In the previous lesson, you learned how you can create a cumulative distribution function for discrete and continuous random variables. In this lab, you'll try to calculate a CDF for a dice roll ...

The PMF function that we saw before works great for inspecting discrete random variables and calculating their expected values. However, we did see that when moving towards continuous random variables ...

The first step is to ensure that the given distribution actually is a valid probability distribution. As mentioned earlier, ...

Example 1: A coin is flipped. Random variable X takes the value 1 if the coin lands heads, and X takes the value 0 if the coin shows tails. Example 2: Three balls are drawn without replacement from a ...

Abstract: A cumulative distribution function (CDF) states the probability that a sample of a random variable will be no greater than a value x, where x is a real value. Closed form expressions for ...

A discrete random variable is a type of random variable that can take on a countable set of distinct values. Common examples include the number of children in a family, the outcome of rolling a die, ...

Abstract: This study analyzes the rainfall profile in terms of discrete-time series using a binomial distribution. In the first part of the study, the descriptive statistics of binomial distribution ...

One confusing question over a long period of time is how transfer the discrete function transfers into continuous function. Recently the issue has been resolved but some details of the transformation ...