Applying regression & correlation : a guide for students and researchers Jeremy Miles and Mark Shevlin

By:

Miles, Jeremy [author]

Contributor(s):

Shevlin, Mark [author]

Material type: Text

TextPublication details: London Sage 2001Description: 253 pagesContent type:

text

Media type:

unmediated

Carrier type:

volume

ISBN:

9780761962304

Subject(s):

LOC classification:

QA278.2 MIL

Summary: The authors start with the basics and begin by re-visiting the mean, and the standard deviation, with which most readers will already be familiar, and show that they can be thought of a least squares model. The book then shows that this least squares model is actually a special case of a regression analysis and can be extended to deal with first one, and then more than one independent variable.

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Holdings
Item type	Current library	Call number	Copy number	Status	Barcode
Core Collection	Main Library Core Collection	QA278.2 MIL (Browse shelf(Opens below))	144393	Available	BK130558
Book	Main Library Open Shelf	QA278.2 MIL (Browse shelf(Opens below))	144395	Available	BK130588
Book	Main Library Open Shelf	QA278.2 MIL (Browse shelf(Opens below))	144394	Available	BK130528
Book	Main Library Open Shelf	QA278.2 MIL (Browse shelf(Opens below))	110174	Available	BK80856
Book	Main Library Open Shelf	QA278.2 MIL (Browse shelf(Opens below))	110175	Available	BK80626
Book	Main Library Open Shelf	QA278.2 MIL (Browse shelf(Opens below))	110176	Available	BK80845

Browsing Main Library shelves, Shelving location: Core Collection Close shelf browser (Hides shelf browser)

Previous	No cover image available		No cover image available					Next
Previous	QA278.2 APP Applied quantitative analysis in education and the social sciences	QA278.2 HAR Regression modeling strategies :	QA 278.2 KEE Semiparametric regression for the social sciences. /	QA278.2 MIL Applying regression & correlation :	QA278.3 BLU Introduction to structural equation modeling using IBM SPSS statistics and EQS	QA278.5 JOL Linear mixed models for longitudinal data	QA278.8 GIB Nonparametric statistical inference	Next

Includes bibliographical references and index.

The authors start with the basics and begin by re-visiting the mean, and the standard deviation, with which most readers will already be familiar, and show that they can be thought of a least squares model. The book then shows that this least squares model is actually a special case of a regression analysis and can be extended to deal with first one, and then more than one independent variable.

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