Finding the P(2x1>x2) for a bivariate normal distribution

In summary, the homework statement is that given a bivariate normal distribution with E(x1)=4 and E(x2)=6 and Var(X)= [3 2.5], find P(2*x1>x2). The Attempt at a Solution suggests using the cdf given by: f(x1,x2)=1/(2*pi*var(x1)*var(x2)*sqrt(1-rho^2)) * e^(-0.5*(z/(2*1-rho^2))) to approximate the cdf of the bivariate normal distribution. Taking the integral from (-infinity to 2*x) dy and then
  • #1
ahuds001
3
0

Homework Statement



Given a bivariate normal distribution with E(x1)=4 and E(x2) = 6 and Var(X) = [3 2.5]
[2.5 7]
Find P(2*x1>x2)

Homework Equations



The cdf of this bivariate normal distribution is given by:

f(x1,x2)=1/(2*pi*var(x1)*var(x2)*sqrt(1-rho^2)) * e^(-0.5*(z/(2*1-rho^2)))

where var(x1) = 3, var(x2) = 7, E(x1)=4 and E(x2) = 6, and rho = 2.5/(sqrt(3)*sqrt(7))

and z = ((x1-E(x1))^2)/var(x1) + ((x2-E(x2))^2)/var(x2) - (2*rho*(x1-E(x1))*(x2-E(x2)))/sqrt(var(x1)*var(x2))

The Attempt at a Solution



The approximate cdf of this bivariate normal distribution is given (in terms of x and y) by:

.041440417*%e^(-0.71186*((((x-4)^2)/3)+(((y-6)^2)/7)-(1.09109*(x-4)*(y-6))/4.582576))

Taking the integral from (-infinity to 2*x) dy and then from (-infinity to + infinity) dx should do the trick, but I have been unable to do so even using approximations.
 
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  • #2
ahuds001 said:

Homework Statement



Given a bivariate normal distribution with E(x1)=4 and E(x2) = 6 and Var(X) = [3 2.5]
[2.5 7]
Find P(2*x1>x2)

Homework Equations



The cdf of this bivariate normal distribution is given by:

f(x1,x2)=1/(2*pi*var(x1)*var(x2)*sqrt(1-rho^2)) * e^(-0.5*(z/(2*1-rho^2)))

where var(x1) = 3, var(x2) = 7, E(x1)=4 and E(x2) = 6, and rho = 2.5/(sqrt(3)*sqrt(7))

and z = ((x1-E(x1))^2)/var(x1) + ((x2-E(x2))^2)/var(x2) - (2*rho*(x1-E(x1))*(x2-E(x2)))/sqrt(var(x1)*var(x2))

The Attempt at a Solution



The approximate cdf of this bivariate normal distribution is given (in terms of x and y) by:

.041440417*%e^(-0.71186*((((x-4)^2)/3)+(((y-6)^2)/7)-(1.09109*(x-4)*(y-6))/4.582576))

Taking the integral from (-infinity to 2*x) dy and then from (-infinity to + infinity) dx should do the trick, but I have been unable to do so even using approximations.

Can you figure out the distribution of the single random variable Y = 2*X1 - X2?

RGV
 
  • #3
I can't, not sure if I am missing something or just being thick.
Working only on z=((x1-4)^2)/3 + ((x2-6)^2)/7 - 5*(x1-4)(x2-6)/21

Distributing I get z=(7x1^2+3x2^2-26x1-16x2-5x1x2+100)/21

My best attempt has z = (2y^2-4y-x1^2+x2^2+3x1x2-20x2-18x1+100)/21
 
  • #4
Figured it out, thanks for the idea, it was extremely helpful :-)
 

What is a bivariate normal distribution?

A bivariate normal distribution is a type of probability distribution that describes the relationship between two variables. It is often used in statistics to model the joint distribution of two continuous variables.

How do you find the P(2x1>x2) for a bivariate normal distribution?

To find the probability that the first variable (x1) is greater than twice the second variable (x2), you can use the cumulative distribution function (CDF) of the bivariate normal distribution. This function takes in the two variables and returns the probability of getting a value greater than or equal to the given values.

What does P(2x1>x2) represent?

P(2x1>x2) represents the probability that the first variable (x1) will be greater than twice the second variable (x2). This is also known as the conditional probability, as it is the probability of one event occurring given that another event has already occurred.

What is the significance of finding P(2x1>x2) for a bivariate normal distribution?

Finding P(2x1>x2) allows us to understand the relationship between the two variables in the bivariate normal distribution. It can help us make predictions and draw conclusions about the data. Additionally, it is often used in hypothesis testing and decision making in various fields such as economics, finance, and social sciences.

What are some assumptions made when finding P(2x1>x2) for a bivariate normal distribution?

Some assumptions made when finding P(2x1>x2) for a bivariate normal distribution include that the two variables are normally distributed, the variables are independent or have a known correlation, and the variables have a linear relationship. These assumptions are important to consider when interpreting the results of the calculation.

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