Reference no: EM13902930

Question 1) Define each of the following terms.

Business Intelligence

Business Analytics

Data Mining.

Question 2) How is business analytics different to data mining?

How is business analytics different from Business Intelligence?

Question 3) Suppose a credit-card company wants to create a model to predict whether a customer will default. After some investigation they decided that they will only use two attributes: Age and Income. They also decided to use a decision tree to build the model. Discuss the pros and cons of this decision. You can create a small data set to justify your answer.

Question 4.

A winery maintains a dataset containing information about customers who subscribe to its tasting events and special offers for wine cases. The winery occasionally mails tasting samples of new wines in an effort to increase sales. The chief marketing officer is aiming to send samples of a newly produced wine to customers who are NOT likely to place an order for the new wine (when probability of purchase is less than 0.5). Based on a survey conducted amongst its customers and their willingness to buy a case of the new wine before tasting it, the firm collected tree attributes - whether customers prefer dry, or have preference for red wine and their ages. The classification problem is to predict whether customers will buy or not buy wine, with certain confidence/probability. The following classification tree was induced:

4.1) After running a business analytics software (example SAS Enterprise Miner), suppose the above tree is generated. If you have to choose a single attribute to predict which customer is likely to place an order or not, which attribute would you use (check one and briefly justify your choice)?

A. Preference for red wine

B. Age (whether the customer is older or younger than 50 years old)

C. Preference for dry wine

D. Impossible to determine given the information provided.

4.2) The company would like to have a few, simple, English language rules that embody the decisions represented by the tree. Write out succinctly the rule you would suggest from the highlighted path on the picture above.

4.3) The Winery manager, Mary Sue, wants to consult with you if she should send a sample to a new customer named Thomas. She tells you that Thomas prefers dry wine and strongly prefers red wine. What's your recommendation (on whether to send a sample to Thomas)? Justify. (Note: The chief marketing officer is aiming to send samples of a newly produced wine to customers who are NOT likely to place an order for the new wine (when probability of purchase is less than 0.5).)

4.4) Assume that the cost of producing and shipping a wine sample to a customer is $8 and the profit from each order is $100. Assume the company ascertained that the probability of purchase will become 17% for a customer who receives a sample of a new wine. Would you suggest shipping a sample to customers preferring dry and red wines? Explain your answer.

5. Tom, the new manager of supermarket Einstein is trying to understand association rule with your help. He presented you the following information. In April 2005, 80 transactions contain eggs; 40 transactions contain milk and 15 transactions contain sugar. Moreover, 10 transactions contain all three items (egg, milk and sugar) altogether; 20 transactions contain egg and milk together; 15 transactions contain egg and sugar together and 10 transactions contain milk and sugar together. (Note: these transactions may overlap, e.g. the 15 transactions with both egg and sugar are among the 80 transactions with egg.) Tom got a rule as follows: egg ? milk with support 20%. Now he asks you to show him the support, confidence and lift for the rule: {egg, milk} ? sugar. (Note: You may not need all the numbers provided here. This question is used to test your understanding about the concept of support, confidence and lift. You should be able to get the answer with a few simple calculations).

Question 5a and 5b are based on the following scenario. A professor in the department of Business conducted an experiment for eBay to examine customer satisfaction with eBay. He used two measures - ease-of-use of eBay's auction mechanism and usefulness of online reviews - to segment eBay's customers. Suppose he identified the following two clusters, each one with 3 customers.

Customer ID	Ease-of-use	Usefulness		Customer ID	Ease-of-use	Usefulness
A	8	9		D	6	5
B	6	8		E	4	5
C	7	10		F	2	8

Cluster A - satisfied customers Cluster B - Unsatisfied customers

Question 5a. What are the centers for cluster A and cluster B?

Question 5b. Now there is a 7th customer, and you want to identify which cluster she belongs to. You are given the following information - her rating of ease-of-use is 5 and usefulness is 7. Which cluster do you think she belongs to?

Question 6:

Take the bank-data-final.text file posted on blackboard (in the Final Exam folder), and perform association rule analysis using SAS Enterprise Miner to answer the following questions.

6.1). What type of customers have a higher chance of buying a personal equity plan (pep=YES)? What types of customers have a lower chance of buying a personal equity plan?

6.2). Please examine the results and identify two interesting rules and explain why you think they are interesting.

In addition to writing down the answers to the questions, please also explain the steps you've taken to reach the answers (i.e. how you set the parameters in SAS Enterprise Miner, and how you read the results to get the conclusion). You may need your own judgment when deciding how to set the parameters (e.g. thresholds for support and confidence).

Below is the description of the attributes in the data.

age	age of customer in years
sex	MALE / FEMALE
region	inner_city/rural/suburban/town
income	income of customer
married	is the customer married (YES/NO)
children	number of children
car	does the customer own a car (YES/NO)
save_acct	does the customer have a saving account (YES/NO)
current_acct	does the customer have a current account (YES/NO)
mortgage	does the customer have a mortgage (YES/NO)
pep	did the customer buy a PEP (Personal Equity Plan) after the last mailing (YES/NO)

Question 7:

Consider the following scenario. A baseball manager wants to identify and group players on the team who are very similar with respect to several statistics of interest. The manager simply wants to identify different groups of players. The manager also wants to learn what differentiates players in one group from players in a different group.

The statistics of the players is provided in the Baseball.text file posted on blackboard (in the Final Exam folder). Please use SAS Enterprise Miner to help the manager understand the player groups better. When you cluster, please specify the number of clusters to be 2, 3, 4 respectively. Please describe the differences in results for k=2, 3, 4 with respect to the quality of the clusters you get and whether the clusters you obtained make sense.

When writing your report for this question, please be as specific as possible. Please also copy the results generated by SAS Enterprise Miner in the report, and point out which part of the results you used to reach certain conclusion.

Note: Please make sure you exclude Name from the attributes you use for clustering. There are also a few missing values from the data, just let SAS Enterprise Miner takes care of the missing values automatically.