Statistics Report Part 3
Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 2
PREVIOUS ASSIGNMENT TO BE USED FOR REFERENCE
Median Housing Price Prediction Model for D.M. Pan Real Estate Company
NAME
Southern New Hampshire University
Median Housing Price Prediction Model for D.M. Pan Real Estate Company 1
Module Two Notes
Random sample of 30 from the Mountain region.
|
|
Mean |
Median |
Standard deviation |
|
Median Square Feet |
2193 |
2104 |
380.90 |
|
Median Listing Price |
$367,828 |
$321,639 |
134739 |
The predictor variable is the median square feet and the response variable is the median listing price.
The regression equation on this graph is Y = 74.683X + 204027
Regression Equation
The regression equation on this graph is Y = 204027 + 74.683X.
(median listing price) Y = 204027 + 74.638X (median sq feet).
Determine r
The meaning of r is to show the strength of the correlation between two variables.
R2 = 0.0446 so r = 0.211187.
Zybooks (2019) shows a table explaining how to determine strength of correlation below.
Based on the fact that r = 0.211187, the strength of the correlation is weak. r is positive, so the direction between the variables is positive.
Examine the Slope and Intercepts
The slope is 204027. The intercept is 74.683X. Based on the slope, land is worth $204,027.
R-squared Coefficient
The purpose of R-squared is to tell you what percentage of data is closest to the regression line. Using R-squared is helpful because you can determine how accurate the regression line is to predicting the value of the response variable. In this particular analysis, R-squared = 0.0446. This means that only 4.46% of the data I used is closest to the regression line and fits the model. So, the rest of the data, 95.54%, is farther from the regression line. R-squared explains that the regression line is not very useful in this analysis.
Conclusions
In conclusion, the lower the square feet the lower the listing price will be. However, because only 4% of my data is closest to the regression line I would recommend the sales team at D.M. Pan Real Estate Company consider some other variables that can have a significant effect on selling prices. I recommend considering variables like quality of the neighborhood, for example, which would factor in nearby schools or walkability to grocery stores and malls. The graph best for this kind of analysis is a box plot because it depicts a much clearer image of the medians.
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