Courses
STAT-110 R for Statistics (1)
This course will introduce the R statistical environment to students who have completed an introductory statistics course without R. Students will learn how to perform calculations in R, read and write data sets, and how to clean and process data for analysis.
Attributes: YLIB
Pre-requisites: ECON-221 D- OR PSYC-201 D-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 introduces data science as a field incorporating statistics, programming and computational thinking, data skills, and disciplinary problem solving. Statistics for data science are covered, including exploratory data analysis through graphical and statistical methods, inferential methods for one and two-sample designs with continuous and categorical outcomes, and simple least-squares and logistic regression with an emphasis on modeling and prediction. Simulation-based statistical methods are emphasized throughout the course. Students learn basic skills for manipulating and managing data, and carry out a data science project in real disciplinary data. Statistical computing is done in R and R Studio, and communication via R Markdown.
Formerly titled: Quantitative Methods for Social Research
Attributes: LEST P4 YLIBSTAT-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: YLIB
Pre-requisites: STAT-160 D- OR STAT-110 Y D- AND (ECON-221 D- OR PSYC-201 D-)STAT-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: YLIB
Pre-requisites: STAT-160 D-STAT-222 Nonparametric Statistics (3)
This course covers nonparametric statistical methods, with emphasis on applications, data analysis, and statistical computing. Topics include binomial and sign tests, rank tests for 1-sample and 2-sample designs, contingency table analysis, Kolmogorov-Smirnov tests, nonparametric correlation coefficients, nonparametric regression methods, and computationally-intensive approaches to nonparametric analysis. Cross-listed with ECON 222.
Attributes: YLIB
Pre-requisites: STAT-160 D- OR ECON-221 D-STAT-250 Geographic Info Systems (3)
Spatial awareness is a key to understanding our world. This class looks at the use of Geographic Information Systems (GIS) to analyze and answer real world problems. GIS is a multidisciplinary tool that can be utilized by pretty much any researcher because it can showcase a lot of information in addition to the geographic location such as demographics of an area for marketing, looking at population needs. This class is an introduction to the possibilities of GIS, and the goal is to come away with new tools and thought process to help look at data in a different way.
Attributes: YLIBSTAT-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 Predictive Analytics (3)
This is a course in supervised statistical learning and predictive modeling, emphasizing the application of statistical learning methods for understanding complex datasets and for addressing regression and classification problems. Methods covered include k-nearest neighbors models, nonlinear regression and spline models, generalized additive models, penalized regression models, tree models including random forests and boosting, and support vector models. Resampling methods for model validation, model bias/variance issues, and using models for prediction are emphasized throughout the course. The course is taught in R, and students are introduced to many R packages for statistical learning.
Formerly titled Exploratory Data Analysis
Attributes: YLIB
Pre-requisites: STAT-160 D- OR STAT-210 D- OR STAT-220 D-STAT-355 Social Network Analysis (3)
This course is an introduction to the statistical analysis of social networks; the structure of network connections introduces a number of unique statistics to networks. Beginning with an introduction to graph theory, it will look at the representation of networks, appropriate descriptive statistics for networks, issues related to sampling networks, and how networks can be compared and modeled. Specific topics to be examined include measures of importance in networks, community structure, and inferential and predictive modeling of networks
Attributes: YLIB
Pre-requisites: STAT-160 D-STAT-370 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. Students who have earned credit for STAT 270 may not earn credit for STAT 370.
Attributes: YLIB
Pre-requisites: 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: STAT-160 D- OR (ECON-221 D- AND STAT-110 D-) OR (PSYC-201 D- AND STAT-110 D-) AND (STAT-210 D- OR STAT-220 D- OR STAT-222 D- OR STAT-345 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, SeniorSTAT-1132 Education Analytics (3)
Education Analytics is an introductory course in using quantitative methods for inquiry in education. The course covers statistical and graphical tools for summarizing educational data, basic statistical inference, and foundational concepts in the statistical modeling of educational outcomes. Students develop competence in reading the statistical aspects of educational research. Students are introduced to educational data mining, learning analytics, and other uses of data to predict educational outcomes. Students work with the National Childhood Longitudinal Study data and other large educational datasets in R/RCommander and develop data analytic and computing skills. The course also covers principles of data ethics through a set of five case studies.
Attributes: DA YLIB
Restrictions: Including: -Class: Freshman, Sophomore -Attribute: New Core 20-21STAT-1134 Healthcare Analytics (3)
Healthcare Analytics is an introductory course in using quantitative methods for inquiry in healthcare. The course covers statistical and graphical tools for summarizing healthcare data, basic statistical inference, and foundational concepts in the statistical modeling of healthcare outcomes.Students develop competence in reading and understanding the statistical aspects of healthcare research. Students work with the community Health Status Indicators and other large healthcare data sets in R and develop data analytic and computing skills. The course also covers how to communicate effectively with data and consider ethical issues inherent in healthcare data analysis.
Attributes: DA YLIB
Restrictions: Including: -Class: Freshman, Sophomore -Attribute: New Core 20-21