Linear Regression: An Introduction to Statistical Models

✍ Scribed by Peter Martin

• Publisher: SAGE Publications
• Year: 2022
• Tongue: English
• Leaves: 201
• Series: The SAGE Quantitative Research Kit
• Category: Library

✦ Synopsis

Part of The SAGE Quantitative Research Kit, this text helps you make the crucial steps towards mastering multivariate analysis of social science data, introducing the fundamental linear and non-linear regression models used in quantitative research. Peter Martin covers both the theory and application of statistical models, and illustrates them with illuminating graphs, discussing:

·       Linear regression, including dummy variablesand predictor transformations for curvilinear relationships

·       Binary, ordinal and multinomial logistic regression models for categorical data

·       Models for count data, including Poisson, negative binomial, and zero-inflated regression

·       Checking model assumptions and the dangers of overfitting

✦ Table of Contents

LINEAR REGRESSION: AN INTRODUCTION TO STATISTICAL MODELS – FRONT
COVER
LINEAR REGRESSION: AN INTRODUCTION TO STATISTICAL MODELS
COPYRIGHT
CONTENTS
LIST OF FIGURES, TABLES AND BOXES
ABOUT THE AUTHOR
ACKNOWLEDGEMENTS
PREFACE
CHAPTER 1 - WHAT IS A STATISTICAL MODEL?
CHAPTER 2 - SIMPLE LINEAR REGRESSION
CHAPTER 3 - ASSUMPTIONS AND TRANSFORMATIONS
CHAPTER 4 - MULTIPLE LINEAR REGRESSION: A MODEL FOR MULTIVARIATE
RELATIONSHIPS
CHAPTER 5 - MULTIPLE LINEAR REGRESSION: INFERENCE, ASSUMPTIONS AND
STANDARDISATION
CHAPTER 6 - WHERE TO GO FROM HERE
GLOSSARY
REFERENCES
INDEX