Expected value from a density function

In summary, to find the expected value from a density function in the form of y^2 with different intervals, you can split the integral into disjoint intervals and apply the different definitions of the function in each interval. This can be done by using the linearity of the Riemann integral and the continuity of the integrand.
  • #1
mind0nmath
19
0
Hey,
I know how to find the expected value from the density function when it is in the form:

(example)

| y^2 -1<y<1
|
fy =|
| 0 elsewhere

Ey = integral(upper limit 1, lower limit -1)[y*y^2 dy)

but, what if the density function looks like this:

| y^2 -1<y<0
|
fy =| y^2 - y 0<y<1
|
| 0 elsewhere

how do you approach here?
 
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  • #2
The expectation value of Y is given by

[tex]E(Y) = \int_{-\infty}^{+\infty}yf(y)dy[/itex]

If I understood your question correctly, you just have to split the integral into disjoint intervals and apply the different definitions of [itex]f(y)[/itex] in each such interval. This is immediate from the linearity of the Riemann integral and the continuity of the integrand.
 
  • #3
E(Y) = \int_{-\infty}^{+\infty}yf(y)dy
 

1. What is the definition of expected value from a density function?

The expected value from a density function is the average or mean value that would be obtained from a large number of samples from the underlying probability distribution.

2. How is the expected value calculated from a density function?

The expected value is calculated by taking the integral of the product of the density function and the variable over the entire range of the variable. This can also be represented by the sum of the product of each possible outcome and its corresponding probability.

3. What is the significance of the expected value in probability and statistics?

The expected value is important in probability and statistics as it provides a measure of central tendency and can be used to make predictions about future outcomes. It is also used to compare different probability distributions and to calculate other statistical measures such as variance and standard deviation.

4. Can the expected value ever be negative?

Yes, the expected value can be negative if the probability distribution has a high likelihood of producing negative outcomes, even if the overall distribution is centered around a positive value. This can also occur if the density function has a long tail towards negative values.

5. How does the shape of a density function affect the expected value?

The shape of a density function can greatly affect the expected value. A skewed or asymmetric density function may result in a different expected value compared to a symmetric distribution. Additionally, the presence of outliers or extreme values can also impact the expected value.

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