Data Science and Machine Learning (Theory and Projects) A to Z - Expectations: Definition

The video tutorial introduces the concept of expectation in statistics, explaining it as the mean or average associated with a random variable. It covers how to calculate expectation for both discrete and continuous random variables, using summation and integration respectively. An example with a Bernoulli random variable is provided to illustrate the concept. The tutorial concludes with a preview of a Python experiment to compute expectation.

What is another term commonly used for expectation in statistics?

• Variance

• Mode

• Standard Deviation

• Mean

How is the expected value of a continuous random variable calculated?

• By summing the probabilities of all outcomes

• By integrating the product of the variable and its density function

• By multiplying the variable by its probability

• By averaging the outcomes

In the context of a Bernoulli random variable, what does the expected value represent?

• The probability of success

• The variance of the distribution

• The total number of trials

• The probability of failure

If a biased coin is tossed 10,000 times with a probability of heads being 0.7, how many times would you expect heads to appear?

• 7,000 times

• 10,000 times

• 5,000 times

• 3,000 times

What is the probability of getting a 0 in a Bernoulli random variable with a probability of 0.7 for getting a 1?

• 0.3

• 1.0

• 0.5

• 0.7

What is the next step mentioned in the video after discussing the Bernoulli example?

• Exploring standard deviation

• Discussing variance

• Learning about mode

• Performing experiments in Python

What programming language is suggested for performing experiments on expectation?

• Java

• C++

• JavaScript

• Python