Multiple Linear Regression

William H. Holmes () and William C. Rinaman ()
Additional contact information
William H. Holmes: Le Moyne College

Chapter 14 in Statistical Literacy for Clinical Practitioners, 2014, pp 367-396 from Springer

Abstract: Abstract This chapter provides an overview of multiple linear regression, a statistical technique that predicts values of a quantitative dependent variable from values of two or more independent variables. By including more than one independent variable, a multiple linear regression can often account for more variability in the dependent variable than can a simple regression, can assess the relationship between the dependent variable and an independent variable after controlling for the presence of other independent variables, and can determine whether the effect of an independent variable varies across levels of another. Topics reviewed include the multiple correlation coefficient, adjusted R 2,interpreting and testing unstandardized and standardized slope coefficients, using categorical and dummy variables as predictors, and testing for the presence of interaction effects.

Keywords: Multiple Regression Analysis; Prediction Equation; Forced Expiratory Volume; Slope Coefficient; Multiple Correlation Coefficient (search for similar items in EconPapers)
Date: 2014
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:978-3-319-12550-3_14

Ordering information: This item can be ordered from
http://www.springer.com/9783319125503

DOI: 10.1007/978-3-319-12550-3_14

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 ().