CDF of X: Probability Pr{X < b}

In summary: In this case we have a non-decreasing sequence of probabilitiesbut the continuity of the CDF implies that the sequence of probabilities convergesto 0.In summary, the conversation discusses the relationship between the probability of a Chi-square random variable being less than or equal to a certain value and its corresponding cumulative distribution function (CDF). It is established that for continuous distributions, such as the Chi-square, the two probabilities are exactly equal. However, for non-continuous random variables, the equality may not hold due to the possibility of non-zero probability for specific values.
  • #1
EngWiPy
1,368
61
Hello,

Suppose X is a Chi-square random variable. Then what is:

[tex]\text{Pr}\left\{X<b\right\}[/tex]?

Does the above probability is the CDF of X? The only difference is that there is no equality!

Thanks
 
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  • #3
My question can we say that Pr[X<b] approximately equal Pr[X<=b]?
 
  • #4
P(X < b) = P(X <= b) for continuous distributions, for example the chi-square distribution.
 
  • #5
S_David said:
My question can we say that Pr[X<b] approximately equal Pr[X<=b]?
Not approximately equal. Exactly equal.

The only time this isn't the case is with those non-continuous random variables for which P(x=b) can be non-zero for some values of b. This doesn't apply to the chi square distribution, which is an absolutely continuous probability distribution. "Absolutely continuous" essentially means it has a PDF; this a stronger constraint than merely being continuous.
 
  • #6
Thanks awkward and D_H, that is really relieving, since in my analysis I have Pr[X<b], and I was afraid it won't be correct to equate this with the CDF of Chi-square, i.e., Pr[X<=b], which has a closed form.

Thanks all
 
  • #7
Just a quick comment: one of the obstacles to assigning non-zero probability to

singletons is that an uncountable sum cannot converge unless only countably-many

terms are non-zero.
 

Related to CDF of X: Probability Pr{X < b}

What is a CDF?

A cumulative distribution function (CDF) is a mathematical function that shows the probability of a random variable falling below a certain value. It is used to describe the distribution of a continuous random variable.

What is X in the CDF?

X refers to the random variable being studied. It can represent any numerical value, such as height, weight, or income, and is used to calculate the probability of the variable falling below a specific value.

What does Pr{X < b} mean?

Pr{X < b} represents the probability that the random variable X is less than a given value, b. This is the area under the curve of the CDF up to the point b.

How is the CDF related to the probability distribution function (PDF)?

The CDF and PDF are two different mathematical representations of the same information. The CDF shows the cumulative probability of a random variable, while the PDF shows the probability density at a specific point. The CDF can be obtained by integrating the PDF, and the PDF can be derived from the CDF by taking the derivative.

What is the range of values that the CDF can take?

The CDF always ranges from 0 to 1, as it represents the probability of a random variable falling below a certain value. At 0, there is no probability of the variable being less than any value, and at 1, there is a 100% probability of the variable being less than or equal to the maximum value of the variable.

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