Data Analytics and Management Part I: Regression a. Linear Regression: For the linear regression analysis, refer to the daily sales of products at Mao’s Palace, a local Chinese restaurant. Mao’s main

product is bowls filled with rice, vegetables, and meat made to the customer’s order. The file Maospalace.xlsx gives daily unit sales of bowl prices and the daily sales of bowls. Your goal is to determine how the price of bowls (independent variable) affects daily sales (dependent variable) using the technique of linear regression. b. Differentiate between linear regression and logistic regression in your own words emphasizing at least two points or differences. Part II: Forecasting and using the S curve to forecast sales of new products a. Forecasting Refer to the shampoo_sales.csv time-series dataset. This data shows sales of shampoo over a three-year period monthly. Carry out simple exponential smoothing on this data and determine whether the data shows any trend Assume the initial value for the level smoothing parameter (alpha) is 0.5. b. Using S curve to Forecast Sales of New Products The excel file Facebook.xlsx gives the number of users of Facebook in millions during the years 2004 – 2012. Based on the S curve, use this data to predict the future growth of Facebook for the years 2013 – 2016. Part III: Outlier Detection a. What is the significance or importance of outlier detection? Explain, in short, in your own words with an example (example may be in an excel sheet or within this same word document). (1 point) b. Consider the following data values: 2.2, 7.8, -4.4, 0.0, -1.2, 3.9, 4.9, 2.0, -5.7, -7.9, -4.9, 28.7, 4.9. Which data points are outliers, according to Tukey’s fences method? Part IV: Price Bundling a. Provide some real-life examples of price bundling used by business organizations for profit maximization? (1 point) b. Software company ABC technologies is going to sell two versions of their software. A basic version and a pro version. Assume that there are three market segments whose size and price for each software version are given in the following table. What price for each version of the software can maximize revenue for ABC technologies? Part V: Customer Lifetime Value a. Assume a customer has been with a company for 5 years and has made purchases at times 0.2, 1.2, 0.8, 1.8, 2.4, and 4.8. Estimate the probability that the customer is still active. b. Customers 1 and 2 have been with the company for 12 months. Customer 1 has made 6 purchases and customer 2 has made four purchases. Customer 1’s last purchase was at the end of month 6 and customer 2’s last purchase was at the end of month 8. Which customer is most likely to be still active? Explain your result. Part VI: Market Basket Analysis a. How could use the concept of lift to make product recommendations? ( b. How did the concept of lift factor into Netflix’s decision to make a show (such as the House of Cards TV series)? c. The excel file marketbasketdata.xls contains sales transactions at an upscale grocery store. Determine the three-product combination with the largest three-product lift.