Scenario:
You have been hired by the D. M. Pan National Real Estate Company to develop a model to predict median housing prices for homes sold in 2019. The CEO of D. M. Pan wants to use this information to help their real estate agents better determine the use of square footage as a benchmark for listing prices on homes. Your task is to provide a report predicting the median housing prices based square footage. To complete this task, use the provided real estate data set for all U.S. home sales as well as national descriptive statistics and graphs provided.
Generate:
generate a report including your tables and graphs to determine if the square footage of a house is a good indicator for what the listing price should be. Reference the National Statistics and Graphs document for national comparisons and the Real Estate County Data spreadsheet that is provided.
Introduction:
-Describe the report: Give a brief description of the purpose of the report.
-Define a question that the report is trying to answer.
-Explain when using linear regression is most appropriate.
-When using linear regression, how would the scatterplot to look like?
-Explain the difference between response and predictor variables in a linear regression to justify the selection of variables.
Data Collection:
-Sampling the data: Selecting the Northeast.
Identify your response and predictor variables.
-Scatterplot: Create a scatterplot of the Northeast response and predictor variables to ensure they are appropriate for developing a linear model.
Data Analysis:
-Histogram: For the two variables, create histograms.
-Summary statistics: For the two variables, create a table to show the mean, median, and standard deviation.
-Interpret the graphs and statistics:
–Based on the graphs and sample statistics, interpret the center, spread, shape, and any unusual characteristic (outliers, gaps, etc.) for the two variables.
–Compare and contrast the shape, center, spread, and any unusual characteristic for the sample of house sales with the national population. Is the sample representative of national housing market sales?
Develop Your Regression Model:
-Scatterplot: Provide a graph of the scatterplot of the data with a line of best fit.
–Explain if a regression model is appropriate to develop based on the scatterplot.
-Discuss associations: Based on the scatterplot, discuss the association (direction, strength, form) in the context of the model.
–Identify any possible outliers or influential points and discuss their effect on the correlation.
–Discuss keeping or removing outlier data points and what impact the decision would have on the model.
-Find r : Find the correlation coefficient (r).
–Explain how the r value that was calculated supports what was noticed in your scatterplot.
Determine the Line of Best Fit. Clearly define the variables. Find and interpret the regression equation. Assess the strength of the model.
-Regression equation: Write the regression equation (i.e., line of best fit) and clearly define the variables.
-Interpret regression equation: Interpret the slope and intercept in context.
-Strength of the equation: Provide and interpret R-squared.
–Determine the strength of the linear regression equation you developed.
-Use regression equation to make predictions: Use the regression equation to predict how much should list the home for based on the square footage of the home.
Conclusions:
Summarize findings: In one paragraph, summarize the findings in clear and concise plain language for the CEO to understand.
-Summarize the results.
-Did the results were expected, or was anything different from the expectations or experiences?
-What changes could support different results, or help to solve a different problem?
-Provide at least one question that would be interesting for follow-up research.
