MATH 324 — Statistics

Sampling distributions, including Normal, Chi-squared, Student’s t, and F distributions. Sampling properties of the sample mean and variance. Point estimation: bias, variance, mean squared error, consistency, sufficiency, UMVUE. Method of moments and maximum likelihood estimation. One- and two-sample inference. Confidence intervals and sample size determination. Hypothesis testing, Type 1 and Type 2 errors, power, and p-values. The Neyman Pearson framework and likelihood ratio tests. Linear models used as illustrative examples, linking them to topics in estimation and hypothesis testing. One-way analysis of variance (ANOVA) and contingency tables.

• Rating: 3.47 out of 5 from 843 student reviews
• Difficulty: 3.48 out of 5
• Credits: 3
• Faculty: Faculty of Science
• Department: Mathematics and Statistics
• Taught by: Christian Genest
• Prerequisite: MATH 323 or equivalent

Sections offered

• Section 001 (Lec), Tue Thu 11:35 am-12:55 pm — 16 seats open