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Understanding regression analysis

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Cover of 'Understanding regression analysis'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 The origins and uses of regression analysis
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    Chapter 2 Basic matrix algebra: Manipulating vectors
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    Chapter 3 The mean and variance of a variable
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    Chapter 4 Regression models and linear functions
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    Chapter 5 Errors of prediction and least-squares estimation
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    Chapter 6 Least-squares regression and covariance
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    Chapter 7 Covariance and linear independence
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    Chapter 8 Separating explained and error variance
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    Chapter 9 Transforming variables to standard form
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    Chapter 10 Regression analysis with standardized variables
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    Chapter 11 Populations, samples, and sampling distributions
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    Chapter 12 Sampling distributions and test statistics
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    Chapter 13 Testing hypotheses using the t test
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    Chapter 14 The t test for the simple regression coefficient
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    Chapter 15 More matrix algebra: Manipulating matrices
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    Chapter 16 The multiple regression model
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    Chapter 17 Normal equations and partial regression coefficients
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    Chapter 18 Partial regression and residualized variables
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    Chapter 19 The coefficient of determination in multiple regression
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    Chapter 20 Standard errors of partial regression coefficients
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    Chapter 21 The incremental contributions of variables
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    Chapter 22 Testing simple hypotheses using the F test
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    Chapter 23 Testing compound hypotheses using the F test
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    Chapter 24 Testing hypotheses in nested regression models
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    Chapter 25 Testing for interaction in multiple regression
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    Chapter 26 Nonlinear relationships and variable transformations
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    Chapter 27 Regression analysis with dummy variables
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    Chapter 28 One-way analysis of variance using the regression model
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    Chapter 29 Two-way analysis of variance using the regression model
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    Chapter 30 Testing for interaction in analysis of variance
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    Chapter 31 Analysis of covariance using the regression model
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    Chapter 32 Interpreting interaction in analysis of covariance
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    Chapter 33 Structural equation models and path analysis
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    Chapter 34 Computing direct and total effects of variables
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    Chapter 35 Model specification in regression analysis
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    Chapter 36 Influential cases in regression analysis
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    Chapter 37 The problem of multicollinearity
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    Chapter 38 Assumptions of ordinary least-squares estimation
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    Chapter 39 Beyond ordinary regression analysis
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Title
Understanding regression analysis
Published by
Plenum Press, January 1997
DOI 10.1007/b102242
ISBNs
978-0-306-45648-0, 978-0-585-25657-3
Authors

Allen, Michael Patrick, Michael Patrick Allen

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Croatia 1 2%
United States 1 2%
Unknown 45 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 9 19%
Researcher 6 13%
Student > Ph. D. Student 6 13%
Student > Bachelor 5 11%
Student > Doctoral Student 4 9%
Other 3 6%
Unknown 14 30%
Readers by discipline Count As %
Economics, Econometrics and Finance 4 9%
Psychology 4 9%
Social Sciences 4 9%
Engineering 4 9%
Business, Management and Accounting 3 6%
Other 12 26%
Unknown 16 34%