Exam 3 Sample

1. Exam 3 Sample Decision Trees Cluster Analysis Association Rules Data Visualization SAS

2. SAS • When to Use Which Analysis (D, C or A)? • When someone gets an A in this class, what other classes do they get an A in? • What predicts whether a company will go bankrupt? • If someone upgrades to an iPhone, do they also buy a new case? • Which party will win the election? • Can we group our website visitors into types based on their online behaviors? • Which customers will purchase our product? • Can we identify different product markets based on customer demographics?

3. SAS • When to Use Which Analysis (D, C or A)? • When someone gets an A in this class, what other classes do they get an A in? • What predicts whether a company will go bankrupt? • If someone upgrades to an iPhone, do they also buy a new case? • Which party will win the election? • Can we group our website visitors into types based on their online behaviors? • Which customers will purchase our product? • Can we identify different product markets based on customer demographics?

4. Decision Trees • Which is the Root Node? • # Leafs Nodes?

5. Decision Trees • Which is the Root Node? • # Leafs Nodes? 1 2 5 3 4

6. Probability of Purchase?i) Female, 130 lbs, 12 ft? ii) 120 lbs, 5 feet, male? • Best predictor variable? >=6’ <6’ Height <170 >=170 <150 >=150 Weight Weight Outcome Data 0 62%1 38%n 350 Outcome Data 0 40%1 60%n 150 Outcome Data 0 55%1 45%n 250 Outcome Data 0 60%1 40%n 250 Male Female Gender Outcome Data 0 45%1 55%n 75 Outcome Data 0 35%1 65%n 75

7. Probability of Purchase?i) Female, 130 lbs, 12 ft? ii) 120 lbs, 5 feet, male? • Best predictor variable? >=6’ <6’ Height <170 >=170 <150 >=150 Weight Weight Outcome Data 0 62%1 38%n 350 Outcome Data 0 40%1 60%n 150 Outcome Data 0 55%1 45%n 250 Outcome Data 0 60%1 40%n 250 Male Female Gender Outcome Data 0 45%1 55%n 75 Outcome Data 0 35%1 65%n 75

8. Probability of Purchase?i) 5 ft 5 inches? ii) 6 ft 5 inches 190 lbs? >=6’ <6’ Height <170 >=170 <150 >=150 Weight Weight Outcome Data 0 62%1 38%n 350 Outcome Data 0 40%1 60%n 150 Outcome Data 0 55%1 45%n 250 Outcome Data 0 60%1 40%n 250 Male Female Gender Outcome Data 0 45%1 55%n 75 Outcome Data 0 35%1 65%n 75

9. Decision Trees • What does it mean that Gender is only on the right side of the tree? Why is it not on both sides? • Based on the tree, which demographic is MOST likely to buy the product? Least likely to buy the product?

10. Decision Trees • What does it mean that Gender is only on the right side of the tree? Why is it not on both sides? • Gender only has predictive/explanatory power for customers who are greater than or equal to 6 feet and below 170lbs. • That is, in other subsets of the population, it does no better than chance at predicting behavior. • Based on the tree, which demographic is MOST likely to buy the product? Least likely to buy the product? • Biggest Leaf Node Probability (1): Over 6 ft, below 170 lbs, female (1 = 65% probability) • Biggest Leaf Node Null Probability (0): below 6 ft, below 150 lbs(0 = 62% probability)

11. Decision Trees • What Statistics are Used to Determine Splits for Decision Trees? • Gini Coefficient, Chi-Square Statistics (p-value) • What does it mean when the Gini = 1? • What does it mean when the Chi-square is bigger? • What happens to the p-value as the Chi-square gets bigger?

12. Decision Trees • What Statistics are Used to Determine Splits for Decision Trees? • Gini Coefficient, Chi-Square Statistics (p-value) • What does it mean when the Gini = 1? • The predictor is no better than flipping a coin (you want a small Gini) • What does it mean when the Chi-square is bigger? • The variable is better at predicting the outcome (you want a big Chi-square) • What happens to the p-value as the Chi-square gets bigger? • The p-value gets smaller as the Chi-square gets bigger (you want a small p-value)

13. Clustering • What statistics do we care about in cluster analysis? What do they represent? • What happens to these statistics as the number of clusters is increased? • Why do we standardize data? Why do we eliminate outliers?

14. Clustering • What statistic do we care about in cluster analysis? What does it represent? • Sum of Squared Errors – SSE (or Root Mean Square Std Dev.) • Within SSE = cohesion, Between SSE = distinctiveness • What happens to these statistics as the number of clusters is increased? • SEE goes down (both within and between) • More cohesive clusters, less distinct though • Why do we standardize data? Why do we eliminate outliers? • Standardize else variables with bigger values will have greater weighting • Elimination outliers because they can skew results

15. Clustering • What are the pros and cons of having only a few clusters (compared to having many clusters)? • What is bad about the below cluster analysis result? How would you improve it?

16. Clustering • What are the pros and cons of having only a few clusters (compared to having many clusters)? • Easier to interpret/analyze, but they may be less informative • What is bad about the below cluster analysis result? How would you improve it? • Clusters should be fairly round! • Add more clusters.

18. Association Rules • How would you describe the following association rule? • {Meat, Dairy}  {Vegetables} • How many items are in this item set? • What is (are) the antecedents? What are the consequents? • What are the statistics we care about when evaluating an association rule?

19. Association Rules • How would you describe the following association rule? • {Meat, Dairy}  {Vegetables} • When someone eats meat and dairy they also eat vegetables. • How many items are in this item set? • This is a 3 item set. • What is (are) the antecedents? What are the consequents? • Meat and Dairy are the antecedents, vegetables is the consequent. • What are the statistics we care about when evaluating an association rule? • Support count, Support Percent, Confidence and Lift

20. Association Rules • Do the following two rules have to have the same Confidence? The same Support? The same Lift? • {Meat, Dairy}  {Vegetables} • {Vegetables}  {Meat, Dairy} • What does Lift > 1 mean? Would you take action on such a rule? • What about Lift < 1? • What about Lift = 1?

21. Association Rules • Do the following two rules have to have the same Confidence (NO) ? The same Support (Yes)? The same Lift (Yes)? • {Meat, Dairy}  {Vegetables} • {Vegetables}  {Meat, Dairy} • What does Lift > 1 mean? Would you take action on such a rule? • More co-purchase observed than chance would predict (+ association) • What about Lift < 1? Less than chance predicts (- association) • What about Lift = 1? Chance explains the observed co-purchase (no apparent association)

22. Association Rules • What might you do as a manager if you saw a very high Lift and Confidence for the following rule about product purchase? Why would you do this? • {Pasta}  {Orange Juice}

23. Association Rules • What might you do as a manager if you saw a very high Lift and Confidence for the following rule about product purchase? Why would you do this? • {Pasta}  {Orange Juice} • Encourage pasta buyers to see OJ (placement) • Get them in and milk ‘em (discount pasta, premium OJ) • Target market (advertise new OJ to Pasta customers)

24. Association Rules • What is the most reliable association rule below?

25. Association Rules • What is the most reliable association rule below? • Rule 2 – Tied for best Lift (3.60), but has Better confidence!

26. Data Visualization • Look at In-Class Exercise Answers...