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# Linear Analysis to Predict Selling Prices of Homes

See attached data file.

You want to develop a model to predict the selling price of homes based on assessed value. A sample of 20 recently sold single-family houses in a small city is selected to study the relationship between selling price (in thousands of dollars) and assessed value (in thousands of dollars). The houses in the study were assessed at full value one year prior to the study.

a. Construct a scatter plot and assuming a linear relationship, use the least square 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 selling price for a house whose assessed value is \$ 170,000.

d. Determine the coefficient of determinations, r squared, and interpret its meaning.

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 selling price and assessed value?

g. Construct a 95% confidence interval estimate of the population slope.

#### Solution Summary

The solution provides a linear analysis to predict selling prices of homes.

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