The number of wins for a high school football team is measured for the season. When the team plays at home, it is generally beli
eved that they will win. Comparing the location of the game and the number of wins, a correlation coefficient of −0.11 is calculated. What would this imply about the football team winning at home? The scatter plot would closely resemble a straight line with a negative slope. The data has a strong, negative correlation, and a causal relationship exists between the team playing at home and winning.
A.The scatter plot would closely resemble a straight line with a negative slope. The data has a strong, negative correlation, but causation cannot be determined.
B. The scatter plot would not be represented by a line of best fit with a negative slope. C.There is a weak correlation between the football team playing at home and winning, and a causal relationship exists between the team playing at home and winning.
D. There is no causation and almost no correlation between the football team playing at home and winning.
D. There is no causation and almost no correlation between the football team playing at home and winning.
Step-by-step explanation:
A correlation coefficient can be greather than or equal to -1, and less than or equal to 1.
If you have a negative correlation coefficient it means that the correlation between the two variables is a reverse relationship. When one of the variables increase the other one decrease.
The closes the correlation coefficient is to -1 or 1, the correlation is higher,
As -0,11 is not close to -1 it means that the correlation is almost 0 and it is not a correlation between the two variables.
What I did is first used the 45 minutes to make the time 10:00 P.M. which leaves you with 8 hours and 25 minutes, then i added the 8 hours onto 10:00 P.M. which leaves you at 6:00 A.M. with 25 minutes left, then you add the 25 minutes onto 6:00 A.M. which puts it at 6:25 A.M.
