R Tutorial: Discrete 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 and welcome to Preparing for Statistics Interview Questions in R. My name is Zuzanna Chmielewska, and this course will prepare you to answer R statistical interview questions. This course is a little bit more challenging than a typical DataCamp course because it aims to get you ready for a job interview. The first chapter of this course focuses on probability theory. Probability theory is the foundation of statistics, so interviewers like to test your knowledge of this topic. There are plenty of statistical distributions out there, but we will focus on these distributions which are popular among interviewers. This video and the following exercises focus on discrete distributions. The next video focuses on continuous distributions. Let's kick off with the structure of probability functions in R for both discrete and continuous distributions. Probability functions consist of a prefix and an abbreviated name of a distribution. For example, d stands for density and the norm is an abbreviated name of a normal distribution, so the dnorm function returns the density of a normal distribution. The other prefixes are: p for distribution function,  q for quantile function, and r for random variates. Awesome! Now, let's review some of the discrete distributions starting with the discrete uniform distribution. Discrete uniform distribution is a probability distribution whereby a finite number of values have equal probability. A simple example of the discrete uniform distribution is throwing a fair die. The possible outcomes are 1 to 6. Each time you throw the dice, the probability of a given score is 1/6. There are multiple ways to generate random numbers from a discrete uniform distribution. You can use the sample function, which