Scenario Smart businesses in all industries use data to provide an intuitive analysis of how they can get a competitive advantage. The real estate industry heavily uses linear regression to estimate home prices, as cost of housing is currently the largest expense for most families. Additionally, in order to help new homeowners and home sellers with important decisions, real estate professionals need to go beyond showing property inventory. They need to be well versed in the relationship between price, square footage, build year, location, and so many other factors that can help predict the business environment and provide the best advice to their clients. Prompt You have been recently hired as a junior analyst by D.M. Pan Real Estate Company. The sales team has tasked you with preparing a report that examines the relationship between the selling price of properties and their size in square feet. You have been provided with a Real Estate County Data document that includes properties sold nationwide in recent years. The team has asked you to select a region, complete an initial analysis, and provide the report to the team. Note: In the report you prepare for the sales team, the response variable (y) should be the median listing price and the predictor variable (it) should be the median square feet. Specically you must address the following rubric criteria, using the Module Two Assignment Template: 0 Generate a Representative Sample of the Data 0 Select a region and generate a simple random sample of 30 from the data. 0 Report the mean, median, and standard deviation of the median listing price and the Marlin.\" crII Irn 'an" llriklar