Statistics: Applied Probability

Foundations of probability, conditioning, and independence. Business, computing, biological, engineering reliability, and quality control applications. Classical discrete and continuous models. Pseudo-random number generation. Prerequisites: MATH 1224 or MATH 1248 or MATH 1242.

Prereqs enforced by the system: MATH 1224 or MATH 1248 or MATH 1242; Open to Degree and PACE students

We will discuss a variety of theory and applications including topics in combinatorics, conditional probability, random variables, mathematical expectation, limit theorems, and statistics. The class will be partially flipped with a mixture of in-class group work, lecture (pre-class videos with a recap during class), discussion, and projects to highlight particular topics. Key topics are: *Properties of Probability ( Conditional Probability, Independent Events, Bayes' Rule) *Discrete Distributions ( Bernoulli, Binomial, and Poisson Distributions) *Continuous Distributions (Uniform, Exponential, Gamma, Chi-square, & Normal) *Bivariate Distributions (Conditional Distributions, Sums of Independent Random Variables) *Distributions of Functions of Random Variables

The class will be partially flipped. Reading the text and watching pre-class videos, along with submitting pre-class notes, before each class will be an ongoing homework assignment throughout the course. Students should expect between two and four hours of work outside of class for every hour of class time. Homework assignments will be listed on the class webpage. Late assignments will not be accepted.

Final grades will be determined by exams/quizzes, homework, and participation in class discussions.