This book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data.

Contrary to other textbooks on regression, this book is based on the idea that you do not necessarily need to know much about statistics and mathematics to get a firm grip on regression and perform it to perfection. This non-technical point of departure is complemented by practical examples of real-life data analysis using statistics software such as Stata, R and SPSS. Parts 1 and 2 of the book cover the basics, such as simple linear regression, multiple linear regression, how to interpret the output from statistics programs, significance testing and the key regression assumptions. Part 3 deals with how to practically handle violations of the classical linear regression assumptions, regression modeling for categorical y-variables and instrumental variable (IV) regression. Part 4 puts the various purposes of, or motivations for, regression into the wider context of writing a scholarly report and points to some extensions to related statistical techniques.

This book is written primarily for those who need to do regression analysis in practice, and not only to understand how this method works in theory. The book’s accessible approach is recommended for students from across the social sciences.

This book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data.

Contrary to other textbooks on regression, this book is based on the idea that you do not necessarily need to know much about statistics and mathematics to get a firm grip on regression and perform it to perfection. This non-technical point of departure is complemented by practical examples of real-life data analysis using statistics software such as Stata, R and SPSS. Parts 1 and 2 of the book cover the basics, such as simple linear regression, multiple linear regression, how to interpret the output from statistics programs, significance testing and the key regression assumptions. Part 3 deals with how to practically handle violations of the classical linear regression assumptions, regression modeling for categorical y-variables and instrumental variable (IV) regression. Part 4 puts the various purposes of, or motivations for, regression into the wider context of writing a scholarly report and points to some extensions to related statistical techniques.

This book is written primarily for those who need to do regression analysis in practice, and not only to understand how this method works in theory. The book’s accessible approach is recommended for students from across the social sciences.

Doing Statistical Analysis looks at three kinds of statistical research questions – descriptive, associational, and inferential – and shows students how to conduct statistical analyses and interpret the results. Keeping equations to a minimum, it uses a conversational style and relatable examples such as football, COVID-19, and tourism, to aid understanding. Each chapter contains practice exercises, and a section showing students how to reproduce the statistical results in the book using Stata and SPSS. Digital supplements consist of data sets in Stata, SPSS, and Excel, and a test bank for instructors. Its accessible approach means this is the ideal textbook for undergraduate students across the social and behavioral sciences needing to build their confidence with statistical analysis.

Doing Statistical Analysis looks at three kinds of statistical research questions – descriptive, associational, and inferential – and shows students how to conduct statistical analyses and interpret the results. Keeping equations to a minimum, it uses a conversational style and relatable examples such as football, COVID-19, and tourism, to aid understanding. Each chapter contains practice exercises, and a section showing students how to reproduce the statistical results in the book using Stata and SPSS. Digital supplements consist of data sets in Stata, SPSS, and Excel, and a test bank for instructors. Its accessible approach means this is the ideal textbook for undergraduate students across the social and behavioral sciences needing to build their confidence with statistical analysis.

Statistical Analysis: The Basics provides an engaging and easy‑to‑read primer on this sometimes daunting subject. Intended for those with little or no background in mathematics or statistics, this book explores the importance of statistical analysis in the modern world by asking statistical questions about data and explains how to conduct such analyses and correctly interpret the results.

Packed with everyday examples from sport, health, education, and leisure, it reinforces the understanding of core topics while avoiding the heavy use of equations and formulae. Written in a highly accessible style and adopting a hands‑on approach, each chapter is accompanied by a summary of key points, illustrations and tables, and recommendations for further reading, with the final chapter delving into the practicalities of conducting a real‑life statistical research project.

Statistical Analysis: The Basics is essential reading for anyone who wishes to master the fundamentals of modern‑day statistical analysis.

Statistical Analysis: The Basics provides an engaging and easy‑to‑read primer on this sometimes daunting subject. Intended for those with little or no background in mathematics or statistics, this book explores the importance of statistical analysis in the modern world by asking statistical questions about data and explains how to conduct such analyses and correctly interpret the results.

Packed with everyday examples from sport, health, education, and leisure, it reinforces the understanding of core topics while avoiding the heavy use of equations and formulae. Written in a highly accessible style and adopting a hands‑on approach, each chapter is accompanied by a summary of key points, illustrations and tables, and recommendations for further reading, with the final chapter delving into the practicalities of conducting a real‑life statistical research project.

Statistical Analysis: The Basics is essential reading for anyone who wishes to master the fundamentals of modern‑day statistical analysis.

