We now learn eabout discrete cumulative probability distributions and cumulative distribution function.
At times, rather than having to calculate the probability of a specific value of \(X\) occurring, we'll need to calculate the probability that \(X\) be less than or equal to some value: \[P\begin{pmatrix}X \leq k \end{pmatrix}\] For such probabilities we'll need to use the cumulative distribution function.
Given a discrete random variable \(X\), and its probability distribution function \(P \begin{pmatrix}X = x \end{pmatrix}=f(x)\), we define its cumulative distribution function, CDF, as: \[F(x) = P \begin{pmatrix} X \leq k \end{pmatrix}\] Where: \[P\begin{pmatrix}X \leq x \end{pmatrix} = \sum_{t=x_{\text{min}}}^x P \begin{pmatrix} X = t \end{pmatrix}\]
This function allows us to calculate the probability that the discrete random variable is less than or equal to some value \(x\).
In practice, we rarely speak of \(F(x)\) and usually just write refer to: \[P\begin{pmatrix} X \leq x \end{pmatrix} = \sum_{t=x_{\text{min}}}^x P \begin{pmatrix} X = t \end{pmatrix}\]
A discrete random variable \(X\) whose probability distribution function is: \[P\begin{pmatrix}X=x\end{pmatrix} = \frac{x}{15}, \quad x \in \left \{ 1, \ 2, \ 3, \ 4, \ 5 \right \} \] Find \(F(3)\), in other words: find \(P\begin{pmatrix}X \leq 3 \end{pmatrix}\).
We use the cumulative distribution function and state: \[P\begin{pmatrix}X \leq 3 \end{pmatrix} = \sum_{t=1}^3 P\begin{pmatrix}X=t \end{pmatrix}\] That's: \[P\begin{pmatrix}X \leq 3 \end{pmatrix} = P\begin{pmatrix}X=1 \end{pmatrix}+P\begin{pmatrix}X=2 \end{pmatrix}+P\begin{pmatrix}X=3 \end{pmatrix}\] Using the fact that \(P\begin{pmatrix}X = x \end{pmatrix} = \frac{x}{15}\), we find: \[\begin{aligned} P\begin{pmatrix}X \leq 3 \end{pmatrix} & = P\begin{pmatrix}X=1 \end{pmatrix}+P\begin{pmatrix}X=2 \end{pmatrix}+P\begin{pmatrix}X=3 \end{pmatrix}\\ & = \frac{1}{15} + \frac{2}{15} + \frac{3}{15} \\ P\begin{pmatrix}X \leq 3 \end{pmatrix} & = \frac{6}{15} \end{aligned}\] Finally we can state \(P\begin{pmatrix}X \leq 3 \end{pmatrix} = \frac{6}{15} =0.4\).
Given an event \(A\)
\(P\begin{pmatrix}X > k \end{pmatrix} = 1 - P\begin{pmatrix}X \leq x \end{pmatrix}\)
We now know how to calculate the probability that \(X\) be less than, or less than or equal to, a certain value.
But what if we were asked to find the probability that \(X\) be greater than a certain value, or the probability that \(X\) is "at least" some amount?
To answer such questions we'll need to use the complement formula, which we show here:
To calculate the probability \(P\begin{pmatrix} X > a \end{pmatrix}\) we use the complement formula, which states: \[P\begin{pmatrix} X > a \end{pmatrix} = 1 - P\begin{pmatrix} X \leq a \end{pmatrix}\]
A discrete random variable, \(X\), has a probability distribution function defined as: \[f(x) = P \begin{pmatrix} X = x\end{pmatrix} = \frac{x^3}{36}\] Find the probability that \(X > 0 \).
Now that we know how to use cumulative distribution formula, we learn how to illustrate them.
This can be done in either:
At times we'll need to calculate the probability that the discrete random variable is between two specfic values, a lower bound and an upper bound.
Typically we'll need to calculate:
\[P \begin{pmatrix} a < X \leq b \end{pmatrix} \]
To do this we'll need the formula we learn here:
\[\begin{aligned} P \begin{pmatrix} a < X \leq b \end{pmatrix} &= F(b) - F(a) \\ & = \sum_{x = x_{\text{min}}}^b f(x) - \sum_{x = x_{\text{min}}}^a f(x) \\ P \begin{pmatrix} a < X \leq b \end{pmatrix} &= \sum_{x = x_{\text{min}}}^b P \begin{pmatrix} X = x \end{pmatrix} - \sum_{x = x_{\text{min}}}^a P\begin{pmatrix} X = x \end{pmatrix}\ \end{aligned}\]