Regression and Forecasting Models for Business Applications

Download as PDF

Overview

Subject area

STA

Catalog Number

9000

Course Title

Regression and Forecasting Models for Business Applications

Description

This course provides a thorough review of regression and forecasting approaches as applied to business applications. Among the topics covered are residual and influence analysis; multiple regression models, including selection criteria, curvilinear regression, dummy variables, and logistic regression; and time series models, including the classical multiplicative model, moving averages, exponential smoothing, and the autoregressive model.

Typically Offered

Fall, Spring, Summer

Academic Career

Graduate

Liberal Arts

No

Credits

Minimum Units

3

Maximum Units

3

Academic Progress Units

3

Repeat For Credit

No

Components

Name

Lecture

Hours

3

Requisites

023422

Course Schedule