Statistics expectation of discrete variable.

In summary, the conversation discusses how to determine the convergence of the series Ʃ(m=1 to ∞) m/2m-1 and how it relates to finding the expected value of a discrete variable Y with probability P(Y) = 1/2m-1. The conversation also mentions the use of calculus in evaluating the series and suggests starting with f(x) = ∑ mx-(m-1).
  • #1
peripatein
880
0
Hi,

Homework Statement



How may I find to what number Ʃ(m=1 to ∞) m/2m-1 converges?
Further, suppose I know it converges to 4, why would then E(Y), given that P(Y) = 1/2m-1, be equal to 2 (thus asserted the answer) and not 4?

Homework Equations





The Attempt at a Solution


I am really not sure how to analyse that series. I have managed to determine, through the ratio test, that it indeed converges, but I am not sure how to break it down so that it could be easily determined to what number it converges. I'd truly appreciate some assistance.
 
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  • #2
hi peripatein! :smile:
peripatein said:
How may I find to what number Ʃ(m=1 to ∞) m/2m-1 converges?

calculus? :wink:
Further, suppose I know it converges to 4, why would then E(Y), given that P(Y) = 1/2m-1, be equal to 2 (thus asserted the answer) and not 4?

i don't understand :redface:

what is Y ? :confused:
 
  • #3
Y is just a discrete variable for which the probability is P(Y) = 1/2m-1. But how is E(Y) 2 (based on the answers in the book)?! Why is it not 4?
And I realize calculus would render the evaluation of Ʃ(m=1 to ∞) m/2m-1 possible, yet I am not sure how to break it down. Could you help?
 
  • #4
hi peripatein! :smile:
peripatein said:
And I realize calculus would render the evaluation of Ʃ(m=1 to ∞) m/2m-1 possible, yet I am not sure how to break it down. Could you help?

start by writing f(x) = ∑ mx-(m-1) :wink:
Y is just a discrete variable for which the probability is P(Y) = 1/2m-1.

do you mean P(Y=m) ? :confused:
 
  • #5
peripatein said:
P(Y) = 1/2m-1
Do those probabilities add up to 1?
 

Related to Statistics expectation of discrete variable.

1. What is the definition of expectation in statistics?

In statistics, the expectation of a discrete variable is a numerical value that represents the average or central tendency of the possible outcomes of the variable. It is calculated by multiplying each possible outcome by its corresponding probability and summing them together.

2. How is the expectation of a discrete variable different from the mean?

The expectation of a discrete variable is sometimes referred to as the mathematical expectation or the expected value, and it is calculated using probabilities. On the other hand, the mean is calculated by summing the values of a variable and dividing by the total number of values. In some cases, the expectation may be the same as the mean, but in others, they can differ significantly.

3. Can the expectation of a discrete variable be negative?

Yes, the expectation of a discrete variable can be negative. This is because it is calculated by multiplying each outcome by its probability, and the probabilities can be negative for some outcomes. For example, in a game of chance, the expectation of winning a certain amount may be negative if the probability of winning is low.

4. How is the expectation used in decision-making?

The expectation of a discrete variable can be used in decision-making by comparing it to a certain threshold or benchmark. If the expectation is higher than the threshold, it may be a favorable decision, while if it is lower, it may not be a favorable decision. For example, in a business decision, if the expected profit is higher than the expected cost, it may be a good decision to pursue.

5. Is the expectation of a discrete variable affected by outliers?

Yes, the expectation of a discrete variable can be affected by outliers. Outliers are values that are significantly different from the rest of the data, and they can skew the expected value. For example, if a dataset has a few extremely high values, the expectation may be higher than the typical central tendency of the data.

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