DRE 2011 Marketing Models
DRE 2011 Marketing Models
This course is designed to provide an introduction to econometric models for the analysis of secondary data sources containing marketing actions and outcomes. It will give students a fundamental understanding and hands-on experience with commonly used empirical models in marketing in particular, and business and social science in general.
A fundamental understanding of commonly used empirical models in marketing applications in particular, and business and social science in general. This includes linear regression, binary logit, conditional logit, nested logit, Poisson regression, latent classes, hierarchical linear models, and spatial models.
After completion of the seminar, participants should be able to understand the properties of these models and the conditions under which the various models are appropriate to apply,
Being able to implement (or code up) the models using statistical software and interpret the model outcomes.
Being able to read and appreciate quantitative (econometric) papers in the good marketing journals.
This course will discuss a broad range of quantitative models that can be applied to a wide variety of marketing decisions. The model selection reflects the variety of secondary data common in marketing; it distinguishes between individual consumer-level and aggregate sales-level models.
Computer-based tools: Standard statistical software and possibly a programming environment.
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Enrollment in a PhD Programme is a general requirement for participation in PhD courses at BI Norwegian Business School.
External candidates are kindly asked to attach confirmation of enrollment in a PhD programme when signing up for a course. Other candidates may be allowed to sit in on courses by approval of the courseleader. Sitting in on a course does not permit registration for the course, handing in exams or gaining credits for the course. Course certificates or confirmation letters will not be issued for sitting in on courses.
Covid-19
Due to the Covid-19 pandemic, there may be deviations in teaching and learning activities as well as exams, compared with what is described in this course description.
Teaching
Information about what is taught on campus and other digital forms will be presented with the lecture plan before the start of the course each semester.
It is assumed that every student has basic statistical working knowledge.
| Assessments |
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Exam category: Submission Form of assessment: Written submission Invigilation Weight: 100 Grouping: Individual Support materials:
Duration: 3 Hour(s) Exam code: DRE20112 Grading scale: ECTS Resit: Examination when next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Prepare for teaching | 140 Hour(s) | |
Teaching | 30 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.