![]() Linear regression makes some assumptions about the data that need to be checked before applying the method. What are the assumptions of linear regression? The choice of method depends on the assumptions and properties of the data. There are different methods to estimate the coefficients β0 and β1, such as ordinary least squares (OLS), maximum likelihood estimation (MLE), or gradient descent 5. Where y is the dependent variable, x is the independent variable, β0 is the intercept (the value of y when x is zero), β1 is the slope (the change in y for a unit change in x), and ϵ is the error term (the difference between the observed and predicted values). This line is called the regression line, and it has the form: Linear regression works by finding the line that minimizes the sum of squared errors between the observed data and the predicted values. It can also help us understand the causal effects of variables, test hypotheses, and evaluate the quality of our predictions. Linear regression is simple, yet powerful. It has many applications in various fields, such as economics, finance, epidemiology, machine learning, and environmental. Or, if we want to predict how much soil erosion occurs at different levels of rainfall, we can use linear regression to fit a line that best describes the data. Linear regression is a technique that allows us to model the relationship between a dependent variable (also known as the response or outcome) and one or more independent variables (also known as the predictors or features).įor example, if we want to study how income affects happiness, we can use linear regression to estimate how much happiness changes as income increases or decreases. In this newsletter, we will introduce you to one of the most fundamental and widely used statistical methods: linear regression.
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