Linear Regression §

Group: 5 #group-5

Relations §

• Heteroscedasticity: Heteroscedasticity refers to the violation of the assumption of constant variance of the error terms in a linear regression model.
• Heteroscedasticity: Heteroscedasticity is a common issue in linear regression models, where the assumption of constant variance of errors is often violated.
• Least Squares Method: The least squares method is used to estimate the parameters of a linear regression model by minimizing the sum of squared residuals.
• Residual Analysis: Residual analysis is used to evaluate the assumptions and goodness-of-fit of a linear regression model.
• Regression: Linear regression is a type of regression analysis that models the linear relationship between a dependent variable and one or more independent variables.
• Multicollinearity: Multicollinearity refers to the presence of high correlations among independent variables in a linear regression model, which can lead to unstable parameter estimates.