x=square footage of a house
y=asking price in thousands of the house.
x goes from 1200 square feet to 1500 square feet. the regression equation is
y^= .0686x+83.24
A. what are the explanatory and response variables?
B. Interpret the slope.
C. Is it reasonable to interpret the y-intercept? why or why not?
D. Is it safe to use x=3000 to predict y? why or why not?
the explanatory variable would be X the square footage of a house which is .0686
and the response variable would be Y the asking price in thousands of the house which is 83.24 .
I am confused on how to figure out the rest of the problem.
the slope x is the amount price per square slope increase per sqauare foot.
the y intercept is a base price for buying a house at all, even with zero sq ft.
It is safe to use the equation to predict for other sg feet.
A. The explanatory variable (independent variable) is x, which represents the square footage of a house. The response variable (dependent variable) is y, which represents the asking price in thousands of the house.
B. The slope of the regression equation, represented by 0.0686, indicates the rate of change in the asking price of the house for every unit increase in square footage. Specifically, for each additional square footage (x), the asking price (y) is expected to increase by 0.0686 units.
C. It may not be reasonable to interpret the y-intercept, which is 83.24, without further context. In this case, it represents the predicted asking price when the square footage is zero, which is not practically possible for a house. Therefore, interpreting the y-intercept as the starting price or base price of a house may not be meaningful in this context.
D. It is not safe to use x = 3000 to predict y because the regression equation is only valid for the range of square footage values between 1200 and 1500. Extrapolating beyond the observed range may lead to inaccurate predictions as the regression equation is based on the data within this range. Therefore, using x = 3000 may not provide reliable predictions for y.