Module 3-3 Assignment
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Mathematics
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Feb 20, 2024
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Housing Price Prediction Model for D.M. Pan National Real Estate Company
Module 3-3 Assignment
Darnell Harper
Department of Mathematics, SNHU
MAT 240: Applied Statistics
Vincent Frazier
Jan 28, 2024
Housing Price Prediction Model for D.M. Pan National Real Estate Company
Module Two Notes
Based on the data from the regional sample, the sample of the listing prices are lower compared to the national listing prices contrary to the square foot sample which is higher than the national average. I ensured that the sample was random by utilizing the random number method. This was done by using the random feature on excel to assign each sample a random number.
Regression Equation
The regression equation for the line from the scatterplot from the previous report is y = 103.34x + 39861
Determine r
The correlation coefficient (r) has been determined to be 0.975627149. With r being between 0.8 and 1.0, it is determined to be a strong correlation. It also describes the direction as a
positive direction on the scatterplot. Examine the Slope and Intercepts
The interpretation of the slope estimate is the change in the listing price per square footage of the property. After calculations, the price of the land is $103.34 per 100 square feet.
R-squared Coefficient
R-squared in this analysis is 0.9518 or 95.18%. This means that in this analysis, the listing price moves relatively in line with the index.
Housing Price Prediction Model for D.M. Pan National Real Estate Company
Conclusions
In conclusion, the relationship between the sales price is in line with the price increase in relation to the square footage of the property. As the selected region compares to the rest of the United States, the prices vary throughout each region however, in each region, the price of the property is determined by square footage. As for the slope, in comparison to the rest of the United States, there is an increase of the price per square footage. Throughout the United States however, the correlation is still strong in comparison to the selected region with a r of 0.974. As for the r-squared coefficient, the analysis has shown a value of 0.9487 or 94.87% which shows the listing price still moves relatively with the index. As for what square footage range would be best used, it would be a range of 1,000 sq ft to 3,000 sq ft.
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