Help with total expectation formula

AI Thread Summary
The discussion revolves around applying the law of total expectation to calculate the expected number of people giving money to a beggar. The scenario involves a Poisson process with a rate of 10 people per hour and a 20% chance of each person donating. The beggar's sitting time is uniformly distributed between 3 to 8 hours. The provided solution uses the formula E(N(X)) = E(E(N(X)|X)) = E(2X) to find the expectation, which simplifies to 2 times the average of the sitting time. Clarification is sought on the application of the law of total expectation in this context.
yevi
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I need some help with "law of total expectation".

Sorry for my English, I don't know the right English expressions.

The Problem is:
People come (show in) with with Poisson rate of 10 people per hour.
There is a 0.2 chance that a person will give money to a beggar sitting in the corner.
The time that beggar is sitting is U[3,8] (continuous) hours.

Need to find the expectation E of people that will give money.

I mark:
N(t)~P(10*0.2*t) as the number of people giving money per hour.
X~U(3,8) time that beggar is sitting.

The given solution is following : E(N(X))=E(E(N(X)|X))=E(2X)=2*(3+8)/2

What I don't understand is their usage law of total expectation, according to the formula it should be like this:

E(Y)=E[E[Y|X]]=\intE[Y|X=x]*f_{x}(x)dx

And this is not what they have...
 
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