Statistics (STAT)

STAT-140 Introduction to the Ideas and Applications of Statistics

Fall and Spring. Credits: 4

This course provides an overview of statistical methods, their conceptual underpinnings, and their use in various settings taken from current news, as well as from the physical, biological, and social sciences. Topics will include exploring distributions and relationships, planning for data production, sampling distributions, basic ideas of inference (confidence intervals and hypothesis tests), inference for distributions, and inference for relationships, including chi-square methods for two-way tables and regression.

Applies to requirement(s): Math Sciences
A. Foulkes, J. Gifford, E. Ray
Advisory: 2 years of high school algebra

STAT-240 Elementary Data Analysis and Experimental Design

Spring. Credits: 4

A fundamental fact of science is that repeated measurements exhibit variability. The course presents ways to design experiments that will reveal systematic patterns while 'controlling' the effects of variability and methods for the statistical analysis of data from well-designed experiments. Topics include completely randomized, randomized complete block, Latin Square and factorial designs, and their analysis of variance. The course emphasizes applications, with examples drawn principally from biology, psychology, and medicine.

Applies to requirement(s): Math Sciences
J. Gifford
Prereq: Any 100-level mathematics or statistics course.

STAT-241 Methods in Data Science

Fall. Credits: 4

This course introduces methods in data science, including exploring problems, developing and implementing possible data analytic solutions and interpreting findings. Statistical programming and computational reasoning are emphasized. Topics include data visualization, data manipulation, data analysis and presentation. Reproducible research methods are explored and case studies are emphasized.

Applies to requirement(s): Math Sciences
A. Foulkes
Prereq: STAT-140 and MATH-101.

STAT-242 Intermediate Statistics

Fall and Spring. Credits: 4

In this course, students will learn how to analyze data arising from a broad array of observational and experimental studies. Topics covered will include exploratory graphics, description techniques, the fitting and assessment of statistical models, hypothesis testing, and communication of results. Specific topics may include multiple regression, ANOVA, and non-linear regression. Statistical software will be used.

Applies to requirement(s): Math Sciences
C. Hosman, The department
Prereq: STAT-140 or equivalent.

STAT-295 Independent Study

Fall and Spring. Credits: 1 - 4

The department
Instructor permission required.
Advisory: The permission of the department is required for independent work to count toward the major or minor.

STAT-340 Applied Regression Methods

Fall. Credits: 4

This course includes methods for choosing, fitting, evaluating, and comparing statistical models; introduces statistical inference; and analyzes data sets taken from research projects in the natural, physical, and social sciences.

Applies to requirement(s): Math Sciences
A. Foulkes
Prereq: MATH-211 and STAT-242.

STAT-343 Mathematical Statistics

Spring. Credits: 4

This course is an introduction to the mathematical theory of statistics and to the application of that theory to the real world. Topics include probability, random variables, special distributions, introduction to estimation of parameters, and hypothesis testing.

Applies to requirement(s): Math Sciences
E. Ray
Prereq: MATH-102 and MATH-342.

STAT-344 Seminar in Statistics and Scientific Research

STAT-344BT Seminar in Statistics and Scientific Research: 'Topics in Biostatistics'

Spring. Credits: 4

This course serves as an introduction to advanced topics in Biostatistics. In this course, students will learn about a range of topics, including: applied Bayesian techniques, e.g. the Gibbs sampler; multiple testing adjustments for high-dimensional data; the expectation-maximization algorithm; multiple imputation for missing data; the bootstrap for hypothesis testing; and simulation techniques for characterizing algorithm performance, including power and type-1 error rates. Areas of application will include, but are not limited to, statistical genetics and genomics. This is a project-oriented course with an emphasis on statistical programming with R.

Applies to requirement(s): Math Sciences
A. Foulkes
Prereq: Any 200- or 300-level Statistics course.

STAT-395 Independent Study

Fall and Spring. Credits: 1 - 8

The department
Instructor permission required.
Advisory: The permission of the department is required for independent work to count toward the major or minor.