Binary Logistic Regression for Predict In-full Delivery

Use Binary Logistic Regression to use multiple predictors to predict in-full delivery.

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

Example

In-full delivery is the proportion of orders that are delivered completely in the first shipment. To calculate in-full delivery rate, divide the total number of in-full deliveries by the total number of deliveries.

In this worksheet, In Full is the response. The response event is In Full. Number of Items is a continuous predictor, and Type is a categorical predictor.

C1-T	C2	C3-T
`In Full`	`Number of Items`	`Type`
In Full	55	Porcelain
In Full	64	Ceramic
In Full	62	Ceramic
Partial	61	Vinyl

How-to

Choose Solutions Modules > Functions > Supply Chain KPIs, then select Launch.
Under Delivery, select In-full delivery.
Select Predict in-full 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 in-full 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 a partial delivery. The predictors are also called X variables.
In Categorical predictors, enter the categorical variables that may explain or predict whether a delivery is a partial delivery. The predictors are also called X variables.
Click OK.

Tip

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