Statistics

Introduction to the probability and statistical theory underlying the estimation of parameters and testing of statistical hypotheses, including those arising in the context of simple and multiple regression models. Students will use computers and statistical programs to analyze data. Examples and applications are drawn from economics, business, and other fields. Students will not receive credit for both STAT 2120 and ECON 3710. Prerequisite: MATH 1210 or equivalent; co-requisite: Concurrent enrollment in a discussion section of STAT 2120. "

Most elementary statistics courses start with a technique & present various surface level examples. This course will use relatively complicated data sets and approach them from multiple angles with elementary statistical techniques. Simulation techniques such as the bootstrap will also be used. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using R statistical software. Prerequisite: An introductory statistics course. "

This course provides a calculus-based introduction to mathematical statistics with some applications. Topics include: sampling theory, point estimation, interval estimation, testing hypotheses, linear regression, correlation, analysis of variance, and categorical data. Prerequisite: MATH 3100 or APMA 3100. "

Main designs & estimation techniques used in sample surveys; including simple random sampling, stratification, cluster sampling, double sampling, post-stratification, ratio estimation; non-response problems, measurement errors. Properties of sample surveys are developed through simulation procedures. Uses SUDAAN software package for analyzing sample surveys. "

This course provides a survey of regression analysis techniques, covering topics from simple regression, multiple regression, logistic regression, and analysis of variance. The primary focus is on model development and applications. Prerequisite: STAT 1100 or STAT 1120 or STAT 2120. "

This course provides an introduction to data analysis using the Python programming language. Topics include using the IPython development environment; data analysis packages NumPy and pandas; data loading, storage, cleaning, merging, transformation, and aggregation; data plotting and visualization and time series data. No prior experience with programming or statistics is required. "

This course provides an introduction to databases. Topics include traditional relational databases and SQL (Structured Query Language) for retrieving information from them, and several noSQL databases built on different organizational structures, such as PostgreSQL (an open source relational database), MongoDB and CouchDB (key-document), Redis (key-value), HBase (column family), and Neo4J (graphs). "

Introduces various topics in machine learning, including regression, classification, resampling methods, linear model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. The statistical software R is incorporated throughout. Prerequisite: STAT 3220, STAT 5120, or ECON 3720, and previous experience with R. "

Linear regression models, inferences in regression analysis, model validation, selection of independent variables, multicollinearity, influential observations, autocorrelation in time series data, polynomial regression, and nonlinear regression. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite:STAT 3120, and either MATH 3351 or APMA 3080 "

This course develops fundamental methodology to the analysis of multivariate data. Topics include the multivariate normal distributions, multivariate regression, multivariate analysis of variance (MANOVA), principal components analysis, factor analysis, and discriminant analysis. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission. "