Answer Internal Staff
The two types of regression are: Simple regression analysis and Multiple regression analysis. Simple regression analysis assumes that one variable (variable x) is dependent upon another single independent variable (variable Y). A simple regression model is presented below: y = β0+ β1x + μ Where Y is the dependent variable; x is the independent variable – also known as explanatory variable, regressor, control variable or predictor; B0 is the intercept parameter also known as the constant term; B1 is the slope parameter and u is the error term. So, the change in y is simply B1* change in x. An example is: crime = B0 +B1wage + u, where wage is wage that could be earned in legitimate employment.
Multiple regression analysis uses a similar methodology as simple regression, but includes more than one explanatory variable.
Furthermore, regression analysis examines the relationship between two variables. Thus it must be remembered that while a correlation may exist between two variables, this does not necessarily mean a dependence of one on the other; simply put, correlation does not equal causation (Smith, 2011).
ReferencesSmith, G. (2011). Essential Statistics, Regression, and Econometrics, California: Elsevier