Property assessments. The following software results provide information about the size (in square feet) of 12 homes in Budapest, Hungary, and the city’s assessed value of those homes (in thousands of Hungarian forint, Ft), where the response variable is Assessment.

Predictor Coeff SE(Coeff) t-stat P-Value Intercept 103.4372 50.1267 2.0635 0.0660 Size 0.0700 0.0213 3.2923 0.0081 s = 36.65 R-Sq = 52.1%

Variable Mean SD Size 2306.0 264.75 Assessment 520.2 50.45

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 95% 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 270,000 Ft assessed value is too high. Do you agree? Explain your reasoning.

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Assessed Value 400 350 300 250 200 150 100 50 0 + 0 2000 4000 Size Price Linear (Price)