Suppose I want to predict the Blue Waves basketball team points scored. Ive 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.). Ive 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, 0 if the game was Away.

A quick example from Game 1: On January 25^th, 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	X1	X2	X3
Points Scored by Blue Wave:	Home Game	Opponent Wins:	Game:
74	0	10	1
61	0	22	2
69	1	10	3
61	1	19	4
78	1	10	5
63	0	4	6
49	0	22	7
53	1	14	8

Regression Statistics
Multiple R	0.888484552
R Square	0.789404798
Adjusted R Square	0.631458397
Standard Error	6.000981542
Observations	8
ANOVA
	df	MS	F	Significance F
Regression	3	179.9843	4.997	0.07706
Residual	4	36.01178
Total	7
	Coefficients	t Stat	P-value
Intercept	84.70660029	12.25014	0.0002
Home Game	5.168662297	1.182086	0.3026
Opponent Wins:	-0.834971851	-2.4	0.07435
Game:	-2.712377111	-2.85755	0.04604

1. Write the Regression Model for Points (round numbers to 3 decimal points to make it easier to read) from the results.
2. 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, its at the end of my Regression video.