Question

1. Why is a multivariate regression model usually better to use than a univariate regression model? Define both and discuss at least two reasons.

Answer 1

Univariate Regression: It describes the relationship between two variables using a straight line. Univariate regression aims to draw a line that is nearest to the data by detecting a slope and intercepting the line and reducing regression errors. It's also called simple linear regression.

Multivariate Regression: It is unusual that only one variable determines the dependent variable. In this case, the analyst uses multivariate regressions, which seek to describe the dependent variable by using more than one independent variable. Multiple regressions can be linear and non-linear. Multivariate regressions are based on the premise that there is a linear relationship between dependent and independent variables.

The multivariate regression model is usually better to use than a univariate regression model as:

1) There is principal adventage of using multiple regression model which is, it gives us more of the information available to us who estimate the dependent variable.

2) Multiple regression is a larger class of regressions that includes linear and nonlinear regressions with numerous explanatory variables.