Data science simplified: Simple linear regression models

In the previous posts of this series, we discussed the concepts of statistical learning and hypothesis testing. In this article, we dive into linear regression models.

Before we dive in, let us recall some important aspects of statistical learning.

Independent and dependent variables:

In the context of statistical learning, there are two types of data:

• Independent variables are data that can be controlled directly.
• Dependent variables are data that cannot be controlled directly.

The data that can’t be controlled (i.e. dependent variables) need to be predicted or estimated.

Model:

A model is a transformation engine that helps us express dependent variables as a function of independent variables.

Parameters:

Parameters are ingredients added to the model to estimate the output.

Concept

Linear regression models provide a simple approach toward supervised learning and are simple yet effective.

Wait, what do we mean by linear?

Linear implies that things are arranged in or extending along a straight or nearly straight line. Linear suggests that the relationship between dependent and independent variable can be expressed in a straight line.

Recall your geometry lesson from high school. What is the equation of a line?

y = mx + c

Formulation

Model building and interpretation

Interpretation

Evaluation

Conclusion

---