MA 2631
Probability Theory
Assignment 7
due on Friday, September 24
based on Lectures of Chapter 4.1–4.3
1. (continuing problem 2, Assignment 6): Consider the random variable X with the
probability mass distribution
P[X = 1] = 0.3, P[X = 4] = 0.25, P[X = 7] = 0.4, P[X = 10] = 0.05.
Calculate the variance of X and Y with Y = 3X + 2.
2. Suppose we pick a month at random from a non leap-year calendar and let X be the
number of days in that month. Find the mean and the variance of X.
3. Let Y be a binomial distributed random variable with n trials of success probability p.
Show that
Var[Y ] = np(1 − p).
4. Let Z be a geometric distributed random variable with success probability p. Calculate
Var[Z].
5. Assume that X is a random variable taking values on the non-negative integers that
satisfies
P[X ≥ n + i| X ≥ n] = P[X ≥ i].
Show that X is a geometric distributed random variable.
2
6. The number of errors on a book page follow a Poisson distribution. It has been
determined that on 10% of the pages there is at least one error.
a) Determine the parameter of the Poisson distribution.
b) What is the expected number of errors on a page?
8 points per problems
Standard Carlton and Devore, Section 2.3: 32, 39 44; Section 2.4: 50, 54, 59, 61, 66, Section 2.5:
76, 81; Section 2.6: 98, 99, 102
Extra Random Walk: Consider the whole numbers and start at 0. You consecutively flip a
coin, and if it shows ’heads’ you move one to the right (+1) while for ’tails’ you move
one to the left (−1). This models a random movement, e.g., approximately the behavior
of a (very) drunk person. Denote by Xn the random variable that marks your position
after n steps.
• What are expectation and variance of Xn? What happens if you consider for them
the limit n → ∞?
• After k steps, you are at some point x. How likely is it that you will return to the
starting point zero before time n? How does this probability behave for n → ∞?
