Regression model: Definition, Types and examples CSAT

Regression model: Definition, Types and examples

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What is regression model?

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 its age increases, they have a linear relationship. 

Regression models are commonly used as a statistical proof of claims regarding everyday facts. In this article we will take a deeper look at regression model and its types. 

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Types of regression models

  • Linear regression model

A linear regression model is used to depict a relationship between variables which are proportional to each other. Meaning, 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 out of it. 

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. 

Example: Chances of a student failing their test can be dependent on various input variables like hard work, family issues, health issues, etc. 

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  • Non-linear regression model 

In the non-linear regression model, the graph doesn’t show a linear progression. Depending on how the response variable reacts to the input variable, the line will rise or fall showing the height or depth of the effect of the response variable. 

To know that a non-linear regression model is the best fit for your scenario, make sure you look into your variables and their patterns. If you see that the response variable is showing not so constant output to the input variable, you can choose to use a non-linear model for your problem.

Example: A patient’s response to treatment can be good or bad depending on their body tendency and willpower. 

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Stepwise regression modelling

Unlike the above-mentioned regression model types, stepwise regression modelling I 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. 

Example: vegetable prices have increased in a certain area. 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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