Continuous Random Variable
Consider a generalized experiment rather than taking some particular experiment. Suppose that in your experiment, the outcome of this experiment can take values in some interval (a, b). That means that each and every single point in the interval can be taken up as the outcome values when you do the experiment. Hence, you do not have discrete values in this set of possible values but rather an interval.
Thus, X= {x: x belongs to (a, b)}
Example of a Continuous Random Variable
Some examples of Continuous Random Variable are:
- The height of an adult male or female.
- The weight of an object.
- The time is taken to complete a task.
- The temperature of a room.
- The speed of a vehicle on a highway.
Discrete Random Variable
Discrete Random Variables are an essential concept in probability theory and statistics. Discrete Random Variables play a crucial role in modelling real-world phenomena, from the number of customers who visit a store each day to the number of defective items in a production line. Understanding discrete random variables is essential for making informed decisions in various fields, such as finance, engineering, and healthcare. In this article, we’ll delve into the fundamentals of discrete random variables, including their definition, probability mass function, expected value, and variance. By the end of this article, you’ll have a solid understanding of discrete random variables and how to use them to make better decisions.