The video tutorial introduces the Bernoulli distribution, a discrete probability distribution, using a coin toss example. It explains the probability mass function, calculating probabilities for success and failure, and the mean and variance of a Bernoulli random variable. An example involving the probability of randomly selecting a lawyer in the US is provided. The tutorial concludes with an introduction to related distributions built on Bernoulli trials, such as the binomial, geometric, and negative binomial distributions.

What is the Bernoulli distribution used to model?

• The number of tails in a single coin toss

• The number of heads in a single coin toss

• The number of heads in multiple coin tosses

• The number of tails in multiple coin tosses

In a Bernoulli trial, what are the two possible outcomes?

• Pass and fail

• Win and lose

• Heads and tails

• Success and failure

How is the probability of success represented in a Bernoulli trial?

• By the letter s

• By the letter r

• By the letter p

• By the letter q

What is the probability mass function of a Bernoulli distribution?

• p^x * (1-p)^(1-x)

• p^(1-x) * (1-p)^x

• p^x + (1-p)^(1-x)

• p^(1-x) + (1-p)^x

What values can the random variable X take in a Bernoulli distribution?

• 0 and 1

• 1 and 3

• 1 and 2

• 0 and 2

What is the probability of success in a Bernoulli trial if p = 0.3?

• 0.5

• 1.0

• 0.7

• 0.3

If p = 0.4 in a Bernoulli trial, what is the probability of failure?

• 0.5

• 0.6

• 0.4

• 0.7

In a Bernoulli distribution, what does the random variable X represent?

• The outcome of a single trial

• The probability of success

• The number of trials

• The number of successes

What is the probability of getting 0 successes in a Bernoulli trial with p = 0.2?

• 0.4

• 0.6

• 0.8

• 0.2

What is the mean of a Bernoulli random variable?

• 1-p

• p

• p(1-p)

• p^2