Describe the difference in roles assumed by the validation partition and the test

partition.

v3.4 Laptop Sales at a London Computer Cbain: Interactive Visualization. The file

is a comma-separated file with nearly 300,000 rows. ENBIS

(the Europeao Network for Business aod Iodustrial Statistics) provided these data as

part of a contest organized in the fall of 2009.

Scenario: Imagine that you are a new aoalyst for a compaoy called Acell (a

compaoy selling laptops). You have been provided with data about products aod

sales. You need to help the compaoy with their business goal of planning a product

strategy aod pricing policies that will maximize Acell’s projected revenues in 2009.

Import the data into JMP (for details on importing data into JMP, search for “import

text files” in the JMP documentation or at jmp.com/leam). Check to ensure that the

data aod modeling types in the data table are correct for each of the variables, aod

aoswer the following questions.

a. Price Questions:

i. At what price are the laptops actually selling?

ii. Does price chaoge with time? (Hint: Make sure that the date column is recognized

as such. JMP will then allow dynamic traosformations aod allow you to

plot the data by weekly or monthly aggregates, or even by day of week.)

iii. Are prices consistent over retail outlets?

iv. How does price chaoge with configuration?

b. Location Questions:

i. Where are the stores aod customers located?

ii. Which stores are selling the most?

iii. How far would customers travel to buy a laptop?

Hint 1: You should be able to aggregate the data, such as by a plot of the

sum or average of the prices.

Hint 2: Use the coordinated highlighting between multiple visualizations

in the same page, for example, select a store in one view to see the matching

customers in another visualization.

Hint 3: Explore the use of filters to see differences. Be sure to filter in the

zoomed-out view. For example, try to use “store location” as an alternative

way to dynamically compare store locations. This might be more useful to

spot outlier patterns if there were 50 store locations to compare.

iv. Try ao alternative way of looking at how far customers traveled. Do this by

creating a new data column that computes the distance between customer aod

store. (For information on creating formulas in JMP, search for “Creating

Formulas” in the JMP documentation or see jmp.com/leam.)

c. Revenue Questions:

i. How do the sales volume in each store relate to Acell’s revenues?

ii. How does this depend on the configuration?

d. Configuration Questions:

i. What are the details of each configuration? How does this relate to price?

ii. Do all stores sell all configurations?