statistic1

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Multiple Regression

Take me out to the ball game.
In baseball a teams success is often thought to be a function of the team's hitting and pitching performance. One measure of hitting performance is the number of home runs that team hits, and one measure of pitching performance is the earned run average for the teams pitching staff. It is generally believed that teams that hit more home runs and have a lower earned run average will win a higher percentage of games played. The following data show the proportion of games won, the number of home runs (HR), and the earned run average (ERA) for the 16 teams in the National League for the 2003 Major League Baseball season.
Team Won HR ERA
Arizona 0.519 152 3.857
Atlanta 0.623 235 4.106
Chicago 0.543 172 3.842
Cincinnati 0.426 182 5.127
Colorado 0.457 198 5.269
Florida 0.562 157 4.059
Houston 0.537 191 3.880
Los Angeles 0.525 124 3.162
Milwaukee 0.420 196 5.058
Montreal 0.512 144 4.027
New York 0.410 124 4.517
Philadelphia 0.531 166 4.072
Pittsburgh 0.463 163 4.664
San Diego 0.395 128 4.904
San Francisco 0.621 180 3.734
St Louis 0.525 196 4.642
1 Determine the estimated regression equation that could be used to predict the proportion of games won given the number of team home runs.
2 Determine the estimated regression equation that could be used to predict the proportion of games won given the earned run average for the team's pitching staff..
3 Determine the estimated regression equation that could be used to predict the proportion of games won given the number of team home runs and the earned run average for the team's pitching staff..
4 Use the estimated regression equation developed in part (3) to estimate the percentage of games San Diego will win if they have 180 team home runs and have an earned run average of 4.0.
5 What is the multiple coefficient of determination which measures the goodness of fit for the estimated multiple regression equation.
6 Setup the hypothesis test for the multiple regression problem.