Applied regression analysis: doing, interpreting and reporting

By:

Thrane, Christer

Material type: Text

TextPublication details: New York Routledge 2020Description: viii, 191 pISBN:

9781138335486

Subject(s):

DDC classification:

519.536 THR

Summary: 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 (https://www.routledge.com/Applied-Regression-Analysis-Doing-Interpreting-and-Reporting/Thrane/p/book/9781138335486)

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	Operations Management & Quantitative Techniques	519.536 THR (Browse shelf(Opens below))	1	Checked out	01/03/2026	009056

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Previous	519.536 LON Regression models for categorical dependent variables using Stata	519.536 OSB Regression and linear modeling: best practices and modern methods	519.536 SEN Linear models and regression with R: an integrated approach	519.536 THR Applied regression analysis: doing, interpreting and reporting	519.54 AIT Introduction to statistical modelling and inference	519.54 KIM Statistical methods for handling incomplete data	519.54 MAV Probability and statistical inference:	Next

Table of contents:
Part 1: The Basics 1. What is regression analysis? 2. Linear regression with a single independent variable 3. Linear regression with several independent variables: Multiple regression Part 2: The Foundations 4. Samples and populations, statistical uncertainty and testing of statistical significance 5. The assumptions of regression analysis Part 3: The Extensions 6. Beyond linear regression: Non-additivity, non-linearity and mediation 7. A categorical dependent variable: Logistic (logit) regression and related methods 8. An ordered (ordinal) dependent variable: Logistic (logit) regression 9. The quest for a causal effect: Instrumental variable (IV) regression Part 4: Regression Purposes, Academic Regression Projects and the Way Ahead 10. Regression purposes in various academic settings and how to perform them 11. The way ahead: Related techniques

[https://www.routledge.com/Applied-Regression-Analysis-Doing-Interpreting-and-Reporting/Thrane/p/book/9781138335486]

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

(https://www.routledge.com/Applied-Regression-Analysis-Doing-Interpreting-and-Reporting/Thrane/p/book/9781138335486)

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