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Introduction to statistical data analysis with R

18 Recensioni
(18 valutazioni)
1 Recensione
Lingua:  English
The book contains an introduction to descriptive and inferential statistics applying the statistical software R. The analyses are introduced and discussed using real data.
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The book offers an introduction to statistical data analysis applying the free statistical software R, probably the most powerful statistical software today. The analyses are performed and discussed using real data. After a brief description of the statistical software R, important parameters and diagrams of descriptive statistics are introduced. Subsequently, recommendations for generating diagrams are provided, where special attention is given to the selection of appropriate colors. The second half of the book addresses the basics of inferential statistics. First, a number of probability distributions are introduced and their applicability is illustrated by examples. Next, the book describes how the parameters of these distributions, which are unknown in practice, may be estimated from given data. The final chapter introduces statistical tests and reviews the most important tests for practical applications.

  1. Statistical Software R
    1. R and its development history
    2. Structure of R
    3. Installation of R
    4. Working with R
    5. Exercises
  2. Descriptive Statistics
    1. Basics
    2. Excursus: Data Import and Export with R
    3. Import of ICU-Dataset
    4. Categorical Variables
    5. Metric Variables
    6. Exercises
  3. Colors and Diagrams
    1. Colors
    2. Excursus: Export of Diagrams
    3. Diagrams
    4. Exercises
  4. Probability Distributions
    1. Discrete Distributions
    2. Continuous Distributions
    3. Exercises
  5. Estimation
    1. Introduction
    2. Point Estimation
    3. Condence Intervals
    4. Exercises
  6. Statistical Tests
    1. Introduction
    2. Examples
    3. Exercises
This book is safe as an airbag while I ride through the jungle of R. Its a great book.
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Matthias Kohl

Matthias Kohl ist Professor für Mathematik in Biology und Medizin und leitet das Masterprogramm “Precision Medicine Diagnostics (M.Sc.)” an der Hochschule Furtwangen. Er ist Mitglied des Institute of Precision Medicine und des Baden-Württemberg Center of Applied Research (BW-CAR). Er hat mehr als 15 Jahre Forschungserfahrung auf den Gebieten Robuste Statistik, Statistisches Lernen, Biostatistik und statistische Bioinformatik. In den vergangenen Jahren war er an vielen klinischen und experimentellen Studien sowie F&E Projekten beteiligt. Er ist Autor von mehr als 80 wissenschaftlichen (peer-reviewed) Artikeln, mehr als 20 Erweiterungspaketen für die Statistiksoftware R, drei Erweiterungspaketen für die Bioconductor Software und sieben Patenten / Patentanmeldungen.


(Bio-)Statistik, Bioinformatik, Statistisches (maschinelles) Lernen, Robuste Statistik


Mathematikstudium (Nebenfach Informatik) an der Universität Bayreuth

Promotion in Robuster Statistik an der Universität Bayreuth (summa cum laude)


Wissenschaftlicher Mitarbeiter (Universität Bayreuth, Universitätsklinikum Jena)

Freiberuflicher statistischer und mathematischer Berater

Head of Biostatistics bei SIRS-Lab GmbH (Jena)


Leiter des Steinbeistransferzentrums "Personalisierte Medizin"

Professor für Mathematik in Biologie und Medizin an der Hochschule Furtwangen, Fakultät Medical and Life Sciences