Question

Describe and explain what are the roles of simple linear regression in Data Science. Illustrate with a real-world application for each of the role described. Briefly explain how they perform the roles.( Detailed Explanation 1000 words)

Answer 1

Here there are roles of simple linear regression in Data Science are -

Linear Regression is a Machine Learning algorithm that is used to predict the value of a quantitative variable. Simple linear regression is actually a basic regression analysis where we have just two variables, an independent variable and a dependent variable. Based on the changes made to the independent variable, we predict the value of the dependent variable. Consider a simple example of predicting the amount of crop yield based on the amount of rainfall received. This is pure case of Simple Linear Regression because we have just two variables : amount of rainfall (independent variable) and the crop yield (dependent variable).

Below are some real world applications of Simple Linear Regression:

1. Linear Regression can be used to predict the sale of products in the future based on past buying behaviour.

2. Economists use Linear Regression to predict the economic growth of a country or state.

3. Sports analyst use linear regression to predict the number of runs or goals a player would score in the coming matches based on previous performances.

4. An organisation can use linear regression to figure out how much they would pay to a new joinee based on the years of experience.

5. Linear regression analysis can help a builder to predict how much houses it would sell in the coming months and at what price.

6. Petroleum prices can be predicted using Linear Regression.