The following software outputs provide information about the Size (in square feet) of 18 homes in Ithaca, New York, and the city's assessed Value of those homes.

Dependent variable is Value R-squared = 32.5%
s = 4682 with 18 - 2 = 16 degrees of freedom

a) Explain why inference for linear regression is appropriate with these data.
b) Is there a significant association between the Size of a home and its assessed Value! Test an appropriate hypothesis and state your conclusion.
c) What percentage of the variability in assessed Value is explained by this regression?
d) Give a 90% confidence interval for the slope of the true regression line, and explain its meaning in the proper context.
e) From this analysis, can we conclude that adding a room to your house will increase its assessed Value! Why or why not?
f) The owner of a home measuring 2100 square feet files an appeal, claiming that the $70,200 assessed Value is too high. Do you agree? Explain your reasoning.

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Variable Count Mean StdDev Range Size Value 18 2003.39 264.727 890 18 60946.7 5527.62 19710 Variable Coefficient SE(Coeff) Intercept 37108.8 8664 Size 11.8987 4.290 72000 68000 64000 60000 56000 00 2000 2200 2400 Size (sg t 4000 -1 Normal Scores 4000 4000 57500 2500 Predicted (5)