Help with use of Chebyshev's inequality and sample size

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The discussion revolves around applying Chebyshev's inequality to determine the probability of a sample mean being within a certain range of a population mean. The user calculates the value of k, finding that k equals approximately 3.162 for a probability of 0.9. They express uncertainty about how to proceed after this calculation and seek clarification on the relationship between the sample mean, variance, and the population mean. The conversation emphasizes the need to define the probability to be calculated and how to express the average in terms of given variables. Ultimately, the user attempts to connect Chebyshev's inequality with their specific problem involving sample averages and variance.
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?
 
Question: A clock's minute hand has length 4 and its hour hand has length 3. What is the distance between the tips at the moment when it is increasing most rapidly?(Putnam Exam Question) Answer: Making assumption that both the hands moves at constant angular velocities, the answer is ## \sqrt{7} .## But don't you think this assumption is somewhat doubtful and wrong?

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