1. Identify from Chapter 1 a quantitative application discussed by the author that you were already familiar with.
• Brigs Myers  A test based on psychiatrist Carl Jung's theory of personality types. This test categorizes people into 16 different types.
• eHarmony  eHarmony used the brig myers theory to find match people who historically have highest probability of having successful relationship.
• Job applicants  Companies today supercrunching their potential employees data to uncover the underlying personality traits of each applicant: conscientiousness, agreeableness, and extroversion. These traits are better predictors of worker productiv- ity than more traditional aptitude test.
• Contintental Airlines  by using data mining program, Continental can keep track of customers’ negative experience and use the data to make the customers happy in their next flight with Continental.
• UPS Predicting when a customer wants to move to another switching company by using a very sophisticated algorithm.
• Harrah’s and the “pain point”  Harrah’s using the customers data such as age, average income and area with the gambling data to predict how much money they can expect from the customers, how many times the customers can lose and still want to play. This is called “pain point”.
• Hertz and gas left in tank  Supercrunching sales data so they will know how much gas the customers would leave if they prepay for the gas.
• Cingular and anytime minutes  Can predict the probability of customers who would used up the anytime minutes.
• Best Buy and extended warranty claims  Supercrunching the data so they would know when customers would claim on an extended warranty.
• Farecast--Prediction of airfares  Counteract the number-crunching price manipulation of sellers so the customers know when to buy the tickets in cheapest price possible.
• Google personalized search  personalized data mining. Predicting what the customers would like to see.
• Page rank  link is like a vote in google. Those who have the highest vote will be on the top of Google page.

2. Which quantitative application discussed by the author surprised you?
Hertz. I was always thinking why would they “lose” money by asking the customers to prepay the gas in much cheaper price. But now I know why. They actually do not lose money because they know how much gas a customer would use.

3. In just 3-4 sentences summarize what the author is trying to tell us in Chapter 1.
The author talks about 2 main things: “wisdom of crowds” and “regression”. These two things can be used in a lot of things such as retaining customers, choosing employees, ranking web pages. The author wants the readers to know that supercrunching your data is very useful.