A regression analysis was done of residential property values in a portion of a city. The dependent variable is Sale Price (Y) and the independent variable is Square Footage (X). Data for 157 properties were used in the regression. The results are listed below.
Predicted Y = 96,600 + 22.5(X) R-square = .77
What is the predicted value of Sale Price if the property has 2500 square feet?

A. $56,250.00
B. $152,850.00
C. $96,622.50
D. $96,600.00

So then the correct option for this case would be:

B. $152,850.00

Step-by-step explanation:

They adjust a linear model using the least squares method

For this case we assume that they calculate the slope with the following formula:

Where:

And we can find the intercept using this:

After apply the method they obtain the following regression equation:

Where Y represent the sale price and X the square footage.

And we want to estimate the predicted value of Sale price if the property has a 2500 square footage.

So for this case we just need to replace into our original equation the value of X = 2500 and we got:

So then the correct option for this case would be:

B. $152,850.00

i think it is 18

step-by-step explanation:

3x + 4y -25 =0 represents the amount of change sianna should recieve