Psychology | Statistical Techniques
K310 | 9886 | Rickert, M


PSY K310 (K310) Statistical Techniques (3 cr.)
M. Rickert

Introduction to Statistical Inference
Prerequisite: M119 or equivalent.

The course begins with a brief review of commonly used methods for
organizing and graphing data, i.e., data visualization. Basic
descriptive statistics for summarizing the main characteristics of
frequency distributions are then introduced, including measures of
location, dispersion, and shape.  Properties of the normal
(Gaussian) distribution as well as important applications of the
standard (unit) normal distribution will be presented.  Next, we
examine measures of the association between two variables, and
develop a simple model for prediction based on linear regression.
The critical conceptual shift to inferential statistics begins with
a discussion of sampling procedures, elementary probability, and
sampling distributions. Here, focus is on the binomial distribution
and on the normal distribution as an approximation to the binomial.
Methods for point estimation, interval estimation, and hypothesis
testing (i.e., statistical decision-making under uncertainty) will
be drawn primarily from the classical or "frequentist" perspective
of statistical inference. A brief introduction to Bayesian inference
will be presented; we will compare frequentist and Bayesian
inference for hypothesis tests of a mean and for a test of the
difference between two means.  Other topics presented include
estimation of effect size, power analysis, multiple-linear
regression and correlation, analysis of variance and covariance, and
non-parametric techniques such as chi-square tests for
frequencies.

Format:	Two lectures per week (no lab); download assignments,
supplementary materials, etc. from Oncourse.

Homework: Five assignments (due dates TBA) 	

Tests: Three in-class examinations, one final

Text: Hinkle, D.E., Wiersma, W., and Jurs, S.G. (2003) Applied
Statistics for the Behavioral Sciences (5th edition). Boston:
Houghton Mifflin.

Computer packages:  (TBA but some computer exercises will require
use of SPSS, Excel, and/or Matlab)

Instructor(s):  Regular office hours; other times by appointment;
drop-ins and e-mail welcome.