Respuesta :
We can estimate the expected value of the various percentiles assuming the normal distribution.
The median, 50th percentile, is the mean, 0.785 mm.
Typically we remember ±1 standard deviation is 68% of the probability, so one standard deviation below the mean is 16th percentile (50-68/2) and one standard deviation above the mean is 84th percentile.
The quartiles, 25th and 75th percentile, are about 2/3 of a standard deviation away from the mean. In other words ±.67 standard deviations is 50% of the probability. It's not a commonly remembered number like 68-95-99.7, but perhaps it should be.
The 25th percentile is 0.785 - (.67)(.07) = 0.7381 mm.
The 75th percentile is 0.785 + (.67)(.07) = 0.8319 mm.
The median, 50th percentile, is the mean, 0.785 mm.
Typically we remember ±1 standard deviation is 68% of the probability, so one standard deviation below the mean is 16th percentile (50-68/2) and one standard deviation above the mean is 84th percentile.
The quartiles, 25th and 75th percentile, are about 2/3 of a standard deviation away from the mean. In other words ±.67 standard deviations is 50% of the probability. It's not a commonly remembered number like 68-95-99.7, but perhaps it should be.
The 25th percentile is 0.785 - (.67)(.07) = 0.7381 mm.
The 75th percentile is 0.785 + (.67)(.07) = 0.8319 mm.
In this exercise we have to use the knowledge of distribution to calculate the value of the first and third quartiles, in this way we can say that:
[tex]25^{th}= 0.7381 mm\\75^{th}= 0.8319 mm[/tex]
We can estimate the expected value of the various percentiles assuming the normal distribution, so we have to make the conversion as:
[tex]50th =0.785 mm[/tex]
The quartiles, 25th and 75th percentile, happen about 2/3 of a predictable difference external the mean. In other words ±.67 standard departure exist 50% of the likelihood of something happening:
[tex]25th =0.785 - (.67)(.07) \\75th = 0.785 + (.67)(.07) = 0.8319 mm[/tex]
See more about distrribuition at brainly.com/question/1620226