Question

a game developer tracks how many hours players practice each week and how far they typically advance in tournaments. the company creates a scatterplot showing each players average hours of weekly practice and their average tournament round reached over the course of a year. question 1 what does the line of best fit help the developer understand? which players had perfect attendance at practice how many tournaments each player entered during the year whether there is a general pattern between practice time and performance which players practiced the most each week question 2 two players both practiced 12 hours. one reached round 9, and the other reached round 6. what does this suggest about the data? the data is too scattered to make predictions practicing more always leads to round 9

Explanation:

• Question 1: A line of best fit in a scatterplot is used to show the general trend or pattern between two variables. In this case, the two variables are average hours of weekly practice and average tournament round reached. It doesn't show perfect attendance (which would be about presence, not a trend between two variables), the number of tournaments entered (not related to the two - variable relationship), or which players practiced the most (a single - variable maximum, not a relationship).
• Question 2: When two players have the same practice time (12 hours) but different tournament round outcomes (Round 9 and Round 6), it shows that there is variability in the data. This variability implies that the data is scattered. Just because two players have the same practice time doesn't mean the outcome is the same, so we can't make a definite prediction. Also, the statement “Practicing more always leads to Round 9” is false as we have a counter - example (the player who practiced 12 hours but only reached Round 6).

Answer:

• Question 1: Whether there is a general pattern between practice time and performance
• Question 2: The data is too scattered to make predictions