MATH 465 - Experimental Design and Regression Analysis

Unit(s): 4

This course will build on ideas from your past statistics classes, focusing on linear and non-linear regression analysis, design of experiments and analysis of variance, non-parametric techniques and multiple comparison methods. The building blocks are your knowledge of statistical inference and probability distributions and their behavior that you learned in Math 265 and 345. In particular, knowledge of and proficiency with definitions, results, and probability distributions presented in Math 345 is expected upon entering Math 465. Along the way, we’ll learn about and use the programming language R, but no prior knowledge of it is needed. In order to best facilitate your learning, this course will employ active learning methods that support such activities. At every class meeting, you will be asked to engage in classroom discussions, work on problems with peers in class, and actively engage with the material. The Conference Board of the Mathematical Sciences published a result in July 2016 stating that “classroom environments in which students are provided opportunities to engage in mathematical investigation, communication, and group problem-solving, while also receiving feedback on their work from both experts and peers, have a positive effect on learning.” In the Math and Stats Department, we invite and welcome students from all educational and cultural backgrounds to join us in creating an active, collaborative learning community that celebrates the complexity, beauty, and applicability of mathematics and statistics.

Prerequisite(s): Grade of C- of better in (MATH 241 or MATH 222) and MATH 265 and MATH 345 or consent of instructor. Completion of GE Golden Four (A1, A2, A3, B4) with a C- or better and completion of B1, B2 and at least 45 units.
GE Category: Upper Division B
Typically Offered Spring Only
Teaching Mode: Face-to-Face Grading: Student Option