Let’s now delve deeper into linear regression. You will now learn to identify whether a given problem is a regression or a classification problem. Imagine that you need to predict the final score of a team before the end of its innings in a cricket match. You can build a regression model to be able to make a decent prediction.

Let’s hear Prof. Dinesh talk about this and some other scenarios in a bit more detail in the next video.

Refer to this link to revise the physical significance of an equation of a straight line and also to understand how to find the slope and intercept of a straight line from its graph.

Answer the following questions in the context of the straight-line plot given below.

Since you now know what the equation of a straight line is, let’s look at the general equation of a straight line, which is fitted during **simple linear regression**.

A simple linear regression model attempts to explain the relationship between a dependent variable and an independent one using a straight line.

The independent variable is also known as the **predictor variable**, and the dependent variables are also known as the **output variables**.