Probability and Statistics Final Exam, Exams of Probability and Statistics

A final exam for a statistics course, covering topics such as probability density functions, cumulative distribution functions, variance, multiple choice tests, independent variables, poisson processes, normal variables, and beta distributions.

Typology: Exams

2012/2013

Uploaded on 02/20/2013

rangith
rangith 🇮🇳

4.7

(7)

46 documents

1 / 3

Toggle sidebar

This page cannot be seen from the preview

Don't miss anything!

bg1
Statistics 134 Fall 2005 Final Exam
Professor James Pitman
1. A random variable Xwith values between 1 and 1 has probability density
function f(x) = cx2for xin that range, for some constant c.
(a) Find cas a decimal.
(b) Give a formula for the cumulative distribution function of X.
(c) Find Var(X) as a decimal.
(d) Let Y=X2. Find the probability density function of Y.
2. A multiple choice test has 4 possible answers for each question, exactly
one of which is right. The test has 20 questions. A student knows the
correct answer to 14 questions and guesses at random for the other 6. Let
Xbe the number of questions the student gets right.
(a) Describe the distribution of Xby a formula.
(b) Give a numerical expression for P(X19).
(c) Evaluate E(X) as a decimal.
(d) Evaluate Var(X) as a decimal.
3. Suppose Xand Yare independent variables, such that Xhas uniform
distribution on [0,3], and Yhas exponential distribution with rate λ= 1.
(a) Find P(X < Y ).
(b) Find the probability density function for Z:= min(X, Y ), the mini-
mum of Xand Y.
4. Dan and Stan each roll a six-sided die. If they roll different numbers, the
one who rolled the higher number wins the difference between the numbers
from the other, in dollars. If they roll the same number, they roll again
until they get different numbers. As soon as they exchange money , that
completes one game. They play 100 games. Let Ribe the number of times
they both have to roll during the i-th game, and let Xibe the amount
that Dan wins in the i-th game. Note that Ri1 and that Xican be
negative, but not 0.
(a) Describe the distribution of R1.
Transcribed by Chris Pennello on December 21, 2006
1
pf3

Partial preview of the text

Download Probability and Statistics Final Exam and more Exams Probability and Statistics in PDF only on Docsity!

Statistics 134 Fall 2005 Final Exam

Professor James Pitman∗

  1. A random variable X with values between −1 and 1 has probability density function f (x) = cx^2 for x in that range, for some constant c.

(a) Find c as a decimal. (b) Give a formula for the cumulative distribution function of X. (c) Find Var(X) as a decimal. (d) Let Y = X^2. Find the probability density function of Y.

  1. A multiple choice test has 4 possible answers for each question, exactly one of which is right. The test has 20 questions. A student knows the correct answer to 14 questions and guesses at random for the other 6. Let X be the number of questions the student gets right.

(a) Describe the distribution of X by a formula. (b) Give a numerical expression for P (X ≥ 19). (c) Evaluate E(X) as a decimal. (d) Evaluate Var(X) as a decimal.

  1. Suppose X and Y are independent variables, such that X has uniform distribution on [0, 3], and Y has exponential distribution with rate λ = 1.

(a) Find P (X < Y ). (b) Find the probability density function for Z := min(X, Y ), the mini- mum of X and Y.

  1. Dan and Stan each roll a six-sided die. If they roll different numbers, the one who rolled the higher number wins the difference between the numbers from the other, in dollars. If they roll the same number, they roll again until they get different numbers. As soon as they exchange money , that completes one game. They play 100 games. Let Ri be the number of times they both have to roll during the i-th game, and let Xi be the amount that Dan wins in the i-th game. Note that Ri ≥ 1 and that Xi can be negative, but not 0.

(a) Describe the distribution of R 1.

∗Transcribed by Chris Pennello on December 21, 2006

(b) Describe the distribution of X 1. (c) Find P (R 1 + R 2 + R 3 + R 4 = 7). (d) Find Var

100 i=1 Xi

(e) Find the approximate value of P

100 i=1 Xi^ ≤^3

  1. The joint density of X and Y is f (x, y) = (^4) xy for 0 < y < x < 1, and 0 otherwise. Find the following.

(a) E(XY ). (b) The marginal density of X. (c) E(Y |X = x) for 0 < x < 1.

  1. Each vehivle arriving at a toll both is either a car or a truck. Cars arrive as a Poisson process with rate λ = 3 per minute. Independently of the cars, trucks arrive as a Poisson process with rate λ = 1 per minute.

(a) What is the probability that exactly 10 vehicles arrive in a two minute interval? (b) Consider the first vehicle to arrive after time t = 0. What is the probability that this vehicle arrives after time t = 1 and is a truck? (c) Give a formula for the probability density of the length of time be- tween arrivals of the 5th and 7th cars after some fixed time. (d) Sketch a graph of the density found in c), with a properly labeled horizontal axis.

  1. Let X and Y be independent normal variables, with E(X) = 0, SD(X) = 3, E(Y ) = 0, SD(Y ) = 4. Let S = X + Y and D = X − Y.

(a) Find P (S < 1). (b) Find the covariance between S and D. (c) Find E(S|D). (d) Find Var(S|D). (e) What is the conditional distribution of S given D = 1?

  1. A non-negative random variable X has mean 100 and variance 100.

(a) Give an explicit example of a distribution of X consistent with these properties. (b) What does Markov’s inequaltiy say about P (X ≥ 400)? (c) What does Chebychev’s inequality say about P (X ≥ 400)? (d) Let Sn be the sum of n independent variables, each with the same distribution as X. Find a sequence xn so that P (Sn/n > 100 + xn) converges to 1/4 as n → ∞.