A PDF (Probability Density Function) is a mathematical function that describes the probability that a random variable takes on a certain value.
A CDF (Cumulative Distribution Function) is a mathematical function that describes the probability that a random variable is less than or equal to a certain value.
The main difference between a PDF and a CDF is that a PDF gives the probability of a random variable taking on a specific value, while a CDF gives the probability of a random variable being less than or equal to a specific value. Additionally, a CDF is the integral of a PDF, meaning that the area under the PDF curve up to a certain point is equal to the value of the CDF at that point.
PDF and CDF are important tools in statistics as they allow us to describe and analyze the behavior of random variables. PDFs are used to calculate probabilities and make predictions, while CDFs are used to determine the probability of a random variable falling within a certain range of values.
PDF and CDF can be used with any type of data that can be represented by a random variable. This includes continuous data, such as height or weight, as well as discrete data, such as the number of siblings a person has. However, they may not be appropriate for data that does not follow a typical distribution, such as outliers or skewed data.