3
Determine r
R = 0.958
The correlation is positive, as it goes in an upward diagonal direction. This is a strong correlation because it is close to 1. The association between the two variables is positive. As the square feet increases, the listing price increase as well.
Examine the Slope and Intercepts I can gather that if the square footage of a house is 0, it would be listed at $56,456. Based
on my observation of the line of best fit, I believe the intercept does not make sense. The value of
only the land is $56,456. R-squared Coefficient
R-squared lets us know that 92% of the variation in listing price is explained by the variation in square feet area. Conclusions
The mean of the square feet in the data sample I selected of the East North Central region
is lower than that of the national mean. The East North Central region is also lower than the national in Q1, median, Q3, and max. The East North Central region is greater than the national in standard deviation and minimum. For every 100 square feet the listing price will increase by $9,653.50. You can use the slope to help identify price changes by multiplying it by 100. The graph would best be used for the square footage range of 1,201 and 5,146.
