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Unformatted text preview: 1 Multiple Regression Part 4: Indicator Variables Section 14.6 Indicator Variables 2 Indicator Variables h Suppose some observations have a particular characteristic or attribute, while some others do not. h We want to include this information in the regression model. 2 Indicator Variables 3 Use an indicator variable h Use a binary variable to “indicate” when the characteristic is present c X i = 1 if observation i has the attribute c X i = 0 if observation i does not have it Indicator Variables 4 Subdivision Prices h In the data set OakKnoll.XLS are prices of 20 homes in either the OakKnoll or Hidden Hills subdivisions. h We want to predict price (in $1000s) by the size of the house, but allow for neighborhood differences. h The variable OakKnoll indicates which homes are in that subdivision. 3 Indicator Variables 5 The Oak Knoll data Hidden Hills Oak Knoll 4000 3000 2000 800 700 600 500 400 SqFt Price Price versus Home Size Indicator Variables 6 The twovariable regression h b 1 = .1987 has the standard interpretation. Each square foot is worth .1987 × $1000 or $198.70 h What is the meaning of b 2 = 33.5383? 4 Indicator Variables 7 An intercept adjustment h For a indicator variable, the coefficient is an “intercept adjustment”. To see this, evaluate the Yhat equation.evaluate the Yhat equation....
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This note was uploaded on 02/14/2011 for the course QMB 3250 taught by Professor Thompson during the Spring '08 term at University of Florida.
 Spring '08
 Thompson

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