Justin and Brian were investigating the relationship between the number of games a baseball team won in a season and the number of hours the team practiced each week during the season. They each drew a line of best fit in the scatterplot.
 
	

Who drew the line of best fit correctly to make predictions? Based on the scatterplot, what is the best prediction of the number of games a baseball team won if the team practiced 3 hours each week during the season?
A.Justin; 14 games
B.Brian; 12 games
C.Justin; 15 games
D.Brian; 13 games


To determine who drew the correct line of best fit and make the best prediction, we would need to:

1. Analyze the scatterplot to see which line better fits the data points.
2. Identify how many games a team would win if they practiced for 3 hours a week based on the correct line of best fit.

Since I cannot directly see the scatterplot, I'll explain how you can evaluate this:

- **Line of best fit**: This line shows the overall trend between two variables—in this case, the number of hours practiced and the number of games won.
- **Correct line**: The correct line minimizes the distance between the line and all the points on the scatterplot, representing the relationship between the variables as accurately as possible.

The answer options give possible predictions for a team practicing 3 hours a week:
- If Justin's line of best fit correctly models the relationship, the team would win either 14 games (A) or 15 games (C).
- If Brian's line of best fit is correct, the team would win either 12 games (B) or 13 games (D).

To determine the correct answer, you should:
1. Look at the scatterplot to see whose line (Justin's or Brian's) better fits the overall trend of the data.
2. Check the point where the line crosses 3 hours of practice on the x-axis and note the corresponding number of games on the y-axis.

### Do you have access to the scatterplot? If so, you can describe it or upload it, and I can provide further guidance.

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Here are some related questions to deepen your understanding:

1. How do you calculate the line of best fit from a set of data points?
2. What is the equation of a line of best fit, and how can it be used for predictions?
3. How does correlation help in understanding the relationship between two variables like practice hours and game wins?
4. How can you use residuals to check if a line of best fit is accurate?
5. How can the slope of a line of best fit be interpreted in real-world terms, like in this scenario?

**Tip**: When analyzing scatterplots, always look for trends like positive or negative correlations to guide you in drawing a line of best fit!

Justin and Brian were investigating the relationship between the number of games a baseball team won in a season and the number of hours the team practiced each week during the season. They each drew a line of best fit in the scatterplot. Who drew the line of best fit correctly to make predictions? Based on the scatterplot, what is the best prediction of the number of games a baseball team won if the team practiced 3 hours each week during the season?
A.Justin; 14 games
B.Brian; 12 games
C.Justin; 15 games
D.Brian; 13 games

This problem investigates the relationship between baseball team wins and practice hours using scatterplots and the line of best fit. Learn how to determine the best prediction for games won based on practice hours. Includes key concepts like the slope of a line and correlation, suitable for students in Grades 9-11.

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