Question

Evaluate some background research on the various methods of linear and multiple regression techniques. Then discuss in scholarly detail using examples researched or based on life experiences how linear and multiple regression techniques are used to create data models to help organizations make decisions based on how these models output analyzed data.

Answer 1

Linear Regression:

It is also called simple linear regression. It establishes the relationship between two variables using a straight line. Linear regression attempts to draw a line that comes closest to the data by finding the slope and intercept that define the line and minimize regression errors.

Multiple Regression:

It is rare that a dependent variable is explained by only one variable. In this case, an analyst uses multiple regression, which attempts to explain a dependent variable using more than one independent variable. Multiple regressions can be linear and nonlinear.

Multiple regressions are based on the assumption that there is a linear relationship between both the dependent and independent variables. It also assumes no major correlation between the independent variables.

Consider an analyst who wishes to establish a linear relationship between the daily change in a company's stock prices and other explanatory variables such as the daily change in trading volume and the daily change in market returns. If he runs a regression with the daily change in the company's stock prices as a dependent variable and the daily change in trading volume as an independent variable, this would be an example of a simple linear regression with one explanatory variable.

If the analyst adds the daily change in market returns into the regression, it would be a multiple linear regression.