Courses

STAT 100

Course Name

Numbers and reason

Surveys the standard ways in which "arithmetic turns into understanding" across examples from the natural and the social sciences. Main concepts include abduction (inference to the best explanation), consilience (numerical agreement across multiple measurement levels), bell curves, linear models, and the likelihood of hypothesis. Offered: A.

Credits
5
Quarter(s)
Autumn

STAT 111

Course Name

Lectures in applied statistics

Weekly lectures illustrating the importance of statisticians in a variety of fields, including medicine and the biological, physical, and social sciences. Credit/no-credit only. Offered: jointly with BIOST 111; Sp.

Credits
1
Quarter(s)
Spring

STAT 180

Course Name

Introduction to data science

Survey course introducing the essential elements of data science: data collection, management, curation, and cleaning; summarizing and visualizing data; basic ideas of statistical inference, machine learning. Students will gain hands-on experience through computing labs. Prerequisite: Either a minimum grade of 2.5 in MATH 098, a minimum grade of 3.0 in MATH 103, a score of 151-169 on the MPT-GS placement test, or score of 145-153 on the MPT-AS placement test. Offered: AWSp.

Credits
4
Quarter(s)
Autumn

STAT 220

Course Name

Principles of statistical reasoning

Introduces statistical reasoning. Focuses primarily on the what and why rather than the how. Helps students gain an understanding of the rationale behind many statistical methods, as well as an appreciation of the use and misuse of statistics. Encourages and requires critical thinking. (Students may receive credit for only one of STAT 220, STAT 221/CS&SS 221/SOC 221, STAT 311, and ECON 311.) Offered: AWSpS.

Credits
5
Quarter(s)
Autumn

STAT 221

Course Name

Statistical concepts and methods for the social sciences

Develops statistical literacy. Examines objectives and pitfalls of statistical studies; study designs, data analysis, inference; graphical and numerical summaries of numerical and categorical data; correlation and regression; and estimation, confidence intervals, and significance tests. Emphasizes social science examples and cases. (Students may receive credit for only one of STAT 220, STAT 221, STAT 311, STAT 221/CS&SS 221/SOC 221, ECON 311.) Offered: jointly with CS&SS 221/SOC 221; AWSp.

Credits
5
Quarter(s)
Autumn

STAT 302

Course Name

Statistical software and its applications

Introduction to data structures and basics of implementing procedures in statistical computing packages, selected from but not limited to R, SAS, STATA, MATLAB, SPSS, and Minitab. Provides a foundation in computation components of data analysis. Prerequisite: either STAT 311/ECON 311 or STAT 390/MATH 390. Offered: W.

Credits
3
Quarter(s)
Winter

STAT 311

Course Name

Elements of statistical methods

Elementary concepts of probability and sampling; binomial and normal distributions. Basic concepts of hypothesis testing, estimation, and confidence intervals; t-tests and chi-square tests. Linear regression theory and the analysis of variance. (Students may receive credit for only one of STAT 220, STAT 221, STAT 311, and ECON 311.) Prerequisite: either MATH 111, MATH 120, MATH 124, MATH 127, or MATH 144. Offered: AWSpS.

Credits
5
Quarter(s)
Autumn

STAT 316

Course Name

Design of experiments and regression analysis

Introduction to the analysis of data from planned experiments. Analysis of variance for multiple factors and applications of orthogonal arrays and linear graphs for fractional factorial designs to product and process design optimization. Regression analysis with applications in engineering. Prerequisite: IND E 315. Offered: jointly with IND E 316; W.

Credits
4
Quarter(s)
Winter

STAT 320

Course Name
Evaluating Social Science Evidence

A critical introduction to the methods used to collect data in social science: surveys, archival research, experiments, and participant observation. Evaluates "facts and findings" by understanding the strengths and weaknesses of the methods that produce them. Case based. Offered: jointly with CS&SS 320/SOC 320.

Credits
5

STAT 321

Course Name
Case-Based Social Statistics I

Introduction to statistical reasoning for social scientists. Built around cases representing in-depth investigations into the nature and content of statistical and social-science principles and practice. Hands-on approach: weekly data-analysis laboratory. Fundamental statistical topics: measurement, exploratory data analysis, probabilistic concepts, distributions, assessment of statistical evidence. Offered: jointly with CS&SS 321/SOC 321.

Credits
5

STAT 322

Course Name
Case-Based Social Statistics II

Continuation of CS&SS 321/SOC 321/STAT 321. Progresses to questions of assessing the weight of evidence and more sophisticated models including regression-based methods. Built around cases investigating the nature and content of statistical principles and practice. Hands-on approach: weekly data analysis laboratory. Prerequisite: CS&SS 321/SOC 321/STAT 321, or permission of instructor. Offered: jointly with CS&SS 322/SOC 322.

