What are Cumulative Distribution Functions?

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Discussion Overview

The discussion centers around the concept of Cumulative Distribution Functions (CDFs) in probability theory, exploring their definitions, properties, and interpretations. Participants engage with both theoretical and practical aspects of CDFs, including their mathematical formulation and graphical representation.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Mathematical reasoning

Main Points Raised

  • One participant states that Cumulative Distribution Functions provide the probability that a value X is lower than a certain estimate.
  • Another participant clarifies that the CDF gives the probability of obtaining a value at most x, represented mathematically as P(X ≤ x), and mentions the integral of the probability density function for continuous cases.
  • A question is posed regarding the interpretation of a graph where the Y-axis represents the CDF, suggesting that as the X values increase, the Y values also increase.
  • Further clarification is provided that the Y-axis on the CDF graph represents P(X ≤ x) and that the CDF ranges from 0 to 1, using the example of a coin toss to illustrate how probabilities accumulate.
  • The example of flipping a coin twice is used to demonstrate how to calculate specific probabilities and their corresponding CDF values.

Areas of Agreement / Disagreement

Participants generally agree on the basic definition and properties of CDFs, but there are varying levels of understanding and interpretation, particularly regarding graphical representations and specific examples.

Contextual Notes

Some assumptions about the nature of the probability distributions (discrete vs. continuous) and the specific examples used may not be fully articulated, leading to potential ambiguities in interpretation.

xeon123
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As I understand, Cumulative Distribution Functions gives the probability that a value X be lower than what is estimated. Is this right?
 
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It tells you the probability of getting at most x (ie P(X ≤ x)). If the function is continuous it will be:
∫ p(x)dx from -inf to x
 
If I've graph where the Y axis represents the CDF, and the YY values goes from 0 to 1, what it means?

Imagine a graph that as the XX values grow, the YY values also grow.
 
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The Y-axis on the CDF graph represents P(X ≤ x)
The CDF always goes from 0 to 1 (because you cannot have less than 0% chance of getting something and no more than 100%)

Well, let's take the classic coin toss as a discrete example:

You flip the coin twice. You count the number of heads (i.e. heads = 1, tails = 0)
You have four possibilities:

0+0 = 0
0+1 = 1
1+0 = 1
1+1 = 2

I guess you know that the probability of getting any of these specific outcomes is equal to 1/4. The probability of getting P(X ≤ 0) = 1/4 (i.e. only 0+0)

The probability of getting P(X ≤ 1) is 3/4. Why? Because three of the possibilities above are equal to or less than 1 (0+0, 0+1 and 1+0).

P(X ≤ 2) = 1 because all of the possibilities above are equal to or less than 2..
 
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