In this section, we shall mainly focus on simple regression to build a sound understanding.

A Simple Linear Regression uses a single feature (independent variable) to predict a target (dependent variable) by fitting a best linear relationship, whereas Multiple Linear Regression uses more than one features to predict a target variable by fitting a best linear relationship. Linear suggests that the relationship between dependent and independent variable can be expressed in a straight line.

Identifying which variables contribute more towards the outcomes.Īs we saw in pevious lesson, linear implies that the model functions along a straight or nearly straight line.

Finding an association, relationship between variables.

Regression Analysis helps us with analytics in following ways: Regression is proven to give credible results if the data follows parametric assumptions which will be covered in upcoming lessons. Regression is a parametric technique used to predict the value of a target variable Y based on one or more input feature variables X. We shall now build on these ideas to explain the regression process. So far, we have covered topics like basic hypothesis testing, variable relationships, statistical learning.

Predict the target of a previously unseen input feature.

Draw a regression line based on calculated slope and intercept.

Calculate the slope and y-intercept using the slope value.

Explain simple linear regression as solving for equation y=mx+c.

Describe statistical modeling with simple regression.

This lesson will introduce you to regression process based on the statistical ideas we have discovered so far. It is one of the simplest algorithms to master but still requires some statistical understanding of the underlying.

For aspiring data scientists, like yourselves, regression analysis the first real 'learning' algorithm that they come across with.

The simple linear regression equation