Difference between random variable and observation

In summary, the conversation discusses considering sampled observations of a random variable as separate random variables with the same p.d.f. and computing the expected value of a given estimator based on these observations. The person confirms that this approach is correct for the given calculation.
  • #1
mnb96
715
5
Hello,
I am given a random variable X with a p.d.f. fX(x;[itex]\theta[/itex]) (depending on a certain deterministic parameter [itex]\theta[/itex]) and I want to consider N sampled observations of that variable: x1,...,xN.
Is it correct to consider each observation as a separate random variable xi with the same pdf fX(x,[itex]\theta[/itex]) associated with it?

I am asking this question because I have got an exercise in which I have to compute the expected value of a given estimator:

[tex]\hat{\theta}(N)=s(x_1,\ldots,x_N)[/tex]

where x1,...,xN are the sampled observations from the distribution of X.
 
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  • #2
For your calculation, yes.
 

What is the definition of a random variable?

A random variable is a numerical quantity that takes on different values as a result of a random process or experiment. It is typically denoted by a capital letter, such as X or Y.

How is a random variable different from an observation?

An observation is a specific outcome or measurement obtained from a random variable. In other words, an observation is a value that the random variable can take on. The key difference is that a random variable is a theoretical concept, while an observation is an actual measurement or data point.

Can an observation be a random variable?

No, an observation is not a random variable. As mentioned earlier, a random variable is a theoretical concept, while an observation is an actual measurement. However, multiple observations can be used to determine the characteristics of a random variable.

Why is it important to distinguish between random variables and observations?

Distinguishing between random variables and observations is important because it helps us understand the nature of the data we are working with. Identifying and defining a random variable allows us to make predictions and draw conclusions about the data and its underlying distribution. On the other hand, observations provide us with concrete data points that can be used to analyze and validate our hypotheses about the random variable.

What are some examples of random variables and observations?

An example of a random variable could be the outcome of flipping a coin, as it can take on two values: heads or tails. An observation in this scenario could be the result of a specific coin flip, such as heads. Another example could be the heights of students in a class. The random variable in this case would be the height, while each student's height would be an observation.

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