This course provides an elementary introduction to probability and statistics with an emphasis on experimentation using computer programming. Topics: permutation, combination, random variables, probability distributions, Bayesian inference, hypothesis testing and confidence intervals.

Course Objectives

Elementary probability theory, conditional probability and independence, random variables, distribution functions, joint and conditional distributions, law of large numbers, central limit theorem, parameter estimation, confidence intervals, and hypothesis testing.

Recommended or Required Reading

Peter Olofsson, Mikael Andersson, Probability, Statistics, and Stochastic Processes, Wiley. ,Michael Baron, Probability and Statistics for Computer Scientists, CRC Press. ,Allen B. Downey, Think Stats: Probability and Statistics for Programmers, Green Tea Press.

Learning Outcomes:

1.To be able to explain and apply the concepts of probability and random variables.
2.To be able to describe and use common probability distributions and their properties.
3.To be able to employ descriptive statistics.
4.To be able to estimate distribution parameters.
5.To be able to calculate confidence intervals.
6.To be able to conduct hypothesis testing.
7.To be able to utilize computation as a tool to explore concepts in probability and statistics and to do quantitative analysis.

Grading

Final Exam 40%

Quiz 10%

Homework 20%

Midterm 30%