Normal Distribution and Log Transformations

The video tutorial explains the concept of normal and log normal distributions, highlighting their differences and real-life examples. It discusses how income data is right-skewed and can be normalized using a log transform. The tutorial also covers the application of log normal distribution in data science, particularly in machine learning models, and demonstrates how to visualize these distributions using Python libraries like seaborn and pandas.

What is a common shape associated with normal distribution?

• Skewed curve

• Bell curve

• Exponential curve

• Flat line

Which of the following is an example of normal distribution?

• Test scores

• Income levels

• Hospitalization days

• Advertising budgets

What does a bell curve represent in statistics?

• A skewed distribution

• A normal distribution

• A flat distribution

• An exponential distribution

What is the primary characteristic of a normal distribution?

• Right-skewed

• Left-skewed

• Symmetrical

• Uniform

What does a bell curve represent in statistics?

• An exponential distribution

• A flat distribution

• A normal distribution

• A skewed distribution

What is the primary characteristic of a normal distribution?

• Left-skewed

• Symmetrical

• Uniform

• Right-skewed

Why is income distribution considered right-skewed?

• Most people earn the same amount

• There are no high earners

• A few individuals earn significantly more than the average

• Income is evenly distributed

In the context of the video, what does a 'long tail' in a distribution indicate?

• A few extremely low values

• A few extremely high values

• A large number of high values

• A large number of low values

In the context of the video, what does a 'long tail' in a distribution indicate?

• A large number of low values

• A few extremely high values

• A few extremely low values

• A large number of high values

What happens to a right-skewed distribution when a log function is applied?

• It remains right-skewed

• It becomes a flat line

• It becomes left-skewed

• It becomes a normal distribution