Final answer:
Multiple linear regression is an advanced form of linear regression where multiple independent variables are used to predict a single dependent variable. It's used in various fields, from GIS to economics, to analyze complex relationships and can simultaneously account for many factors influencing a dependent variable.
Step-by-step explanation:
An extension of simple linear regression analysis that uses multiple variables to relate to employment is called multiple linear regression. In multiple linear regression, one dependent variable is predicted based on several independent variables. Unlike simple linear regression, which uses just one independent variable to predict an outcome, multiple linear regression allows for a more comprehensive analysis of the relationship between a dependent variable and multiple independent variables. This technique is essential for understanding the nature of relationships between cause and effect variables.
For instance, in the context of geographic information systems (GIS), regression analysis can be used to understand complex issues such as the impact of fast food joints, ethnicity, and income on obesity rates. Moreover, economists might use multiple linear regression models when investigating wage discrimination, accounting for differences in occupation, education, and other characteristics, with the unexplained residuals potentially indicating the presence of discrimination.
A basic linear equation in statistics is expressed as y = a + bx, where 'a' represents the y-intercept, 'b' is the slope coefficient reflecting the rate of change in the dependent variable 'y' as the independent variable 'x' changes. This form extends to multiple linear regression with additional terms for each independent variable included in the model.