Introduction to Statistics
and Data Analysis
-
with exercises, solutions and
applications in R -
by Christian Heumann, Michael Schomaker
and Shalabh
This introductory statistics textbook conveys
essential concepts 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. The revised and
extended second edition, published in 2023,
features new chapters on logistic regression,
simple random sampling, bootstrapping, and
causal inference. Alternatives to making binary
decisions with statistical tests, using the
concepts of compatibility and functions of p-
and S-values for varying hypotheses, are
introduced as well.
The text is intended for undergraduate students
and self-learners in disciplines like business
administration, the social sciences, medicine,
politics, psychology and others. It features a
wealth of examples; each chapter contains
numerous exercises and, importantly, clear and
detailed solutions. Many examples and exercises
are illustrated with computer code in the
statistical programming language R.
The book is available from Springer here
(and the popular first edition here)
Prof. 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. 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.
Prof. 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 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.
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