Answer:
See explanation below.
Step-by-step explanation:
If we have a continuous variable the expected value is defined as:

Where a and b are the limits for the distribution and
represent the density function.
If we have a discrete random variable X, the expected value is defined as:

The mean is the most common measure of central tendency in order to describe a probability distribution.
The expected value also represent the first central moment of the random variable defined as:
![\mu_1= E[(X-E[X])^1] =\int_{-\infty}^{\infty} (x-\mu)^n f(x) dx](https://tex.z-dn.net/?f=%20%5Cmu_1%3D%20E%5B%28X-E%5BX%5D%29%5E1%5D%20%3D%5Cint_%7B-%5Cinfty%7D%5E%7B%5Cinfty%7D%20%28x-%5Cmu%29%5En%20f%28x%29%20dx)
If we assum that X is a continuous random variable.