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Competence area statistics

We pursue the overarching goal of promoting data literacy in teaching and research and focus on the area of applied statistics. Within this area, knowledge and skills are taught that enable learners to effectively apply statistical methods to analyze data, identify patterns and draw sound conclusions. This includes understanding basic and advanced statistical concepts and applying these concepts to real-world data sets from a variety of disciplines. By fostering data literacy, students and researchers are enabled to critically evaluate data, formulate research questions and generate empirical findings that contribute to the development of knowledge in their respective fields.

Summenzeichen

About our working group

Campus Unteres Schloß, Gebäudeteil D

"In our working group, we deal with the quantitative analysis of economic and social processes, in particular the effects of working conditions and individual resources on health, well-being and performance. In doing so, we combine methodologically sound data analysis with economic theory.

The focus is on both stable relationships and dynamic developments, for example in panel data. Special emphasis is placed on the identification of causal effects and the critical evaluation of empirical results.

As an interdisciplinary team from applied statistics, economics and mathematics, we work on the development and application of modern quantitative methods in order to provide reliable empirical evidence for economic and social science issues."

Our research profile

The competence area of statistics includes the analysis of complex real-world data in the research areas of medical-technical research, health economics, entrepreneurship and SME management. In addition, the area of expertise includes the interdisciplinary application of statistical methods in clinical, economic and entrepreneurial contexts, including experimental study designs and simulation-based methods. The aim is to generate robust, evidence-based findings to support scientific analyses and well-founded decision-making processes in research and practice.

Main research areas

  • Entrepreneurship
  • SME Management
  • Health economics
  • Medical-technical research

 

Publications

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Artur Barsumyan, Christian Soost, Jan Adriaan Graw, Rene Burchard

The relationship between cadence decline, cardiovascular drift and aerobic decoupling as a marker of fatigue in well trained cyclists

Bianca Valentina Schlesier, Christian Soost, Jan Adriaan Graw, Rene Burchard, Artur Barsumyan

Influence of Carving Ski Base Preparation on Patient-Reported Outcomes in Recreational Skiers with Knee Osteoarthritis: A Pilot Study

Artur Barsumyan, Anton Saukkonen, Christian Soost, Jan Adriaan Graw, Rene Burchard

Predicting Next Day Heart Rate Variability Based on Training Load in Cyclists Using Machine Learning

Claudia Döhner, Christian Soost, Sam Steinhöfer, Jan A. Graw, Christopher Bliemel, Artur Barsumyan, et al.

Pressure-Relief Effect of Post-Op Shoes Depends on Correct Usage While Walking

Floating-embedded stems reduce tibial stress shielding in total knee revision arthroplasty

Enhanced durability predicts success in amateur road cycling: evidence of power output declines

Konrad Meisner, Christian Soost, Petra Moog, Juni Mohos

Broadening the horizon for gender studies in family business and entrepreneurship research: QCA as an additional approach

Higher rate of undetected intraoperative damage of latex-free surgical gloves worn by scrub nurses

Thorsten Lehnhardt, Christian Soost, Jan Adriaan Graw, Rene Burchard, Christopher Bliemel, Artur Barsumyan

Median Nerve Diameter Ratio on Ultrasound as a Complementary Tool to Electrodiagnostic Testing in Carpal Tunnel Syndrome

Nico Ries, Christian Soost

Flexibilisierung von Finanzierungslösungen im Mittelstand durch IoT-Technologien

Durability as an independent parameter of endurance performance in cycling

Quantifying training response in cycling based on cardiovascular drift using machine learning

Christian Soost

AOR Dr. Christian Soost

Senior Academic Councillor
Foto von Sam Steinhöfer

Sam Steinhöfer

Research assistant

Academic Advisor für Volkswirtschaftslehre und Mathe-Vorkurs

Contact the working group

Postal address

University of Siegen
Schools III - Statistics
Kohlbettstr. 15
57072 Siegen

Visitor address

University of Siegen
Faculty III - Statistics

US-D 005
Kohlbettstr. 15
57072 Siegen

Secretariat

Please contact statistik@uni-siegen.de