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Housing prices The following regression model was found for the houses in upstate New York considered in the chapter: \(\widehat{\text { Price }}=20,986.09-7483.10\) Bedrooms +93.84 Living Area. a. Find the predicted price of a 2 -bedroom, 1000-sq-ft house from this model. b. The house just sold for \(\$ 135,000\). Find the residual corresponding to this house. c. What does that residual say about this transaction?

More indicators For each of these potential predictor variables say whether they should be represented in a regression model by indicator variables. If so, then suggest what specific indicators should be used (that is, what values they would have). a. In a regression to predict income, the age of respondents in a survey b. In a regression to predict the square footage available for rent, whether a commercial building has an elevator or not c. In a regression to predict annual medical expenses, whether a person was a child (in pediatric care), an adult, or a senior (over 65 years old)

Interpretations A regression performed to predict the selling price of houses found the equation $$ \widehat{\text { Price }}=169,328+35.3 \text { Area }+0.718 \text { Lotsize }-6543 \mathrm{Age} \( where Price is in dollars, Area is in square feet, Lotsize is in square feet, and Age is in years. The \)R^{2}\( is \)92 \%\(. One of the interpretations below is correct. Which is it? Explain what's wrong with the others. a. Each year a house Ages it is worth \)\$ 6543\( less. b. Every extra square foot of Area is associated with an additional \)\$ 35.30\( in average price, for houses with a given Lotsize and Age. c. Every dollar in price means Lotsize increases 0.718 square feet. d. This model fits \)92 \%$ of the data points exactly.

More interpretations A household appliance manufacturer wants to analyze the relationship between total sales and the company's three primary means of advertising (television, magazines, and radio). All values were in millions of dollars. They found the regression equation $$ \widehat{\text { Sales }}=250+6.75 \mathrm{TV}+3.5 \text { Radio }+2.3 \text { Magazines. } $$ One of the interpretations below is correct. Which is it? Explain what's wrong with the others. a. If they did no advertising, their income would be \(\$ 250\) million. b. Every million dollars spent on radio makes sales increase \(\$ 3.5\) million, all other things being equal. c. Every million dollars spent on magazines increases TV spending \$2.3 million. d. Sales increase on average about \(\$ 6.75\) million for each million spent on TV, after allowing for the effects of the other kinds of advertising.