University:
Southern New Hampshire University
Course:
MAT240 | Applied Statistics
Academic year:

2020
Views:

421

Pages:

2
Author:

Kristopher Elliott

Median Housing Price Prediction Model for D.M. Pan National Real Estate Company 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 Regression equation: y = 146.52x - 95611 Determine r The value of [r] determines the association between variables. The [r2] value is 0.713, meaning the square root (or [r] value) is equal to 0.988. An [r] value between 0.8 and 1 means the correlation between the variables is strong. [r] being a positive number means the slope is positive and travels in an upward right direction. This also tells us that as one variable increases, the other variable increases. If X increases then Y increases, or if Y increases then X increases. In other words, as the square footage of the house increases, the listing price of the house increases. Examine the Slope and Intercepts The slope intercept formula is y = 146.52x – 95611. We determined the slope based on 2 positions on the line and the Y intercept. We can take 2 points on the linear regression line to calculate slope as well. The Y-intercept is 95611. b0 is 95611. b1 is the slope (rise over run) which is 146.52. This slope intercept doesn’t seem accurate to me, as it looks to me like it should be around 50000. R-squared Coefficient [Explain what R-squared means in the context of this analysis.] The r2 value is the represents the proportionate relationship between the independent and dependent variable in a regression model. It explains the extent of how one variable determines Median Housing Price Prediction Model for D.M. Pan National Real Estate Company the other. The r2 value being 0.713 means almost ¾ of the observed variation can be explained by the inputs. Conclusions In conclusion, we know that there is a direct relationship between the square footage and listing price of the houses. If the square footage increases, it has a direct, strong, positive relationship with the listing price (causing it to increase as well). The relationship between the random selections I chose, and the national average is that the national average yields higher costs per square foot. Choosing a different random sample might yield different results. Plugging in the national average into a regression model would give me a more accurate idea of the deviation. Learning the slope and r value can pinpoint exactly where the listing price of a house should be. A chart that identified the regression line and slope for the average house listing price in the immediate area would better put us in a position to identify what the house should be listed at, as it pertains to the average in the area. For every 100 square feet, the listing price of a house should increase by roughly $8,300. The median price per square foot is $83, so for 100 square feet, it would increase by roughly $8300. Plugging in a 1200 square foot house into the regression equation gets us a house that should be listed at $80,213. The graph would best be used to illustrate houses between 1500 and 2000 square feet.

Median Housing Price Prediction Model Assignment

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