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 Data Spreadsheet spreadsheet 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 listing price and the predictor variable (x) should be the square feet.
Specifically you must address the following rubric criteria, using the Module Two Assignment Template:
Generate a Representative Sample of the DataSelect 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 SampleDiscuss 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 ScatterplotCreate a scatterplot of the x and y variables noted above. Include a trend line and the regression equation. Label the axes.
Observe patternsAnswer the following questions based on the scatterplot: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?
Category: Graphs
Tiered Polyester Diminishment
Numeric Targets for Polyester Reduction (created by our think tank):40% total reduction by 2030
70% total reduction by 2040
90% total reduction by 2050
100% elimination by 2060
Additional Statistics from Textile Exchange:Increase the market share of recycled polyester from 14% in 2019 to 45% by 2025.
Polyester accounted for 52% of the global fiber market in 2020.
In the US, just over 13% of clothes were recycled in 2018.
More than 11 million tonnes of clothing are incinerated or dumped in the US every year.
Only about 0.5% of the global fiber market comes from pre and post-consumer recycled textiles.
Contextual Information:These targets are part of Step 2, which involves tiered polyester diminishment.
The targets are proposed for the fashion industry as a whole, with additional reduction targets potentially necessary for specific sectors such as fast fashion.
Emphasize the significance of these targets in the broader context of achieving sustainability in the fashion industry.
Additional Points:Emphasize the importance of these targets as part of a structured approach towards sustainability.
Mention the need for legislative and enforcement mechanisms to support these targets.
Emphasize the role of consumers in driving change and achieving these reduction goals.