Linear Regression Models

Alfred DeMaris and Steven H. Selman
Additional contact information
Alfred DeMaris: Bowling Green State University
Steven H. Selman: University of Toledo, Department of Urology

Chapter Chapter 6 in Converting Data into Evidence, 2013, pp 79-114 from Springer

Abstract: Abstract The correlation coefficient discussed in the last chapter is a component of one of the most important techniques in statistics: linear regression modeling. In this section, we introduce this topic and the subject of statistical modeling, in general. We begin with the familiar step of analyzing the association between a study endpoint and one explanatory variable, with both as quantitative variables. We then expand our model to include several explanatory variables, using the multiple linear regression model. Examples drawn from the GSS and the journal literature help to flesh out this topic.

Keywords: Waist Circumference; Depressive Symptomatology; Study Endpoint; Exam Score; Spousal Support (search for similar items in EconPapers)
Date: 2013
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-7792-1_6

Ordering information: This item can be ordered from
http://www.springer.com/9781461477921

DOI: 10.1007/978-1-4614-7792-1_6

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().