Understanding Regression Analysis

by Assuming It Is Possible To Understand Regression Analysis Without Fully Comprehending All Its Underlying Proofs And Theories, This Introduction To The Widely Used Statistical Technique Is Accessible To Readers Who May Have Only A Rudimentary Knowledge Of Mathematics. Chapters Discuss: -descriptive Statistics Using Vector Notation And The Components Of A Simple Regression Model; -the Logic Of Sampling Distributions And Simple Hypothesis Testing; -the Basic Operations Of Matrix Algebra And The Properties Of The Multiple Regression Model; -testing Compound Hypotheses And The Application Of The Regression Model To The Analyses Of Variance And Covariance, And -structural Equation Models And Influence Statistics. booknews for Students, Researchers, And Policy-makers With Basic Familiarity, Allen (washington State U., Pullman) Provides A Guide To Correctly Applying And Interpreting The Results Of This Statistical Technique Ubiquitous In The Social Sciences by Explaining The Underlying Logic Of Sample Distributions, Hypothesis Testing, Matrix Algebra, And Regression Models (linear And Multiple). Mercifully, Statistical Derivations Are Relegated To The Appendices. Offers Suggested Readings And Standard Statistical Tables, But No Sample Examples Or Exercises. Annotation C. By Book News, Inc., Portland, Or. By assuming it is possible to understand regression analysis without fully comprehending all its underlying proofs and theories, this introduction to the widely used statistical technique is accessible to readers who may have only a rudimentary knowledge of mathematics. Chapters -descriptive statistics using vector notation and the components of a simple regression model; -the logic of sampling distributions and simple hypothesis testing; -the basic operations of matrix algebra and the properties of the multiple regression model; -testing compound hypotheses and the application of the regression model to the analyses of variance and covariance, and -structural equation models and influence statistics. Proceeding on the assumption that it is possible to develop a sufficient understanding of this technique without resorting to mathematical proofs and statistical theory, Understanding Regression Analysis explores Descriptive statistics using vector notation and the components of a simple regression model; the logic of sampling distributions and simple hypothesis testing; the basic operations of matrix algebra and the properties of the multiple regression model; the testing of compound hypotheses and the application of the regression model to the analysis of variance and covariance; and structural equation models and influence statistics