Data Analysis Concept Overview

Question 1 (20 points) (Please use the excel data file “Q1 FAB  Data” to answer this question) 

Fab Machines produces a light weight, highly reliable laser color printer. The technology for this printer was developed in an in-house R&D program, and no competitor has been able to match the quality of Fab printers. Demand has been growing rapidly and the estimated production for 2021 is 1,000,000 units. The excel file “Q1 FAB Data” contains data on the number of printers produced per year and the unit cost. Fab wants to provide an analyst forecast for the 2021unit cost per printer. Additionally, Fab wants to develop a cost budget for 2022 based on the data. Based on the data: 

1. Use learning curve theory prepare an analyst forecast of unit cost per printer for 2021 given the data in the excel file. You must prepare a separate forecast using (a) the cumulative average-time learning model, and (b) the individual unit-time (incremental) learning model. 
2. Use learning curve theory prepare a budgeted unit cost per printer for 2022 given the data in the FAB excel file model. You must prepare a separate forecast using (a) the cumulative average-time learning model, and (b) the individual unit-time (incremental) learning model. 
3. Express the learning curve for the company in percentage terms under (a) the cumulative average-time learning model, and (b) the individual unit-time (incremental) learning model 
4. Discuss the implications of these results for the company with respect to its human resource policy (e.g., retention, turnover, training, incentive bonus, etc.). Be concrete in your suggestions and show numbers where possible. 

Question 2 (Please use the Excel data file labeled “Q2 MSI Store Data” for this question) 

MSI, a department store chain, is trying to upgrade its customer service in order to compete with a rival chain which has recently moved into its territory and has a very strong customer-service reputation. MSI management knows that customer service is currently high in some of its stores but low in others. On average, its current reputation for service is less than outstanding. In order to build support for better customer service throughout the chain, MSI management decides to analyze existing data to show how much more profitable its own high-service stores are than its low-service stores. 

MSI has created a customer-service indicator which is composed of a combination of ratings from “mystery shoppers” and surveys of customers by an independent organization. The scale for this indicator ranges from 1 to 60, which is a continuous variable with higher numbers indicating higher quality. MSI also has data on a number of factors that are likely to influence store profits. These include store size, rural versus urban location, manager performance rating (1 to 5 scale, where 5 is high), per capita income in the surrounding region (low to high ranges, summarized on a 1 to 5 scale, where 5 is high), non-managerial employee skill index (a measurement the Human Resources department has created, which ranges from 1 to 20; high numbers are better) and age of the store (which implies how long it has been in operation). 

Based on regression analysis, what can you tell MSI about customer service? For example: 

1. How big an effect on profit does customer service have? 
2. Does customer service have a bigger effect on profits in some portions of the customer- 

service range than others? That is does the effect of customer service on profit have 

diminishing or increasing returns? 

1. Is the effect of customer service on profit similar for large versus small stores? 
2. Is the effect of customer service on profit similar for urban versus rural stores? 
3. What are the factors that influence the level of customer-service quality? Be concrete and 

Question 4  Please use the Excel data file labeled “Q4 Rochester Auto Parts Data” for this question) 

Rochester Auto Parts is a supplier of windshield wipers to the auto industry. Its customers include all the major auto companies. You have been hired by the company’s board to study the company’s salary structure. The file “Q4 Rochester Auto Parts 2021 Data” contains data on the following variables. 

Answer the following questions, using appropriate analytical techniques. 

a. How do the years of experience and gender influence salary? 

b. Does each additional year of experience have the same impact on salary? 

c. Does the effect of years of experience on salary vary for women, relative to men? 

d. What changes would you recommend to the board based on your analysis in (a) – (c). Be specific and use numerical answers to build support for your arguments, where feasible.