Why is the cdf G considered a strictly increasing function?

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The cumulative distribution function (cdf) G is defined as a strictly increasing function, meaning that for any two random numbers v1 and v2, if v1 < v2, then G(v1) < G(v2). The discussion clarifies that the author uses the equivalence symbol (<=>) to emphasize that both implications hold: v1 < v2 if and only if G(v1) < G(v2). This is crucial as it establishes a two-way relationship, reinforcing that if v1 equals v2, then G(v1) must equal G(v2), which is a fundamental property of cdfs.

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PAHV
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Let's say we have a cumulative distribution function (cdf) G and random numbers v1 and v2.

The definition of strict increasing function is: v1 < v2 => G(v1) < G(v2).

In a statistics book, the author writes:

"...but with the additional assumption that the cdf G is a strictly increasing function. That is, v1 < v2 <=> G(v1) < G(v2)".

a) He writes the definition with an equivalence (<=>) and not an implication (=>). Could someone explain why? Does in fact the definition also imply that its is an equivalence?

b) The authors definition: "v1 < v2 <=> G(v1) < G(v2)" must imply that:
v1 = v2 <=> G(v1) = G(v2). Correct?

Any help is very appreciated!
 
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PAHV said:
Let's say we have a cumulative distribution function (cdf) G and random numbers v1 and v2.

The definition of strict increasing function is: v1 < v2 => G(v1) < G(v2).

In a statistics book, the author writes:

"...but with the additional assumption that the cdf G is a strictly increasing function. That is, v1 < v2 <=> G(v1) < G(v2)".

a) He writes the definition with an equivalence (<=>) and not an implication (=>). Could someone explain why? Does in fact the definition also imply that its is an equivalence?

b) The authors definition: "v1 < v2 <=> G(v1) < G(v2)" must imply that:
v1 = v2 <=> G(v1) = G(v2). Correct?

Any help is very appreciated!

I think it is because "false implies true" is true, so he wanted to avoid that by using <->. Or maybe it is just an insignificant detail. I think that your 2 conclusion is correct and is easy to prove.
 
PAHV said:
Let's say we have a cumulative distribution function (cdf) G and random numbers v1 and v2.

The definition of strict increasing function is: v1 < v2 => G(v1) < G(v2).

In a statistics book, the author writes:

"...but with the additional assumption that the cdf G is a strictly increasing function. That is, v1 < v2 <=> G(v1) < G(v2)".

a) He writes the definition with an equivalence (<=>) and not an implication (=>). Could someone explain why? Does in fact the definition also imply that its is an equivalence?

b) The authors definition: "v1 < v2 <=> G(v1) < G(v2)" must imply that:
v1 = v2 <=> G(v1) = G(v2). Correct?

Any help is very appreciated!

The reverse implication (<=) is true because G is a cdf.
 
As bpet said, CDF's have this property.
 

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