Expectation of Continuous Random Variable [word problem]

In summary, the conversation discusses a problem with the solution provided for a calculus question. The conversation focuses on taking the derivative of integrals using the Fundamental Theorem of Calculus and the Leibniz Integral Rule. The conversation concludes with the correct solution using the Leibniz Integral Rule.
  • #1
izelkay
115
3

Homework Statement


Here's the problem with the solution provided:
V2pcSB6.png


Homework Equations


Fundamental Theorem of Calculus (FToC)

The Attempt at a Solution


So I understand everything up to where I need to take the derivative of the integral(s).
Couple of things I know is that the derivative of the CDF, F(T) is the PDF, f(t). So naturally the integral of the PDF would be the CDF, right?

So for the first integral, ct∫f(x)dx [0,t]
I would need to use the product rule for this I think, so I'd have:

c∫f(x)dx [0,t] + ctf(t)

And since the integral of the PDF is the CDF, this would be:

cF(t) + ctf(t) right? That's what they have so far

For the second integral, -c∫xf(x)dx[0,t]

The derivative of this would just be
-ctf(t)

by the Fundamental Theorem of Calculus.

The third integral, k∫xf(x)dx [t,∞]

I believe I would need to switch the limits of integration for this to be able to differentiate using the FToC:

-k∫xf(x)dx [∞,t]

Then, differentiating this:

-ktf(t)

For the fourth integral I'm not sure what they did. I thought we just needed to switch the limits of integration again to get

kt∫f(x)dx [∞,t]

And then differentiate this to get

k∫f(x)dx [∞,t] + ktf(t)
=
kF(t) + ktf(t)

so in total, I'd have:

cF(t) + ctf(t) - ctf(t) - ktf(t) + kF(t) + ktf(t)

which matches what they have except for the last two terms. I'm not sure what's going on there, can someone explain?
 
Physics news on Phys.org
  • #2
Hm I'm looking at where I switched the limits of integration, and it doesn't really make sense to integrate something from infinity to t does it? Could that be the problem?
 
  • #3
izelkay said:

Homework Statement


Here's the problem with the solution provided:
V2pcSB6.png


Homework Equations


Fundamental Theorem of Calculus (FToC)

The Attempt at a Solution


So I understand everything up to where I need to take the derivative of the integral(s).
Couple of things I know is that the derivative of the CDF, F(T) is the PDF, f(t). So naturally the integral of the PDF would be the CDF, right?

So for the first integral, ct∫f(x)dx [0,t]
I would need to use the product rule for this I think, so I'd have:

c∫f(x)dx [0,t] + ctf(t)

And since the integral of the PDF is the CDF, this would be:

cF(t) + ctf(t) right? That's what they have so far

For the second integral, -c∫xf(x)dx[0,t]

The derivative of this would just be
-ctf(t)

by the Fundamental Theorem of Calculus.

The third integral, k∫xf(x)dx [t,∞]

I believe I would need to switch the limits of integration for this to be able to differentiate using the FToC:

-k∫xf(x)dx [∞,t]

Then, differentiating this:

-ktf(t)

For the fourth integral I'm not sure what they did. I thought we just needed to switch the limits of integration again to get

kt∫f(x)dx [∞,t]

And then differentiate this to get

k∫f(x)dx [∞,t] + ktf(t)
=
kF(t) + ktf(t)

so in total, I'd have:

cF(t) + ctf(t) - ctf(t) - ktf(t) + kF(t) + ktf(t)

which matches what they have except for the last two terms. I'm not sure what's going on there, can someone explain?

The exerpt you quote has a very weird way of doing the derivative, and you are over-thinking the problem. In fact, using the general result
[tex] \frac{d}{dt} \int_{a(t)}^{b(t)} h(x,t) \, dx = h(b(t),t) b'(t) - h(a(t),t) a'(t)
+ \int_{a(t)}^{b(t)} \frac{\partial}{\partial t} h(x,t) \, dt [/tex]
we have
[tex] \frac{d}{dt} \int_0^t c(t-x) f(x)\, dx = c(t-x) f(x)|_{x=t} + \int_0^t \frac{\partial}{\partial t} c(t-x) f(x) \, dx\\
= c \:0 \:f(t) + c \int_0^t f(x) \, dx = c F(t)[/tex]
and
[tex] \frac{d}{dt} \int_t^{\infty} k(x-t) f(x) \, dx = -k(x-t) f(x)|_{x=t}
+ \int_t^{\infty} \frac{\partial}{\partial t} k(x-t) f(x) \, dx \\
= - k \: 0 \: f(t) - k \int_t^{\infty} f(t) \, dt.[/tex]
Since ##\int_t^{\infty} f(x) \, dx = 1 - F(t)##, we have
[tex] \frac{d}{dt} E C_t(X) = c F(t) - k[1-F(t)]= (c+k) F(t) - k [/tex]
 
  • #4
Mm okay it makes sense with the formula you used, though I've never seen that formula before.
 
  • #6
Okay, thank you very much.
 

FAQ: Expectation of Continuous Random Variable [word problem]

1. What is a continuous random variable?

A continuous random variable is a type of random variable that can take on any numerical value within a specific range, as opposed to discrete random variables which can only take on specific values. An example of a continuous random variable would be the height of a person, which can range from 4 feet to 7 feet.

2. How is the expectation of a continuous random variable calculated?

The expectation of a continuous random variable is calculated by taking the integral of the variable's probability density function over its entire range. This can also be thought of as the weighted average of all possible values of the variable, where the weights are determined by the probability of each value occurring.

3. What is the significance of the expectation of a continuous random variable?

The expectation of a continuous random variable represents the mean or average value of the variable. It is an important measure as it provides a single value that summarizes the central tendency of the variable's distribution. It is also used in many statistical calculations and analyses.

4. Can the expectation of a continuous random variable be negative?

Yes, the expectation of a continuous random variable can be negative. This is because the expectation is calculated by taking the weighted average of all possible values, and if the variable has a distribution that includes negative values, then the expectation can also be negative.

5. How does the expectation of a continuous random variable differ from the expectation of a discrete random variable?

The main difference between the expectation of a continuous random variable and a discrete random variable is the way it is calculated. For a continuous random variable, the expectation is calculated by taking an integral, while for a discrete random variable it is calculated by taking a sum. Additionally, the range of values for a continuous random variable is infinite, while a discrete random variable can only take on a finite number of values.

Back
Top