Course Descriptions

An introduction to the language of statistics and some statistical methods, including random variables and their distribution; random sampling, normal distribution, estimation of parameters and testing hypotheses.

A calculus-based study of probability, discrete and continuous random variables, mathematical expectation, moment generating functions, Central Limit Theorem, sampling from normal populations, confidence intervals, hypothesis testing.

The ability to analyze data is an increasingly important skill in many fields of study. This problem-based course introduces common inferential statistics and data analysis techniques to students interested in improving critical thinking about data and evidence-based decision making. Students will explore different data visualization and data analysis techniques in the context of practical problems and students will investigate predictive analysis techniques using statistical software or spreadsheet add-ins.

This course introduces modern tools and techniques used for manipulation, analysis and visualization of large datasets using software. Students will study regression models to make predictions and analyze the relationship between variables. Students will gain hands-on experience visualizing, analyzing, and interpreting large datasets from disciplines such as social sciences, life sciences, physical sciences, economics, education, and engineering.

This course introduces students to models, design and analysis of small to medium-sized experimental data using software. Students will design single-factor and factorial experiments as well as analyze the effect of each factor and the interaction among factors. Statistical methods such as t-tests and ANOVAs will be used to analyze data. Simple and multiple linear regression models will be used to make predictions and analyze relationships between variables. Students will gain hands-on experience applying these techniques to data from disciplines such as social sciences, life sciences, physical sciences, economics, education, and engineering.