Bernoulli Distribution Concepts

The video tutorial by Krishnak introduces the Bernoulli distribution, explaining its characteristics and how it differs from other distributions. It covers the probability mass function, probability calculations, and provides examples with diagrams. The tutorial also explains how to calculate the mean, variance, and standard deviation of a Bernoulli distribution. The video concludes with a summary of key points and potential interview questions related to the topic.

What are the possible outcomes in a Bernoulli Distribution?

• Three outcomes

• Two outcomes

• Only one outcome

• Multiple outcomes

Which function is used to describe the probability of discrete outcomes?

• Probability Density Function

• Probability Mass Function

• Cumulative Distribution Function

• Exponential Function

In a Bernoulli Distribution, what does the PMF represent?

• The cumulative probability of all outcomes

• The probability of a random variable being a specific value

• The probability of continuous outcomes

• The average of all possible outcomes

What is the probability of getting a head in a fair coin toss?

• 0.25

• 0.5

• 0.75

• 1.0

If the probability of success is 0.4, what is the probability of failure?

• 0.8

• 0.2

• 0.6

• 0.4

How is the mean of a Bernoulli Distribution calculated?

• As the probability of failure

• As the sum of all probabilities

• As the probability of success

• As the square root of the variance

What is the formula for the variance in a Bernoulli Distribution?

• p+q

• p^2

• p(1-p)

• 1-p

What is the standard deviation of a Bernoulli Distribution?

• sqrt(p(1-p))

• p(1-p)

• 1-p

• p

What is the expected value of a Bernoulli Distribution?

• The probability of failure

• The probability of success

• The average of all outcomes

• The sum of all possible outcomes

What are the two additional interview questions mentioned in the video?

• Probability and statistics

• Standard deviation and variance

• Mean and variance

• Median and mode