Version A. Problem 1. Let X be the continuous random variable defined by the following pdf: 1 x/2 when 0 x 2, f(x) = 0 otherwise.

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1 Math 224 Q Exam 3A Fall 217 Tues Dec 12 Version A Problem 1. Let X be the continuous random variable defined by the following pdf: { 1 x/2 when x 2, f(x) otherwise. (a) Compute the mean µ E[X]. E[X] x f(x) dx x(1 x/2) dx (x x 2 /2) dx (x 2 /2 x 3 /6) (b) Compute the variance σ 2 Var(X) E[X 2 ] E[X] 2. First we compute the second moment: E[X 2 ] x 2 f(x) dx 2 x 2 (1 x/2) dx (x 2 x 3 /2) dx (x 3 /3 x 4 /8) 2 4/2 8/6 2/3. 8/3 /8 2/3. Then we have: Var(X) E[X 2 ] E[X] 2 (2/3) (2/3) 2 2/9. (c) Draw the graph of f(x), labeled with the mean µ and standard deviation σ. From parts (a) and (b) we have µ 2/3.67 and σ 2/9.47. Here is the picture:

2 Problem 2. Let X 1, X 2,..., X be independent samples from an underlying distribution with mean µ 1 and standard deviation σ 8. Consider the sample mean: X X 1 + X X. (a) Compute the expected value of the sample mean: E[X]. Since expected value is linear we have E[X] E[X 1] + E[X 2 ] + + E[X ] (b) Compute the variance of the sample mean: Var(X). Since the observations X i are independent we have Var(X) Var(X 1) + Var(X 2 ) + + Var(X ) (c) Assuming that n is large enough, use the Central Limit Theorem to estimate the probability P (8 < X < 11). Assuming that X is approximately normal, we find that (X E[X])/ Var(X) (X 1)/2 is approximately standard normal. Hence ( P (8 < X < 11) P ( 2 < X 1 < 1) P 1 < X 1 ) <. 2 Φ(.) Φ( 1) Φ(.) [1 Φ(1)].691 (1.8413) 3.28%. [Remark: I treated the underlying distribution as continuous.] Problem 3. Suppose that Z is a standard normal random variable. Use the provided tables to solve the following problems. (a) Find α such that P (Z.) α and draw a picture to illustrate your answer.

3 (b) Find t such that P (Z t).2 and draw a picture to illustrate your answer. (c) Find c such that P ( c Z c).7 and draw a picture to illustrate your answer. Problem 4. Flip a fair coin 2 times and let X be the number of heads that you get. (a) Write a formula for the exact value of P (X 12). 12 ( ) 2 P (X 12) /2 2. k k [Remark: My laptop evaluates this to 86.84%.] (b) Since np and n(1 p) are both 1 we can assume that X is approximately normal. Use a continuity correction to approximate the probability from part (a) by the integral of some function: P (X 12) π e (x 1)2 /1 dx. [Remark: The lower limit gives the same answer to many decimal places.]

4 (c) Use the provided tables to compute the value of this integral. Note that X is binomial with n 2 and p 1/2, hence with µ np 1 and σ 2 np(1 p). Let X be a continuous random variable with the same mean and standard deviation so that (X 1)/ is standard normal. Then we have P (X 12) P ( X 12.) P ( X ) P Φ(1.12) 86.86%. ( X ) Problem. Suppose that the following five independent observations come from a normal distribution with mean µ and variance σ 2 : (a) Compute the sample mean X and sample standard deviation S. X , S 2 ( )2 + (1. 1.8) 2 + ( ) 2 + ( ) 2 + ( ) 2 4 S , (b) Suppose for some reason that you know the population standard deviation σ.. In this case, find an exact 9% confidence interval for the unknown µ. X ± z./2 σ 1.8 ± ±.438. n (c) Now suppose that the population standard deviation σ is unknown. In this case, find an exact 9% confidence interval for the unknown µ. X ± t./2 (n 1) S n 1.8 ± ±.686.

