Understanding Regression Assumptions (Quantitative Applications in the Social Sciences)
Product Description
Understanding Regression Assumptions (Quantitative Applications in the Social Sciences)
<p>Through the use of careful explanation and examples, Berry demonstrates how to consider whether the assumptions of multiple regression are actually satisfied in a particular research project. Beginning with a brief review of the regression assumptions as they are typically presented in text books, he moves on to explore in detail the <i>substantive </i>meaning of each assumption; for example, lack of measurement error, absence of specification error, linearity, homoscedasticity, and lack of auto-correlation. </p>
Technical Specifications
You might also like
Regression Diagnostics: An Introduction (Quantitative Applications in the Social Sciences)
Applied Regression: An Introduction (Quantitative Applications in the Social Sciences)
Multiple Regression in Practice (Quantitative Applications in the Social Sciences)
Understanding Regression Analysis: An Introductory Guide (Quantitative Applications in the Social Sciences)
Logistic Regression: A Primer (Quantitative Applications in the Social Sciences)
Regression Models for Categorical Dependent Variables Using Stata, Third Edition
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, 3rd Edition
Fixed Effects Regression Models (Quantitative Applications in the Social Sciences)
Principal Components Analysis (Quantitative Applications in the Social Sciences)
Interpreting and Using Regression (Quantitative Applications in the Social Sciences, No. 29)