Regression Analysis: Unified Concepts, Practical Applications, Computer Implementation is a concise and innovative book that gives a complete presentation of applied regression analysis in approximate
Table of Contents: 1. Data and Distributions 2. Numerical Summary Measures 3. Bivariate and Multivariate Data 4. Obtaining Data 5. Probability and Sampling Distributions 6. Quality and Reliability 7. Estimation and Statistical Intervals 8. Testing Statistical Hypotheses 9. The Analysis of Variance 10. Experimental Design 11. Inferential Methods in Regression and Correlation
The eighth edition of Multivariate Data Analysis provides an updated perspective on the analysis of all types of data as well as introducing some new perspectives and techniques that are foundational
Regression analysis is the most commonly used statistical method in the world. It is used in economics and many other fields. Although few would characterize this technique as simple, regression is in
APPLIED REGRESSION ANALYSIS applies regression to real data and examples while employing commercial statistical and spreadsheet software. Covering the core regression topics as well as optional topics
This book creates a balance between the theory, practical applications, and computer implementation behind Regression—one of the most widely used techniques in analyzing and solving real world problem
A basic understanding of multiple regression is helpful in carrying out good business practices—specifically in the areas of demand management and data analysis. This book on correlation and regressio
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
This book covers essential elements of building and understanding regression models within the context of business and economics. It is a nonmathematical treatment that is accessible, even to readers
Regression analysis has become a mainstay of research in diverse fields; in this edition the authors take this popularity into consideration along with the fact that computers are everywhere. They cov
This introductory text also serves as a reference for professionals in such fields as marketing, finance, and production forecasting. The authors cover basic statistical concepts, regression analysis
Statistical Analysis in the Social Sciences imbeds basic descriptive and inferential statistical concepts within a unified framework: the linear regression model. The regression model is used not only
One of the few case studies of undocumented immigrants available, this insightful anthropological analysis humanizes a group of people too often reduced to statistics and stereotypes. The hardships of
A basic understanding of multiple regression is helpful in carrying out good business practices-- specifically in the areas of demand management and data analysis. This book on correlation and regress
VMware ESXi is the easiest way to get started with virtualization -- and it's free. It allows administrators to consolidate their applications onto fewer servers and start saving money through reduced
Taking an integrated-skills approach, Imagenes offers a strong emphasis on developing oral proficiency and cultural awareness and appreciation. The text's reduced scope and sequence combined with ampl
This text is a descriptive presentation of how the largest and most influential police department in the nation, the NYPD, has significantly reduced crime over the last 10 years. As COMPSTAT is a crit
In THEORY AND APPLICATION OF THE LINEAR MODEL, Franklin A. Graybill integrates the linear statistical model within the context of analysis of variance, correlation and regression, and design of experi
The technique of regression analysis is used so often in business and economics today that an understanding of its use is necessary for almost everyone engaged in the field. This book covers essential
Part of the Duxbury Classic series, Franklin A. Graybill's MATRICES WITH APPLICATIONS TO STATISTICS focuses primarily on matrices as they relate to areas of multivariate analysis and the linear model.
There are several powerful statistical analysis software, such as Stata, SAS, and a host of other packages with different levels of elegance, completeness, and support. Preferred is Stata because of i
One of the larger challenges of teaching modeling is the issue of balancing quantitative skill sets with qualitative concepts. Students require familiarity with specific mathematical concepts and form
