Understanding Probability Distributions

The video tutorial introduces discrete probability distributions using a six-sided die. It explains how to draw a probability distribution for a fair die, highlighting the concept of uniform distribution where each outcome is equally likely. The tutorial then explores a modified die with altered probabilities, demonstrating a non-uniform distribution. This provides a practical understanding of how changes in a die's faces affect probability distributions.

What is a discrete probability distribution?

• A distribution where outcomes are continuous

• A distribution where outcomes are discrete

• A distribution with only one outcome

• A distribution with infinite outcomes

How many equally likely outcomes are there when rolling a fair six-sided die?

• Seven

• Four

• Five

• Six

What is the probability of rolling a 3 on a fair six-sided die?

• 1/2

• 1/3

• 1/4

• 1/6

What term describes a probability distribution where all outcomes are equally likely?

• Uniform distribution

• Poisson distribution

• Normal distribution

• Binomial distribution

What change was made to the six-sided die in the modified example?

• A side with 3 dots was erased

• A side with 1 dot was added

• A side with 2 dots was erased

• A side with 6 dots was added

How does the probability of rolling a 1 change in the modified die?

• It becomes 1/6

• It becomes 2/6

• It remains unchanged

• It becomes 3/6

What is the probability of rolling a 2 on the modified die?

• 1/2

• 1/3

• 0

• 1/6

Which outcomes remain unchanged in probability on the modified die?

• 2 and 3

• 3, 4, 5, and 6

• 1 and 2

• 4 and 5

What type of distribution does the modified die represent?

• Uniform distribution

• Non-uniform distribution

• Normal distribution

• Exponential distribution

What is the key difference between uniform and non-uniform distributions?

• Non-uniform distributions have equal probabilities

• Uniform distributions have unequal probabilities

• Uniform distributions have equal probabilities

• Non-uniform distributions have no probabilities