Bivariate Regression

 

Regression analysis is a powerful and commonly used tool in business research. One important step in regression is to determine the dependent and independent variable(s).
In a bivariate regression, which variable is the dependent variable and which one is the independent variable?
What does the intercept of a regression tell? What does the slope of a regression tell?
What are some of the main uses of a regression?
Provide an example of a situation wherein a bivariate regression would be a good choice for analyzing data.
Question Two: Types of Regression Analyses
There are two major types of regression analysis—simple and multiple regression analysis. Both types consist of dependent and independent variables. Simple linear regression has two variables—dependent and independent. Multiple regression consists of dependent variable and two or more independent variables.
How does a multiple regression compare with a simple linear regression?
What are the various ways to determine what variables should be included in a multiple regression equation?
Compare and contrast the following processes: forward selection, backward elimination, and stepwise selection.

 

 

Sample Solution

Bivariate Regression

Regression analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables. Bivariate regression analysis is a type of statistical analysis that can be used during the analysis and reporting stage of quantitative market research. It is often considered the simplest form of regression analysis. Essentially, bivariate regression analysis involves analyzing two variables to establish the strength of the relationship between them. The two variables are frequently denoted as X and, with one being an independent variable (an explanatory variable), while the other is a dependent variable (or outcome variable). Y is the dependent variable while X is the independent variable.

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