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Drawing Conclusions Designing an Experiment

2 Choose Window ÿ Interaction Plot for Hours to make the interaction plot active.

The vertical scale (y-axis) is in units of the response (Hours).

Interaction Plot for Hours Data Heans

13

J lO

9 This point is the mean time j required to prepare packages using the new order processing system and packing procedure A.

This legend displays the levels of the first factor (OrderSystem).

A B

\ / The horizontal scale (x-axis) shows the levels of the second factor (Pack).

An interaction plot shows the impact that changing the settings of one factor has on another factor. Because an interaction can magnify or diminish main effects, evaluating interactions is extremely important.

The plot shows that book orders processed with the new order processing system and packing procedure B took the fewest hours to prepare (about 9 hours). Orders processed with the current order processing system and packing procedure A took the longest to prepare (about 14.5 hours). Because the slope of the line for the new order processing system is steeper, you conclude that the packing procedure has a greater effect when the new order processing system is used versus the current order processing system.

Based on the results of the experiment, you recommend that the Western shipping center use the new order processing system and packing procedure B to

speed up the book shipping process.

Save Project 1 Choose File ÿ Save Project As.

2 Navigate to the folder in which you want to save your files.

3 In File name, enter My_DOE.MPJ.

4 Click Save.

/j

Meet Minitab 5-11

Chapter 5 What's Next

What's Next

The factorial experiment indicates you can decrease the time it takes to prepare orders at the Western shipping center by using the new order processing system and packing procedure B. In the next chapter, you learn how to use command language and create and run Execs to quickly rerun an analysis when new data are collected.

5-12 Meet Minitab

6 Using Session Commands

Objectives In this chapter, you:

[] Enable and type session commands, page 6-2

[] Conduct an analysis using session commands, page 6-3

[] Rerun a series of session commands with Command Line Editor, page 6-5

[] Create and run an Exec, page 6-7

Overview

Each menu command has a corresponding session command. Session commands

consist of a main command and, in most cases, one or more subeommands. Commands are usually easy-to-remember words, such as PLOT, CHART, or SORT. Both main commands and subcommands can be followed by a series of arguments, which can be columns, constants, or matrices, text strings, or numbers.

Session commands can be:

[]

[]

Typed into the Session window or the Command Line Editor.

Copied from the History folder to the Command Line Editor. (When you use menu commands, Minitab generates and stores the corresponding session commands in the History folder.)

Copied and saved in a file called an Exec, which can be reexecuted and shared with others or used in future sessions.

Meet Minitab 6-1

Chapter 6 Enabling and Typing Commands

Use session commands to quickly rerun an analysis in current or future sessions or as an alternative to menu commands. Some users find session commands quicker to use than menu commands once they become familiar with them.

The Western shipping center continuously collects and analyzes shipping time when new data are available. In Chapter 4, Assessing Quality, you conducted a capability analysis on data from March. In this chapter, you conduct a capability analysis on data from April using session commands.

To learn more about session commands, choose Help ÿ Help, then click Session Commands Eÿ under References.

Enabling and Typing Commands One way to use session commands is to directly type the commands and subeommands at the command prompt in the Session window. However, Minitab does not display the command prompt by default. To enter commands directly into the Session window, you must enable this prompt.

Enable session