Jack L. Vevea (jvevea@ucmerced.edu)
Social Science and Management Building, Room 306-A
Office hours: TBD. Telephone: (209) 658-1706
There is no required text for the class.
We will meet Tuesdays and Thursdays from 10:30 to 11:45 A.M. in room 264 of the Classroom Building.
Psychology 213 will introduce the student to some of the mathematical tools often used by quantitative psychologists. The class will use calculus, but software support will be available for those who feel insecure in their calculus skills.
In the class, you will:
By the end of the class, you will be able to use the tools and methods described above under "course learning goals" in your own work.The learning goals will be assessed via (roughly) weekly homework assignments. There will be no exams.
Psychology 202a or consent of intructor.
We will use two programs in this class: R and Mathematica. Information (and downloads) related to R is available here. Information related to obtaining Mathematica through the campus site license is here. (Warning: do not try to put this on your computer until you have time for a long download and installation. A hardwire internet connection is recommended.)
Grading will be based entirely on your written homework. In cases where the homework-based grade is on or near the cusp between two grades, attendance and participation will be considered.
Students should be familiar with University policies on academic honesty. Here is a link to the most recent policy on acedemic honesty.
For this class, my expectations are:
Where does this fit into my general graduate eduation at UC?
Here are the UC Merced Guiding Principles for General Education. Although they are thought of as particularly relevant to undergraduate training, they still provide a reasonable framework for thinking about graduate education. This class is particularly relevant to the first (Scientific Literacy), second (Decision Making), and eighth (Developing Personal Potential).
The course outline for this class is likely to change as we proceed through the semester.In Week One of the class, we will introduce the concept of expectations, working with the Bernoulli distribution and the closely related binomial distribution. In this context, we will introduce the moment generating function.
In Week Two, we will explore some more results related to the moment generating function and begin to discuss functions of random variables.
In Week Three, we will introduce joint and conditional distributions (discrete case on Tuesday; continuous case on Thursday).
In Week Four, we will discuss sequences and series, and introduce Taylor Series approximations.
In Week Five, we will introduce the Delta Method for approximating the mean and variance of functions of random variables.
In Week Six, the distribution of the week will be the univariate normal distribution and related distributions. We will introduce the method of moments.
In Week Seven, we will continue our discussion of distributions related to the normal distribution and introduce maximum likelihood estimation.
In Week Eight, we will continue with maximum likelihood estimation, investigating various optimization approaches for situations where the MLEs lack closed-form solutions. The distribution of the week is the Poisson distribution.
In Week Nine, we discuss Newton and Quasi-Newton methods for optimization.
In Week Ten, we will consider the Box-Cox maximum-likelihood approach to finding optimal transformations.
In Week 11, we will discuss algorithms for random number generation. The distribution of the week is the uniform distribution. We will play with some simple C programs to illustrate simple linear congruential random generators (mainly for historic interest).
In Week 12, we will introduce simple approaches to Bayesian statistical methods.
In Week 13, we will demonstrate the agony of
Homework One is available 9/7/2017, due 9/14/2017.
Homework Two is available 9/28/2017, due 10/5/2017.