MAT 240 Project One

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Southern New Hampshire University *

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240

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Economics

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Jan 9, 2024

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Uploaded by BrigadierKuduMaster186

Median Housing Price Prediction Model for D. M. Pan National Real Estate Company 1 Report: Housing Price Prediction Model for D. M. Pan National Real Estate Company Amiee Sizemore Southern New Hampshire University

Median Housing Price Model for D. M. Pan National Real Estate Company 2 Introduction Describe the report: D. M. Pan National Real Estate Company has requested that we create a house pricing model that will predict house values for sales in 2019. The CEO plans to use this data to make decisions about the square footage as a real estate listing price benchmark. In my report, I will compare housing costs and its relationship to square footage. Data Collection S ampling the data: I generated 50 random house data sets by using Excel's "=rand()" function, and then I randomly rearranged each row using the Data tab's sorting options. To collect my final data, I selected the first 50 rows that were generated. Sampling the data: In the scatter plot below, "X" stands for square footage (the predictors) and "Y" for listing price (the response). Scatterplot: A linear regression model provides a deeper understanding of the relationships between two variables. The scatterplot should show a fairly random pattern, if appropriate. While the predictor variable (x) is fixed (non-random), the response variable (y) is a random variable. The scatterplot shows a weak, but positive correlation due to the random and positive pattern. This shows that while one variable increases, so does the other. 1,000 2,000 3,000 4,000 5,000 - 100,000 200,000 300,000 400,000 500,000 600,000 700,000

Median Housing Price Model for D. M. Pan National Real Estate Company 3 Data Analysis Histogram: Histogram: Summary statistics: Interpret the graphs and statistics: The results in the data analysis support the theory that the listing price of homes will increase with the square footage amount. The mean listing

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Median Housing Price Model for D. M. Pan National Real Estate Company 4 price across the country is $342,365, while the mean listing price for the data I randomly selected is $333,2712. The mean square footage of my selected data is 2,110, whereas the mean square footage of the country is 2,111, which is almost spot on. Given the very few variations in square footage and average price between the two variables, I can determine that my randomly selected sample accurately represents sales in the US housing market. Develop Regression Model Scatterplot: 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 - 100,000 200,000 300,000 400,000 500,000 600,000 700,000 f(x) = 93.68 x + 136066.89 R² = 0.52 Discuss associations: As stated above, a linear regression model provides a deeper understanding of the relationships between two different variables. The scatterplot I’ve created shows a random pattern, confirming that a regression model is appropriate. An obvious outlier that I can see is the gap between about 2,500 SF and 3.500 SF. This outlier seems to be from lack of data reported in my random selection. Otherwise, this scatterplot shows a positive correlation that is weak to moderate.

Median Housing Price Model for D. M. Pan National Real Estate Company 5 Determine the Line of Best Fit Regression equation: y = 93.685x + 136067 Strength of the equation: The correlation coefficient, which equals .718003167, indicates a strong correlation between the two listed variables. The r-squared indicates that there is a good degree of variation between the variables (about 72%).

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