"Linear Regression Analysis" - Information and Links:

Linear Regression Analysis - Info and Reading Options

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Book's cover
The cover of “Linear Regression Analysis” - Open Library.

"Linear Regression Analysis" was published by NASW Press, National Association of Social Workers in May 5, 2015 - Washington, USA, it has 240 pages and the language of the book is English.


“Linear Regression Analysis” Metadata:

  • Title: Linear Regression Analysis
  • Authors:
  • Language: English
  • Number of Pages: 240
  • Publisher: ➤  NASW Press, National Association of Social Workers
  • Publish Date:
  • Publish Location: Washington, USA

“Linear Regression Analysis” Subjects and Themes:

Edition Specifications:

  • Format: Paperback
  • Weight: 15 ounces
  • Dimensions: 10 x 7 x 1 inches
  • Pagination: ➤  xiv, 226 pages : illustration ; 23 cm

Edition Identifiers:

AI-generated Review of “Linear Regression Analysis”:


"Linear Regression Analysis" Table Of Contents:

  • 1- -A review of some elementary statistical concepts --
  • 2- -Simple linear regression --
  • 3- -Multiple linear regression analysis --
  • 4- -The ANOVA table and goodness-of-fit --
  • 5- -Comparing linear regression models --
  • 6- -Dummy variables in linear regression models --
  • 7- -Specification errors in linear regression models --
  • 8- -Measurement errors in linear regression models --
  • 9- -Collinearity and multicollinearity --
  • 10- -Nonlinear associations and interaction terms --
  • 11- -Heteroscedasticity and autocorrelation --
  • 12- -Influential observations : leverage points and outliers --
  • 13- -A brief introduction to logistic regression --
  • 14- -An introduction to multilevel models --
  • 15- -Conclusion.

"Linear Regression Analysis" Description:

The Open Library:

Social work and other social and behavioral science students and researchers need to have a suite of research tools to conduct studies. Regression analysis is a popular tool that is used in numerous studies to examine statistical relationships among variables. Yet there are few books that offer straightforward and easy-to-follow instruction regarding this type of analysis. Most books rely too much on mathematical and symbolic representations of regression analysis, even though many students do not have a sufficient background in mathematics and are often put off by the high level of sophistication required to master these techniques. This book offers a conceptual and software-driven approach to understanding linear regression analysis, with only a slight familiarity with algebra required even for self-study. Students and researchers will find this to be an accessible, yet thorough, introduction to the linear regression model.

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