Question

In: Statistics and Probability

Expectation 1 Compute E(X) for the following random variable X : X=Number of tosses until getting...

Expectation 1

Compute E(X) for the following random variable X

: X=Number of tosses until getting 4 (including the last toss) by tossing a fair 10-sided die.

E(X)= ?

Expectation 2

Compute E(X) for the following random variable X : X=Number of tosses until all 10 numbers are seen (including the last toss) by tossing a fair 10-sided die.

To answer this, we will use induction and follow the steps below:

Let E(i) be the expected number of additional tosses until all 10 numbers are seen (including the last toss) given i distinct numbers have already been seen.

Find E(10) .

E(10)= ?

Write down a relation between E(i) and E(i+1) .

Answer by finding the function f(i) in the formula below. For i=0,1,…,9 : E(i)=E(i+1)+f(i)

where f(i)= ?

Finally, using the results above, find E[X] .

(Enter an answer accurate to at least 1 decimal place.)

E[X]=?

Solutions

Expert Solution

Expectation 1

The probability of getting 4 by tossing a fair 10-sided die = 1/10

X ~ Geom(p = 1/10)

E(X)= 1/p = 1/(1/10) = 10

Expectation 2

E(10) = Expected number of additional tosses until all 10 numbers are seen given 10 distinct numbers have already been seen

= 0

f(i) = E(i) - E(i+1) = Expected number of additional tosses until all 10 numbers are seen given i distinct numbers have already been seen - Expected number of additional tosses until all 10 numbers are seen given i+1 distinct numbers have already been seen

= Expected number of additional tosses to get the next (i+1)th distinct number given i distinct numbers have already been seen

When i distinct numbers have already been seen, remaining distinct numbers = 10 - i

Probability to find next distinct number = (10 - i) / 10

Let X be the number of trails to get the next (i+1)th distinct number. Then X ~ Geom(p = (10 - i) / 10)

E(X) = 1/p = 10/(10-i)

Thus,

f(i) = Expected number of additional tosses to get the next (i+1)th distinct number given i distinct numbers have already been seen = 10/(10-i)

E[X] = E[0] = E[1] + f(0)

= E[2] + f(1) + f(0)

= E[3] + f(2) + f(1) + f(0)

.....

= E[10] + f(9) + .... + f(1) + f(0)

= 0 + f(9) + f(8) + ... + f(1) + f(0)


Related Solutions

Expectation 1 Compute ?(?) for the following random variable ? : ?=Number of tosses until getting...
Expectation 1 Compute ?(?) for the following random variable ? : ?=Number of tosses until getting 4 (including the last toss) by tossing a fair 10-sided die.          ?(?)= ? ------------------------------------------------------------------------------------------- Expectation 2 Compute ?(?) for the following random variable ? : ?=Number of tosses until all 10 numbers are seen (including the last toss) by tossing a fair 10-sided die.          To answer this, we will use induction and follow the steps below: Let ?(?)...
Compute ?(?) for the following random variable ?: ?=Number of tosses until all 10 numbers are...
Compute ?(?) for the following random variable ?: ?=Number of tosses until all 10 numbers are seen (including the last toss) by tossing a fair 10-sided die. To answer this, we will use induction and follow the steps below: Let ?(?) be the expected number of additional tosses until all 10 numbers are seen (including the last toss) given ? distinct numbers have already been seen. 1. Find ?(10). ?(10)=? 2. Write down a relation between ?(?) and ?(?+1). Answer...
compute the sampling distribution for three tosses of an unfair coin. Assume the random variable x=1...
compute the sampling distribution for three tosses of an unfair coin. Assume the random variable x=1 if the coin is tails and x=0 if it is head. The probability of coin is head facing upward is 0.47. a) compute the mean and standard deviation of the population. b) what is the sampling distribution of the sample mean c) compute the mean and standard deviation of the sampling distribution d) as n gets large does the distribution of sample mean approach...
compute the sampling distribution for three tosses of an unfair coin. Assume the random variable x=1...
compute the sampling distribution for three tosses of an unfair coin. Assume the random variable x=1 if the coin is tails and x=0 if it is head. The probability of coin is head facing upward is 0.47. a) compute the mean and standard deviation of the population. b) what is the sampling distribution of the sample mean c) compute the mean and standard deviation of the sampling distribution d) as n gets large does the distribution of sample mean approach...
If x is a binomial random variable, compute P(x) for each of the following cases:
If x is a binomial random variable, compute P(x) for each of the following cases: (a) P(x≤5),n=7,p=0.3 P(x)= (b) P(x>6),n=9,p=0.2 P(x)= (c) P(x<6),n=8,p=0.1 P(x)= (d) P(x≥5),n=9,p=0.3   P(x)=  
If x is a binomial random variable, compute p(x) for each of the following cases: (a)...
If x is a binomial random variable, compute p(x) for each of the following cases: (a) n=3,x=2,p=0.9 p(x)= (b) n=6,x=5,p=0.5 p(x)= (c) n=3,x=3,p=0.2 p(x)= (d) n=3,x=0,p=0.7 p(x)=
If x is a binomial random variable, compute ?(?) for each of the following cases: (a)  ?(?≤1),?=3,?=0.4...
If x is a binomial random variable, compute ?(?) for each of the following cases: (a)  ?(?≤1),?=3,?=0.4 ?(?)= (b)  ?(?>1),?=4,?=0.2 ?(?)= (c)  ?(?<2),?=4,?=0.8 ?(?)= (d)  ?(?≥5),?=8,?=0.6 ?(?)=
1 point) If xx is a binomial random variable, compute P(x)P(x) for each of the following...
1 point) If xx is a binomial random variable, compute P(x)P(x) for each of the following cases: (a)  P(x≤1),n=5,p=0.3P(x≤1),n=5,p=0.3 P(x)=P(x)= (b)  P(x>3),n=4,p=0.1P(x>3),n=4,p=0.1 P(x)=P(x)= (c)  P(x<3),n=7,p=0.7P(x<3),n=7,p=0.7 P(x)=P(x)=
If x is a binomial random variable, compute P(x) for each of the following cases, rounded...
If x is a binomial random variable, compute P(x) for each of the following cases, rounded to two decimal places: c)  P(x<1),n=5,p=0.1 d)  P(x≥3),n=4,p=0.5
Given that xx is a random variable having a Poisson distribution, compute the following: (a)  P(x=1)P(x=1) when...
Given that xx is a random variable having a Poisson distribution, compute the following: (a)  P(x=1)P(x=1) when μ=4.5μ=4.5 P(x)=P(x)= (b)  P(x≤8)P(x≤8)when μ=0.5μ=0.5 P(x)=P(x)= (c)  P(x>7)P(x>7) when μ=4μ=4 P(x)=P(x)= (d)  P(x<1)P(x<1) when μ=1μ=1 P(x)=P(x)=
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT