LR (Linear Regression)

Information

• LR (Linear Regression) is a statistical technique used to model the relationship between one or more independent variables and a dependent variable.
• LR assumes a linear relationship between the independent and dependent variables, meaning that the change in the dependent variable is proportional to the change in the independent variable.
• LR aims to find the line of best fit that minimizes the distance between the observed data and the predicted values.
• LR can be used for both simple linear regression, where there is only one independent variable, and multiple linear regression, where there are multiple independent variables.
• LR can be used for both continuous and categorical dependent variables, depending on the type of regression used.
• LR can provide information about the strength and direction of the relationship between the independent and dependent variables, as well as the significance of the relationship.
• LR can also provide predictions of the dependent variable based on the values of the independent variables.
• LR assumptions include linearity, independence of errors, homoscedasticity (equal variance), normality of errors, and no multicollinearity among independent variables.
• LR is widely used in data analysis and machine learning applications, and has a long history in statistical modeling and inference.
• LR is supported by a variety of programming languages, including Python, R, and Java, and has a large and active user community.