Course Outline

TMGT 555 : Applied Regression Analysis

Preview Workflow

Viewing: TMGT 555-WW : Applied Regression Analysis

Last approved: Wed, 20 Jan 2016 14:16:21 GMT

Last edit: Wed, 20 Jan 2016 14:16:20 GMT

TMGT 555-WW
Campus
Worldwide
College of Business (WBUAD)
TMGT
555
Applied Regression Analysis
3
Students are challenged in the application of regression analysis - diagnosing practical problems, deciding upon the appropriate regression model and knowing which inferential technique will answer the practical question. Topics covered include Multiple Regression Models, Model Building, Variable Screening Methods, Regression Pitfalls, Residual Analysis and Special Topics in Regression.

Upon completion, a student will be able to select an appropriate regression analysis method to address a simulated management problem, then calculate and interpret the solution. The student should understand the methodology used and be able to defend the rationale for selection. The student will be able to use and correctly apply regression techniques. Knowledge acquired in this course will give students graduate level skills required for completion of other courses.

Upon course completion, the student will be able to:

1. Understand and apply the general form of a multiple regression model, including model assumptions to solve management problems.2. Calculate a first-order regression model with quantitative predictors. Calculate the predictor variance, coefficient of determination and utility of the model using analysis of variance. Interpret results.

3. Apply regression model building utilizing quantitative and qualitative independent variables to reach appropriate solutions to management situations.

4. Apply stepwise regression techniques to a variety of cases and scenarios of management related problems. Interpret results.

5. Differentiate between the various regression models and identify the differences between other models and risks involved in the all-possible regression selection procedure.

6. Compare regression model results when deviating from assumptions, independent variable interaction is suspected, anomalies and lack of fit for residuals. Interpret results as a function of the assumptions used.

7. Use piecewise regression, weighted least squares and robust regression modeling techniques in practical applications.

8. Demonstrate an increased ability to recognize and apply appropriate statistical techniques to test inferential hypotheses and interpret results.

Located on the Daytona Beach Campus, the Jack R. Hunt Library is the primary library for all students of the Worldwide Campus. The Chief Academic Officer strongly recommends that every faculty member, where appropriate, require all students in his or her classes to access the Hunt Library or a comparable college-level local library for research. The results of this research can be used for class projects such as research papers, group discussion, or individual presentations. Students should feel comfortable with using the resources of the library. 


Web & Chat: http://huntlibrary.erau.edu
Email:  library@erau.edu
Text: (386) 968-8843
Library Phone:  (386) 226-7656 or (800) 678-9428
Hourshttp://huntlibrary.erau.edu/about/hours.html
 

N/A
N/A

Written assignments must be formatted in accordance with the current edition of the Publication Manual of the American Psychological Association (APA) unless otherwise instructed in individual assignments.

ActivityPercent of Grade
Input Grading Item100

Undergraduate Grade Scale

90 - 100% A
80 - 89% B
70 - 79% C
60 - 69% D
0 - 60% F

Graduate Grade Scale

90 - 100% A
80 - 89% B
70 - 79% C
0 - 69% F
Bobby McMasters, Ed.D. - 1/2014
mcmas245@erau.edu
Aaron Glassman
glassf10@erau.edu
Aaron Glassman
glassf10@erau.edu
Bobby McMasters, Ed.D. - 1/2014
mcmas245@erau.edu
Key: 388