Credits
5

STAT 340

Course Name

Introduction to probability and mathematical statistics i

Covers the fundamentals of probability and mathematical statistics; axioms of probability, conditional and joint probability, random variables, univariate and multivariate distributions and densities, and moments; bionomial, negative binomial, geometric, Poisson, normal, exponential distributions, and central limit theorem; and basic estimation and hypothesis testing theory. Prerequisite: either STAT 311/ECON 311 or STAT 390/MATH 390; either a minimum grade of 2.5 in MATH 327 or MATH 136. Offered: A.

Credits
4
Quarter(s)
Autumn

STAT 341

Course Name

Introduction to probability and mathematical statistics ii

Brief review of: sample spaces, random variables, probability. Distribution: binomial, normal, Poisson, geometric. Followed by: expectation, variance, central limit theorem. Basic concepts of estimation, testing, and confidence intervals. Maximum likelihood estimators and likelihood ratio tests, efficiency. Introduction to regression. Prerequisite: either STAT 340 or STAT/MATH 394 and STAT/MATH 395; either STAT/ECON 311 or STAT/MATH 390; either a minimum grade of 2.5 in MATH 136 or MATH 327. Offered: W.

Credits
4
Quarter(s)
Winter

STAT 342

Course Name

Introduction to probability and mathematical statistics iii

Brief review of: sample spaces, random variables, probability. Distribution: binomial, normal, Poisson, geometric. Followed by: expectation, variance, central limit theorem. Basic concepts of estimation, testing, and confidence intervals. Maximum likelihood estimators and likelihood ratio tests, efficiency. Introduction to regression. Prerequisite: STAT 341. Offered: Sp.

Credits
4
Quarter(s)
Spring

STAT 390

Course Name

Statistical methods in engineering and science

Concepts of probability and statistics. Conditional probability, independence, random variables, distribution functions. Descriptive statistics, transformations, sampling errors, confidence intervals, least squares and maximum likelihood. Exploratory data analysis and interactive computing. Students may receive credit for only one of STAT 390, STAT 481/ECON 481, and ECON 580. Prerequisite: either MATH 126 or MATH 136. Offered: jointly with MATH 390; AWSpS.

Credits
4
Quarter(s)
Autumn

STAT 391

Course Name

Quantitative introductory statistics for data science

The basic concepts of statistics, machine learning and data science, as well as their computational aspects. Statistical models, likelihood, maximum likelihood and Bayesian estimation, regression, classification, clustering, principal component analysis, model validation, statistical testing. Practical implementation and visualization in data analysis. Assumes knowledge of basic probability, mathematical maturity, and ability to program. Prerequisite: either CSE 312, or STAT 394/MATH 394 and STAT 395/MATH 395. Offered: Sp.

Credits
4
Quarter(s)
Spring

STAT 394

Course Name

Probability i

Sample spaces; basic axioms of probability; combinatorial probability; conditional probability and independence; binomial, Poisson, and normal distributions. Prerequisite: minimum grade of 2.0 in either MATH 126 or MATH 136. Offered: jointly with MATH 394; AWS.

Credits
3
Quarter(s)
Autumn

STAT 395

Course Name

Probability ii

Random variables; expectation and variance; laws of large numbers; normal approximation and other limit theorems; multidimensional distributions and transformations. Prerequisite: minimum grade of 2.0 in STAT/MATH 394. Offered: jointly with MATH 395; WSpS.

Credits
3
Quarter(s)
Winter

STAT 396

Course Name

Probability iii

Characteristic functions and generating functions; recurrent events and renewal theory; random walk. Prerequisite: minimum grade of 2.0 in either MATH 395 or STAT 395, or minimum grade of 2.0 in STAT 340 and in STAT 341. Offered: jointly with MATH 396; Sp.

Credits
3
Quarter(s)
Spring

STAT 403

Course Name

Introduction to resampling inference

Introduction to computer-intensive data analysis for experimental and observational studies in empirical sciences. Students design, program, carry out, and report applications of bootstrap resampling, rerandomization, and subsampling of cases. Experience programming in R is beneficial. Credit allowed for STAT 403 or STAT 503 but not both. Prerequisite: either STAT 311/ECON 311, STAT 341, STAT 390/MATH 390, STAT 481/ECON 481, or Q SCI 381 and Q SCI 482. Offered: jointly with Q SCI 403; Sp.

Credits
4
Quarter(s)
Spring