Expectation values and mean square deviations

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Discussion Overview

The discussion revolves around the computation of expectation values and mean square deviations, specifically in the context of rolling a die. Participants explore the definitions and calculations involved in determining these statistical measures, addressing both theoretical and statistical perspectives.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant seeks clarification on how to compute the expectation value of the number of spots shown by a die, suggesting a formula involving a summation over possible states.
  • Another participant confirms the formula for expectation value but questions the first participant's understanding of the definition of expectation.
  • A participant expresses uncertainty regarding the integral form of expectation values, noting that it may only apply to continuous states, while discrete states like a die have a finite number of outcomes.
  • Discussion includes a distinction between theoretical values, computed directly from probabilities, and statistical values, which are derived from experimental trials.
  • A participant questions the calculation of the expectation value of 3.5, asking for clarification on how this value is derived given equal probabilities for each die face.
  • Another participant explains the calculation of expectation using the distributive law of probability.
  • A participant inquires about the specific value of the expectation of the square of the outcome and poses a question regarding the relationship between expectation values of squares and the square of expectation values.

Areas of Agreement / Disagreement

Participants express varying levels of understanding regarding the definitions and calculations of expectation values and mean square deviations. There is no consensus on all points, particularly regarding the interpretation of theoretical versus statistical values and the specific calculations involved.

Contextual Notes

Some participants mention the importance of the number of trials (N) in achieving accurate probability estimates, suggesting that larger N leads to more accurate results. The discussion also touches on the distinction between theoretical and statistical approaches without resolving the implications of this distinction.

Ed Quanta
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I am having trouble applying this concept to simple things. Like a die where we let s be the number of spots shown by a die thrown at random.



How could I compute the expectation value of s? And how would I compute the mean square deviation. Would the expectation value just be <s>=1/N summation from i=1 to N of si? Here the only possible states are s1,s2,s3,s4,s5, and s6. Help anyone? And how would I calculate the mean square deviation from the equation delta s=<<s-<s>>^2>=<c^2>-<c>^2?
 
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Would the expectation value just be <s>=1/N summation from i=1 to N of si?

Yes, but you seem to be unsure... do you recall the definition of expectation?

<c^2>-<c>^2?

(assuming you mean <s^2> - <s>^2)
You've already got <s>, you just need <s^2>, which you can also compute from the definition.
 
Yeah, I am unsure because I always thought that the expectation value of let's say <x> was the integral of the wave functions squared multiplied by x. But the definition I gave was not in integral form. Does the integral form only have to do with continuous states, where as for the die there are only 6 different possible states? sorry if I am being confusing or just making more of this than there is. Oh and one more question, the only specification of N is that it is much larger than 1. Is this simply because probability of a system of randomness will be more and more accurate as N approaches infinity?
 
There is a distinction between theoretical values, which can be computed directly, and statistical values. To compute the theoretical value, assume each face of a die has probability 1/6, then the expectation values are <s>=3.5, and <s2>=15.166... The statisitcal value is obtained by running lots of trials and averaging the results by the number of trials.

As an afterthought, these concepts have nothing to do with quantum theory, although quantum theory uses them. These are notions from probability and statistics.
 
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How did you get those values? Did you just add 1+2+3+4+5+6 and divide by 6 to calculate <s>? And if so, why would this be accurate for calculating an expectation value where 3.5 be the expectation value when there is an equal probability for each of the six states?
 
Distributive law.

1 * 1/6 + 2 * 1/6 + 3 * 1/6 + 4 * 1/6 + 5 * 1/6 + 6 * 1/6 = (1 + 2 + 3 + 4 + 5 + 6)/6
 
Why is Expectation<s^2> = 15.1666?

Is it possible for there to be an X such that EX^2 < (EX)^2? Please explain.
 
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