CART® Classification for Predict Freight Bill Accuracy

Use CART® Classification to use complex relationships with 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 the continuous predictor, and Carrier, Freight Class, and Auto Invoice are categorical predictors.

C1-T	C2	C3-T	C4-T	C5-T
`Bill Accuracy`	`Weight`	`Carrier`	`Freight Class`	`Auto Invoice`
Accurate	7.5	Speedy Shipping	Class 125	Yes
Accurate	9.5	Global Delivery	Class 100	Yes
Incorrect	5.0	Ship-It-Fast	Class 175	No
Incorrect	9.25	Ship Now	Class 100	No

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 CART® Classification, 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.
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.