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.

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 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. In the report you prepare for the sales team, the response variable (y) should be the listing price and the predictor variable (x) should be the square feet.

address the following: Generate a Representative Sample of the Data

Select a region and generate a simple random sample of 30 from the data.

Report the mean, median, and standard deviation of the listing price and the square foot variables.

Analyze Your Sample

Discuss how the regional sample created is or is not reflective of the national market.

Compare and contrast your sample with the population using the National Summary Statistics and Graphs Real Estate Data PDF document.

Explain how you have made sure that the sample is random.

Explain your methods to get a truly random sample.

Generate Scatterplot

Show scatterplot of the x and y variables noted above and include a trend line and the regression equation

Observe patterns

Define x and y. Which variable is useful for making predictions?

Is there an association between x and y? Describe the association you see in the scatter plot.

What do you see as the shape (linear or nonlinear)?

If you had a 1,800 square foot house, based on the regression equation in the graph, what price would you choose to list at?

Do you see any potential outliers in the scatterplot?

Why do you think the outliers appeared in the scatterplot you generated?

What do they represent?

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Summary Statistics for MAT 240 Real Estate Data (for dataset in Modules 2, 3, and 4) n Mean Median Std. Dev. Listing 1,000 342,365 318,000 125,914 Min 135,300 265,250 Q1 Q3 381,600 Max 987,600 price ($) Cost per 1,000 169 166 41 71 139 191 344 square foot ($) Square 1,000 2,111 1,881 921 1,101 1,626 2,215 6,516 feet This graph shows the frequency for listing price. National Listing Prices (Ss) Frequency 400 300 200 100 200000 400000 600000 800000 1000000 Summary Statistics for MAT 240 Real Estate Data (for dataset in Modules 2, 3, and 4) n Mean Median Std. Dev. Listing 1,000 342,365 318,000 125,914 Min 135,300 265,250 Q1 Q3 381,600 Max 987,600 price ($) Cost per 1,000 169 166 41 71 139 191 344 square foot ($) Square 1,000 2,111 1,881 921 1,101 1,626 2,215 6,516 feet This graph shows the frequency for listing price. National Listing Prices (Ss) Frequency 400 300 200 100 200000 400000 600000 800000 1000000