Question: Complete the PDF and answer the questions. x P(x) 0 0.3 1 0.2 2 3 0.4 a. Find the probability that x= 2. b. Find the expected value. Probability density function (PDF): The chances that a variable will take a value is known as a probability distribution function (PDF). • It is denoted by p(x) • The sum of entire probabilities for all the potential values is always equal to 1. • The values are in the middle of the range 0 and 1.

Question: Complete the PDF and answer the questions. x P(x) 0 0.3 1 0.2 2 3 0.4 a. Find the probability that x= 2. b. Find the expected value. Probability density function (PDF): The chances that a variable will take a value is known as a probability distribution function (PDF). • It is denoted by p(x) • The sum of entire probabilities for all the potential values is always equal to 1. • The values are in the middle of the range 0 and 1.

Essentials of Business Analytics (MindTap Course List)

2nd Edition

ISBN:9781305627734

Author:Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson

Publisher:Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson

Chapter5: Probability: An Introduction To Modeling Uncertainty

Section: Chapter Questions

Problem 22P: Consider a Poisson distribution with . Write the appropriate Poisson probability mass...

See similar textbooks

Similar questions

Consider a Poisson distribution with . Write the appropriate Poisson probability mass function. Compute f(2). Compute f(1). Compute .
An exponential probability distribution _____. a. must be normally distributed b. is a discrete distribution c. is a continuous distribution d. can be either continuous or discrete
The following table provides a probability distribution for the random variable y. a. Compute E(y). b. Compute Var(y) and .
A random variable that can assume only a finite number of values is referred to as a(n) _____. a. infinite sequence b. finite sequence c. discrete probability function d. discrete random variable
Let X be a random variable such that E[X] = μ, V[X] =σ², max[X] = My min(X) = m with 0 Not egual to m < M and consider its normalization: E[Y] =μ M-m Determine whether the following statements are true or false. Perform the corresponding calculations. E[Y] μ V[Y] = M-m 2 σ M-m
Consider two random variables X and Y. The covariance between these two variables is 1.00. We can conclude that there is a perfect positive linear relationship between the two variables. Is this statement true or false? If your answer is false, give reasons.
Consider the formula: A = P(1 + r)t. What can we say about the variable A? it is a future value it is a present value
How do you calculate the value at a node in a binomial tree? A. As a present value of the two possible values from the next period В. Average of the present value of the possible values from the next period OC. Sum of the present values of the two possible values from the next period D. Average of the future values of the two possible values from the next period
The random variable x is known to be uniformly distributed between 10 and 20. a. Show the graph of the probability density function. b. Compute P(x15). c. Compute P(12x18) d. Compute E(x) e. Compute Var(x)
Given the probability density function f(x)=13f(x)=13 over the interval [1,4][1,4], find the standard deviation.Standard Deviation:
H1.
Explain arithmetic mean returns?