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## Über dieses Buch

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.

## Inhaltsverzeichnis

### Chapter 1. Introduction and Framework

Statistics is a collection of methods which help us to describe, summarize, interpret, and analyse data. Drawing conclusions from data is vital in research, administration, and business.
Christian Heumann, Michael Schomaker, Shalabh

### Chapter 2. Frequency Measures and Graphical Representation of Data

In Chap. 1, we highlighted that different variables contain different levels of information.
Christian Heumann, Michael Schomaker, Shalabh

### Chapter 3. Measures of Central Tendency and Dispersion

A data set may contain many variables and observations. However, we are not always interested in each of the measured values but rather in a summary which interprets the data.
Christian Heumann, Michael Schomaker, Shalabh

### Chapter 4. Association of Two Variables

In Chaps. 2 and 3 we discussed how to analyse a single variable using graphs and summary statistics.
Christian Heumann, Michael Schomaker, Shalabh

### Chapter 5. Combinatorics

Combinatorics is a special branch of mathematics. It has many applications not only in several interesting fields such as enumerative combinatorics (the classical application), but also in other fields, for example in graph theory and optimization.
Christian Heumann, Michael Schomaker, Shalabh

### Chapter 6. Elements of Probability Theory

Let us first consider some simple examples to understand the need for probability theory.
Christian Heumann, Michael Schomaker, Shalabh

### Chapter 7. Random Variables

In the first part of the book, we highlighted how to describe data.
Christian Heumann, Michael Schomaker, Shalabh

### Chapter 8. Probability Distributions

We introduced the concept of probability density and probability mass functions of random variables in the previous chapter.
Christian Heumann, Michael Schomaker, Shalabh

### Chapter 9. Inference

The first four chapters of this book illustrated how one can summarize a data set both numerically and graphically.
Christian Heumann, Michael Schomaker, Shalabh

### Chapter 10. Hypothesis Testing

We introduced point and interval estimation of parameters in the previous chapter.
Christian Heumann, Michael Schomaker, Shalabh

### Chapter 11. Linear Regression

We learnt about various measures of association in Chap. 4.
Christian Heumann, Michael Schomaker, Shalabh

### Backmatter

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