Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. This method assumes that the relationship between the variables can be represented by a straight line.
The purpose of linear regression is to identify and quantify the relationship between the dependent variable and independent variable(s) in order to make predictions about the dependent variable. It is commonly used in data analysis and machine learning to understand and make predictions about relationships between variables.
The assumptions of linear regression include: linearity, independence of errors, homoscedasticity (equal variance of errors), and normality of errors. These assumptions must be met in order for the results of linear regression to be valid.
The main sources of error in linear regression include measurement error, random error, and model error. Measurement error occurs when the data collected is not accurate, while random error refers to natural variations in the data. Model error occurs when the chosen model does not accurately represent the relationship between the variables.
No, linear regression is only appropriate for linear relationships between variables. If the relationship between the variables is non-linear, other regression methods such as polynomial regression or logistic regression may be more suitable.