Please use template provided.
You have been hired by the D. M. Pan National Real Estate Company to develop a model to predict 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 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.
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.
Note: Present your data in a clearly labeled table and using clearly labeled graphs.
Specifically, include the following in your report:
Describe the report: Give a brief description 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.
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.
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.
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.
Median Housing Price Model for D. M. Pan National Real Estate Company 3
[Note: To complete this template, replace the bracketed text with your own content. Remove this note before you submit your outline.]
Report: Housing Price Prediction Model for D. M. Pan National Real Estate Company
Median Housing Price Prediction Model for D. M. Pan National Real Estate Company 1
Southern New Hampshire University
[Describe the report: Include in this section a brief overview, including the purpose of the report and your approach.]
[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.]
[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.]
[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.]
project 1 data
Real Estate County Data for 2019
2019 Data (n=1000)
Region State County listing price $’s per square foot square feet
East North Central in grant 219,500 $116 1,898
East North Central il vermilion 254,500 $156 1,632
East North Central in henry 235,000 $148 1,588
East North Central in wayne 203,800 $141 1,441
East North Central il coles 220,800 $117 1,893
East North Central il macoupin 197,600 $111 1,783
East North Central in vigo 165,800 $122 1,362
East North Central oh jefferson 246,500 $136 1,814
East North Central il jackson 154,300 $105 1,463
East North Central oh marion 149,700 $116 1,296
East North Central mi bay 145,100 $117 1,239
East North Central il whiteside 283,700 $136 2,087
East North Central oh trumbull 243,000 $133 1,827
East North Central in madison 229,100 $187 1,224
East North Central il knox 205,100 $118 1,740
East North Central il stephenson 235,600 $140 1,682
East North Central il macon 212,900 $128 1,659
East North Central in delaware 221,600 $134 1,651
East North Central il henry 257,700 $123 2,087
East North Central oh seneca 211,900 $168 1,263
East North Central oh darke 160,800 $114 1,416
East North Central oh scioto 204,200 $131 1,562
East North Central oh belmont 172,500 $101 1,710
East North Central oh sandusky 253,900 $146 1,738
East North Central il rock island 166,300 $127 1,305
East North Central oh clark 240,500 $137 1,752
East North Central oh columbiana 241,400 $164 1,469
East North Central in howard 304,300 $152 1,996
East North Central oh richland 248,900 $132 1,880
East North Central il peoria 187,900 $131 1,434
East North Central il la salle 311,100 $154 2,015
East North Central il madison 254,500 $156 1,628
East North Central mi wayne 213,800 $172 1,243
East North Central in vanderburgh 214,100 $134 1,596
East North Central oh mahoning 207,500 $123 1,688
East North Central il williamson 171,600 $141 1,218
East North Central il winnebago 236,700 $140 1,692
East North Central il adams 266,100 $166 1,599
East North Central mi saginaw 171,800 $118 1,452
East North Central oh montgomery 225,300 $151 1,493
East North Central oh allen 227,600 $147 1,550
East North Central oh lucas 228,300 $115 1,978
East North Central oh ashtabula 177,000 $107 1,658
East North Central oh lawrence 248,300 $156 1,587
East North Central oh huron 199,700 $147 1,359
East North Central il tazewell 278,700 $165 1,693
East North Central oh summit 185,800 $101 1,847
East North Central il sangamon 213,500 $130 1,643
East North Central oh ashland 188,000 $151 1,246
East North Central oh tuscarawas 270,700 $149 1,815
East North Central oh ross 257,200 $127 2,018
East North Central mi shiawassee 192,400 $129 1,494
East North Central mi calhoun 266,200 $130 2,042
East North Central il kankakee 148,700 $115 1,293
East North Central in lawrence 270,600 $137 1,978
East North Central wi manitowoc 181,400 $14
Summary Statistics for MAT 240 Real Estate Data (for dataset in Modules 2, 3, and 4)
n Mean Median Std. Dev. Min Q1 Q3 Max
1,000 342,365 318,000 125,914 135,300 265,250 381,600 987,600
1,000 169 166 41 71 139 191 344
1,000 2,111 1,881 921 1,101 1,626 2,215 6,516
This graph shows the frequency for listing price.
This graph shows the frequency for square feet.
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