Question 4 Consider flipping a (biased) coin for which the probability of head is p. The fraction of heads after n independent tosses is Xn. Law of large numbers imply that Xn → p as n → o. This does not mean that Xn will exactly equal to p, but rather the distribution of X is tightly concentrated around p for large n. (a) Suppose 0.1 0.95? Note: The purpose of this question is to enhance your understanding of Chebyshev's inequality. In practice, the bound provided by Chebyshev's inequality is usually very "loose", in the sense that n actually only needs to be much smaller for P(0.5 < Xn < 0.7) > 0.95 to hold. A tighter bound for the setting we considered can be obtained by Hoeffding's inequality (Example 6.15 in Wasserman) or Central Limit Theorem (we will study this in class). Note that the bounds from Chebyshev's inequality or Hoeffding's inequality are finite sample in that they hold for any finite n. On the contrary, the bound from Central Limit Theorem is asymptotic, in that it is a statement about n o, and it only holds approximately for finite n.
Question 4 Consider flipping a (biased) coin for which the probability of head is p. The fraction of heads after n independent tosses is Xn. Law of large numbers imply that Xn → p as n → o. This does not mean that Xn will exactly equal to p, but rather the distribution of X is tightly concentrated around p for large n. (a) Suppose 0.1 0.95? Note: The purpose of this question is to enhance your understanding of Chebyshev's inequality. In practice, the bound provided by Chebyshev's inequality is usually very "loose", in the sense that n actually only needs to be much smaller for P(0.5 < Xn < 0.7) > 0.95 to hold. A tighter bound for the setting we considered can be obtained by Hoeffding's inequality (Example 6.15 in Wasserman) or Central Limit Theorem (we will study this in class). Note that the bounds from Chebyshev's inequality or Hoeffding's inequality are finite sample in that they hold for any finite n. On the contrary, the bound from Central Limit Theorem is asymptotic, in that it is a statement about n o, and it only holds approximately for finite n.
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![Question 4
Consider flipping a (biased) coin for which the probability of head is p. The fraction of heads
after n independent tosses is Xn. Law of large numbers imply that Xn → p as n → o. This
does not mean that Xn will exactly equal to p, but rather the distribution of X is tightly
concentrated around p for large n.
(a)
Suppose 0.1 < p< 0.9. Use Chebyshev's inequality to obtain a lower bound
on
P(p- 0.1 < X, <p+0.1).
(b)
Suppose p 0.6. Using the above lower bound derived using Chebyshev's
inequality, how large should n be so that P(0.5 < Xn < 0.7) > 0.95?
Note: The purpose of this question is to enhance your understanding of Chebyshev's
inequality. In practice, the bound provided by Chebyshev's inequality is usually very "loose",
in the sense that n actually only needs to be much smaller for P(0.5 < Xn < 0.7) > 0.95 to
hold. A tighter bound for the setting we considered can be obtained by Hoeffding's inequality
(Example 6.15 in Wasserman) or Central Limit Theorem (we will study this in class). Note
that the bounds from Chebyshev's inequality or Hoeffding's inequality are finite sample in
that they hold for any finite n. On the contrary, the bound from Central Limit Theorem
is asymptotic, in that it is a statement about n o, and it only holds approximately for
finite n.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ff0d90012-896b-4f52-8f0b-e38034636c35%2F0d643d78-5e95-43bf-b717-aca10c6d3b92%2Ftem7hy4_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Question 4
Consider flipping a (biased) coin for which the probability of head is p. The fraction of heads
after n independent tosses is Xn. Law of large numbers imply that Xn → p as n → o. This
does not mean that Xn will exactly equal to p, but rather the distribution of X is tightly
concentrated around p for large n.
(a)
Suppose 0.1 < p< 0.9. Use Chebyshev's inequality to obtain a lower bound
on
P(p- 0.1 < X, <p+0.1).
(b)
Suppose p 0.6. Using the above lower bound derived using Chebyshev's
inequality, how large should n be so that P(0.5 < Xn < 0.7) > 0.95?
Note: The purpose of this question is to enhance your understanding of Chebyshev's
inequality. In practice, the bound provided by Chebyshev's inequality is usually very "loose",
in the sense that n actually only needs to be much smaller for P(0.5 < Xn < 0.7) > 0.95 to
hold. A tighter bound for the setting we considered can be obtained by Hoeffding's inequality
(Example 6.15 in Wasserman) or Central Limit Theorem (we will study this in class). Note
that the bounds from Chebyshev's inequality or Hoeffding's inequality are finite sample in
that they hold for any finite n. On the contrary, the bound from Central Limit Theorem
is asymptotic, in that it is a statement about n o, and it only holds approximately for
finite n.
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