Undergraduate Course Listings

For your first course in statistics, see also placement information

STA 10: Basic Statistics and Quantitative Literacy
Explore the use of information from surveys concerning health, behavior, and attitudes, as well as studies revealing scientific or technological breakthroughs. Focus is on quantitative literacy and understanding basic statistics.

STA 10 is not open to those with credit for AP statistics or other statistics courses. Students who take STA 10 can take a 100 level course after completion of this course. Prerequisite: Placement Exam.

STA 49S: Reasoning in the Presence of Uncertainty
Investigate how probability and decision theory can help us make decisions in science, business, law, medicine, and daily life. Appropriate for students interested in mathematics, statistics, computer science, and the natural and social sciences.

Prerequisite: Math 31 or equivalent

STA 101: Data Analysis and Statistical Inference
First principles in the construction and critique of quantitative arguments for research questions in the social and behavioral sciences and public policy. Topics include: descriptive statistics, graphical methods for exploring distributions and relationships between variables, elementary probability, point and interval estimation in one-, two- and multi-sample problems, and statistical inference from frequentist and Bayesian perspectives. Historical and philosophical developments of classical and Bayesian statistics are discussed. Applications in education, sports, law, environment, government, discrimination, psychology, sociology, and public policy included.

Prerequisite: Placement Exam.

STA 102: Introductory Biostatistics
Reading and interpretation of statistical analyses from life science and medical literature. Conceptual bases for using data and understanding uncertainty when making treatment decisions about patients. Includes extensive reading and class discussion of articles from the medical literature. Topics include: basic concepts and tools of probability and conditional probability, independence, two-by-two tables, Simpson's paradox, medical diagnosis, ROC curves, study designs for medical problems, inference and hypothesis testing for RCTs, decision analysis and decision trees, and basic survival analysis. Emphasizes role of biostatistics, drug testing, and clinical trials in modern society.

Prerequisite: Placement Exam.

STA 102B: Statistics in the Courtroom
Learn how statistical analyses are used in legal disputes. The course interlaces case studies with a mathematical exposition of statistical methods. Case studies include topics in discrimination, fraud, polygraphs, jury selection, sentencing, and more. Topics include graphical displays, study design, probability, confidence intervals, hypothesis tests, Bayes rule, regression, survival analysis, and more.

Prerequisite: Placement Exam.

STA 103: Probability and Statistical Inference
Basic laws of probability - random events, independence and dependence, expectations, Bayes theorem. Discrete and continuous random variables, density, and distribution functions. Binomial and normal models for observational data. Introductions to maximum likelihood estimation and Bayesian inference. One- and two-sample mean problems, simple linear regression, multiple linear regression with two explanatory variables. Applications in economics and quantitative social sciences, and natural sciences emphasized.

Prerequisites: MTH 31 or equivalent.

STA 104: Probability
Probability models, random variables with discrete and continuous distributions, independence, joint distributions, conditional distributions, expectations, functions of variables, and the central limit theorem. This is a basic, undergraduate level, calculus based first course in probability. The primary goal is the development of the ability to analyze problems involving uncertainty through the use of applied probability theory.

Crosslisting: MTH 135. Prerequisite: MTH 103

STA 113: Probability and Statistics in Engineering
Introduction to probability; independence; conditional independence and Bayes Theorem; nonlinear transformations of random variables; classical and Bayesian inference; decision theory; comparisons of hypotheses; experimental design; linear regression; statistical quality control and other engineering applications. This course is an introduction to statistical reasoning and includes a review of the necessary elements of probability theory. Applications in various branches of engineering and natural sciences are stressed throughout. Students apply important statistical tools such as regression and design of experiments to real life problems. Computation is emphasized, presently using Matlab.

Prerequisite: MTH 103

STA 114: Statistics
An introduction to the concepts, theory, and application of modern statistical inference. Topics developed include the basic structure of statistical problems, probability modeling and statistical inference, elements of data analysis and statistical computing, and linear regression Modeling. Inference is developed from the viewpoints of modern Bayesian statistical science, with some development of sampling theory methods and comparative inference. Specific statistical models and methods draw on motivating applications from various fields (such as medicine, health policy, finance, neurosciences, astronomy,...) This is the second semester of a two-semester sequence in probability and statistics, the first semester course being STA 104 (cross-listed as MTH 135). It is strongly recommended that students take the prerequisite course STA 104/MTH 135 in the Fall semester directly preceding STA 114 in Spring.

Crosslisting: MTH 136. Prerequisite: recent completion of STA 104/MTH 135; also an introduction to linear algebra such as that in MTH~104 or MTH~107 and some interest in computing.

STA 121: Regression Analysis
An extensive study of linear regression modeling, with emphasis on applications in various fields. Topics include: ordinary and weighted least squares regression, logistic regression, basic analysis of variance, properties of regression coefficients, prediction, model diagnostics and model selection. Statistical software will be used extensively for numerous applied studies.

Prerequisite: 100-level STA course.

STA 122: Bayesian Inference and Modern Statistical Methods
The principles of data analysis and advanced statistical modeling. Topics include: Bayesian methods, prior and posterior distributions, hierarchical models, model checking and selection, elementary stochastic simulation by Markov Chain Monte Carlo.

Prerequisites: STA 104, STA 114, and STA 121 or ECO 139/239.

STA 130: Design and Analysis of Causal Studies
Design of randomized experiments and observational studies, including the role of randomization, block designs, factorial designs, fractional factorial designs, split plot designs, matching. ANOVA, contrasts, nonparametric methods, propensity score matching, instrumental variables models.

Prerequisite: STA 121 or ECO 139/239.

STA 135: Design and Analysis of Surveys
Design of surveys, including random sampling, stratified sampling, cluster sampling, multi-stage sampling. Design-based and model-based inference. Methods of handling missing data.

Prerequisite: STA 121 or ECO 139/239.

STA 140: Introduction to Statistical Decision Analysis
Modern quantitative methods for decision making under uncertainty. Topics include: probability theory, personal probabilities and utilities; decision trees; ROC curves, sensitivity analysis, dominant strategies, Bayesian networks and influence diagrams; Markov models and time discounting; cost-effectiveness analysis; multi-agent decision-making and game theory.

Pre-requisite: 100-level STA course.

STA 145S: Undergraduate Statistical Consulting Seminar
Participation in data analysis projects from the Statistical Consulting Center . Projects led and directed by Duke Statistics faculty and graduate students.

Pre-requisite: STA 122 or consent of instructor

STA 175S: Computational Data Analysis
Data analysis, graphical data exploration and representation, and elements of scientific modeling and computation in scientific problems arising in areas such as genomics, biotechnology, climatology, neuroscience, finance, and others. Methods include: regression methods for data-mining for large-data sets, algebraic decomposition methods in statistics, stochastic simulation tools for temporal models of dynamic processes, graphical and network data, and computational methods development. Problems and data are drawn from research projects of the instructor and research collaborators.

Pre-requisite: STA 122, and some computer programming expertise.

STA 180S: Statistical Methods in Bioinformatics
Explore statistical models and analytical tools for bioinformatics and genomics. Topics include functional inference for DNA, RNA, and protein sequences, and the analysis of genetic pedigrees, gene expression experiments, and families of molecular sequences and structures.

Prerequisites: STA 104/MATH 135 and a 100-level statistics course.

STA 190AS: Research Seminar in Statistical Science I
Initiation of year-long statistical research project, mentored by Duke Statistics faculty members. Student presentations of statistical research. Assignments related to understanding other students' research.

STA 190BS: Research Seminar in Statistical Science II
Continuation of STA 190AS. Final product is statistical research paper required for major.