a) here std deviation =(16)1/2 =4
| for normal distribution z score =(X-μ)/σ | |
| here mean= μ= | 5 |
| std deviation =σ= | 4.0000 |
hence P(|X-5|<6) =P(-6<X-5<6) =P(-1<X<11) =P(-1.5<Z<1.5)=
| 0.9332-0.0668= | 0.8664 |
b)
from Chebyshev's: P(|X-5|<6) =P(-6<X-5<6) =P(-1<X<11) =P(-1.5<Z<1.5) =(1-1/1.52) =1-0.4444 =0.5556
Exercise 2 Consider a random variable X with E]5 and VarX 16 (a) Calculate P(lz-5 <...
Let X be a positive random variable with E(X) = 2 and VarX= 20: (a) Use Markov’s inequality to obtain an upper bound onP(X≥25). (b) Use Chebyshev’s inequality to obtain an upper bound onP(X≥25).
6. If E(x) 16 and E(X?) -292, use Chebyshev's inequality to determine a) A lower bound for P(8< X < 24). (b) An upper bound for P(X 162 18)
Apply Chebyshevs Inequality to lower bound P(O< X < 4) when E(X) 2 and E(X2)-5
5. Let X > 0 be a random variable with EX = 10 and EX2 = 140. a. Find an upper bound on P(X > 14) involving EX using Markov's inequality. b. Modify the proof of Markov's inequality to find an upper bound on P(X > 14) in- volving EX? c. Compare the results in (a) and (b) above to what you find from Chebyshev's inequality.
2 5. Suppose X is a discrete random variable that has a geometric distribution with p= a. Compute P(X > 6). [5] b. Use Markov's Inequality to estimate P(X > 6). [5] c. Use Chebyshev's Inequality to estimate P(X > 6). [5]
2. Suppose that is an exponential random variable with pdf f(y)= e), y>0. a. Use Chebyshev's Inequality to get an upper bound for the probability that takes on a value more than two standard deviations away from the mean. b. Use the given pdf to compute the exact probability that takes on a value more than two standard deviations away from the mean.
5. A random variable X follows a binomial distribution with n 35 and p-4. Use the normal approximation to the binomial distribution to find P(X < 16)
2 of 3 01- 5. Suppose X is a discrete random variable that has a geometric distribution with p= a. Compute P(X > 6). [5] b. Use Markov's Inequality to estimate P(X > 6). [5] c. Use Chebyshev's Inequality to estimate P(X > 6). (5)
5) Let X be a random variable with mean E(X) = μ < oo and variance Var(X) = σ2メ0. For any c> 0, This is a famous result known as Chebyshev's inequality. Suppose that Y,%, x, ar: i.id, iandool wousblsxs writia expliiniacy" iacai 's(%) fh o() airl íinic vaikuitx: Var(X) = σ2メ0. With Υ = n Ση1 Y. show that for any c > 0 Tsisis the celebraed Weak Law of Large Numben