Binary Logistic Regression for Predict On-time Shipping Rate

Use Binary Logistic Regression to use multiple predictors to predict on-time shipping rate.

This example applies to the Supply Chain Module. For more information, go to www.minitab.com/supply-chain-module.

Example

On-time shipping rate is the proportion of orders that are received within the promised shipping window. To calculate on-time shipping rate, divide the total number of on-time shipments by the total number of shipments.

In this worksheet, On Time is the response. The response event is On Time. Number of Items is a continuous predictor, and Zone is a categorical predictor.

C1-T	C2	C3-T
`On Time`	`Number of Items`	`Zone`
On Time	55	Central
On Time	64	Priority
Late	62	Secondary
Late	61	Secondary

How-to

Choose Solutions Modules > Functions > Supply Chain KPIs, then select Launch.
Under Delivery, select On-time shipping rate.
Select Predict on-time shipping rate, then click OK.
Select Binary Logistic Regression, then click OK.
In Response, enter the binary variable that contains the damage 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 on-time shipment.
(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 shipment is late. The predictors are also called X variables.
In Categorical predictors, enter the categorical variables that may explain or predict whether a shipment is late. The predictors are also called X variables.
Click OK.

Tip

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