STAT - Statistics

Introduction to the field of statistics, including sampling, experiments, measurement, descriptive statistics, probability, inference, correlation, regression and prediction. The emphasis will be on data and concepts rather than on calculations and mathematical theory. Not open to students who have completed a 200-level statistics course with a grade of C- or better. Background assumed: N.Y.S. Algebra II and Trigonometry (or Math B), or equivalent.

An introductory study of statistical methods with applications to business, economics, education, and the social sciences. Topics covered include: descriptive statistics and graphs, probability and probability distributions, estimation, confidence intervals, hypothesis testing and linear regression. The course focuses on when to use each of the different methods. Note: Credit for at most one of the following courses may be applied towards a student's requirements for graduation: BUAD 200, ECON 200, EDU 200, POLI 200, SOC 200, and STAT 200. Background assumed: N.Y.S. Algebra II and Trigonometry (or Math B), or equivalent.

Introduction to statistical methods with special emphasis on uses in the natural sciences. Topics will include descriptive statistics, data collection, probability distributions, confidence intervals, hypothesis testing, regression, and analysis of variance. The course will include use of analytical labs and statistical computer packages. Background assumed: N.Y.S. Algebra II and Trigonometry (or Math B), or equivalent.

An introduction to the art and science of transforming data into information. Working with data using R and RStudio; data collection, wrangling, modelling, and visualization; data "storytelling." Background assumed: N.Y.S. Algebra II or equivalent.

Continuation of STAT 200. Review of the basics of estimation, confidence intervals and hypothesis testing. Simple and multiple regression, time series, analysis of variance and non-parametric methods. A statistical software package will be used extensively.

Basics of probability; descriptive statistics; discrete and continuous distributions; confidence intervals and tests of hypotheses concerning means and proportions; simple linear regression; statistical software. MATH 210 is recommended, in addition to the prerequisites listed.

Simple linear regression and multiple regression including inference, diagnostics and transformations. One-way and multi-way analysis of variance including inference, diagnostics and transformations. Use of professional statistical software.

Techniques for analyzing categorical response data â€" confidence intervals, tests of significance for a proportion, the difference of two proportions, contingency tables, regression, odds, odds ratios, logistic regression, logit models, loglinear models and diagnostics.

Probability, probability distributions, moments and moment-generating functions, conditional expectations, multivariate distributions, functions of random variables, sampling distributions, theory of estimation and hypothesis testing.

Independent study of a selected list of readings approved by the faculty advisor. Departmental approval required.

This is a capstone course for the statistics minor. Students will complete a major statistics project. It will include designing an experiment, collecting the data, analyzing the data, and giving oral and written reports explaining the analysis and conclusions. Departmental permission is required.

Selected readings, discussions, data analysis on a topic in statistics. Permission of department required.

Selected readings, discussions, data analysis on a topic in statistics. Permission of department required.

Study of linear time series, moving averages and auto regressive models. Estimation, confidence intervals, forecasting and data analysis with time series models will be examined.

Development of fundamental mathematical tools and language of quantitative risk management. Multivariate probability distributions including joint, conditional and marginal distributions, probabilities, moments, variance and covariance.