Random Variable over probability space

In summary, the answer is 3. Statements (1), (2), and (4) are true, but statements (3) and (5) are false.
  • #1
Francobati
20
0
Hello. Can you help me solve it? ($F$ is a $\sigma $ algebra).
Let $X$ be a rv over $(\Omega ,F,P)$. Set $Y:= min\left \{ 1,X \right \}$. What statement is TRUE?

(1): $\left \{ Y=X \right \}\neq \Omega $;
(2): $F_Y(x)=F_X(x)$ for every $x\epsilon \Re $;
(3): $Y\leqslant X$ for every outcome;
(4): $E(Y)=\int_{-\infty}^1xd F_{X}(x)$;
(5): $E(Y)=\int_{\Re }max\left \{ 1,x \right \}d F_{X}(x)$.
 
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  • #2
What are your thoughts on this problem? Can you eliminate any of the answer choices?
 
  • #3
I have to find the true answer among these five.
 
  • #4
That's clear, but you haven't mentioned which answer choices you know can be eliminated.
 
  • #5
I have no idea. What information do I need to resolve it? Help me, please.
 
  • #6
Ok, let's start with what is the definition of $E[X]$ and $F(x)$? What do these two concepts represent in general for random variables?
 
  • #7
$E(X)=:x_{1}P(A_{1})+...+x_{k}P(A_{k})\geqslant 0$
$F(x)=P(X\leqslant x)$
 
  • #8
By the very definition of $Y$, isn't it the case that $Y(\omega) \le X(\omega)$ for all $\omega\in \Omega$?
 
  • #9
Yes, the answer is 3. But as I explain in an exhaustive way?
 
  • #10
For all pairs of real numbers $a$ and $b$, $\min\{a,b\} \le a$ and $\min\{a,b\} \le b$. So, for all $\omega\in \Omega$, $Y(\omega) \le 1$ and $Y(\omega) \le X(\omega)$. In particular, for every $\omega\in \Omega$, $Y(\omega) \le X(\omega)$.

For the other cases, you can explain why they are false using counterexamples. Take for instance statement (1). If we let $\Omega = [0,1]$ and $X$ the indicator of $[0,1/2]$, then $X\le 1$ and hence $Y$ is identical to $X$; therefore, the event $(Y = X)$ is equal to the whole sample space $\Omega$.
 
  • #11
Ok. Many thanks.
 

FAQ: Random Variable over probability space

What is a random variable?

A random variable is a mathematical concept used in probability theory to represent a numerical outcome of a random phenomenon. It can take on different values based on the outcome of a particular event or experiment.

What is the difference between a discrete and a continuous random variable?

A discrete random variable can only take on a finite or countably infinite number of values, while a continuous random variable can take on any value within a certain range. For example, the number of heads obtained when flipping a coin is a discrete random variable, while the weight of a person is a continuous random variable.

How is a random variable related to a probability space?

A probability space is a mathematical construct that defines all possible outcomes of a random phenomenon and assigns probabilities to each outcome. A random variable is a function that maps these outcomes to numerical values, allowing us to calculate probabilities of certain events occurring.

What is the difference between a random variable and a probability distribution?

A random variable is a concept, while a probability distribution is a mathematical function that describes the probabilities of the values that a random variable can take on. In other words, a random variable is the variable itself, while a probability distribution is a tool used to analyze its behavior.

How is a random variable used in statistical analysis?

Random variables are used in statistical analysis to model and analyze real-world phenomena. They provide a framework for calculating probabilities and making predictions based on data. By using random variables, scientists can make informed decisions and draw conclusions about the underlying processes that generate data.

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