Problem #2 (20 points) Suppose I want to predict the Blue Wave's basketball team points scored. I've listed their points scored in their last 8 games. Those games are in order (Game 1 was 1/25....Game 8 was 2/22, etc.). I've also listed how many points the team scored in those games, and how many Wins their opponent had this season. Finally, I have a counting variable: 1 if the game was at Home, o if the game was Away. A quick example from Game 1: On January 25th, the Blue Wave traveled Away (so Home = 0) to Juanita, where they scored 74 points against a Juanita team that won 10 games this year. My model would have predicted about 73.7 points would be scored. Y X3 X1 Home Game X2 Opponent Wins: Points Scored by Blue Wave: Game: 1 N 74 61 69 61 78 10 22 10 19 vo 0 0 1 1 1 0 0 1 4 5 10 63 5 6 7 8 4 22 14 49 53 Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations 0.888484552 0.789404798 0.631458397 6.000981542 8 ANOVA df F 4.997 Significance F 0.07706 Regression Residual Total MS 3 179.9843 4 36.01178 7 Intercept Home Game Opponent Wins: Game: Coefficients t Stat 84.70660029 12.25014 5.168662297 1.182086 -0.834971851 -2.4 -2.712377111 -2.85755 P-value 0.0002 0.3026 0.07435 0.04604 a) Write the Regression Model for Points (round numbers to 3 decimal points to make it easier to read) from the results. b) Suppose I brag to you that this is a good regression, because my R^2 is .79. Explain to me two good reasons why this regression is not as good as I think it is. Be detailed enough for your answers, using the Excel output to make your points. Hint: this information is not in the book, it's at the end of my Regression video