ERIC Number: EJ1061632
Record Type: Journal
Publication Date: 2013-Jul
Abstractor: As Provided
Reference Count: N/A
An Excel Solver Exercise to Introduce Nonlinear Regression
Pinder, Jonathan P.
Decision Sciences Journal of Innovative Education, v11 n3 p263-278 Jul 2013
Business students taking business analytics courses that have significant predictive modeling components, such as marketing research, data mining, forecasting, and advanced financial modeling, are introduced to nonlinear regression using application software that is a "black box" to the students. Thus, although correct models are estimated, students often do not obtain a thorough understanding of the nonlinear estimation process. The exercise presented in this article was created to demonstrate to students the need for nonlinear regression estimation--rather than using linear transformations and Ordinary Least Squares (OLS) and subsequently demonstrate the nonlinear optimization process to estimate nonlinear regression models. Using the spreadsheet exercise, students can see effects on the fit of the model by changing the model parameters as they change the values of the decision variables. After applying the spreadsheet to further exercises, students have expressed a deep understanding of the linear regression software. This exercise is innovative because the active learning exercise requires the students to make the logical connections between the structure of the model, the model's parameters, and the objective function.
Descriptors: Spreadsheets, Computer Software, Regression (Statistics), Business Administration Education, Computer Uses in Education, Computation, Least Squares Statistics, Models, Learning Activities, Active Learning, College Students
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Publication Type: Journal Articles; Reports - Descriptive
Education Level: Higher Education; Postsecondary Education
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