Welcome to MKTG 3850 - Marketing Analytics - at Otterbein University. In this course, we will learn how to select an analytical tool, apply it to a problem or issue, and communicate our conclusions. The required textbook remains Multivariate Data Analysis (7th edition) by Joseph Hair, Jr., William Black, Barry Babin, and Rolph Anderson. You should also register for a six-month access to Laerd Statistics.
You should find resources that aid in your understanding and with your learning.
The crypto currency dataset and other datasets used in-class can be accessed by clicking on this link.
The MotoWorld dataset and mini case, and the dataset and case for the final can be accessed by clicking on this link. The folder contains the case, the assignment, and the datasets.
The box office dataset, course topics along with due dates, and the reflection essay assignment can be viewed by clicking on this link.
The required readings can be downloaded by clicking on this link.
If you are looking for datasets to play with, then click on this link. More can be found through this curated list.
The course syllabus is available by clicking this link.
Students are strongly encouraged to consult these resources to help them succeed in the course.
Description & Objective
The primary objective of this course is to introduce you to data analysis techniques. These techniques include cross tabulations, t-test and ANOVA, correlation and regression, cluster analysis, as well as factor analysis. If time permits, we will review conjoint analysis.
Although a variety of analytical techniques are available, the selected techniques represent tools used most frequently by marketing and management professionals. The tools that you learn in the course go beyond simple data descriptors such as mean, median, mode, and variance. Such descriptors serve as a starting point for us to explore the data set. Techniques learned in this course will allow you to make a more informed decision and recommendation. Data analysis represents a critical thinking tool. As consumers of knowledge, you will need to assess whether you have good or not so good knowledge. By understanding data analysis, you can assess whether the analysis is appropriate and, therefore, whether you have good knowledge.