Almost every industry uses linear regression in data science. From Finance to Human Resources, finds applications because of its simplicity. This diverse tool predicts outcomes, studies trends, and performs feature selection. If you wish to establish a career in data science, this topic is important. It eases the complex statistical modelling techniques for you.
Keep reading to learn the meaning and types of linear regression. Explore the assumptions, model evaluation, and learn the importance of using this technique. Finally, get a tip to boost your resume with a jobguaranteed program.
Let's begin!
Important terms
Before moving forward, let's take a quick stop. The terms listed below will help you understand the concept better.

Dependent variable: It is also known as a response or target variable.

Independent variable: Also known as a predictor or explanatory variable.
What is Linear Regression?
Technically, linear regression is a statistical modelling technique. You use it to establish a linear relationship between two variables. These variables are dependent and independent. When we use one dependent variable, the model evaluates its relationship with multiple independent variables. The main aim of linear regression is to find a linear equation that best fits the dependent and independent variables.
1. Types
Before jumping to the equations, you must know the types of linear regression.

Simple linear regression: In a Cartesian coordinate system, a simple linear equation is seen as a straight line. Similarly, simple linear regression displays a straight linedrelationship between one dependent and one independent variable.
The equation of simple linear regression is y = a + bx + c. The terms are explained below.

y is the dependent variable

x is the independent variable

a is the yintercept

b is the slope (or coefficient)

c is the error term


Multiple linear regression: As the name suggests, in multiple linear regression, you'll find more than one independent variable. As there are multiple independent variables, this regression is represented in a hyperplane.
The equation is y = a + b₁x₁ + b₂x₂ + ... + bₚxₚ + c. Here, the terms are explained below.

y is the dependent variable

x₁,… are the dependent variables

a is the yintercept

b₁, b₂, ..., bₚ are the slopes corresponding to each variable

c is the error term

2. Assumptions
To present accurate results, linear regression makes the following assumptions.

The dependent and independent variables are related linearly.

Every observation in the model is not dependent on any other observation.

At every level of independent variables, the variance of errors is constant.

The errors in the model are normally distributed.

Multiple independent variables are not highly correlated with each other.
3. Model Evaluation & Interpretation
Once you've fitted a linear regression model, it's time to evaluate performance and interpret results. The evaluation metrics listed below help in this process.


Coefficient of determination

Root mean squared method

Hypothesis testing of coefficients

Why is Linear Regression used in Data Science and Analytics?
Linear regression is used for building models that predict the value of a dependent variable based on the independent variables. It also helps analyse trends by fitting a linear regression equation to historical data points. You will also find its use in assessing variables and their impact on each other.
In Real Estate, linear regression helps in predicting house prices. It considers variables like location, number of rooms, and size for this task. Similarly, the technique predicts the relationship between advertising spending and customer behaviour. In such cases, linear regression helps companies in making the right decisions that lead to better results. On a national level, this mathematical model is also used to identify risk factors in healthcare. For this, it uses age, gender, and costs as the variables.
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In short, linear regression is a statistical technique that helps several industries. It does that by predicting the relationship between variables, allowing feature selection, and much more. From Finance and Banking to Healthcare, every industry uses it for prediction analysis.
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