Conditional Expectation problem

In summary, the conversation discusses the expected length of a telephone conversation with a certain firm's executive, given a probability density function. The formula for finding the expected value is used, but a mistake is made in the expression. The correct expression is provided, and the use of $\LaTeX$ on the site is also discussed.
  • #1
JGalway
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Q The amount of time (in minutes) that an executive of a certain firm talks on the telephone is a random variable having the probability density:


$$f(x) = \begin{cases} \dfrac{x}{4}&\text{for $0 < x \le 2$}\\
\dfrac{4}{x^3}&\text{for $x > 2$}\\
0&\text{elsewhere}\end{cases}$$


with reference to part (b) of Exercise $4.59$, find the expected length of one of these telephone conversations that has lasted for 1 minute.

A: The formula from $4.59$(b) is $$E[u(x)|a<x \le b]= \frac{\int_a^b u(x)f(x)\, dx}{\int_a^b f(x)\, dx}$$

I tried $$E[x|x \ge 1]= \frac{\int_1^2 x(x/4)\, dx}{\int_1^2 x/4\, dx} + \frac{\int_2^\infty x(4/x^3)\, dx}{\int_2^\infty 4/x^3 \, dx}
= \frac{14/6}{9/6}+4=\text{5.55555 minutes}$$

but the back of the books says the answer is $2.95$ mins so i don't know where i went wrong.
 
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  • #2
Welcome, JGalway! (Wave)

The expression you wrote for $E[X|X \ge 1]$ should instead be

$$\frac{\int_1^2 x\cdot \dfrac{x}{4}\, dx + \int_2^\infty x\cdot \dfrac{4}{x^3}\, dx}{\int_1^2 \dfrac{x}{4}\, dx + \int_2^\infty \dfrac{4}{x^3}\, dx}$$
 
  • #3
Euge said:
Welcome, JGalway! (Wave)

The expression you wrote for $E[X|X \ge 1]$ should instead be

$$\frac{\int_1^2 x\cdot \dfrac{x}{4}\, dx + \int_2^\infty x\cdot \dfrac{4}{x^3}\, dx}{\int_1^2 \dfrac{x}{4}\, dx + \int_2^\infty \dfrac{4}{x^3}\, dx}$$
Thanks for that, I sometimes make silly mistakes like that when I get tired.
Also is the maths formatting used here the same as most sites(showing integral sign,etc)? Just not sure if I want to learn it just for this site.
 
  • #4
JGalway said:
...Also is the maths formatting used here the same as most sites(showing integral sign,etc)? Just not sure if I want to learn it just for this site.

Yes, we use $\LaTeX$ powered by MathJax, which is what you'll find on most other math sites. The only difference is, unlike other sites, we provide you with easy to use tools for creating the code/markup for displaying math expressions, and a means of previewing it in real time before putting it in your post. Thus, MHB is the perfect environment to learn how to use $\LaTeX$, which you will find useful pretty much everywhere else. (Yes)
 

FAQ: Conditional Expectation problem

What is conditional expectation?

Conditional expectation is a statistical concept that calculates the expected value of a variable based on certain conditions or information. It is the expected value of one variable given the value of another variable.

How is conditional expectation calculated?

Conditional expectation is calculated using the formula E[X|Y] = ∑ x P(X=x|Y), where X and Y are random variables, E[X|Y] is the conditional expectation of X given Y, and P(X=x|Y) is the probability of X taking on the value x given the condition Y.

What is the difference between conditional expectation and regular expectation?

Regular expectation, or unconditional expectation, calculates the expected value of a variable without taking into account any conditions or information. In contrast, conditional expectation takes into account certain conditions or information when calculating the expected value.

How is conditional expectation used in data analysis?

Conditional expectation is commonly used in data analysis to understand the relationship between variables and to make predictions. It can help identify patterns and trends in the data and can be used to estimate the value of one variable based on the values of other variables.

What are some real-life applications of conditional expectation?

Conditional expectation has various applications in fields such as economics, finance, and engineering. It is used to predict stock prices, analyze consumer behavior, and model financial risk. It is also used in machine learning and artificial intelligence algorithms to make predictions and improve decision-making processes.

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