Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 2

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Median Housing Price Prediction Model for D.M. Pan Real Estate Company

[Your Name]

Southern New Hampshire University

Median Housing Price Prediction Model for D.M. Pan Real Estate Company 1

Module Two Notes

[Copy and paste any relevant information from your Module Two assignment here to assist you in completing this assignment. This section is not graded and is only provided to help you easily review Module Two assignment information while completing this assignment.]

Regression Equation

[Insert the regression equation for the line of best fit using the scatterplot from your Module Two assignment.]

Determine r

Examine the Slope and Intercepts

R-squared Coefficient

Selling Price Analysis for D.M. Pan National Real Estate Company 2

Report: Selling Price and Area Analysis for D.M. Pan National Real Estate Company

Chelsey Welch

Selling Price and Area Analysis for D.M. Pan National Real Estate Company 1

Southern New Hampshire University

Introduction

The purpose of this report is to provide the sales team at D.M. Pan Real Estate Company with data that examines the relationship between the selling price of properties and their size in square feet. The data will be from Mountain region of the US.

Representative Data Sample

This is my random sample of 30 from the Mountain region.

Data Analysis

When comparing median listing price my sample has a larger mean and median than the regional sample but a smaller standard deviation. When comparing square feet my sample had a larger mean, median, and standard deviation. For my sample I used systematic sampling, which is a method of random sampling where you select data based on every nth unit from a population. The Mountain region consists of a population of 63. I took every 2nd entry and used it to create the sample of 30.

Scatterplot

The Pattern

The variables I have are the median square feet and the median listing price. The predictor variable is the median square feet and the response variable is the median listing price because the “response variable is the variable being modeled or predicted, while the predictor variable is the variable used to predict the response” (Zybooks).

There is one outlier and I marked it with a blue glow. It’s from the sample of Eagle, CO listing price $699,443 and 1765 square feet. This might have occurred due to other housing price determinants such as age, locality, etc.

The regression equation on this graph is Y = 74.683X + 204027

So, if I plug X for a 1,200 square foot this is the equation:

Y= 74.683(1200) + 204027 = $293.592.60

If I had a 1,200 square foot house, based on the regression equation the price I would list it at is $293.592.60.

References

ZyBooks. (2016). MAT 240: Applied Statistics. Zyante. ISBN: 978-1-394-04892-2

Median Square Feet and Median Listing Price

Listing Price

1792.375 1943.8511904999998 1765.4523808333333 2026.9821429166666 2216.0773810000001 2575.8035713333334 2687.3809523333334 1679.6666666666667 1765.3154761666665 2445.1726190833333 2092.4821428333335 2953.3452380833332 2505.7083332500001 2024.0476190833335 2011.3690476666668 2241.7142856666665 2173.2857143333335 2301.5476189999999 1944.36309525 1797.7142858333334 1821.8928572499999 2271.2678570833336 2033.5297620000001 1837.6011904166669 1935.61309525 2115.0357143333335 3101.0654761666665 2950.8392856666669 2243.2619047499998 2544.5416666666665 200260.64881666665 360235.11904166668 304891.14880000002 291768.57738333335 457533.88691666658 418734.79762499995 620894.32739166671 534393.24404999998 699442.85713333322 611189.88095000002 540468.00595000002 417120.20832500001 255125 287786.03570833331 265805.3571416667 485857.14285833336 318840.47619166668 281365.47619166668 223966.2321416667 186790.83333333334 190429.16666666666 304790.68452499999 231238.77976666667 319031.48215 290651.14880833332 467923.03572500002 398894.40476666664 336614.95832500001 408546.97618333326 324247.51189999998