The number of wins for a high school football team is measured for the season. When the team plays at home, it is generally believed that they will win. Comparing the location of the game and the number of wins, a correlation coefficient of 0.891 is calculated. What would this imply about the football team winning at home? The scatter plot would closely resemble a straight line with a positive slope. The data has a strong, positive correlation, but causation cannot be determined.
The scatter plot would closely resemble a straight line with a positive slope. The data has a strong, positive correlation, and a causal relationship exists between the team playing at home and winning.
The scatter plot would not be represented by a line of best fit with a positive slope. There is a weak correlation between the football team playing at home and winning.
There is no causation and almost no correlation between the football team playing at home and winning.

The scatter plot would closely resemble a straight line with a positive slope. The data has a strong, positive correlation, but causation cannot be determined.

Step-by-step explanation:

As we know that the correlation coefficient(r) tells about the direction and the strength of the correlation but nothing about the causation.

So, if we have only correlation coefficient(r) , then causation cannot be determined.

Given , Comparing the location of the game and the number of wins, a correlation coefficient of 0.891 is calculated.

Since , correlation coefficient is positive , it means it shows a positive coreelation.

Also, 0.891 lies between 0.7 and 1 , it means it indicates a strong correlation between the location of the game and the number of wins.

Hence, the correct summarized statement about the football team winning at home would be :

"The scatter plot would closely resemble a straight line with a positive slope. The data has a strong, positive correlation, but causation cannot be determined."

The scatter plot would closely resemble a straight line with a positive slope. The data has a strong, positive correlation, but causation cannot be determined.

The answer is A

Step-by-step explanation:

incomplete question. how do you expect anyone to you when you do not post the question in its entirety?

step-by-step explanation:

12: 00 p.m. to 7: 00 p.m. is a total of 7 hours.

4: 00 p.m. to 7: 00 p.m. is a total of 3 hours.

this means that from 4: 00 p.m to 7: 00 p.m. this represents 3/7 of what was lost from 12: 00 p.m. to 7: 00 p.m.

after knowing this, after doing 11,600 - 6,800 = 4,800

4,800 represents 3/7 of what was lost for 7 hours.

we then divide 4,800 by 3 which gets 1,600.

because we know that 1,600 represents 1/7 of how much water was lost, we just have to multiply by 7, which gets 11,200.   after adding 11,200 to how much water was left at 7: 00 p.m. which was 6,800. 11,200 + 6,800 = 18,000

this means that the pool originally had 18,000 gallons in the pool.

you can also check your work by doing 18,000 - 4(1600), which becomes           1800 - 6400 which gets you 11,600.