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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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(p.1) 1 Introduction
Basic Statistics in Multivariate Analysis

Karen A. Randolph

Laura L. Myers

Oxford University Press

Chapter 1 defines important terms including basic statistics, multivariate analysis, and inferential statistics, as a way to introduce readers to the book’s premise—that a thorough understanding of basic statistics is critical in the successful application of more advanced statistical methods. Readers are also introduced to assumptions and other requirements necessary for inferential statistical testing, making predictions about relationships between variables, and making causal inferences. The chapter then provides a description of each of the multivariate methods—multiple linear regression, Analysis of variance and Covariance, and path analysis—covered in the book. The chapter concludes with an overview of subsequent chapters and a description of the National Educational Longitudinal Study of 1988 (NELS: 88), which is the data set used for the book’s examples of each method.

Keywords:   basic statistics, causal inference, inferential statistics, multivariate analysis, statistical assumptions

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