In statistics, you have to determine if the pattern you identify in the data is significantly different from no pattern at all. There are a bunch of ways to do this, but the most common is the use of probability functions. Probability functions permit you to determine the chance that your model is different. Among […]
What do you do when you’re lost? You use tools like a compass and GPS to figure out where you are and how to get where you are going. Well, in statistics there are ways to figure out where a data point or set falls. These are called measures of position. Once we know where […]
A discrete probability distribution describes a random variable that can only produce distinct and finite outcomes. In this article, we explore what a discrete probability distribution looks like, and how to calculate the expected value of a random variable from the discrete probability distribution.
The geometric distribution formula can be used to calculate the probability of success after a given number of failures. The probabilities it generates form a geometric sequence, hence its name. Check out this article to learn more about the geometric distribution formula!
The word “geometric” might remind you of the triangles and squares learned about back in ninth grade geometry class. However, elsewhere in mathland, “geometric” simply refers to multiplication. Thus, “geometric probability distribution” will involve the multiplication of probabilities. Let’s consider some examples.
What is a continuous probability distribution? It will be helpful to first define the terms continuous versus discrete, and then compare the two. According to Apple Dictionary, discrete means, “individually separate and distinct,” whereas continuous means, “forming an unbroken whole; without interruption.” A great way to visualize the difference is to look at a fretted […]
The binomial distribution describes random variables with only two possible outcomes. This post explains the binomial distribution, and how to use it!
The normal distribution, or bell curve, has wide applications in inferential statistics. This post will help you understand what it is, and how to use it!