The video tutorial by Krish Naik covers key statistical concepts relevant to data science interviews, focusing on descriptive and inferential statistics. It explains the probability density function (PDF), probability mass function (PMF), and cumulative distribution function (CDF), providing examples to illustrate these concepts. The tutorial also explores various statistical distributions, such as normal, Bernoulli, and exponential distributions, and discusses their properties and applications.

Why is statistics important in data science interviews?

• To test programming skills

• To evaluate understanding of data

• To assess communication skills

• To check teamwork abilities

What are the two main parts of statistics?

• Qualitative and quantitative

• Theoretical and applied

• Descriptive and inferential

• Basic and advanced

What does PDF stand for in statistics?

• Probability Density Function

• Probability Distribution Function

• Probability Decision Function

• Probability Data Function

When is a probability density function used?

• For continuous random variables

• For categorical random variables

• For discrete random variables

• For binary random variables

What is the relationship between PDF and CDF?

• PDF is the derivative of CDF

• PDF is the integral of CDF

• PDF is the sum of CDF

• PDF is the product of CDF

What type of random variable is associated with PMF?

• Categorical

• Binary

• Continuous

• Discrete

Which distribution is used for a coin toss?

• Exponential distribution

• Poisson distribution

• Normal distribution

• Bernoulli distribution

What is a key characteristic of a normal distribution?

• It is bimodal

• It is symmetric

• It is skewed to the left

• It is skewed to the right

How can a log-normal distribution be converted to a normal distribution?

• By using a power transformation

• By using a log transformation

• By using a square root transformation

• By using a reciprocal transformation

What parameter affects the shape of a Poisson distribution?

• Lambda

• Beta

• Alpha

• Gamma