How Do Conditional Probabilities and Expectations Relate in Statistics?

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SUMMARY

This discussion focuses on the relationship between conditional probabilities and expectations in statistics, specifically addressing two key equations: Pr(Y = yi) = ∑ from 1 to l Pr(Y = yi | X = xi) Pr(X = xi) and E(Y) = E(E(Y | X)). The participants emphasize the importance of understanding conditional expectations and probabilities, suggesting that a Wikipedia page provides a comprehensive explanation for the second equation. The discussion highlights the necessity of precise notation and terminology when formulating statistical problems.

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  • Understanding of random variables and their distributions
  • Familiarity with conditional probability notation
  • Knowledge of expected value calculations in statistics
  • Ability to interpret mathematical expressions accurately
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  • Research "Conditional Expectation in Probability Theory"
  • Study "Law of Total Expectation" in statistics
  • Explore "Bayes' Theorem and its Applications"
  • Review "Statistical Notation and Common Errors" for clarity in communication
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Students and professionals in statistics, data analysts, and anyone looking to deepen their understanding of conditional probabilities and expectations in statistical analysis.

MaxManus
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Homework Statement


Consider two random variables X and Y. Suppose that Y takes on k values yi,...,yk and X takes l values xi,...,xl

Hey, I am supposed to show that
1) Pr(Y = yi = [tex]\sum[/tex],from 1 to l Pr(Y = yi(givrn)(X=xi)Pr(X=xi)


and
2) E(Y) = E(E(Y(given)X)


I am not sure where to start on neither of them,
 
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You need to learn how to do a little research, bro. That second problem is worked out completely on a wikipedia page, which I was able to find in about 20 seconds. I will let you think about what keywords to search for (you should at least know what E(Y) is called). As for the first problem, I'm guessing that you didn't type it in correctly because I can't parse what you're trying to say. Try again?
 

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