Without seeing the box plot, it's difficult to provide an exact answer. However, I can explain how to interpret a box plot and estimate the percentage of players who weigh less than or equal to 200 pounds.
In a box plot, the box represents the middle 50% of the data, with the median (50th percentile) indicated by a line inside the box. The whiskers extend from the box to the minimum and maximum values within 1.5 times the interquartile range (IQR) of the box. Any data points beyond the whiskers are considered outliers.
Assuming the weight data is roughly normally distributed and there are no extreme outliers, we can estimate the percentage of players who weigh less than or equal to 200 pounds by finding the z-score corresponding to 200 pounds and using a standard normal distribution table to find the percentage of data below that z-score. The z-score can be calculated using the formula:
z = (x - μ) / σ
where x is the weight (200 pounds), μ is the mean weight, and σ is the standard deviation of the weight data.
If we assume that the mean weight is around 180-200 pounds and the standard deviation is around 20-30 pounds, then a weight of 200 pounds is around 1 to 2 standard deviations above the mean. Looking at a standard normal distribution table, we can estimate that roughly 84-98% of the data falls below a z-score of 1-2.
Therefore, approximately 84-98% of the football players weigh less than or equal to 200 pounds.