Regression model: Definition, Types and examples - Voxco (2024)

• December 17, 2021

A regression model determines a relationship between an independent variable and a dependent variable, by providing a function. Formulating a regression analysis helps you predict the effects of the independent variable on the dependent one.

Example: we can say that age and height can be described using a linear regression model. Since a person’s height increases as age increases, they have a linear relationship.

Regression models are commonly used as statistical proof of claims regarding everyday facts.

In this article, we will take a deeper look at the regression model and its types.

There are three different types of regression models:

1. Linear
2. Non-linear
3. Multiple

Let’s look at them in detail:

Linear regression model

A linear regression model is used to depict a relationship between variables that are proportional to each other. Meaning, that the dependent variable increases/decreases with the independent variable.

In the graphical representation, it has a straight linear line plotted between the variables. Even if the points are not exactly in a straight line (which is always the case) we can still see a pattern and make sense of it.

For example, as the age of a person increases, the level of glucose in their body increases as well.

Multiple regression model

A multiple regression model is used when there is more than one independent variable affecting a dependent variable. While predicting the outcome variable, it is important to measure how each of the independent variables moves in their environment and how their changes will affect the output or target variable.

For example, the chances of a student failing their test can be dependent on various input variables like hard work, family issues, health issues, etc.

Unlike the above-mentioned regression model types, stepwise regression modeling is more of a technique used when various input variables are affecting one output variable. The analyst will automatically proceed to measure the variable that is directly correlated input variable and build a model out of it. The rest of the variables come into the picture when he decides to perfect the model.

The analyst may add the remaining inputs one after the other based on their significance and the extent to which it affects the target variable.

For example, vegetable prices have increased in a certain areas. The reason behind the event can be anything from natural calamities to transport and supply chain management. When an analyst decides to put it out on a graph, he will pick up the most obvious reason, heavy rainfall in the agricultural regions.

Once the model is built, he can then add the rest of the affecting input variables into the picture based on their occurrence and significance.

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