Help with use of Chebyshev's inequality and sample size

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Homework Help Overview

The discussion revolves around the application of Chebyshev's inequality in the context of determining sample size and understanding probability distributions. Participants are exploring how to express probabilities related to sample means and variances.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Participants are attempting to apply Chebyshev's inequality to find probabilities related to sample means and variances. They are questioning how to express the average of measurements and the implications of the inequality in terms of standard deviations.

Discussion Status

Some participants have provided guidance on identifying the necessary probabilities and formulating the average of measurements. There is an ongoing exploration of how to express the event of the average being within a certain range of a target value, as well as discussions about the definitions of mean and variance in this context.

Contextual Notes

Participants note constraints such as the lack of specific values for variance and the need to express certain events probabilistically. There is also mention of a known variance for one of the variables, which is under discussion.

penguinnnnnx5
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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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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!
 
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?##
 
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?
 

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