction to probability and its applications. Topics will include combinatorial and axiomatic probability, conditional probability and Bayes' Theorem, random variables, expectation and variance, discrete and continuous probability distributions, joint and conditional distributions, and limit laws. PREREQUISITES: MATH 200, and either MATH 220 or MATH 230

Advanced work in statistics on topics not covered in regular offerings. PREREQUISITES: To register student must complete the Student Initiated Course Form with consultation of supervising instructor.

Directed study follows a syllabus set primarily by the instructor to meet the needs or interests of an individual student or small group of students. The main goal of directed study is knowledge or skill acquisition, not research or creative work. PREREQUISITES: To register student must complete the Student Initiated Course Form with consultation of supervising instructor.

Guided independent study of an advanced topic in undergraduate statistics or supervised work on an undergraduate research project, generally culminating in a final presentation and/or paper. PREREQUISITES: To register student must complete the Student Initiated Course Form with consultation of supervising instructor.

This course builds on CMSC/STAT 205, providing a deeper exploration of statistical computing in R. Topics might include efficient programming techniques, parallelization, statistical algorithms, advanced data visualization, and creation of R packages. Statistical software will be used extensively in this course. The class will be taught in an interactive lab-based format. There will be regular assignments and a project. PREREQUISITES: CMSC 205

Development of the mathematical theory of statistics and its application to the real world. The course will focus on the principles of estimation and testing from both the frequentist and Bayesian perspectives. Resampling methods (permutation tests and bootstrap intervals) will also be explored. PREREQUISITES: MATH/STAT 340

A study of the Bayesian statistical philosophy, contrasting it with the traditional frequentist approach taught in other statistics courses. Topics include Bayes' Theorem, prior and posterior probability distributions, hierarchical models, and Markov Chain Monte Carlo methods. The course will involve a mixture of lecture, discussion, and use of statistical software. Requirements include exams, a project, and assignments involving the use of statistical software. PREREQUISITES: MATH/STAT 340

This course expands on STAT 255, and introduces more sophisticated models, meant to capture complicated correlation structure in data. Topics might include generalized linear models, mixed-effects models, hierarchical models, spatial models, and time series. The course will involve the use of statistical software. There will be regular assignments, exams, and possibly projects. PREREQUISITES: STAT 255 or instructor permission

Directed study follows a syllabus set primarily by the instructor to meet the needs or interests of an individual student or small group of students. The main goal of directed study is knowledge or skill acquisition, not research or creative work. PREREQUISITES: To register student must complete the Student Initiated Course Form with consultation of supervising instructor.

Guided independent study of an advanced topic in undergraduate statistics or supervised work on an undergraduate research project, generally culminating in a final presentation and/or paper. PREREQUISITES: To register student must complete the Student Initiated Course Form with consultation of supervising instructor.

Intended for students majoring in MATH or MAEC, who wish to complete a senior experience project in statistics. Students will work on a research project, applying skills learned throughout the statistics curriculum. Requirements include a written report and periodic oral presentations. A student must submit a written proposal, and have it approved by the instructor(s) prior to the start of the course. PREREQUISITES: For MATH majors: Senior standing and a project proposal approved by instructor(s) through one-unit indepenent study, and STAT 255, and MATH 340, and one 400-level STAT class, and one of CMSC 205, or CMSC 208. Either MATH 340 or the 400-level STAT class may be taken concurrently with STAT 698 . For MAEC majors: Senior standing and a project proposal approved by the instructor(s) through one-unit independent study, and STAT 255, and MATH 340, and ECON 380. MATH 340 may be taken concurrently with STAT 698.

Guided independent study of an advanced topic in undergraduate statistics or supervised work on an undergraduate research project, generally culminating in a final presentation and/or paper. PREREQUISITES: To register student must complete the Student Initiated Course Form with consultation of supervising instructor.