Data Science and Machine Learning (Theory and Projects) A to Z - Continuous Random Variables: Uniform Distribution Solut

The video tutorial explains the concept of uniform random variables, focusing on the distinction between discrete and continuous types. It uses the example of a fair die to illustrate a discrete uniform random variable, where each outcome has an equal probability of 1/6. The Probability Mass Function (PMF) is detailed, showing how it applies to discrete variables. The tutorial emphasizes that uniform random variables are not limited to continuous types, highlighting their presence in discrete scenarios as well.

What type of random variable is X when considering the role of a fair die?

• Discrete uniform random variable

• Continuous uniform random variable

• Exponential random variable

• Normal random variable

What is the probability of rolling a 3 with a fair die?

• 1/3

• 1/4

• 1/5

• 1/6

How many possible outcomes does a fair die have?

• 6

• 7

• 5

• 4

Which of the following statements is true about uniform random variables?

• They can only be continuous.

• They can only be discrete.

• They can be either discrete or continuous.

• They are always normally distributed.

What does the PMF of a discrete uniform random variable represent?

• The cumulative distribution function

• The probability of each possible outcome

• The mean of the distribution

• The variance of the distribution