Linear regression modeling explained in detail.
Linear Regression Modeling
Linear regression modeling is used to determine the relationship between a dependent variable and one or more independent variables, assuming a linear relationship between them. It is one of the most often used regression models we use for analyzing and predicting the relationship between variables.
The goal of linear regression modeling is to estimate the parameters of a linear equation that best fits the observed data. The linear equation is of the form Y = β0 + β1X1 + β2X2 + … + βn*Xn where:
- Y is the dependent variable, or response variable, that we want to model or predict.
- X1, X2, …, Xn are the independent variables, or predictors, that we believe may have an effect on the dependent variable.
- β0, β1, β2, …, βn are the coefficients, or parameters,of the linear equation that represent the strength and direction of the relationship between the variables.
- ε is the error term or residual, which represents the unexplained variation in the dependent variable that is not accounted for by the linear relationship with the independent variables.
The goal of linear regression is to estimate the values of the coefficients (β0, β1, β2, …, βn) that best fit the observed data. We often use ordinary least squares (OLS) estimation, which minimizes the sum of squared errors between the observed values and the predicted values from the linear equation.
Once the linear regression model is estimated, we use it in prediction, estimation, hypothesis testing, and inference. We use linear regression to predict the value of the dependent variable for new observations, assess the significance and direction of the relationship between variables, identify influential variables, and evaluate the overall fit of the model.
We also use linear regression modeling for sales forecasting, demand estimation, pricing optimization, customer segmentation, risk assessment, and performance predictions.