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 theReal Estate Dataspreadsheet (both attached bellow) for your statistical analysis.

Note:Present your data in a clearly labeled table and using clearly labeled graphs.

Specifically, include the following in your report:

Introduction

1. Describe the report:Give a brief description of the purpose of your report.
2. Define the question your report is trying to answer.
3. Explain when using linear regression is most appropriate.
4. When using linear regression, what would you expect the scatterplot to look like?
5. Explain the difference between response and predictor variables in a linear regression to justify the selection of variables.

Data Collection

1. Sampling the data:Select a random sample of 50 houses.
2. Identify your response and predictor variables.
3. Scatterplot:Create a scatterplot of your response and predictor variables to ensure they are appropriate for developing a linear model.

Data Analysis

1. Histogram:For your two variables, create histograms.
2. Summary statistics:For your two variables, create a table to show the mean, median, and standard deviation.
3. Interpret the graphs and statistics:
4. Based on your graphs and sample statistics, interpret the center, spread, shape, and any unusual characteristic (outliers, gaps, etc.) for the two variables.
5. 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

1. Scatterplot:Provide a graph of the scatterplot of the data with a line of best fit.
2. Explain if a regression model is appropriate to develop based on your scatterplot.
3. Discuss associations:Based on the scatterplot, discuss the association (direction, strength, form) in the context of your model.
4. Identify any possible outliers or influential points and discuss their effect on the correlation.
5. Discuss keeping or removing outlier data points and what impact your decision would have on your model.
6. Findr:Find the correlation coefficient (r).
7. Explain how thervalue 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.

1. Regression equation:Write the regression equation (i.e., line of best fit) and clearly define your variables.
2. Interpret regression equation:Interpret the slope and intercept in context.
3. Strength of the equation:Provide and interpretR-squared.
4. Determine the strength of the linear regression equation you developed.
5. 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

1. Summarize findings:In one paragraph, summarize your findings in clear and concise plain language for the CEO to understand. Summarize your results.
2. Did you see the results you expected, or was anything different from your expectations or experiences?
3. What changes could support different results, or help to solve a different problem?
4. Provide at least one question that would be interesting for follow-up research.

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Fill out the brackets bellow:

Introduction

[Describe the report: Include in this section a brief overview, including the purpose of the report and your approach.]

Data Collection

[Sampling the data: Outline how you obtained your sample data, including the response and predictor variables.]

[Scatterplot: Insert a correctly labeled scatterplot of your chosen variables.]

Data Analysis

[Histogram: Insert the histogram of the two variables. Be sure to include appropriate labels.]

[Summary statistics: Insert a table to show the summary statistics.]

[Interpret the graphs and statistics: Describe the shape, center, spread, and any unusual characteristic (outliers, gaps, etc.) and what they mean based on your sample data and the graphs you created.]

[Explain how these characteristics of the sample data compare to the same characteristics of the national population. Also, determine whether your sample is representative of the national housing market sales.]

The Regression Model

[Scatterplot: Include the scatterplot graph of the sample with a line of best fit and the regression equation.]

[Based on your graph, explain whether a regression model can be developed for the data and how.]

[Discuss associations: Explain the associations in the scatterplot, including the direction, strength, form in the context of your model.]

[Find r: Calculate the correlation coefficient and explain how it aligns with your interpretation of the data from the scatterplot.]

The Line of Best Fit

[Regression equation: Insert the regression equation.]

[Interpret regression equation: Interpret the slope and intercept in context.]

[Strength of the equation: Interpret the strength of the regression equation, R-squared.]

[Use regression equation to make predictions: Use the regression equation to make a sample prediction.]

Conclusions

[Summarize findings: Summarize your findings in clear and concise plain language. Outline any questions arising from the study that might be interesting for follow-up research.]

Excel MAT 240 Real Estate Data - View-only v Search (Alt + Q) File Home Insert Draw Page Layout Formulas Data Review View Help Viewing Comments D v Q v Arial 11 B A . . . ab General .00 00 Ov E AY OV . . . A105 v fx East North Central B G H N O P Q R V W X Z AA AB AC 5 Region State County listing price square foot square feet 6 East North Central in grant 219,500 $116 1,898 East North Central vermilion 254,500 $156 1,632 8 East North Central henry 235,000 $148 1,588 9 East North Cent wayne 203,800 $141 1,441 10 East North Central coles 220,800 $117 1,893 East North Central macoupin 197,600 $111 1,783 East North Central vigo 165,800 $122 1.362 13 East North Central oh jefferson 246,500 $136 1.814 East Nor jackson 154,300 $105 1,463 East North Central oh marion 149,700 $116 1,296 mi bay 145, 100 $11 1.239 East North Central whiteside 283,700 $136 2,087 East North Central oh trumbull 243,000 $133 1.827 East North Central madison 229,100 $187 1.224 East North Central knox 205,100 $118 1,740 East North Central stephenson 235,600 $140 1,682 East North Central macon 212,900 $128 ,659 delaware 221,600 $134 1,65 24 East North Central henry 257,700 $123 2,087 East North Central oh seneca 211,900 $168 1,263 East North Central oh darke 60,800 $114 1,416 East North Central oh scioto 204,200 $131 1,562 East North Central belmont 172,500 $101 1,710 East North Central oh sandusky 253,900 $146 1,738 East North Centra rock island 166,300 $127 1,305 31 East North Central oh clark 240,500 $137 1,752 32 East North Central oh columbiana 241,400 $164 1,469 33 East North Central in howard 304,300 $152 1.996 34 East North Central oh richland 248,900 $132 1.880 35 East North Central peoria 187,900 $131 1.434 Fast North Central la salle 311 100 $154 2 015