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Unformatted text preview: 4/15/2010 1 Dummy Variables  I We next consider the case when X i is a dummy variable , or binary variable. A dummy variable is a variable that takes on only the values 0 or 1. Often categorical variables are quantified in this way, e.g. X i = 1 if female, X i = 0 if male, X i = 1 if use advertising campaign A, X i = 0 if use advertising campaign B. Dummy Variables  II Nothing major changes when X i is a dummy variable. However, the interpretation of the estimated coefficients and 1 is a bit different. Example: Sales i = + 1 AdCampaign i + u i AdCampaign i =1 if use advertising campaign A in region i AdCampaign i =0 if use advertising campaign B in region i Sales i = unit sales in region i Suppose we regress Sales i on AdCampaign i and get estimated coefficients and 1 . What do these coefficients tell us? Recall predicted value formula 4/15/2010 2 Dummy Variables  III The predicted value formula says that when advertising campaign A is used, (i.e. when AdCampaign i =1) predicted sales are: When advertising campaign B is used (i.e. AdCampaign i =0), predicted sales are: So the estimated coefficient 1 measures the difference in predicted sales between the two campaigns. circumflexnosp4char 1 Sales AdCampaign i i = + circumflexnosp4char 1 Sales i = + circumflexnosp4char Sales i = Dummy Variables  IV Therefore, if we want to test whether the two ad campaigns generate the same amount of sales, we should test the null hypothesis that the slope coefficient 1 =0. Suppose regression results are: The t STAT for the hypothesis test that 1 =0 is 1.46. Hence we cannot reject the null hypothesis that the campaigns generate equivalent amounts of sales....
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This note was uploaded on 06/17/2010 for the course ECON 103 taught by Professor Sandrablack during the Spring '07 term at UCLA.
 Spring '07
 SandraBlack
 Econometrics

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