CART® Regression for Predict Freight Cost Per Unit

Use CART® Regression to use complex relationships with multiple predictors to predict freight cost per unit.

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

Example

Freight cost per unit is the total freight costs divided by the number of units shipped.

In this worksheet, Freight Cost/Unit ($) is the response. Load Times, Weight, and Dunnage Weight are the continuous variables. Product, Carrier, Pickup Time, and Shipping Contract are the categorical variables. The predictors may explain differences in freight cost per unit.

C1	C2	C3	C4	C5-T	C6-T	C7-T	C8-T
`Freight Cost/Unit ($)`	`Load Times`	`Weight`	`Dunnage Weight`	`Product`	`Carrier`	`Pickup Time`	`Shipping Contract`
1.38	2	758.7	75.0	Mix	Speedy Shipping	Peak	Yes
1.22	4	995.3	82.5	Mix	Global Delivery	Normal	Yes
2.36	8	1150.6	327.6	Flavored Oils	Ship-It-Fast	Peak	No
1.75	3	925.1	68.1	Flavored Vinegars	Ship Now	Normal	Yes

How-to

Choose Solutions Modules > Functions > Supply Chain KPIs, then select Launch.
Under Costs, select Freight cost per unit.
Select Predict freight cost per unit, then click OK.
Select CART® Regression, then click OK.
In Responses, enter the column that contains the freight cost per unit data. The response is also called the Y variable.
In Continuous predictors, enter the columns of numeric data that may explain or predict changes in freight cost. The predictors are also called X variables.
In Categorical predictors, enter the categorical classifications or group assignments that may explain or predict changes in freight cost. The predictors are also called X variables.
Click OK.

Tip

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