What Linear regression is
Linear regression is a method used to model the relationship between a dependent variable (y) and one or more independent variables (x). It is one of the most widely used statistical techniques for predicting the value of a dependent variable based on the values of one or more independent variables.
Steps for linear regression:
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Identify the dependent and independent variables: The dependent variable is the one whose value is being predicted, while the independent variables are the ones used to make the prediction.
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Collect data: Collect data for the dependent and independent variables.
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Choose a linear regression model: Choose the type of linear regression model that best fits the data.
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Estimate the parameters of the model: Estimate the parameters of the model by using the collected data.
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Evaluate the model: Evaluate the accuracy of the model by measuring the residual sum of squares and other metrics.
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Make predictions: Use the estimated parameters of the model to make predictions for unseen data.
Examples
- Linear regression can be used to assess the strength of the relationship between two quantitative variables, such as height and weight.
- Linear regression can be used to determine the effect of changes in the independent variable on the dependent variable, such as the effect of temperature on crop yield.
- Linear regression can be used to predict the value of a continuous variable based on the values of other variables, such as predicting a person’s income based on their education and experience.
- Linear regression can be used to identify the most influential variables responsible for the variation in a dependent variable, such as identifying which factors have the greatest effect on customer satisfaction.
- Linear regression can be used to test the validity of a hypothesis, such as testing the hypothesis that the price of a stock is related to its volume.