# Introduction to Statistics

## Contents

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.

## Portrait of the authors

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 Chair of Statistics at Humboldt-Universität zu Berlin. He received his PhD in computational statistics from the Catholique University 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.

# Interactive examples from the book

## Use of the examples

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.

## Old and new apps

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