Bivariate Regression Regression analysis is a powerful and commonly used tool in business research. One important step in r
- Week 4 DiscussionDiscussion Topic
Overdue – Dec 24, 2021 12:59 AMDiscussion
The discussion assignment provides a forum for discussing relevant topics for this week based on the course competencies covered.
For this assignment, choose one of the following questions and post your initial response to the Discussion Area by the due date assigned.
To support your work, use your course and text readings and also use outside sources. As in all assignments, cite your sources in your work and provide references for the citations in APA format.
Start reviewing and responding to the postings of your classmates as early in the week as possible. Respond to at least two of your classmates. Participate in the discussion by asking a question, providing a statement of clarification, providing a point of view with a rationale, challenging an aspect of the discussion, or indicating a relationship between two or more lines of reasoning in the discussion. Complete your participation for this assignment by the end of the week.
Question One: 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.
- Justify your answers using examples and reasoning. Comment on the postings of at least two peers and state whether you agree or disagree with their views.
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