2024-11-06
Distinguish between discrete and continuous random variables.
Calculate probabilities for continuous random variables.
Calculate and graph a density (i.e., probability density function, PDF).
Calculate and graph a CDF (i.e., a cumulative distribution function)
For a discrete RV, the set of possible values is either finite or can be put into a countably infinite list.
Continuous RVs take on values from continuous intervals, or unions of continuous intervals
Discrete RV \(X\):
Continuous RV \(X\):
Probability density function
The probability distribution, or probability density function (pdf), of a continuous random variable \(X\) is a function \(f_X(x)\), such that for all real values \(a,b\) with \(a \leq b\),
\[\mathbb{P}(a \leq X \leq b) = \int_a^b f_X(x)dx\]
Remarks:
Note that \(f_X(x) \neq \mathbb{P}(X=x)\)!!!
In order for \(f_X(x)\) to be a pdf, it needs to satisfy the properties
\(f_X(x) \geq 0\) for all \(x\)
\(\int_{-\infty}^{\infty} f_X(x)dx=1\)
Example 1.1
Let \(f_X(x)= 2\), for \(a \leq x \leq 3\).
Example 1.2
Let \(f_X(x)= 2\), for \(a \leq x \leq 3\).
Example 1.3
Let \(f_X(x)= 2\), for \(a \leq x \leq 3\).
Example 1.4
Let \(f_X(x)= 2\), for \(a \leq x \leq 3\).
Example 1.5
Let \(f_X(x)= 2\), for \(a \leq x \leq 3\).
Chapter 24 Slides