Listed in: Mathematics and Statistics, as MATH-430
Formerly listed as: MATH-30
Amy S. Wagaman (Section 01)
This course examines the theory behind common statistical inference procedures including estimation and hypothesis testing. Beginning with exposure to Bayesian inference, the course will cover Maximum Likelihood Estimators, sufficient statistics, sampling distributions, joint distributions, confidence intervals, hypothesis testing and test selection, non-parametric procedures, and linear models. Four class hours per week.
Requisite: MATH 360 or consent of the instructor. Spring semester. Professor Wagaman.
Section 01
M 09:00 AM - 09:50 AM SMUD 202
Tu 09:00 AM - 09:50 AM SMUD 202
W 09:00 AM - 09:50 AM SMUD 202
F 09:00 AM - 09:50 AM SMUD 202
This is preliminary information about books for this course. Please contact your instructor or the Academic Coordinator for the department, before attempting to purchase these books.
| ISBN | Title | Publisher | Author(s) | Comment | Book Store | Price |
|---|---|---|---|---|---|---|
| Mathematical Statistics with Applications | Brooks/Cole | Wackerly, Mendenhall, and Scheaffer | Same book from Math 360 - working on the second half | Amherst Books | TBD |
These books are available locally at Amherst Books.