![]() If there are two or more inputs, this is multiple linear regression. When there is only one input variable we call this simple linear regression. However, all it means is that the predicted value you are looking for (the output variable) depends on the values that you put into the model (the input variables). It uses one or more independent input variables to predict a single, dependent output variable. In the simplest terms, a linear regression model allows us to see how one thing changes based on how other things change. We don’t want to presume any prior knowledge here, so before getting into how we can perform linear regression, let’s cover the basics. Ready to get all the basics of linear regression down? Then let’s dive in. Preparing a linear regression model: 4 techniques.How do you prepare data for performing linear regression?.What are the key assumptions of effective linear regression?.Where necessary, we’ve also included links to some excellent free statistics resources that will explain the math clearly. To simplify things, we’ll use plain English, explaining any jargon as we go. It’s also relatively easy to grasp and can be applied in many disciplines, from finance and marketing to medicine.īut how does linear regression work? What are its strengths and weaknesses? In this beginner’s guide, we’ll cover everything you need to know to get started with linear regression. Linear regression is useful as it allows us to model the relationship between one or more input variables and a dependent output variable. ![]() One of the most common statistical techniques used in predictive analytics is linear regression. By identifying trends and patterns, predictive analytics helps forecast future events and can even fill in the gaps in datasets by automating otherwise time-consuming imputation tasks. Predictive analytics is undoubtedly one of the most lauded tools in data science and machine learning. ![]()
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