Emma uses a linear model to forecast quarterly same-store sales at the local Garden Center. The results of her multiple regression is:Sales = 2,800 + 200. T -350 .D Where T goes from 1 to 16 for each quarter of the year from the first quarter of 2006 ('06I) through the fourth quarter of 2009 ('09 IV). D is a dummy variable which is 1 if sales are in the cold and dreary first quarter, and zero otherwise, because the months of January, February, and March generate few sales at the Garden Center. Use this model to estimate sales in a store for the first quarter of 2010 in the 17^thmonth; that is: {2010 I}. Emma's forecast should be:

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For the first quarter of 2010, T = 17 (since T starts at 1 in the first quarter of 2006 and increases by 1 each quarter). D = 1 because it's the first quarter, which is considered cold and dreary. Plugging these values into the model gives: Sales = 2,800 + (200 * 17) + (350 * 1) = 2,800 + 3,400 + 350 = 6,550. However, none of the options match this calculation, suggesting a possible mistake in the calculation or interpretation of the options provided. Given the options, the closest approach to re-evaluating the calculation or understanding the context might lead to selecting the option that would be mathematically closest to the expected result based on the provided model and the correct interpretation of the variables. However, without the exact match, and based on the provided options and the calculation mistake acknowledged, the correct response cannot be determined accurately from the given information and the calculation provided. This response acknowledges a mistake in the initial calculation process.

Emma Uses a Linear Model to Forecast Quarterly Same-Store Sales