# Housing Price Model

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Report: Housing Price Prediction Model for D. M. Pan National Real Estate Company
Introduction
[Describe the report: Define the question your report is trying to answer.]
[Describe the report: Explain when using linear regression is the most appropriate.]
[Describe the report: Explain when using linear regression what you would expect the scatterplot to look like.]
[Describe the report: Explain the difference between predictor (x) and response (y) variables in a linear regression to justify the selection of variables.]
Data Collection
[Sampling the data: Select a random sample of 50 houses. Describe how you obtained your sample data (provide Excel formulas as appropriate).]
[Sampling the data: Identify your predictor and response variables.]
[Scatterplot: Create and insert a correctly labeled scatterplot of your predictor and response variables to ensure they are appropriate for developing a linear model.]
Data Analysis
[Histogram: Create and insert a histogram for the first variable. Be sure to include appropriate labels.]
[Histogram: Create and insert a histogram for the second variable. Be sure to include appropriate labels.]
[Summary statistics: Create and insert a table to show the summary statistics (mean, median, standard deviation) for both variables.]
[Interpret the graphs and statistics: Interpret the center, spread, shape, and any unusual characteristic (outliers, gaps, etc.) for house sales and square footage.]
[Interpret the graphs and statistics: Compare and contrast center, spread, shape, and any unusual characteristic for your sample of house sales with the national population. Also, determine whether your sample is representative of the national housing market sales. Note: In the learning management system, under Supporting Materials, see National Summary Statistics and Graphs Real Estate Data PDF.]
Develop Regression Model
[Scatterplot: Create and insert the scatterplot of the variables with a line of best fit and the regression equation. [Based on your scatterplot, explain whether a regression model is appropriate.]
[Discuss associations: Discuss the associations in the scatterplot, including the direction, strength, and form, in the context of your model.]
[Discuss associations: Identify any possible outliers or influential points and discuss their effect on correlation.]
[Discuss associations: Discuss keeping or removing outlier data points and what impact your decision would have on your model.]
[Calculate r: Calculate the correlation coefficient and explain how the calculated r value supports what was noticed in your scatterplot.]
Determine the Line of Best Fit
[Regression equation: Write the regression equation (i.e., line of best fit) and clearly define your variables.]
[Interpret regression equation: Interpret the slope and intercept in context. For example, answer the questions: What does the slope represent in this situation? What does the intercept represent? Revisit the Scenario section in the learning management system.]
[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 you should list your home for based on the assumed square footage of your home at 1500 square feet.]
Conclusions
[Summarize findings: Summarize your findings in clear and concise plain language for the CEO to understand.]
[Summarize findings: Did you see the results you expected, or was anything different from your expectations or experiences?]
[Summarize findings: What changes could support different results, or help to solve a different problem?]
[Summarize findings: Provide at least one question that would be interesting for follow-up research.]

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