If we let x denote the number that the dice lands on, then the probability density function for the outcome can be described as follows: measuring, height, weight, time, etc.) Probability Density FunctionsĪ probability density function (pdf) tells us the probability that a random variable takes on a certain value.įor example, suppose we roll a dice one time. But if you can measure the outcome, you are working with a continuous random variable (e.g. counting the number of times a coin lands on heads). Rule of Thumb: If you can count the number of outcomes, then you are working with a discrete random variable (e.g. There are an infinite amount of possible values for height. Some examples of continuous random variables include:įor example, the height of a person could be 60.2 inches, 65.2344 inches, 70.431222 inches, etc. The number of times a dice lands on the number 4 after being rolled 100 times.Ī continuous random variable is one which can take on an infinite number of possible values.The number of times a coin lands on tails after being flipped 20 times.Some examples of discrete random variables include: There are two types of random variables: discrete and continuous.Ī discrete random variable is one which can take on only a countable number of distinct values like 0, 1, 2, 3, 4, 5…100, 1 million, etc. Random Variablesīefore we can define a PDF or a CDF, we first need to understand random variables.Ī random variable, usually denoted as X, is a variable whose values are numerical outcomes of some random process. This tutorial provides a simple explanation of the difference between a PDF (probability density function) and a CDF (cumulative distribution function) in statistics.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |