Applied Multivariate Data Analysis: Regression and Experimental Design

✍ Scribed by J. D. Jobson (auth.)

Publisher: Springer-Verlag New York
Year: 1991
Tongue: English
Leaves: 645
Series: Springer Texts in Statistics
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

An easy to read survey of data analysis, linear regression models and analysis of variance. The extensive development of the linear model includes the use of the linear model approach to analysis of variance provides a strong link to statistical software packages, and is complemented by a thorough overview of theory. It is assumed that the reader has the background equivalent to an introductory book in statistical inference. Can be read easily by those who have had brief exposure to calculus and linear algebra. Intended for first year graduate students in business, social and the biological sciences. Provides the student with the necessary statistics background for a course in research methodology. In addition, undergraduate statistics majors will find this text useful as a survey of linear models and their applications.

✦ Table of Contents

Front Matter....Pages i-xxv
Introduction....Pages 1-32
Univariate Data Analysis....Pages 33-86
Bivariate Analysis for Quantitative Random Variables....Pages 87-217
Multiple Linear Regression....Pages 219-398
Analysis of Variance and Experimental Design....Pages 399-544
Back Matter....Pages 545-622

✦ Subjects

Statistics for Business/Economics/Mathematical Finance/Insurance; Statistics for Life Sciences, Medicine, Health Sciences

📜 SIMILAR VOLUMES

Applied Regression Analysis and Experime

📁 Applied Regression Analysis and Experimental Design

✍ Brook, Richard J 📂 Library 📅 2018 🏛 Routledge;CRC 🌐 English

For a solid foundation of important statistical methods, the concise, single-source text unites linear regression with analysis of experiments and provides students with the practical understanding needed to apply theory in real data analysis problems. Stressing principles while keeping computationa

Applied Regression Analysis and Other Mu

📁 Applied Regression Analysis and Other Multivariable Methods

✍ David G. Kleinbaum, Lawrence L. Kupper, Azhar Nizam, Eli S. Rosenberg 📂 Library 📅 2013 🏛 Cengage Learning 🌐 English

This bestseller will help you learn regression-analysis methods that you can apply to real-life problems. It highlights the role of the computer in contemporary statistics with numerous printouts and exercises that you can solve using the computer. The authors continue to emphasize model development

Applied Regression Analysis and Other Mu

📁 Applied Regression Analysis and Other Multivariable Methods

✍ David G. Kleinbaum, Lawrence L. Kupper, Azhar Nizam, Eli S. Rosenberg 📂 Library 📅 2013 🏛 Cengage Learning 🌐 English

Applied Multivariate Data Analysis: Volu

📁 Applied Multivariate Data Analysis: Volume II: Categorical and Multivariate Methods

✍ J. D. Jobson (auth.) 📂 Library 📅 1992 🏛 Springer-Verlag New York 🌐 English

<p>A Second Course in Statistics The past decade has seen a tremendous increase in the use of statistical data analysis and in the availability of both computers and statistical software. Business and government professionals, as well as academic researchers, are now regularly employing techniques t

Applied Multivariate Data Analysis, Seco

📁 Applied Multivariate Data Analysis, Second Edition

✍ Brian S. Everitt, Graham Dunn(auth.) 📂 Library 📅 2001 🏛 Wiley 🌐 English

Multivariate analysis plays an important role in the understanding of complex data sets requiring simultaneous examination of all variables. Breaking through the apparent disorder of the information, it provides the means for both describing and exploring data, aiming to extract the underlying patte

Applied Multiway Data Analysis

📁 Applied Multiway Data Analysis

✍ Pieter M. Kroonenberg 📂 Library 📅 2008 🏛 Wiley-Interscience 🌐 English

<p>From a preeminent authority—a modern and applied treatment of multiway data analysis<p>This groundbreaking book is the first of its kind to present methods for analyzing multiway data by applying multiway component techniques. Multiway analysis is a specialized branch of the larger field of mult