Understanding Regression Analysis [Hardcover]

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.

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.

The Origins and Uses of Regression Analysis. Basic Matrix Algebra: Manipulating Vectors. The Mean and Variance of a Variable. Regression Models and Linear Functions. Errors of Prediction and Least-Squares Estimation. Least-Squares Regression and Covariance. Covariance and Linear Independence. Separating Explained and Error Variance. Transforming Variables to Standard Form. Regression Analysis with Standardized Variables. Populations, Samples, and Sampling ls