Statistics: 90th percentile demand

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    Percentile Statistics
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Discussion Overview

The discussion revolves around finding the 90th percentile of demand using a given cumulative probability distribution H(x). Participants explore the interpretation of percentiles, interpolation methods, and the implications of linearity in statistical analysis.

Discussion Character

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

Main Points Raised

  • One participant expresses confusion about determining the 90th percentile from the cumulative distribution and suggests that it may correspond to x = 3.
  • Another participant agrees with the initial approach but clarifies that x = 3 corresponds to the 97.196th percentile, while x = 2 corresponds to the 89.149th percentile, indicating the need for interpolation between these values.
  • A different participant reiterates that x = 3 is correct for the 90th percentile, framing it in terms of a minimum value condition for the cumulative distribution.
  • One participant cautions against relying solely on linear interpolation, suggesting that it may not accurately represent the underlying population or sample due to potential loss of information.
  • Another participant repeats the initial question about finding the 90th percentile, confirming that the interpretation aligns with the earlier definitions provided in the discussion.

Areas of Agreement / Disagreement

There is no consensus on the correct approach to finding the 90th percentile. Some participants support the idea that x = 3 is valid, while others emphasize the need for interpolation and caution against linear assumptions.

Contextual Notes

Participants highlight the importance of interpolation between cumulative probabilities and the potential limitations of linearity in representing the distribution accurately. There are unresolved questions regarding the implications of these methods on the interpretation of the data.

bookworm121
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I'm a little confused on how you would do something like this:

I have a cumulative probability distribution H(x):

0 0.31042
1 0.67825
2 0.89149
3 0.97196
4 0.9942
5 0.999
6 0.99985
7 0.99997
8 1

and I need to find the 90th percentile of demand..
I understand that this means its the minimum x so that H(x) >= 90% but I need an example to make sure I'm doing this right.
Would this mean x >= 3 in this case??
 
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You've got the right idea, but 3 is actually the 97(.196)th percentile whereas 2 is the 89(.149)th.

You need to interpolate between these two points: usually this is a linear interpolation. Moving from 2 to 3 you get from 89.149 to 97.196; how much is this? How far would you have to move from 2 to get to 90?
 
Hey bookworm121.

For this distribution, you are correct in say that x = 3 for the 90th percentile.

It is just a minimum value where x = Min_i P(X <= i) >= p where is your percentile and i is the value corresponding to the value in the distribution and X is the random variable.

Min_i is a function that returns the smallest value of i such that the expression holds.
 
MrAnchovy said:
You've got the right idea, but 3 is actually the 97(.196)th percentile whereas 2 is the 89(.149)th.

You need to interpolate between these two points: usually this is a linear interpolation. Moving from 2 to 3 you get from 89.149 to 97.196; how much is this? How far would you have to move from 2 to get to 90?

I would not recommend this since you are losing information through the definition of the random variable.

Linearity assumes an equal kind of weighting between bins and this may not be representative of the population (or sample).
 
bookworm121 said:
I'm a little confused on how you would do something like this:

I have a cumulative probability distribution H(x):

0 0.31042
1 0.67825
2 0.89149
3 0.97196
4 0.9942
5 0.999
6 0.99985
7 0.99997
8 1

and I need to find the 90th percentile of demand..
I understand that this means its the minimum x so that H(x) >= 90% but I need an example to make sure I'm doing this right.
Would this mean x >= 3 in this case??
By your definition that is correct.
 

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