This course studies introductory statistics and probability, visual methods for summarizing quantitative information, basic experimental design and sampling methods, ethics and experimentation, causation, and interpretation of statistical analyzes. Applications use …
This course provides an introduction to statistical analysis in the context of forensic science. Statistical topics covered include probability distributions, hypothesis testing, confidence intervals, measures of association, and regression. Applications …
This course provides an introduction to the process of collecting, manipulating, exploring, analyzing, and displaying data using the statistical software R. The collection of elementary statistical analysis techniques introduced will …
This course provides an introduction to various topics in data science using the Python programming language. The course will start with the basics of Python, and apply them to data …
This course provides an introduction to sports analytics, including the collection, analysis, and visualization of sports data using the statistical programming language R. Elementary statistical analysis techniques will be introduced …
This course includes a basic treatment of probability, and covers inference for one and two populations, including both hypothesis testing and confidence intervals. Analysis of variance and linear regression are …
This course provides an introduction to the probability & statistical theory underlying the estimation of parameters & testing of statistical hypotheses, including those in the context of simple & multiple …
This course introduces methods to approach uncertainty and variation inherent in elementary statistical techniques from multiple angles. Simulation techniques such as the bootstrap will also be used. Conceptual discussion in …
This course provides an overview of basic probability and matrix algebra required for statistics. Topics include sample spaces and events, properties of probability, conditional probability, discrete and continuous random variables, …
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 …
This course introduces 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 …
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 …
This course provides an introduction to data analysis using the Python programming language. Topics include using an integrated development environment; data analysis packages numpy, pandas and scipy; data loading, storage, …
This course introduces methods for presenting data graphically and in tabular form, including the use of software to create visualizations. Also introduced are databases, with topics including traditional relational databases …
This course includes an overview of parametric vs. non-parametric methods including one-sample, two-sample, and k-sample methods; pair comparison and block designs; tests for trends and association; multivariate tests; analysis of …
This course provides the opportunity to offer a new topic in the subject area of Statistics.
This course includes linear regression models, inferences in regression analysis, model validation, selection of independent variables, multicollinearity, influential observations, and other topics. Conceptual discussion is supplemented with hands-on practice in …
This course develops fundamental methodology to the analysis of multivariate data using computational tools. Topics include multivariate normal distribution, multivariate linear model, principal components and factor analysis, discriminant analysis, clustering, …
This course introduces various topics in experimental design, including simple comparative experiments, single factor analysis of variance, randomized blocks, Latin squares, factorial designs, blocking and confounding, and two-level factorial designs. …
This course introduces topics in time series analysis as they relate to financial data. Topics include properties of financial data, moving average and ARMA models, exponential smoothing, ARCH and GARCH …