Explanation of some terms in Normal(Gaussian) Distribution

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SUMMARY

The discussion clarifies the Normal Distribution Theorem, specifically the expression P(Z ≤ z). Here, Z represents a random variable, while z denotes a specific value or deviation. The expression indicates the probability that the random variable Z is less than or equal to the value z. This relationship is defined by the cumulative density function, represented as Φ(z) = P(Z ≤ z).

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  • Basic concepts of Normal Distribution
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Ein Krieger
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Hello,

I need your help in clarifying some points from Normal Distribution Theorem:

What does this expression exactly says:

P(Z<=z) ?

Z is random variable?

z is deviation?
 
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##P(Z \leq z)## is the probability that the random variable, denoted capital ##Z##, is less than or equal to some specific value, lowercase ##z##. The lowercase ##z## is a variable, so that this is in fact a function

$$\Phi(z) = P(Z \leq z),$$
called the "cumulative density function.
 

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