A. 25% of the monthly returns are below or equal to the first quartile. 50% of the monthly returns are below or equal to the second quartile. 75% of the monthly returns are below or equal to the third quartile.
Answer:
- As explained below, with the individual’s score in the 0.03125 fraction of top candidates, they can anticipate securing a position.
Explanation:
Utilizing Chebyshev’s Theorem is key.
This theorem is valid for any dataset, irrespective of its shape.
Chebyshev's Theorem states that at least 1−1/k² of the data falls within k standard deviations from the mean.
For this data set, the specifics are:
- mean: 60
- standard deviation: 6
- score: 84
The number of standard deviations that 84 is from the mean can be calculated as:
- k = (score - mean) / standard deviation
- k = (84 - 60) / 6 = 24 / 6 = 4
Hence, the individual’s score is 4 standard deviations above the mean.
How significant is this?
According to Chebyshev’s Theorem, at least 1−1/k² of the data is within k standard deviations from the mean. Setting k = 4 gives us:
- 1 - 1/4² = 1 - 1/16 = 0.9375
- This implies that half of 1 - 0.9375 exceed k = 4: 0.03125
- Consequently, 1 - 0.03125 is below k = 4: 0.96875
With 70 job openings and 1,000 applicants, the ratio is 70/1,000 = 0.07, indicating the company seeks the top 0.07 of applicants.
Given the individual scores in the top 0.03125 of applicants, they can expect to obtain a job.
