Data Science / Statistics / R Foundation / Statistics / Inferential Statistics for Probability Analysis and Testing

Statistics - Inferential Statistics for Probability Analysis and Testing


ID 2858620
Classroom 3 days
Webinar 5 days
Method Lecture with examples and exercises.
Prequisite General knowledge of math
Audience Data Analysts


  • Lunch / Catering
  • Assistance for hotel / travel bookings
  • Comelio certificate
  • Flexible: Free cancellation up until 10 days before the training


In statistics, statistical inference is the process of drawing conclusions from data that is subject to random variation, for example, observational errors or sampling variation. Statistical induction helps describing systems of procedures that can be used to draw conclusions from datasets arising from systems affected by random variation, such as observational errors, random sampling, or random experimentation. It is then used to test hypotheses and make estimations using sample data. This training covers all the fundamentals of inductive statistics (probability theory, probability distributions and hypotheses testing) which can be used in marketing, controlling and engineering. You will learn theory and the mathematical foundations in lectures with examples and you will train your new knowledge in practical hands-on labs and exercices.

Training Dates

  • 2020-Oct-26 - Oct-30
  • 2021-Jan-04 - Jan-08
  • 2021-Mar-15 - Mar-19
  • 2021-May-24 - May-28

850 EUR +VAT

Location | Enrollment


Probability Theory

Probability axioms - Probability space Sample space - Elementary event - Random variable - Probability measure - Complementary event - Joint probability - Marginal probability - Conditional probability - Independence - Conditional independence - Law of total probability - Law of large numbers - Bayes' theorem - Venn diagram - Tree diagram

Probability Distributions

Introduction: Probability mass function, Probability density function, Probability distribution function - Discrete univariate distributions: Binomial, Poisson, Geometric, Hypergeometric - Continuous univariate distributions: Uniform, Exponential, Normal (Gaussian)

Frequentist Inference

Unbiased estimators (Mean unbiased minimum variance, Median unbiased) - Confidence interval - Testing hypotheses - Alpha-/Beta-Error and Power

Specific Tests

Z (normal) - Student's t-test - F - Goodness of fit (Chi-squared) - ­Signed-rank (1-sample, 2-sample, 1-way anova)


Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.

  • Grundlagen empirische Sozialforschung ISBN 978-3-939701-23-1
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  • Oracle PL/SQL ISBN 978-3-939701-40-8
  • MS SQL Server - T-SQL Programmierung und Abfragen ISBN 978-3-939701-69-9

- He works as an IT-consultant and project manager. He developed various Business Intelligence systems for industry clients and the public sector. For several years now, he is responsible for a BI-team in India which is mainly involved in BI and OLAP projects, reporting systems as well as statistical analysis and Data Mining.


He led several research projects and was leading scientist and project manager of a publicly funded project about interactive questionnaires and online surveys.