# Introduction to Statistics

## Benefits

• Covers thoroughly all necessary topics for introductory statistics courses

• Guides students to a deeper understanding of statistical methods through diverse examples

• Provides interactive real data examples for independent study via the enhanced online version

Textbook

1. Front Matter
Pages i-xx
2. Wolfgang Karl Härdle, Sigbert Klinke, Bernd Röonz
Pages 1-19
3. Wolfgang Karl Härdle, Sigbert Klinke, Bernd Röonz
Pages 21-68
4. Wolfgang Karl Härdle, Sigbert Klinke, Bernd Röonz
Pages 69-96
5. Wolfgang Karl Härdle, Sigbert Klinke, Bernd Röonz
Pages 97-106
6. Wolfgang Karl Härdle, Sigbert Klinke, Bernd Röonz
Pages 107-148
7. Wolfgang Karl Härdle, Sigbert Klinke, Bernd Röonz
Pages 149-208
8. Wolfgang Karl Härdle, Sigbert Klinke, Bernd Röonz
Pages 209-249
9. Wolfgang Karl Härdle, Sigbert Klinke, Bernd Röonz
Pages 251-309
10. Wolfgang Karl Härdle, Sigbert Klinke, Bernd Röonz
Pages 311-418
11. Wolfgang Karl Härdle, Sigbert Klinke, Bernd Röonz
Pages 419-454
12. Wolfgang Karl Härdle, Sigbert Klinke, Bernd Röonz
Pages 455-475
13. Wolfgang Karl Härdle, Sigbert Klinke, Bernd Röonz
Pages 477-494
14. Back Matter
Pages 495-516

### Introduction

MM*Stat, together with its enhanced online version with interactive examples, offers a flexible tool that facilitates the teaching of basic statistics. It covers all the topics found in introductory descriptive statistics courses, including simple linear regression and time series analysis, the fundamentals of inferential statistics (probability theory, random sampling and estimation theory), and inferential statistics itself (confidence intervals, testing).

MM*Stat is also designed to help students rework class material independently and to promote comprehension with the help of additional examples. Each chapter starts with the necessary theoretical background, which is followed by a variety of examples. The core examples are based on the content of the respective chapter, while the advanced examples, designed to deepen students’ knowledge, also draw on information and material from previous chapters.

The enhanced online version helps students grasp the complexity and t

he practical relevance of statistical analysis through interactive examples and is suitable for undergraduate and graduate students taking their first statistics courses, as well as for undergraduate students in non-mathematical fields, e.g. economics, the social sciences etc.

### Keywords

Descriptive Statistics Estimation Inferential Statistics Probability Theory Sampling Theory Statistical Tests

#### Authors and affiliations

1. 1.Humboldt-Universität zu BerlinC.A.S.E. Centre f. Appl. Stat. & Econ., School of Business and Economics, Humboldt-Universität zu BerlinBerlinGermany
2. 2.Ladislaus von Bortkiewicz Chair of StatisticsHumboldt-Universität zu BerlinBerlinGermany
3. 3.Department of Economics Inst.for Statsitics and EconometricsHumboldt-Universität zu BerlinBerlinGermany

Wolfgang Karl Härdle is the Ladislaus von Bortkiewicz Professor of Statistics at the Humboldt-Universität zu Berlin and director of C.A.S.E. (Center for Applied Statistics and Economics), director of the CRC-649 (Collaborative Research Center) “Economic Risk” and director of the IRTG 1792 “High Dimensional Non-stationary Time Series”. He teaches quantitative finance and semi-parametric statistics.  His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected member of the ISI (International Statistical Institute) and advisor to the Guanghua School of Management, Peking University and a senior fellow of Sim Kee Boon Institute of Financial Economics at the Singapore Management University.

Sigbert Klinke is a postdoctoral research fellow at the Ladislaus von Bortkiewicz Chair of Statistics at Humboldt-Universität zu Berlin. He received his PhD in computational statistics from the Catholique Uni

versity in Louvain-la-Neuve, Belgium. He teaches introductory statistics courses and data analytical courses for bachelor and master students in Economics and Educational Science at Humboldt-Universität zu Berlin’s School of Business and Economics. His research focuses on computational and multivariate statistics and the teaching of statistics.

Bernd Rönz was a Professor of Statistics at the Institute for Statistics and Econometrics, School of Business and Economics, Humboldt University, Berlin. He taught Statistics, Computational Statistics and Generalized Linear Models. His research focused on multivariate statistics, computational statistics and generalized linear models. He previously worked as Associate Professor of Quantitative Methods for Business Decisions at the University of Dar es Salaam, Tanzania for more than two years. Furthermore, he was a Visiting Lecturer at Hosei-University Tokyo and Ritsumeikan-University Kyoto and a Visiting Fellow at the Centre f

or Mathematics and its Applications, School of Mathematical Sciences, The Australian National University, Canberra. He retired in 2006.

### Bibliographic information

• Book Title Introduction to Statistics
• Book Subtitle Using Interactive MM*Stat Elements
• Authors Wolfgang Karl Härdle
Bernd Rönz
• DOI https://doi.org/10.1007/978-3-319-17704-5
• Copyright Information Springer International Publishing Switzerland 2015
• Publisher Name Springer, Cham
• eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
• Hardcover ISBN 978-3-319-17703-8
• Softcover ISBN 978-3-319-79237-8
• eBook ISBN 978-3-319-17704-5
• Edition Number 1
• Number of Pages XX, 516
• Number of Illustrations 32 b/w illustrations, 173 illustrations in colour
• Additional Information Originally published in German with the title: MM*Stat - Eine interaktive Einführung in die Welt der Statistik
• Topics
• Buy this book on publisher's site

## Reviews

“The book is meant for undergraduate and graduate students taking their first statistics courses, as well as for undergraduate students in non-mathematical fields. … The book includes many examples with explanations. … This book will be useful for statistics students.” (S. V. Nagaraj, Computing Reviews, November, 2016)