Explanation of some terms in Normal(Gaussian) Distribution

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P(Z ≤ z) represents the probability that the random variable Z is less than or equal to a specific value z. In this context, Z is the random variable following a normal distribution, while z is a particular value or deviation. The expression is a function known as the cumulative density function, denoted as Φ(z). This function quantifies the likelihood of Z being at or below the value of z. Understanding this relationship is crucial for interpreting data within the framework of normal distribution.
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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