Cumulative distribution function

In probability theory, a cumulative distribution function F(x) of a probability density function say f(x) is a real valued and continuous function whose value is the proportion of probability values of a variable which occur on the part of the real line up and including the value of that variable; i.e.,

Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F(x) = \int_{-\infty}^x \,f(\lambda)\,d\lambda}

Considering this definition in light of the fundamental theorem of the Integral Calculus yields:

$f(x)={\frac {dF(x)}{dx}}$

Due to the properties of the probability density function f(x), the cumulative distribution function F(x) will have the following properties:

(1) $F(x)\geq 0$ Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \forall x } inside its domain of support.

(2) Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle \lim_{x \to \infty}F(x) = 1 } , i.e., finitely convergent (to unity by convention).

(3) Failed to parse (MathML with SVG or PNG fallback (recommended for modern browsers and accessibility tools): Invalid response ("Math extension cannot connect to Restbase.") from server "https://wikimedia.org/api/rest_v1/":): {\displaystyle F(a) \leq F(b) } for a<b, i.e., is non-decreasing

If the domain of the variable is finite, then the upper limit in equation (2) above should be the upper bound of the variables domain of support.