## Course description

This class will provide the necessary probability and statistical background needed for students to go on to study economics, financial statistics, engineering and more advanced methods of quantitative analyses in the natural and social sciences. We will learn about the basic probability laws, random events, independence and conditional independence, expectations, and Bayes theorem. We will also cover discrete and continuous random variables, density and distribution functions, point estimation, maximum likelihood estimation, confidence intervals, bootstrap methods, Bayesian inference, hypothesis testing, simple linear regression, multiple linear regression and much more. A detailed course schedule will be maintained throughout the semester.

Labs will be taught using R and RStudio and will emphasize exploratory data analyis and implementation of inference procedures introduced in lecture.

## Prerequisites

Calculus (MATH21 or equivalent).

## Textbook

Probability and Statistics (4th Edition), by Morris H. DeGroot and Mark J. Schervish

(Optional) OpenIntro Statistics (3rd Edition), by David M. Diez, Christopher D. Barr, and Mine Ã‡etinkaya-Rundel