Data Science and Machine Learning (Theory and Projects) A to Z - Continuous Random Variables: Uniform Distribution Pytho

The video tutorial introduces the numpy random module, focusing on generating uniformly distributed random numbers between 0 and 1. It demonstrates how to visualize these numbers using Matplotlib, adjusting the number of bins to better observe uniformity. The tutorial also covers transforming random numbers to different ranges by scaling and shifting. Finally, it previews upcoming topics on exponential and Gaussian random variables.

What is the range of a single random number generated using np.random.rand()?

• 0 to 1

• 10 to 100

• 1 to 100

• 0 to 10

What does the histogram of 1000 uniformly distributed random numbers between 0 and 1 typically look like?

• A random scatter

• A triangle

• A perfect square

• A bell curve

How does increasing the number of bins in a histogram affect the visualization of uniform random numbers?

• It changes the range of the data

• It makes the distribution look less uniform

• It makes the distribution look more uniform

• It has no effect

What is the effect of increasing the sample size on the visibility of uniformity in a distribution?

• It makes the distribution skewed

• It has no effect

• Uniformity becomes more visible

• Uniformity becomes less visible

What happens to the height of the density function when the range of uniform random numbers is scaled from 0 to 1 to 0 to 100?

• It remains the same

• It doubles

• It becomes 1/100

• It becomes 100 times larger

If you shift all numbers in a uniform distribution by 20, what will be the new range if the original range was 0 to 100?

• 30 to 130

• 10 to 110

• 20 to 120

• 0 to 100

Which of the following is NOT a type of random variable mentioned for future videos?

• Binomial random variable

• Gaussian random variable

• Exponential random variable

• Normal random variable