You want to develop a model to predict the taxes of houses, based on asking price. A sample of 61 single-family houses listed for sale in Silver Spring, Maryland, a suburb of Washington, DC, is selected. The taxes (in $) and the asking price of the houses (in $thousands) are recorded and stored in SilverSpring.

a. Construct a scatter plot and, assuming a linear relationship, use the least-squares method to compute the regression coefficients b₀ and b₁.

b. Interpret the meaning of the Y intercept, b₀, and the slope, b₁, in this problem.

c. Use the prediction line developed in (a) to predict the mean taxes for a house whose asking price is $400,000.

d. Determine the coefficient of determination, r², and interpret its meaning in this problem.

e. Perform a residual analysis on your results and evaluate the regression assumptions.

f. At the 0.05 level of significance, is there evidence of a linear relationship between taxes and asking price?

g. What conclusions can you reach concerning the relationship between taxes and asking price?