The Core Textbook Case Example for Chapter 13

We continue the core textbook example from Chapter 1. Remember that the company is interested in explaining the first-year sales of its services offerings. You might generally assume that sales are at least partially dependent on (explained by) various aspects including:
  • “License” which is a categorical variable measured as “Freeware” or “Premium.”
  • “Size” is an ordinal variable with levels “Small”, “Medium” and “Big.”
  • “Trust” refers to the trust the customer has in your product and company. You originally measured trust through four questions in an online survey that you sent to key account holders at the client (measured on a 0 to 100 Likert scale). As per the discussion in Chapter 9, you established that internal reliability exists for the four items. (You might also wish to analyze these in terms of factor analysis, which is not covered in this book). You have therefore averaged the trust items to give you a single summated score for trust for each customer.
  • “Customer satisfaction” is also measured through four questions (this time on a 1 to 7 scale) in a paper-based customer survey. Like trust, in Chapter 9 we analyzed these four items for internal reliability, however there we found that the fourth item had poor reliability. Therefore, to create the final score we averaged only Satisfaction01-Satisfaction03[1].
  • “Enquiries” refers to the average number of enquiries about the core product logged with the call center or online help by customers, per month, since starting use of the product. This data is provided by your in-house CRM data systems.
Figure 13.1 Reminder of the core textbook model
“Data05_Regression_Initial” in the book materials shows the dataset for the case example, in this case isolating only the final aggregated variables used in the regression, which in total has 279 observations. As can be seen, in its raw format, the table has both character (word) and numerical data points. Words have initially been used for the categorical and ordinal variables.
Now that the scene is set, the following sections discuss the broad ideas behind linear regression.
Last updated: April 18, 2017
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