CASE 3: Car Dealership You are preparing a marketing report for a car dealership who is considering expanding into new markets and first wants to study its existing customer data. The existing dealership has collected data on customers who met with a sales representative in the worksheet "Purchase Data". The data includes some basic information on the customers' visits: the value of the trade-in, the discount the customer was offered, whether the visit occurred on a weekend, the temperature on the day of the visit, and whether the salesperson was the same gender as the customer, and finally whether a purchase was made. For customers who purchased vehicles and sought financing, the dealership has some additional data provided in the worksheet "Finance Data": the age and income of the customer, and whether the customer purchased a sedan, SUV or a pickup truck. Construct a logistic regression to predict the likelihood of the customer purchasing a vehicle based on all statistically significant variables provided in Purchase Data. Use your model to predict the likelihood of a male customer purchasing a vehicle on a Saturday from a female salesperson when the temperature is 70 degrees, provided the customer was offered a 10% discount and the value of their trade-in is $10,000? Perform a cluster analysis on the variables of Age and Income and determine how closely these clusters correspond to the type of vehicle purchased (sedan, SUV or pickup). F G E Same Gender Salesperson Purchase 0 0 1 1 0 0 1 1 1 1 1 1 1 B 1 Discount Offered Weekend 2 21% 3 27% 4 26% 5 9% 6 19% 7 27% 8 239 9 26% 10 1% 11 26% 12 10% 13 11% 14 2% 15 23% 16 28% 17 5% 18 0% 19 16% 20 239 21 6% 22 5% an Purchase Data C D Tradein Value Temperature 0 $ 16.500 OS 9,000 76 1 S 21,000 62 1 $ 9,000 70 0 $ 18,000 75 1 S 15,000 76 OS 18,000 60 1 S 9,000 90 0 $ 13,500 86 1 S 10,500 64 OS 13,500 66 1 $ 12,000 64 0 $ 9,000 0 $ 9,000 87 1 S 21,000 42 1 S 19,500 44 1 $ 10,500 76 OS 21,000 58 0 $ 21,000 43 1 $ 16,500 70 OS 12,000 46 0 0 1 0 1 1 0 1 1 0 1 40 1 1 0 1 1 1 0 0 1 1 1 1 1 1 0 1 0 Vehicle Data I = = Clipboard L2 Font 12 G32 fx 09 C U D 3 4 7 A B 1 Income Age Vehicle 2 $ 80,000 54 Pickup $ 40,200 31 Sedan $ 75,100 35 Sedan 5 $ 152,600 36 SUV 6 $ 103,200 44 SUV 7 $ 19,500 47 Pickup 8 $ 150,900 33 SUV 9 $ 36,000 48 Pickup 10 $ 62,900 26 Sedan 11 $ 78,400 55 Pickup 12 $ 111,300 36 SUV 13 $ 17,200 55 Pickup 14 $ 112,300 34 SUV 15 $ 147,400 38 SUV 16 $ 89,200 54 Pickup 17 $ 107,700 37 SUV 18 $ 139,800 35 SUV 19 $ 158,200 43 SUV 20 $ 103,200 30 SUV 21 $ 119,400 35 SUV 22 $ 85,200 33 SUV -1. Purchase Data Vehicle Data CASE 3: Car Dealership You are preparing a marketing report for a car dealership who is considering expanding into new markets and first wants to study its existing customer data. The existing dealership has collected data on customers who met with a sales representative in the worksheet "Purchase Data". The data includes some basic information on the customers' visits: the value of the trade-in, the discount the customer was offered, whether the visit occurred on a weekend, the temperature on the day of the visit, and whether the salesperson was the same gender as the customer, and finally whether a purchase was made. For customers who purchased vehicles and sought financing, the dealership has some additional data provided in the worksheet "Finance Data": the age and income of the customer, and whether the customer purchased a sedan, SUV or a pickup truck. Construct a logistic regression to predict the likelihood of the customer purchasing a vehicle based on all statistically significant variables provided in Purchase Data. Use your model to predict the likelihood of a male customer purchasing a vehicle on a Saturday from a female salesperson when the temperature is 70 degrees, provided the customer was offered a 10% discount and the value of their trade-in is $10,000? Perform a cluster analysis on the variables of Age and Income and determine how closely these clusters correspond to the type of vehicle purchased (sedan, SUV or pickup). F G E Same Gender Salesperson Purchase 0 0 1 1 0 0 1 1 1 1 1 1 1 B 1 Discount Offered Weekend 2 21% 3 27% 4 26% 5 9% 6 19% 7 27% 8 239 9 26% 10 1% 11 26% 12 10% 13 11% 14 2% 15 23% 16 28% 17 5% 18 0% 19 16% 20 239 21 6% 22 5% an Purchase Data C D Tradein Value Temperature 0 $ 16.500 OS 9,000 76 1 S 21,000 62 1 $ 9,000 70 0 $ 18,000 75 1 S 15,000 76 OS 18,000 60 1 S 9,000 90 0 $ 13,500 86 1 S 10,500 64 OS 13,500 66 1 $ 12,000 64 0 $ 9,000 0 $ 9,000 87 1 S 21,000 42 1 S 19,500 44 1 $ 10,500 76 OS 21,000 58 0 $ 21,000 43 1 $ 16,500 70 OS 12,000 46 0 0 1 0 1 1 0 1 1 0 1 40 1 1 0 1 1 1 0 0 1 1 1 1 1 1 0 1 0 Vehicle Data I = = Clipboard L2 Font 12 G32 fx 09 C U D 3 4 7 A B 1 Income Age Vehicle 2 $ 80,000 54 Pickup $ 40,200 31 Sedan $ 75,100 35 Sedan 5 $ 152,600 36 SUV 6 $ 103,200 44 SUV 7 $ 19,500 47 Pickup 8 $ 150,900 33 SUV 9 $ 36,000 48 Pickup 10 $ 62,900 26 Sedan 11 $ 78,400 55 Pickup 12 $ 111,300 36 SUV 13 $ 17,200 55 Pickup 14 $ 112,300 34 SUV 15 $ 147,400 38 SUV 16 $ 89,200 54 Pickup 17 $ 107,700 37 SUV 18 $ 139,800 35 SUV 19 $ 158,200 43 SUV 20 $ 103,200 30 SUV 21 $ 119,400 35 SUV 22 $ 85,200 33 SUV -1. Purchase Data Vehicle Data