Help with use of Chebyshev's inequality and sample size

In summary: This would make c the mean, yes? Because it is the expected value/ the value you expect to be correct. Would that make ##U_n## the variance then? But if so, how will we find the variance if ##U_n## is not given? We only know that ##Var(U_n) = 3##
  • #1
penguinnnnnx5
36
0

Homework Statement



4307a6c25e65fc6dc596f937b6482d60.png


Homework Equations



P (|Y - μ| < kσ) ≥ 1 - Var(Y)/(k2σ2) = 1 - 1/k2

??

The Attempt at a Solution



using the equation above

1 - 1/k2 = .9

.1 = 1/k2

k2 = 10

k = √10 = 3.162

k = number of standard deviations. After this I don't know where to go.

I don't have a solid understanding of Chebyshev's inequality either.
 
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  • #2
penguinnnnnx5 said:

Homework Statement



4307a6c25e65fc6dc596f937b6482d60.png


Homework Equations



P (|Y - μ| < kσ) ≥ 1 - Var(Y)/(k2σ2) = 1 - 1/k2

??

The Attempt at a Solution



using the equation above

1 - 1/k2 = .9

.1 = 1/k2

k2 = 10

k = √10 = 3.162

k = number of standard deviations. After this I don't know where to go.

I don't have a solid understanding of Chebyshev's inequality either.

First figure out what probability you need to find, then worry about how to find it using Chebyshev or some other method. So, if your measurements are ##M_1, M_2, \ldots, M_n##, what is their average, ##\bar{M}##? In terms of ##c## and ##U_1, U_2, \ldots, U_n##, what would be your formula for ##\bar{M}##? What would be the mean and variance of ##\bar{M}## (expressed in terms of ##c, \:n## and other given quantities)? Now how would you express the event "the average is within half a degree of ##c##"? At this point you are ready to apply some probability!
 
  • #3
Ray Vickson said:
First figure out what probability you need to find, then worry about how to find it using Chebyshev or some other method. So, if your measurements are ##M_1, M_2, \ldots, M_n##, what is their average, ##\bar{M}##? In terms of ##c## and ##U_1, U_2, \ldots, U_n##, what would be your formula for ##\bar{M}##? What would be the mean and variance of ##\bar{M}## (expressed in terms of ##c, \:n## and other given quantities)? Now how would you express the event "the average is within half a degree of ##c##"? At this point you are ready to apply some probability!
I would say that the average ##\bar{M}## ##=## (##n c## + ##U_1, U_2, \ldots, U_n##) / ##n## since you are finding ##c## ##n## times and adding that to all the ##U_i## from each sample. Then to average it, you'd need to divide it by ##n## of course.

If it is half a degree of ##c##, would that mean that ##|\bar{M}## - ##c| >= .5?##
 
  • #4
penguinnnnnx5 said:
If it is half a degree of ##c##, would that mean that ##|\bar{M}## - ##c| >= .5?##

This would make c the mean, yes? Because it is the expected value/ the value you expect to be correct. Would that make ##U_n## the variance then? But if so, how will we find the variance if ##U_n## is not given? We only know that ##Var(U_n) = 3##

But given what I know now, would this mean then that ##P (|\bar{M} - c| >= .5) = 1 -P (|\bar{M} - c| < .5) = 1 - σ^2 / nε^2## where ε = .5?
 

1. What is Chebyshev's inequality and how is it used in statistics?

Chebyshev's inequality is a mathematical theorem that provides a way to estimate the proportion of a population that falls within a certain number of standard deviations from the mean. In statistics, it is used to determine the minimum sample size needed to make a reliable estimate of the population mean or variance.

2. How do you calculate the sample size using Chebyshev's inequality?

To calculate the sample size using Chebyshev's inequality, you will need to know the desired confidence level, the variability of the population, and the margin of error. The formula for sample size is n ≥ (zα/2 * σ / ε)^2, where zα/2 is the critical value for the desired confidence level, σ is the standard deviation of the population, and ε is the margin of error.

3. Can Chebyshev's inequality be used for any type of data?

Yes, Chebyshev's inequality can be used for any type of data as long as the population's standard deviation is known or can be estimated. It is commonly used for continuous data, but it can also be applied to discrete data.

4. What is the advantage of using Chebyshev's inequality for sample size determination?

The advantage of using Chebyshev's inequality for sample size determination is that it provides a conservative estimate of the sample size needed. This means that the calculated sample size will be large enough to ensure a certain level of confidence, even if the population's distribution is unknown or highly skewed.

5. Are there any limitations to using Chebyshev's inequality for sample size determination?

One limitation of using Chebyshev's inequality for sample size determination is that it assumes the population's standard deviation is known or can be estimated. In cases where the standard deviation is not available, other methods such as the central limit theorem or the sample size formula for a normal distribution may be more appropriate.

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