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Basic Statistics in Multivariate Analysis$
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Karen A. Randolph and Laura L. Myers

Print publication date: 2013

Print ISBN-13: 9780199764044

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199764044.001.0001

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Bivariate and Multivariate Linear Regression Analysis

Bivariate and Multivariate Linear Regression Analysis

Chapter:
(p.109) 5 Bivariate and Multivariate Linear Regression Analysis
Source:
Basic Statistics in Multivariate Analysis
Author(s):

Karen A. Randolph

Laura L. Myers

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199764044.003.0005

Chapter 5 provides a description of bivariate and multiple linear regression analysis. The chapter begins with a description of the basic statistics that are important in linear regression analysis (i.e., correlation and the straight line), the role of sums of squares in determining variance, and model estimation and model fit. Next, the authors describe the assumptions and other model requirements for conducting linear regression analysis. In the third section, readers are introduced to the three methods of data entry in conducting multiple linear regression analysis—standard multiple regression, sequential multiple regression, and statistical multiple regression. The authors then present examples of the use of multiple linear regression in social work research. The final section provides step-by-step instructions for running a multiple linear regression model in SPSS, using data from the National Education Longitudinal Study of 1988.

Keywords:   bivariate linear regression analysis, correlation, least squares method, multiple linear regression analysis, proportion of variance explained

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