Advanced Regression Analysis
An Introduction
1st Edition
Will be published approx. on 26. May 2031
Book
Paperback/Softback
224 pages
978-1-4739-1216-8 (ISBN)
Description
Aimed at students who have completed basic statistical training, this book is the perfect gateway to help you move up to more advanced courses. Armed with an understanding of regression analysis, you will be ready to apply regression methods effectively and insightfully any problem.
Benefits of this book:
? Accessible to all students, regardless of their mathematical level
? Rich with examples and problems from a across the social sciences, business and health
? Examples and problem sets on the companion website help you to test your learning and put your skills into practice.
? A guide to Matrix Algebra to help students quickly grasp more advanced procedures
This is the ideal companion into advanced regression analysis for students in the social sciences, business and health.
Benefits of this book:
? Accessible to all students, regardless of their mathematical level
? Rich with examples and problems from a across the social sciences, business and health
? Examples and problem sets on the companion website help you to test your learning and put your skills into practice.
? A guide to Matrix Algebra to help students quickly grasp more advanced procedures
This is the ideal companion into advanced regression analysis for students in the social sciences, business and health.
More details
Series
Language
English
Place of publication
London
United Kingdom
Target group
College/higher education
Dimensions
Height: 242 mm
Width: 170 mm
ISBN-13
978-1-4739-1216-8 (9781473912168)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Content
Chapter 1: Understanding and Applying Regression Analysis - Theory as Well as Practice
Chapter 2: Basic Matrix Algebra for Regression Analysis
Chapter 3: Ordinary Least Squares Regression Derived, and Initial Tenets of Estimation Practice Introduced
Chapter 4: Moving from Ordinary to Generalized Least Squares, Illustrated through the Problem of Heteroskedasticity
Chapter 5: Autocorrelated Errors - A Further Look at Generalized Least Squares
Chapter 6: Finding Unusual Cases in Your Data Set - They Aren't Just 'Outliers'
Chapter 7: Collinearity - Finding and Coping with Very High Correlations Among Explanatory Variables
Chapter 8: Model Specification - How Can We Know When a Model is Good, or Better than a Competing Model?
Chapter 9: Measurement Error in Our Independent and Dependent Variables - How Might This Compromise Your Parameter Estimates, And What Can You Do About It?
Chapter 10: Regression Analysis Is the Gateway - Some Directions for Further Study in Data Science
Chapter 2: Basic Matrix Algebra for Regression Analysis
Chapter 3: Ordinary Least Squares Regression Derived, and Initial Tenets of Estimation Practice Introduced
Chapter 4: Moving from Ordinary to Generalized Least Squares, Illustrated through the Problem of Heteroskedasticity
Chapter 5: Autocorrelated Errors - A Further Look at Generalized Least Squares
Chapter 6: Finding Unusual Cases in Your Data Set - They Aren't Just 'Outliers'
Chapter 7: Collinearity - Finding and Coping with Very High Correlations Among Explanatory Variables
Chapter 8: Model Specification - How Can We Know When a Model is Good, or Better than a Competing Model?
Chapter 9: Measurement Error in Our Independent and Dependent Variables - How Might This Compromise Your Parameter Estimates, And What Can You Do About It?
Chapter 10: Regression Analysis Is the Gateway - Some Directions for Further Study in Data Science