Calculating Expectation Values for Independent Random Variables

In summary, the covariance of two random variables is zero, and the expectation of a product of two random variables is equal to the sum of the expectation of each random variable.
  • #1
toothpaste666
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Homework Statement


If X1 has mean -3 and variance 2 while X2 has mean 5 and variance 4 and the two are independent find
a) E(X1 - X2)
b) Var(X1 - X2)

The Attempt at a Solution


I am not very clear on what I am supposed to be doing for this problem. I don't fully understand this expectation value concept. Can someone help explain what they are asking me to do or give me a hint on how to get started please? Here is what I did so far but I don't know if its worth anything

they are independent so my book says the covariance is 0
so
E[(X1 - μ1)(X2 - μ2)] = 0
E[(X1 + 3)(X2 - 5)] = 0
E[X1X2 - 5X1 + 3X2 -15] = 0

I think that is equal to this but I may be wrong:

E[X1X2] - 5E[X1] + 3 E[X2] - 15 = 0

I don't know what to do from here though. Am I on the right track at all?
 
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  • #2
Yes you are on the right track.

'The mean of X1' is defined to be E[X1].

As regards E[X1X2]: have they given you the rule for the expectation value of a product of independent variables?
If not, it's easy to derive. Start by writing the expectation as a double integral.
 
  • #3
toothpaste666 said:

Homework Statement


If X1 has mean -3 and variance 2 while X2 has mean 5 and variance 4 and the two are independent find
a) E(X1 - X2)
b) Var(X1 - X2)

The Attempt at a Solution


I am not very clear on what I am supposed to be doing for this problem. I don't fully understand this expectation value concept. Can someone help explain what they are asking me to do or give me a hint on how to get started please? Here is what I did so far but I don't know if its worth anything

they are independent so my book says the covariance is 0
so
E[(X1 - μ1)(X2 - μ2)] = 0
E[(X1 + 3)(X2 - 5)] = 0
E[X1X2 - 5X1 + 3X2 -15] = 0

I think that is equal to this but I may be wrong:

E[X1X2] - 5E[X1] + 3 E[X2] - 15 = 0

I don't know what to do from here though. Am I on the right track at all?

No, I don't think you are on the right track. You are making it much too difficult. You don't need to do any calculations with covariance or the expectation of a product. Surely your text has formulas for the expected value and variance of a sum of independent random variables. Have you looked for such formulas?
 
  • #4
Ok sorry my book is very confusingly written but I think I figured it out.
so E(aX1 + bX2) = aE(X1) + bE(X2)
for part a) a = 1 and b = -1
so E(X1-X2) = E(X1) - E(X2) = -3 - 5 = -8

for part b)
Var(aX1+bX2) = a^2Var(X1) + b^2Var(X2)
since a =1 and b = -1
Var(X1-X2) = Var(X1) + Var(X2) = 2 + 4 = 6

is this correct?
 
  • #5
Yes that's correct. Just be careful not to forget that the formula you use in (b) only holds when the two random variables are independent. The formula in (a) holds regardless of dependence.
 
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  • #6
thank you!
 

1. What is an expectation value problem?

An expectation value problem is a concept in quantum mechanics that involves finding the average value of a physical quantity, such as position or momentum, in a quantum state. It is a mathematical tool used to predict the outcome of measurements in quantum systems.

2. How is an expectation value calculated?

An expectation value is calculated by taking the integral of the product of the value of the physical quantity and the probability of obtaining that value in a given quantum state. This integral is also known as the quantum mechanical average.

3. Why is the expectation value important?

The expectation value is important because it allows us to make predictions about the behavior of quantum systems. It gives us a way to understand the probabilities of different outcomes for a measurement, and it is a fundamental concept in quantum mechanics.

4. Can the expectation value be measured in experiments?

Yes, the expectation value can be measured in experiments. However, it is important to note that the outcome of a single measurement may not always match the expectation value. The expectation value is a prediction of the average outcome over a large number of measurements.

5. How does the uncertainty principle relate to expectation values?

The uncertainty principle states that it is impossible to know both the position and momentum of a particle with absolute certainty. The expectation value of position and momentum can be used to calculate the uncertainties in these quantities, and this is one way in which the uncertainty principle relates to expectation values.

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