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Mathematics · Ch 11 — Probability Distributions

Cumulative Distribution Function or Distribution Function

11.3.3

Cumulative Distribution Function or Distribution Function

There are many situations where we want the probability that XX is at most some value xx, not just equal to it.

Definition 11.4 (Cumulative distribution function). For a discrete random variable XX taking the values x1<x2<x3<⋯x_1<x_2<x_3<\cdots with pmf f(xi)f(x_i), the cumulative distribution function (cdf, also called the distribution function) is

F(x)=P(X≤x)=∑xi≤xf(xi).F(x)=P(X\le x)=\sum_{x_i\le x} f(x_i).

While the pmf f(x)f(x) is defined only at the discrete support points x1,x2,…x_1,x_2,\dots, the cdf F(x)F(x) is defined for every real number xx — it is a step function, constant between consecutive support points and jumping by f(xi)f(x_i) at each xix_i. …

Figure 11.5Probability mass function of $f(x)$ for the number of fours when a pair of fair dice is rolled: $f(0)=\frac{25}{36}$, $f(1)=\frac{10}{36}$, $f(2)=\frac{1}{36}$ (Fig. 11.5)
Fig. 11.5 — Probability mass function of $f(x)$ for the number of fours when a pair of fair dice is rolled: $f(0)=\frac{25}{36}$, $f(1)=\frac{10}{36}$, $f(2)=\frac{1}{36}$ (Fig. 11.5)

Drawn by us to help you understand the concept clearly, and verified to make sure it's accurate. For exams, practice from your textbook's own diagram.

What this figure shows. Probability mass function of f(x)f(x) for the number of fours when a pair of fair dice is rolled: f(0)=2536f(0)=\frac{25}{36}, f(1)=1036f(1)=\frac{10}{36}, $f(2)=\frac{1}{ …