Question #2 (40 points) Part A: Read the case Nomis Solutions (A) and answer the questions below: a) Describe briefly (one or two paragraphs) some of the practices in consumer lending at the time that Bob and Simon were founding Nomis Solutions. b) In one paragraph, where do you see the business analytics opportunity in consumer lending? c) In one paragraph, what is your assessment of the business opportunity for Nomis? Part B: Read the case Nomis Solutions (B) and answer the questions below: a) Draw a process flowchart to describe the process through which e-Car currently lends to customers. If needed, add a paragraph below the flowchart to complete the explanation of the process. The following questions are related to the attached file Nomis B_Data_Extract.xlsx, which includes data from a particular market segment: used cars, FICO scores between 684 and 712, loan term of 60 months, and loan amounts between $7,000 and $25,000. b) The attached script Nomis Prelim Charts-students.py generates three charts that provide a preliminary analysis of the data. The code for generating the first two charts is fully completed. REVENUE MANAGEMENT & PRICING Prof. Gustavo Vulcano 3 Figure 1: $ per Cust/Month/Accepted Quote vs. Quoted APR Figure 2: Conversion Rate vs. Quoted APR The code for generating the third chart is incomplete (two lines need to be completed; search the string TO BE COMPLETED BY STUDENTS). Figure 3: Number of Quotes vs. Quoted APR Include the three charts in your main submission file and add just one paragraph with your findings: Do you see an incremental revenue opportunity from these charts? c) Construct a predictive model based on linear regression. To this end, the attached script Nomis Linear Regression-students.py implements a univariate linear regression model to predict the acceptance (or not) of a quoted APR and plots a chart that allows to assess the quality of the prediction. There are three incomplete lines in the script (search the string TO BE COMPLETED BY STUDENTS). Include the results of the linear regression and the figure saved from the script. Comment on the quality of the prediction output and the potential drawbacks for the implementation of this predictive model. d) Propose an alternative to improve the prediction quality and explain why it could be a good candidate. Question #3 (20 points) Answer question #1 at the end of the case What Price Vertigo?, which is included here: a) What are the levels of differential pricing for Vertigo at Stade de France? b) What are the relevant market segments? c) What other segmentation strategies can you propose? What threats do you identify to these?