Courses
STAT-125 Statistical Literacy (3)
Statistics is less about “crunching numbers” than about logical and disciplined thinking about what we can (and cannot) conclude from data in general. This course introduces statistical principles and methods for improving our thinking about data summaries and data-based claims. The course covers graphical and statistical methods for “mining” meaning from data, what questions to ask about statistical claims, how knowledge of the laws of probability help us make better decisions, why sampling is important to good science, what good measurement is and how to recognize it, what the results of a scientific article mean, and many other practical applications of statistical theory and reasoning. Students will learn and use basic statistical computing skills for exploring and analyzing data and testing statistical concepts.
Attributes: YLIBSTAT-160 P4 Intro to Data Science (3)
This course covers techniques for working with data, including getting and cleaning data, exploratory data analysis, data visualization, and statistical modeling and prediction. Students will learn how to ask good questions, apply data to practical problems, and communicate data analytic results. Statistical computing is integrated into the course. The course is intended for social science disciplines.
Formerly titled: Quantitative Methods for Social Research
Attributes: LEST P4 YLIBSTAT-199C RW Research-Based Writing (3)
Students learn the basics of writing an academic research paper in this discipline. Emphasis is on elements of persuasive argumentation, the inclusion of more than one perspective on an issue, the proper use and documentation of sources, and revision. Students also learn how to make an effective oral presentation of their research. Department-determined topic may change from semester to semester and is likely to include literary texts as primary materials.
Restricted to freshmen and transfers.
Note: 199C courses may not be taken for credit more than once.
» Spring Research-based Writing (199) Courses & Topic Descriptions [pdf]
Attributes: RW YLIB
Restrictions: Including: -Class: Freshman, SophomoreSTAT-210 Regression Analysis (3)
This course covers basic and intermediate principles of applied linear regression. The course topics include least-squares estimation; assumptions underlying regression analysis and tests of regression assumptions; residuals analysis; regression with nominal/dummy-coded predictors; stepwise and hierarchical entry strategies; prediction, and testing interaction effects in regression analysis. Emphasis is placed on the analysis of behavioral data using regression methods, the interpretation of regression statistics, and the written communication of results of regression analysis. SPSS and R will be the primary statistics software used in this course.
Attributes: YLIBSTAT-220 Experimental Design (3)
Principles of designing and analyzing experiments with applications to behavioral and health science disciplines. Topics covered include randomized and blocked experimental designs, control, and analysis of variance in between-subjects, repeated-measures, and simple factorial designs. Data analytic, statistical computing, and statistical communication skills are developed in the course.
Attributes: YLIBSTAT-222 Nonparametric Statistics (3)
Basic nonparametric methods will be covered, including binomial and sign tests, rank tests for 1-sample and 2-sample designs, and Kolmogorov-Smirnov type tests. Nonparametric analysis of contingency tables and bivariate association will also be covered. The course will emphasize data analysis and interpretation as well as statistical computing skills. Cross listed with ECON 222.
Attributes: YLIBSTAT-270 Meta-analysis (3)
Meta-analysis refers to statistical methods for analyzing effect sizes across studies, and is widely-used in the social and health sciences for synthesizing research and establishing evidence-based practice and policy. Topics covered in this course include: effect size estimation, coding, weighting schemes, fixed and random effects models, moderation of meta-effects, meta-regression, and methods to evaluate heterogeneity and publication bias. Computing will use meta-analysis packages in R.
Attributes: YLIB
Pre-requisites: MATH-260 D- OR ECON-221 D- OR PSYC-201 D- OR STAT-160 D-STAT-330 Sample Survey Methods (3)
This course covers statistical methods for the collection and analysis of political survey data, including methods associated with sampling, survey design and implementation, and the analysis and presentation of polling data. Students will learn statistical approaches to sampling, measurement, and analysis of survey, as well as how to address common issues involved with population estimation.
Attributes: YLIB
Pre-requisites: STAT-210 D- OR STAT-220 D-STAT-345 Exploratory Data Analysis (3)
This course covers statistical and graphical methods to explore, understand, and communicate the underlying structure in data. Topics include exploratory statistical methods, data smoothing and clustering, plots for exploratory analysis, and data mining and machine learning concepts. The course covers R packages and functions for exploratory data analysis.
Attributes: YLIB
Pre-requisites: MATH-260 D- OR STAT-160 D- OR STAT-210 D- OR STAT-220 D-STAT-375 Data Analysis Stat Comp (3)
Uses statistical models and other data science tools to analyze data for various objectives, including description, prediction, and inference, and develop skill with statistical computing languages and software to analyze data. Students analyze real datasets in areas of disciplinary interest, and apply statistical and computing methods learned in prior courses as well as new methods appropriate to the problem. Emphasis is placed on data analytic and computing skill, communication of findings, and research reproducibility.
Attributes: YLIB
Pre-requisites: MATH-260 D- OR STAT-210 D- OR STAT-220 D-STAT-390 Spec Topics in Statistics (3)
The course will address an advanced topic in statistics which may emphasize mathematical statistics, applied statistics, or computer applications in statistics. Fall 2016 Topic:Statistical Inference (3) Approaches to statistical inference are covered, framed by frequentist and Bayesian perspectives. Topics include: null hypothesis significance testing; robustness and exact tests, point and interval estimation; confidence interval construction methods; goodness of fit tests; maximum likelihood estimation, Bayesian estimation, and inference using resampling methods.
Attributes: YLIB
Pre-requisites: STAT-210 D- OR STAT-220 D-
Restrictions: Including: -Major: Statistics -Class: Junior, SeniorSTAT-405 Statistical Inference (3)
Approaches to statistical inference are covered, framed by frequentist and Bayesian perspectives. Topics include: null hypothesis significance testing; robustness and exact tests, point and interval estimation; confidence interval construction methods; goodness of fit tests; maximum likelihood estimation, Bayesian estimation, and inference using resampling methods.
Attributes: YLIB
Pre-requisites: MATH-122C D- AND STAT-220 D-STAT-480 Statistics Capstone (3)
Students propose, conduct, and present a substantive research project that demonstrates a synthesis of learning accumulated in the statistics major. The research topic is approved by the Program Director and conducted under the supervision of the student’s capstone advisor. The project culminates in a written report and presentation. Permission of the Program Director is required to register.
Attributes: YLIBSTAT-490 Field Experience (2 TO 6)
Provides students with the opportunity to sharpen and use statistical, scientific, report-writing, and communication skills in an organizational setting. Success is based on the student’s report of the field experience and the supervisor’s evaluation. May be repeated for a total of 6 credits. Permission of instructor is required to register.
Attributes: YLIB
Pre-requisites: MATH-122C D- AND STAT-210 D-
Restrictions: Including: -Major: Statistics -Class: Junior, SeniorSTAT-496 Independent Study (1 TO 3)
In-depth study of a statistical topic under the direction of a Statistics faculty member. A written report summarizing the course project, research, or activity is submitted to the supervising faculty member. Completion of the Independent Study/Tutorial Authorization form is required.
Attributes: YLIB
Restrictions: Including: -Major: Statistics -Class: Junior, Senior