Difference between random variable and observation

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SUMMARY

The discussion clarifies the distinction between a random variable and its observations. A random variable X is defined by its probability density function (p.d.f.) fX(x; θ), which depends on a deterministic parameter θ. Each observation xi from N sampled observations can be treated as a separate random variable with the same p.d.f. fX(x; θ). This understanding is crucial for calculating the expected value of an estimator, denoted as \hat{θ}(N) = s(x1, ..., xN), where the observations are derived from the distribution of X.

PREREQUISITES
  • Understanding of random variables and probability density functions (p.d.f.)
  • Familiarity with statistical estimators and expected values
  • Knowledge of deterministic parameters in statistical models
  • Basic concepts of sampling in statistics
NEXT STEPS
  • Study the properties of probability density functions (p.d.f.) in detail
  • Learn about the calculation of expected values for different estimators
  • Explore the concept of sampling distributions and their implications
  • Investigate the relationship between random variables and their observed values
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Statisticians, data scientists, and students studying probability theory and statistical inference will benefit from this discussion.

mnb96
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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.
 
Last edited:
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For your calculation, yes.
 

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