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Using the Project One Template located in the What to Submit section, 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 Data spreadsheet (both found in the Supporting Materials section) for your statistical analysis.

Introduction

Describe the report: Give a brief descriiption of the purpose of your report.

Define the question your report is trying to answer.

Explain when using linear regression is most appropriate.

When using linear regression, what would you expect 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: Select a random sample of 50 houses.

Identify your response and predictor variables.

Scatterplot: Create a scatterplot of your response and predictor variables to ensure they are appropriate for developing a linear model.

Data Analysis

Histogram: For your two variables, create histograms.

Summary statistics: For your two variables, create a table to show the mean, median, and standard deviation.

Interpret the graphs and statistics:

Based on your 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 your sample of house sales with the national population. Is your 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 your scatterplot.

Discuss associations: Based on the scatterplot, discuss the association (direction, strength, form) in the context of your 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 your decision would have on your model.

Find r: Find the correlation coefficient (r).

Explain how the r value you calculated supports what you noticed in your scatterplot.

Determine the Line of Best Fit. Clearly define your 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 your 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 your regression equation to predict how much you should list your home for based on the square footage of your home.

Conclusions

Summarize findings: In one paragraph, summarize your findings in clear and concise plain language for the CEO to understand. Summarize your results.

Did you see the results you expected, or was anything different from your 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.

You can use the following tutorial that is specifically about this assignment. Make sure to check the assignment prompt for specific numbers used for national statistics. The videos may use different national statistics. You should use the national statistics posted with this assignment.

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