Binary Logistic Regression for Predict Perfect Order Delivery Rate

Use Binary Logistic Regression to use multiple predictors to predict perfect order delivery rate.

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

Example

Perfect order delivery rate is the proportion of orders that are error-free from beginning to end.

In this worksheet, Order Status is the response. The response event is Error-free. Number of Items is a continuous predictor, and Region is a categorical predictor.

C1-T	C2	C3-T
`Order Status`	`Number of Items`	`Region`
Error-free	5	Northwest
Error-free	4	Northwest
Error	2	Midwest
Error	1	Midwest

How-to

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

Tip

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