Probability and Statistics

This course covers the basics of probability theory and statistics.

Topics in probability include discrete and continuous random variables, probability distributions, conditional probability and stochastic independence, joint distributions, sums and functions of random variables, expectation, variance and covariance, the law of large numbers, and the central limit theorem.

Topics in statistics include random sample and sample distributions, graphical summaries, estimation and (un)biased estimators, maximum likelihood estimation and least square estimation, confidence intervals, hypothesis testing and type I and II errors and the t-test.

Two exams: midterm in week 5 about first part of the course and a final exam in week 9 about the second part of the course.

The final grade will be the average of the midterm and final exam.