Introduction to Statistics and Data Analysis : With Exercises, Solutions and Applications in R

Christian Heumann

ISBN 10: 3319834568 / ISBN 13: 9783319834566

Verlag: Springer International Publishing, Springer International Publishing, 2018

Beschreibung

Druck auf Anfrage Neuware - Printed after ordering - 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. In the experimental sciences and interdisciplinary research, data analysis has become an integral part of any scientific study. Issues such as judging the credibility of data, analyzing the data, evaluating the reliability of the obtained results and finally drawing the correct and appropriate conclusions from the results are vital.The text is primarily intended for undergraduate students in disciplines like business administration, the social sciences, medicine, politics,macroeconomics,etc. 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 all methods to their own applications.

Bestandsnummer des Verkäufers 9783319834566

Bibliografische Details

• Titel: Introduction to Statistics and Data Analysis : With Exercises, Solutions and Applications in R
• Verlag: Springer International Publishing, Springer International Publishing
• Erscheinungsjahr: 2018
• Sprache: Englisch
• ISBN-10: 3319834568
• ISBN-13: 9783319834566
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

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. In the experimental sciences and interdisciplinary research, data analysis has become an integral part of any scientific study. Issues such as judging the credibility of data, analyzing the data, evaluating the reliability of the obtained results and finally drawing the correct and appropriate conclusions from the results are vital.

The text is primarily intended for undergraduate students in disciplines like business administration, the social sciences, medicine, politics, macroeconomics, etc. 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 all methods to their own applications.

Über die Autorin bzw. den Autor

Dr. Christian Heumann is a professor at the Ludwig-Maximilian-Universität Munich, where he teaches students in Bachelor and Master programs offered by the Department of Statistics, as well as undergraduate students in the Bachelor of Science programs in business administration and economics. His research interests include statistical modeling, computational statistics and all aspects of missing data.

Dr. Michael Schomaker is a Senior Researcher and Biostatistician at the Centre For Infectious Disease Epidemiology & Research (CIDER), University of Cape Town, South Africa. He received his doctoral degree from the University of Munich. He has taught undergraduate students from the business and medical sciences for many years and has written contributions for various introductory textbooks. His research chiefly focuses on missing data, causal inference, model averaging and HIV/AIDS.  

Dr. Shalabh is a Professor at the Indian Institute of Technology Kanpur (India). He received his Ph.D. from the University of Lucknow (India) and completed his post-doctoral work at the University of Pittsburgh (USA) and University of Munich (Germany). He has over twenty years experience in teaching and research. His main research areas are linear models, regression analysis, econometrics, error-measurement models, missing data models and sampling theory.