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are157-homework-2

# are157-homework-2 - ARE 157 Spring 2009 HOMEWORK 2 T~ 1...

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T~, ~I() lo.k ~aQw ARE 157 Spring 2009 HOMEWORK 2 \~ ~ t ~ pOSt<..& I'2-I.SW- ~ t-o 1, Forecasting using data smoothing. James and Kelly are avid basketfall fans. They often argue about whether players experience "momentum" (hot streaks or cold streaks that carry over from one game to the next). James believes momentum is important. If a player achieves a high score one night, he is likely to perform well again in the next game. Kelly thinks scoring fluctuates randomly around the player's mean score, so that knowing how much the player scored in the last few games does not help forecast the next game's points scored. To settle their dispute, they look at 2007 data from two players: Kobe Bryant, a leading player with extensive NBA experience; and Kevin Martin, at that time a second-year player. They use 8 data smoothing models to forecast the next game's points scored, each implying different degrees of momentum from one game to the next. a. Create the following 8 sets of forecasts, using actual data from Kobe Bryanfs first 42 games of the season. Begin your forecasts on game 6 (games 1 through 5 are needed to create some of the forecasts). For your exponential smoothing models, assume your first forecast (Game 6 forecast) is 28.4, Kobe's average points scored in the '05-06 season.

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are157-homework-2 - ARE 157 Spring 2009 HOMEWORK 2 T~ 1...

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