Regression Analysis – Introduction

What is Regression Analysis?

Regression Analysis is a Statistical Technique used to investigate and model the relationship between variables. It helps to identify trends associated with data and quantify them.

In regression analysis, there are 2 main types of variables. Predictor Variable (Independent Variable) and Dependent Variable (Response Variable).

For example, let’s say we use parents’ height to predict the height of their children. In this case, the predictor variable is “Parents’ height” and Dependent Variable is the “height of children”. This is because its value is depending on the value of the Independent (Predictor) variable value.

The above is a simple example where regression analysis comes into life. It is used in numerous disciplines including Machine Learning and Artificial Intelligence. Most of the engineering courses offer Regression Analysis as a subject because of its importance in those respective fields.

There are several types. Simple Linear Regression, Multiple Linear Regression, and Logistic Regression are some.

In simple linear regression, we deal with one Predictor Variable and One Response Variable. We have several predictor variables in Multiple Linear Regression. Also, when the Response Variable is Categorical, we use Logistic regression. But the basic core is the same in all types. If you get the fundamentals right, you are gonna master every aspect easily.

Here is a useful link where you can read more about the Applications of regression analysis.

Statistical software such as Minitab, SPSS, SAS and programming packages like R and Python can be used to do regression analysis. Also, Microsoft Excel can be used to do the same. In future posts, we will be mostly using Microsoft Excel, Minitab, and Python.

Thank You.

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