Regarding indicators in Statistics.

  • Thread starter peripatein
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In summary, the expectation and variance of the number of empty boxes can be calculated using the following formulas: E[Xi] = P(Xi) = (1-1/m)n and Var[Xi] = E[Xi](1-E[Xi]) = (1-1/m)n(1-(1-1/m)n).
  • #1
peripatein
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Hi,

Homework Statement


n different balls are distributed independently between m boxes with unlimited capacity each. I am asked to find the expectation and variance of the number of empty boxes.


Homework Equations





The Attempt at a Solution


The probability of i-th box being empty at the end is (1-1/m)n. Ergo, E[Xi] = P(Xi) = (1-1/m)n. Hence, E[X] = m(1-1/m)n.
As for the Variance, I used Var(Xi) = E[Xi](1-E[Xi])=(1-1/m)n(1-(1-1/m)n). Therefore, Var(X) = m*Var(Xi).
Is that correct?
 
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  • #2
I am wondering why no one has yet replied. Is my formulation inappropriate/incomprehensible?
 
  • #3
If the Xi were independent, then your variance would be correct. They aren't though, so you will have terms like E(Xi Xj) that you will have to deal with (luckily it's not too hard to calculate these) when you expand E(X2)
 
  • #4
So should it be:
m(1-1/m)n(1-(1-1/m)n) + 2m(1-2/m)n?
(I used the expression for the Covariance)
 
  • #5
peripatein said:
I am wondering why no one has yet replied. Is my formulation inappropriate/incomprehensible?

Not incomprehensible, just sloppy and incomplete. What is the meaning of ##X_i##? If you mean that ##X_i = 1## if box i is empty and ##X_i = 0## if box i is not empty, then you should say so. Also, the notation ##P(X_i)## is meaningless; if you mean ##P(X_i = 1)## you should write that.
 
  • #6
I am sorry, I was using my mobile to post that one. I did mean everything you thought I might have meant.
I'd very much appreciate it if you could comment on my attempt at solution now.
Actually, both this one and the Statistics problem I posted earlier.
 
  • #7
I'd truly appreciate some feedback on my recent attempt at solution, namely:
The probability of the i-th box being empty at the end is (1-1/m)n. Ergo, E[Xi] = P(Xi) = (1-1/m)n. Hence, E[X] = m(1-1/m)n.
As for the Variance, I used the following:
Var(X) = m*V(Xi) + 2*SIGMA(where i<j)*Cov(Xi,Xj) = m(1-1/m)n(1-(1-1/m)n) + mC2*(1-2/m)n
I am not sure this is correct but would certainly appreciate any comments.
 

1. What are statistical indicators?

Statistical indicators are measurable values that provide information about a particular phenomenon or population. They are used to summarize and describe data in a meaningful way, and can be used to make comparisons and identify patterns.

2. How are statistical indicators used in research?

Statistical indicators are used in research to analyze and interpret data in order to answer research questions or test hypotheses. They can also be used to identify trends and patterns, and to make predictions about future outcomes.

3. What are some common examples of statistical indicators?

Some common examples of statistical indicators include measures of central tendency, such as mean, median, and mode, as well as measures of variability, such as range, standard deviation, and variance. Other examples include correlation coefficients, regression slopes, and confidence intervals.

4. How do you choose the appropriate statistical indicator for a given dataset?

Choosing the appropriate statistical indicator depends on the type of data and the research question being asked. For numerical data, measures of central tendency and variability may be useful, while for categorical data, proportions and percentages may be more appropriate. It is important to carefully consider the characteristics of the data and the research question in order to select the most relevant statistical indicator.

5. How can statistical indicators be used to communicate results?

Statistical indicators can be used to effectively communicate results by providing a concise and meaningful summary of the data. They can also be used to make comparisons and identify patterns, which can help to support or refute research findings. Graphs and charts can also be used to visually represent statistical indicators, making the results more accessible and understandable to a wider audience.

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