Binary Logistic Regression for Predict Service Level

Use Binary Logistic Regression to use multiple predictors to predict whether a service level agreement is likely to be met.

This example applies to the Customer Contact Center Module. For more information, go to www.minitab.com/customer-contact-center-module.

Example

Service level is the ability to meet the standards in a Service Level Agreement (SLA).

In this worksheet, Service Level is the response. The response event is Not met. Schedule Adherence is a continuous predictor, and Call Center Location is a categorical predictor.

C1-T	C2	C3-T
`Service Level`	`Schedule Adherence`	`Call Center Location`
Not met	70	East
Met	80	East
Met	82	West
Met	76	West

How-to

Choose Solutions Modules > Functions > Customer Contact Center KPIs, then select Launch.
Under Service Level, select Service level.
Select Predict service level, then click OK.
Select Binary Logistic Regression, then click OK.
In Response, enter the binary variable that contains the service level agreement data. Binary variables are categorical variables that have two possible levels, such as pass/fail or true/false. The response is also called the Y variable.
In Response event, select the value that represents an unmet service level agreement.
(Optional) In Frequency, enter the column that contains the counts that correspond to the response and predictor values in the row.
In Continuous predictors, enter the continuous variables that may explain or predict whether a service level agreement is not met. The predictors are also called X variables.
In Categorical predictors, enter the categorical variables that may explain or predict whether a service level agreement is not met. The predictors are also called X variables.
Click OK.

Tip

For more information about this analysis, click Help in the main dialog box.