A probability distribution can be conceived as a theoretical frequency distribution, that is, it is a distribution that describes how the results are expected to vary. Since these kinds of distributions deal with expectations, they are very useful models for making inferences and making decisions under conditions of uncertainty.
The following video introduces you to the discrete probability distribution.
Random variable.
It is one that assumes different values as a result of the results of a random experiment.
These variables can be discrete or continuous. If a random variable is allowed to take on only a limited number of values, it is called a discrete random variable. On the contrary, if it is allowed to assume any value within certain limits, it is called a continuous random variable.
The Expected Value.
The expected value is a fundamental concept in the study of probability distributions. For many years this concept has been widely applied in the insurance business and in the last twenty years it has been applied by other professionals who almost always make decisions under conditions of uncertainty.
To obtain the expected value of a discrete random variable, we multiply each value that it can assume by the probability of occurrence of that value and then add the products. It is a weighted average of the results expected in the future.
