Regression: A General Overview

 Lets talk about Regression...

So the first question that comes to mind is, " what regression actually is ? ".

And the simple answer to that question would be :-

    "A regression is a statistical technique that relates a dependent variable to one or more independent (explanatory) variables."

OK. But what does it actually mean...…..

Let us understand with the help of an example,

You are conducting a case study on a set of college students to understand if students with high CGPA also get a high GRE score. Your first task would be to collect the details of all the students.

We go ahead and collect the GRE scores and CGPAs of the students of this college. All the GRE scores are listed in one column and the CGPAs are listed in another column.

Now, if we are supposed to understand the relationship between these two variables, we can draw a scatter plot.

Here, we see that there’s a linear relationship between CGPA and GRE score which means that as the CGPA increases, the GRE score also increases. This would also mean that a student who has a high CGPA, would also have a higher probability of getting a high GRE score.

But what if I ask, “The CGPA of the student is 8.32, what will be the GRE score of the student?“

This is where Regression comes in. If we are supposed to find the relationship between two variables, we can apply regression analysis.

For the regression analysis is be a successful method, we understand the following terms:

• Dependent Variable: This is the variable that we are trying to understand or forecast.

• Independent Variable: These are factors that influence the analysis or target variable and provide us with information regarding the relationship of the variables with the target variable.

This video will better help you understand about regression,

History of regression

The term "regression" was coined by Francis Galton in the 19th century to describe a biological phenomenon. 

 

        Sir Francis Galton 

 

The phenomenon was that the heights of descendants of tall ancestors tend to regress down towards a normal average (a phenomenon also known as regression toward the mean).For Galton, regression had only this biological meaning, but his work was later extended by Udny Yule and Karl Pearson to a more general statistical context.

 

Modern Day use cases of regression

Regression has many applications in the modern day like,

• Usage in buildings models based on Artificial intelligence and Machine Learning 

•  Predicting weather and releasing reports accordingly. 

•  Usage in economics and sales, can help in improving an organization. 

•  Use cases in commerce, analyzing stocks and trade market trends. 

•  In medical fields, for illness prediction.

etc. 

Conclusion

This wraps up the general overview of regression. In further posts, we will talk about its applications in greater detail and see some use case examples also.