What Linear is
Linear regression is a statistical technique used to analyze the relationship between two or more variables. In linear regression, one variable (dependent variable) is modeled as a function of one or more independent variables. The goal of linear regression is to find the best fitting line (or plane, in higher dimensions) that describes the relationships between the dependent and independent variables.
Steps for Linear Regression:
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Select the type of linear regression model (simple or multiple) that best fits your data.
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Collect data for the independent and dependent variables.
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Create a scatter plot of the data points to visually inspect the relationship between the two variables.
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Calculate the correlation coefficient (r) to quantify the strength of the linear relationship.
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Calculate the linear regression equation (y = mx + b) using the least-squares method.
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Use the regression equation to make predictions about the dependent variable.
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Evaluate the goodness of fit of the linear regression model.
Examples
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Linear regression is a statistical technique that is used to predict a numerical outcome based on an existing set of independent variables.
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Linear discriminant analysis is a technique used to classify data into two or more categories using a linear combination of independent variables.
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Linear mixed models are used to analyze data when there is both fixed and random effects.
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Linear programming is a technique used to optimize an objective function subject to a set of linear constraints.