Overview
- Introduces undergraduate students and self-learners to quantitative data analysis and statistics
- Features new chapters on logistic regression, sampling and bootstrapping, and causal inference
- Provides a wealth of examples, exercises and solutions as well as working computer code in R
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Table of contents (14 chapters)
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Front Matter
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Descriptive Statistics
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Front Matter
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Probability Calculus
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Front Matter
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Inductive Statistics
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Front Matter
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Additional Topics
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Front Matter
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Back Matter
Keywords
- descriptive statistics
- inductive statistics
- quantitative data analysis
- statistical software R
- introduction to statistics
- applications of statistical methods
- statistical inference
- hypotheses testing
- linear regression
- logistic regression
- bootstrapping
- causality
- random sampling
- probability distributions
- random variables
- graphical representation of data
About this book
Now in its second edition, this introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. This revised and extended edition features new chapters on logistic regression, simple random sampling, including bootstrapping, and causal inference.
The text is primarily intended for undergraduate students in disciplines such as business administration, the social sciences, medicine, politics, and macroeconomics. It features a wealth of examples, exercises and solutions with computer code in the statistical programming language R, as well as supplementary material that will enable the reader to quickly adapt the methods to their own applications.
Authors and Affiliations
About the authors
Dr. Christian Heumann is a Professor at the Department of Statistics, LMU Munich, Germany, where he teaches students in both the Bachelor’s and Master’s programs. His research interests include statistical modeling, computational statistics and methods for missing data, also in connection with causal inference. Recently, he has begun exploring statistical methods in natural language processing.
Dr. Michael Schomaker is a Researcher and Heisenberg Fellow at the Department of Statistics, LMU Munich, Germany. He is an honorary Senior Lecturer at the University of Cape Town, South Africa and previously worked as an Associate Professor at UMIT – University for Health Sciences, Medical Informatics and Technology, Austria. For many years he has taught both undergraduate and post-graduate students from various disciplines, including the business and medical sciences, and has written contributions for various introductory textbooks. His research focuses on causal inference, missing data, model averaging, and HIV and public health.
Dr. Shalabh is a Professor at the Indian Institute of Technology Kanpur, India. As a post-doctoral researcher he worked at the University of Pittsburgh, USA and LMU Munich, Germany. He has over twenty-five years of experience in teaching and research. His main research areas are linear models, regression analysis, econometrics, error-measurement models, missing data models and sampling theory.
Bibliographic Information
Book Title: Introduction to Statistics and Data Analysis
Book Subtitle: With Exercises, Solutions and Applications in R
Authors: Christian Heumann, Michael Schomaker, Shalabh
DOI: https://doi.org/10.1007/978-3-031-11833-3
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-11832-6Published: 31 January 2023
Softcover ISBN: 978-3-031-12025-1Published: 01 February 2024
eBook ISBN: 978-3-031-11833-3Published: 30 January 2023
Edition Number: 2
Number of Pages: XVII, 584
Number of Illustrations: 112 b/w illustrations, 6 illustrations in colour
Topics: Statistical Theory and Methods, Statistics, general, Applied Statistics, Statistics and Computing/Statistics Programs