Binary Logistic Regression for Predict Back-order Rate

Use Binary Logistic Regression to use multiple predictors to predict backorder rate.

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

Example

Backorder rate is the proportion of orders that cannot be filled when the customer orders them.

In this worksheet, Inventory Status is the response. The response event is Backorder. Number of Items is a continuous predictor, and Supplier Location is a categorical predictor.

C1-T	C2	C3-T
`Inventory Status`	`Number of Items`	`Supplier Location`
Backorder	7645	Foreign
Backorder	3494	Domestic
Available	2238	Domestic
Available	1871	Foreign

How-to

Choose Solutions Modules > Functions > Supply Chain KPIs, then select Launch.
Under Quality, select Back-order rate.
Select Predict back-order rate, then click OK.
Select Binary Logistic Regression, then click OK.
In Response, enter the binary variable that contains the backorder 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 a backorder.
(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 an order has a backorder status. The predictors are also called X variables.
In Categorical predictors, enter the categorical variables that may explain or predict whether an order has a backorder status. The predictors are also called X variables.
Click OK.

Tip

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