This book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data.

Contrary to other textbooks on regression, this book is based on the idea that you do not necessarily need to know much about statistics and mathematics to get a firm grip on regression and perform it to perfection. This non-technical point of departure is complemented by practical examples of real-life data analysis using statistics software such as Stata, R and SPSS. Parts 1 and 2 of the book cover the basics, such as simple linear regression, multiple linear regression, how to interpret the output from statistics programs, significance testing and the key regression assumptions. Part 3 deals with how to practically handle violations of the classical linear regression assumptions, regression modeling for categorical y-variables and instrumental variable (IV) regression. Part 4 puts the various purposes of, or motivations for, regression into the wider context of writing a scholarly report and points to some extensions to related statistical techniques.

This book is written primarily for those who need to do regression analysis in practice, and not only to understand how this method works in theory. The book’s accessible approach is recommended for students from across the social sciences.

This book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data.

Contrary to other textbooks on regression, this book is based on the idea that you do not necessarily need to know much about statistics and mathematics to get a firm grip on regression and perform it to perfection. This non-technical point of departure is complemented by practical examples of real-life data analysis using statistics software such as Stata, R and SPSS. Parts 1 and 2 of the book cover the basics, such as simple linear regression, multiple linear regression, how to interpret the output from statistics programs, significance testing and the key regression assumptions. Part 3 deals with how to practically handle violations of the classical linear regression assumptions, regression modeling for categorical y-variables and instrumental variable (IV) regression. Part 4 puts the various purposes of, or motivations for, regression into the wider context of writing a scholarly report and points to some extensions to related statistical techniques.

This book is written primarily for those who need to do regression analysis in practice, and not only to understand how this method works in theory. The book’s accessible approach is recommended for students from across the social sciences.

Doing Statistical Analysis looks at three kinds of statistical research questions – descriptive, associational, and inferential – and shows students how to conduct statistical analyses and interpret the results. Keeping equations to a minimum, it uses a conversational style and relatable examples such as football, COVID-19, and tourism, to aid understanding. Each chapter contains practice exercises, and a section showing students how to reproduce the statistical results in the book using Stata and SPSS. Digital supplements consist of data sets in Stata, SPSS, and Excel, and a test bank for instructors. Its accessible approach means this is the ideal textbook for undergraduate students across the social and behavioral sciences needing to build their confidence with statistical analysis.

Doing Statistical Analysis looks at three kinds of statistical research questions – descriptive, associational, and inferential – and shows students how to conduct statistical analyses and interpret the results. Keeping equations to a minimum, it uses a conversational style and relatable examples such as football, COVID-19, and tourism, to aid understanding. Each chapter contains practice exercises, and a section showing students how to reproduce the statistical results in the book using Stata and SPSS. Digital supplements consist of data sets in Stata, SPSS, and Excel, and a test bank for instructors. Its accessible approach means this is the ideal textbook for undergraduate students across the social and behavioral sciences needing to build their confidence with statistical analysis.

Statistical Analysis: The Basics provides an engaging and easy‑to‑read primer on this sometimes daunting subject. Intended for those with little or no background in mathematics or statistics, this book explores the importance of statistical analysis in the modern world by asking statistical questions about data and explains how to conduct such analyses and correctly interpret the results.

Packed with everyday examples from sport, health, education, and leisure, it reinforces the understanding of core topics while avoiding the heavy use of equations and formulae. Written in a highly accessible style and adopting a hands‑on approach, each chapter is accompanied by a summary of key points, illustrations and tables, and recommendations for further reading, with the final chapter delving into the practicalities of conducting a real‑life statistical research project.

Statistical Analysis: The Basics is essential reading for anyone who wishes to master the fundamentals of modern‑day statistical analysis.

Statistical Analysis: The Basics provides an engaging and easy‑to‑read primer on this sometimes daunting subject. Intended for those with little or no background in mathematics or statistics, this book explores the importance of statistical analysis in the modern world by asking statistical questions about data and explains how to conduct such analyses and correctly interpret the results.

Packed with everyday examples from sport, health, education, and leisure, it reinforces the understanding of core topics while avoiding the heavy use of equations and formulae. Written in a highly accessible style and adopting a hands‑on approach, each chapter is accompanied by a summary of key points, illustrations and tables, and recommendations for further reading, with the final chapter delving into the practicalities of conducting a real‑life statistical research project.

Statistical Analysis: The Basics is essential reading for anyone who wishes to master the fundamentals of modern‑day statistical analysis.