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Introduction to Statistics and Data Analysis

With Exercises, Solutions and Applications in R

Author(s):
Publisher:

Springer

Pages: 456
Further Actions:

Recommend to library

AVAILABLE FORMATS

Ebook - 9783319461625

26 January 2017

$54.99

In stock

Hardcover - 9783319461601

22 March 2017

$69.99

In stock

All prices are shown excluding Tax

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...

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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.

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Introduces undergraduate students to quantitative data analysis and statistics  

Includes a wealth of examples, exercises and solutions 

Features working computer code in the statistical software R

Part I Descriptive Statistics: Introduction and Framework
Frequency Measures and Graphical Representation of Data
Measures of Central Tendency and Dispersion
Association of Two Variables
Part I Probability Calculus: Combinatorics
Elements of Probability Theory
Random Variables
Probability Distributions
Part III Inductive Statistics: Inference
Hypothesis Testing
Linear Regression
Part IV Appendices: Introduction to R
Solutions to Exercises
Technical Appendix
Visual Summaries.

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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...

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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.

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