5 Version B Problem 1. Let X be the continuous random variable defined by the following pdf: { x/2 when x 2, f(x) otherwise. (a) Compute the mean µ E[X]. E[X] x f(x) dx x 3 /6 dx (x 2 /6) 2 x(x/2) dx 8/6 4/3. (b) Compute the variance σ 2 Var(X) E[X 2 ] E[X] 2. First we compute the second moment: E[X 2 ] Then we have: x 2 f(x) dx x 3 /2 dx (x 4 /8) 2 x 2 (x/2) dx /8 2. Var(X) E[X 2 ] E[X] 2 2 (4/3) 2 2/9. (c) Draw the graph of f(x), labeled with the mean µ and standard deviation σ. From parts (a) and (b) we have µ 4/ and σ 2/9.47. Here is the picture: Problem 2. Let X 1, X 2,..., X be independent samples from an underlying distribution with mean µ 12 and standard deviation σ 8. Consider the sample mean: X X 1 + X X.

6 (a) Compute the expected value of the sample mean: E[X]. Since expected value is linear we have E[X] E[X 1] + E[X 2 ] + + E[X ] (b) Compute the variance of the sample mean: Var(X). Since the observations X i are independent we have Var(X) Var(X 1) + Var(X 2 ) + + Var(X ) (c) Assuming that n is large enough, use the Central Limit Theorem to estimate the probability P (11 < X < 13). Assuming that X is approximately normal, we find that (X E[X])/ Var(X) (X 12)/2 is approximately standard normal. Hence ( P (11 < X < 13) P ( 1 < X 12 < 1) P. < X 12 ) <. 2 Φ(.) Φ(.) Φ(.) [1 Φ(.)].691 (1.691) 38.3%. [Remark: I treated the underlying distribution as continuous.] Problem 3. Suppose that Z is a standard normal random variable. Use the provided tables to solve the following problems. (a) Find α such that P (Z.) α and draw a picture to illustrate your answer. (b) Find t such that P (Z t).6 and draw a picture to illustrate your answer.

7 (c) Find c such that P ( c Z c).2 and draw a picture to illustrate your answer. Problem 4. Flip a fair coin 2 times and let X be the number of heads that you get. (a) Write a formula for the exact value of P (X 9). P (X 9) 2 k9 ( ) 2 /2 2. k [Remark: My laptop evaluates this to 74.83%.] (b) Since np and n(1 p) are both 1 we can assume that X is approximately normal. Use a continuity correction to approximate the probability from part (a) by the integral of some function: P (X 9) π e (x 1)2 /1 dx. [Remark: The upper limit gives the same answer to many decimal places.]

8 (c) Use the provided tables to compute the value of this integral. Note that X is binomial with n 2 and p 1/2, hence with µ np 1 and σ 2 np(1 p). Let X be a continuous random variable with the same mean and standard deviation so that (X 1)/ is standard normal. Then we have P (X 9) P ( X 8.) P ( X ) P 1 Φ(.67) Φ(.67) 74.86%. ( X ) 1.67 Problem. Suppose that the following five independent observations come from a normal distribution with mean µ and variance σ 2 : (a) Compute the sample mean X and sample standard deviation S. X , S 2 ( )2 + (1. 1.8) 2 + ( ) 2 + ( ) 2 + ( ) 2 4 S , (b) Suppose for some reason that you know the population standard deviation σ.. In this case, find an exact 9% confidence interval for the unknown µ. X ± z./2 σ 1.8 ± ±.438. n (c) Now suppose that the population standard deviation σ is unknown. In this case, find an exact 9% confidence interval for the unknown µ. X ± t./2 (n 1) S n 1.8 ± ±.34.

Math489/889 Stochastic Processes and Advanced Mathematical Finance Homework 5

Math489/889 Stochastic Processes and Advanced Mathematical Finance Homework 5 Steve Dunbar Due Fri, October 9, 7. Calculate the m.g.f. of the random variable with uniform distribution on [, ] and then