R Tutorial: Continuous distributions

Want to learn more? Take the full course at https://learn.datacamp.com/courses/practicing-statistics-interview-questions-in-r at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi again! In the previous video, we focused on discrete distributions. In this video, we will talk about continuous distributions. Let's review the difference between discrete and continuous distributions using the example of a uniform distribution. A random variable from a discrete distribution can assume one of a finite number of values. There is a probability associated with obtaining each of the values. In a continuous distribution, a random variable can assume one of an infinite number of values. The probability of obtaining one specific value amounts to zero. We can calculate the probability that a continuous random variable lies within a range. In the case of continuous distributions, we work with probability density functions. The density function is used to specify the probability of the random variable falling within a particular range of values. The area under the density function sums up to one. The area under the density function in a given range determines the probability that a random variable falls within that range. One of the most famous continuous distributions is the normal distribution. It's a continuous probability distribution with a bell-curve shape. It is fundamental to many statistical concepts like sampling and hypothesis testing. The normal distribution is usually associated with the 68-95-99.7 rule. Questions about this rule are common in interviews. The 68-95-99.7 rule states that 68% of the normally distributed data lies within one standard deviation of the mean, 95% within two standard deviations of the mean, and 99.7% within three standard deviations of the mean. In the last video, we discussed the syntax of probability functions in R. Let's take a look at these functi