Exploring Mean Absolute Deviation

The video tutorial explains how to use mean absolute deviation (MAD) to determine the most valuable player (MVP) on a high school basketball team. Both players, Michael and Austin, scored the same total points over five games. The tutorial demonstrates calculating the mean and MAD for each player. Michael's MAD is lower, indicating more consistent performance, while Austin's scores vary widely. The video concludes that Michael should be the MVP due to his consistent scoring, highlighting the importance of understanding measures of variability like MAD in decision-making.

What was the total score of both Michael and Austin from the last five games?

• 60 points each

• 80 points each

• 50 points each

• 70 points each

What statistical method is used to determine the MVP in the scenario?

• Range

• Mean absolute deviation

• Standard deviation

• Variance

How is the mean calculated for a player's scores?

• Product of scores and number of games

• Sum of scores plus number of games

• Sum of scores minus number of games

• Sum of scores divided by number of games

What was Michael's mean score per game?

• 15 points

• 14 points

• 10 points

• 12 points

What does a lower mean absolute deviation indicate about a player's performance?

• Higher scoring

• More consistency

• Less consistency

• Lower scoring

What was Austin's mean absolute deviation?

• 10

• 2

• 5

• 8

Which player showed more consistency in their scoring?

• Data is insufficient

• Both were equally consistent

• Austin

• Michael

What is the mean score of Austin per game based on the data provided?

• 15 points

• 8 points

• 12 points

• 10 points

Why is mean absolute deviation important in sports analytics?

• It predicts future scores

• It shows scoring trends

• It calculates total scores

• It measures consistency

How can mean absolute deviation help a coach?

• In player selection

• All of the above

• In setting game strategies

• In predicting wins