MATH 60605A

This course aims to introduce advanced statistical methods for making meaningful inferences from data collected in UX contexts. It covers linear models, including mixed and/or generalized models, which are particularly useful for analyzing neurophysiological data commonly encountered in the field.

The course presents key concepts in multiple linear regression relevant to UX data analysis. Topics include appropriate statistical terminology, elements of experimental design, the effects of factors under study on measured responses, interpretation of results, and aspects of statistical modeling that support sound interpretation. The discussion is then extended to the case of correlated observations, enabling the analysis of longitudinal data or repeated measures collected from each participant. The course also provides guidance on how to handle cases where the response variable is binary. In addition to theoretical concepts, the course emphasizes practical applications in UX. To support application, data analysis software (e.g., SAS) is used, without becoming the central focus of the course.