The following software results provide information about the size (in square feet) of 18 homes in Ithaca, New York, and the city€™s assessed value of those homes, where the response variable is Assessment.

a) Explain why inference for linear regression is appropriate with these data.
b) Is there a significant linear association between the Size of a home and its Assessment? Test an appropriate hypothesis and state your conclusion.
c) What percentage of the variability in assessed value is accounted for 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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Predictor Intercept 3710.85 8664.33 size Coeff SE(Coeff t Ratio P-Value 4.28 0.0006 2.77 0.0136 11.90 4.29 Variable Assessment60946.7 5527.62 Size Mean StdDev 2003.39 264.727 72,000 68,000 64,000 60,000 t . 56,000 1800 2000 2200 2400 Size 4000 -4000 Normal Scores 4000 Ф-4000 57,500 62,500 Predicted ($