The book **Introduction
to Statistics** by W. Härdle, S.Klinke and B. Rönz has been
published in 2015 by Springer Verlag (paper/pdf/epub).

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).

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 the practical relevance of statistical analysis through interactive examples (Shiny apps) 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.

This book 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). 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 the 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.

The interactive examples in the book can be accessed via a web link
of the form `https://u.hu-berlin.de/men_xxxx`

. In the package
`HKRbook`

these links have been replaced by the functions
`men_xxxx()`

.

```
# install the package once from CRAN
# install.packages("HKRbook")
library("HKRbook")
men_asso() # calls the Shiny app behind "https://u.hu-berlin.de/men_asso"
# Additionally you may use your data sets, for details see ?men_asso
men_asso(Titanic)
```

If you are running the application, exit it by closing the application window.

However, we have streamlined some apps as they are more or less duplicates.

R function | Parameters | Content | Book link (`https://u.hu-berlin.de/` ) |
---|---|---|---|

men_asso() | data set(s) | Association of categorical data | `men_asso` , `men_tab2` |

men_bin() | parameters | Binomial distribution | `men_bin` |

men_ci1() | data set(s) | Confidence interval for the mean | `men_ci1` |

men_ci2() | data set(s) | Confidence interval for the difference of two means | `men_ci2` |

men_cilen() | – | Necessary sample sizes for confidence interval | `men_cilen` |

men_cipi() | data set(s) | Confidence interval for the proportion | `men_cipi` |

men_cisig() | data set(s) | Confidence interval for the variance | `men_cisig` |

men_corr() | data set(s) | Scatterplots and correlation | `men_corr` , `men_plot` |

men_die() | – | Die rolling sisters (Bayes theorem) | `men_die` |

men_dot() | data set(s) | Dot plot/strip chart | `men_dot1` , `men_dot2` |

men_exp() | parameters | Exponential distribution | `men_exp` |

men_hall() | – | Monty Hall problem | `men_hall` |

men_hist() | data set(s) | Histogram | `men_hist` |

men_hyp() | parameters | Hypergeometric distribution | `men_hyp` |

men_norm() | parameters | Normal distribution | `men_norm` |

men_parn() | parameter | Distribution of sample parameters of a numerical variable | |

men_poi() | parameter | Poisson distribution | `men_poi` |

men_rank() | data set(s) | Rank correlation of ordered variables | `men_rank` |

men_regr() | data set(s) | Simple linear regression | `men_regr` |

men_tab() | data set(s) | Simple linear regression | |

men_terr() | data set(s) | Test of mean with type I and II error | `men_terr` |

men_time() | time series | Classical time series analysis | `men_time1` , `men_time2` ,
`men_time3` |

men_tmu1() | data set(s) | Test for mean | `men_tmu1` |

men_tmu2() | data set(s) | Test for mean difference | `men_tmu2` |

men_tprop() | data set(s) | Binomial test | `men_tprop` |

men_vis() | data set(s) | Visualizations of a numeric variable | `men_vis` |