Binary Logistic Regression for Predict On-time Delivery

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

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

Example

On-time delivery is the proportion of orders that arrive on schedule. To calculate on-time delivery rate, divide the total number of on-time deliveries by the total number of deliveries.

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

C1-T	C2	C3-T
`On Time`	`Number of Items`	`Carrier`
On Time	55	Speedy Shipping
On Time	64	Global Delivery
On Time	52	Ship-It-Fast
Late	61	Ship Now

How-to

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