Chebyshevs theorem : find k so that at most 10%

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In summary: The value of k represents the cut-off point at which at most 10% of the scores are above it. This means that 90% of the scores are below k, and 10% are above it. In other words, k represents the boundary between the top 10% and the remaining 90% of the scores.
  • #1
idioteque
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the test scores for a large statistics class have an unknown distribution with a mean of 70 and a standard deviation of 10

find k so that at most 10% of the scores are more than k standard deviations above the mean.

I'm a bit confused by the question it self.
does the question means :
1-1/k^2 = 0.1
k = 1.05

or

1-1/k^2 = 0.2
k = 1.12

or

1-1/k^2 = 0.8
k = square root 5 = 2.23

pls help, thanks in advance.
 
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to avoid being flamed for homework type of question, I added my own opinion towards this question. your help is very much appreciated.
 
  • #3
idioteque said:
to avoid being flamed for homework type of question, I added my own opinion towards this question. your help is very much appreciated.

I misread your question. The minimal value for k given "at most" 10% k SDs above the mean would be [tex]1-1/k^2=0.8[/tex] so [tex]k=2.236[/tex]. This assumes your distribution is perfectly symmetrical.
 
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  • #4
'at most' 10%.

why take the other remaining 80%?
it asked for 'at most' 10%.
doesn't this mean anything not greater than 10%?
if it asked 'at least' 10% then anything greater than 10%
 
  • #5
idioteque said:
'at most' 10%.

why take the other remaining 80%?m
it asked for 'at most' 10%.
doesn't this mean anything not greater than 10%?
if it asked 'at least' 10% then anything greater than 10%

80% of the test scores are within 2.236 SD of the mean. 10% are at least 2.236 SD above the mean, 10% at least 2.236 SD below the mean. This is the minimal value of k. If you want to be sure that at most 10% are more than k SD above the mean, take any arbitrary value of k greater than 2.236. That's the way I read it anyway.
 
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  • #6
ok thank you. I got it.
the keyword is above the mean.
the value of k before the last 10% of each side(20%). so 80% is in between the 20%
is how I interpreted it as it is.
correct?
 
  • #7
idioteque said:
s how I interpreted it as it is.
correct?

Correct.
 

FAQ: Chebyshevs theorem : find k so that at most 10%

What is Chebyshev's theorem?

Chebyshev's theorem, also known as the Chebyshev's inequality, is a statistical theorem that provides a bound on the proportion of values that lie within a certain number of standard deviations from the mean in any distribution. It states that at least 1 - (1/k^2) of the values in a distribution lie within k standard deviations from the mean, where k is any positive number.

How is Chebyshev's theorem applied?

Chebyshev's theorem is commonly used to determine the minimum proportion of data values within a certain range from the mean. This allows for the estimation of the spread of data in a distribution, even if the exact shape of the distribution is unknown.

What is the significance of finding k in Chebyshev's theorem?

Finding k in Chebyshev's theorem allows us to determine the minimum proportion of values that fall within a certain range from the mean. This is useful for understanding the spread of data in a distribution and making statistical inferences about the data.

How do you find k in Chebyshev's theorem to ensure that at most 10% of values fall outside of a certain range from the mean?

To find k for a given distribution, we can use the formula k = 1/√p, where p is the desired proportion of values that fall within a certain range from the mean. In this case, since we want at most 10% of values to fall outside of the range, p = 0.1. Therefore, k = 1/√0.1 = 3.1623. This means that at least 90% of the values in the distribution will fall within 3.1623 standard deviations from the mean.

Can Chebyshev's theorem be applied to any distribution?

Yes, Chebyshev's theorem can be applied to any distribution, regardless of its shape or characteristics. However, it is most useful for distributions that are unimodal (have a single peak) and are not too heavily skewed. It is also important to note that Chebyshev's theorem gives a conservative estimate and the actual proportion of values within a certain range may be higher than the bound provided by the theorem.

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