**The distribution of an event consists not only of the input values that can be observed, but is made up of all possible values.**

So, the distribution of the event – rolling a die – will be given by the following table. The probability of getting one is 0.17, the probability of getting 2 is 0.17, and so on… you are sure that you have exhausted all possible values when the sum of probabilities is equal to 1% or 100%. For all other values, the probability of occurrence is 0.
Each probability distribution is associated with a graph**Here’s the graph for our example. This type of distribution is called a uniform distribution. It is crucial to understand that the distribution in statistics is defined by the underlying probabilities and not the graph. The graph is just a visual representation. You can learn more about visualizing data in statistics from our articles Visualizing Data with Contingency tables and Scatter Plots and Visualizing Data with Bar, Pie and Pareto Charts. Alright. Now think about rolling two dice. What are the possibilities? One and one, two and one, one and two, and so on. Here’s a table with all the possible combinations. We are interested in the sum of the two dice. So, what’s the probability of getting a sum of 1? It’s 0, as this event is impossible. What’s the probability of getting a sum of 2? There is only one combination that would give us a sum of 2 – when both dice are equal to 1. So, 1 out of 36 total outcomes, or 0.03. Similarly, the probability of getting a sum of 3 is given by the number of combinations that give a sum of three divided by 36. Therefore, 2 divided by 36, or 0.06. We continue this way until we have the full probability distribution.**

*describing the likelihood of occurrence of every event.*
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