Binary Logistic Regression for Predict Freight Bill Accuracy

Use Binary Logistic Regression to use multiple predictors to predict freight bill accuracy.

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

Example

A freight bill, or freight invoice, includes the shipping details and fees for shipments. Freight bill accuracy is the proportion of accurate freight bills.

In this worksheet, Bill Accuracy is the response. The response event is Accurate. Weight is a continuous predictor, and Carrier is a categorical predictor.

C1-T	C2	C3-T
`Bill Accuracy`	`Weight`	`Carrier`
Accurate	7.5	Speedy Shipping
Accurate	9.5	Global Delivery
Incorrect	5.0	Ship-It-Fast
Incorrect	9.25	Ship Now

How-to

Choose Solutions Modules > Functions > Supply Chain KPIs, then select Launch.
Under Quality, select Freight bill accuracy.
Select Predict freight bill accuracy, then click OK.
Select Binary Logistic Regression, then click OK.
In Response, enter the binary variable that contains the freight bill 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 accurate bill.
(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 bill is accurate. The predictors are also called X variables.
In Categorical predictors, enter the categorical variables that may explain or predict whether a bill is accurate. The predictors are also called X variables.
Click OK.

Tip

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