Regression, ANOVA, and the General Linear Model: A Statistics Primer
|Rating||:||4.83 (705 Votes)|
|Number of Pages||:||344 Pages|
Five Stars This is an excellent book.
and Ph.D. in General Psychology from San Diego State University and a M.A. He has authored or co-authored numerous research publications and book chapters. . Vik is Professor of Psychology and Director of the University Honors Program at Idaho State University. in Human Development from the University of California at Davis, an M.A. Currently, Dr. He completed a clinical internship and postdoctoral fellowship with the Department of Psychiatry at the University of Califor
The author does an excellent job breaking down the different components of these statistical techniques while capturing the attention of the reader.” (Manfred van Dulmen 2012-11-21)“…the author really takes the readers step by step and makes the material easy to follow even for readers without extensive mathematics backgrounds.” (Kamala London 2012-11-21) . “I believe that when students are taught about statistics using the approach of this text, they have a MUCH deeper understanding and appreciation of the material. It is really fantastic.” (Jeffrey A. Ciesla 2012-11-21)“The author does a really nice job of explaining the General Linear Model (GLM) by comparing it to hypothesis testing and showing some of its real-world applicability.” (Alfred F. Mancuso 2012-11-21)“The text includes simple descriptions of complex mathematical concepts that are the foundation of statis
By so doing, students will acquire a theoretical and conceptual appreciation for data analysis as well as an applied practical understanding as to how these two approaches are alike. Peter Vik's Regression, ANOVA, and the General Linear Model: A Statistics Primer demonstrates basic statistical concepts from two different perspectives, giving the reader a conceptual understanding of how to interpret statistics and their use. The two perspectives are (1) a traditional focus on the t-test, correlation, and ANOVA, and (2) a model-comparison approach using General Linear Models (GLM). . This book juxtaposes the two approaches by presenting a traditional approach in one chapter, followed by the s