Please find the syllabus here: Syllabus

Probability and statistical inference provide tools needed to answer a very broad range of interesting questions in various fields. For example, how likely was Loyola IL to make the final four at the beginning of the 2017/2018 season or what is the probability that you will make six or more digits annual salary after graduation if you move to New York? This is an introductory level class that aims to provide some of the theoretical probability and statistical inference background needed for students to go on to study advanced quantitative analyses in the natural and social sciences, financial statistics, and so on. You will learn the basic laws of probability, random events, independence, expectations and Bayes theorem. You will also learn discrete and continuous random variables, density and distribution functions, point estimation, confidence intervals, Bayesian inference, one and two-sample mean problems, simple linear regression, multiple linear regression, and much more. While this is an introductory class with most students being freshmen, higher level students can also take the class as a first-level theoretical refresher course for a more advanced class or simply out of interest. All students must have some background in calculus (see prerequisites) to be able to keep up with and understand the materials.

Learning Objectives:
By the end of this class, students should be able to

  • Define probability, random variables, probability density and mass functions, probability distribution functions and use probability results for statistical inference.
  • Derive and verify some well-known statistical results, for example, maximum likelihood estimators for common distributions.
  • Apply the probability results and statistical models developed in class to real data from economics, public policy, social science, and so on.
  • Summarize and analyze data using R or Stata.

Textbook: Probability and Statistics (4th edition) by De Groot and Schervish.

Acknowledgement: This web page contains information, lecture notes, examples, and datasets developed by Dr. David Banks and Dr. Mine Çetinkaya-Rundel.