- Height of an NBA team
- Length of a table
- Time required to finish a marathon

Continuous Random Variable Examples

A probability density function characterizes continuous random variables by assigning a probability density, rather than an exact probability, to possible values. Since the exact probability of any single continuous value is zero, determining the probability that the variable falls within a specific interval requires calculating the definite integral of the density function over that interval.

Probability Density Function

A continuous random variable can take on any value at any point along a given interval. This variable is a real number. Continuous data is measured.

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- Discrete Random Variable
- Continous Random Variable

Types of Random Variables

Goodfellow, I., Bengio, Y., & Courville, A. (2016). $\mathit{Deep \ Learning.}$ MIT Press. Retrieved from [www.deeplearningbook.org](https://www.deeplearningbook.org) 

Deep Learning

A discrete random variable has a countable number of states. It can take on only certain values along a given interval. This variable can either be an integer or a named state that does not have a numerical value. Discrete data is counted.

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