Course: Statistics for Biological and Medical Data – Fundamentals & Practical Application This three-day course covers the basics of statistical analysis for biological and medical data. Instead of complex mathematical formulas, the focus is on practical application. Through hands-on examples, you will learn to understand and confidently apply statistical methods. The course offers a comprehensive overview of statistical analysis methods, their applications, and potential risks – ideal for beginners and professionals looking to refresh their knowledge. What you will learn in this course: 📊 Statistics Fundamentals: Descriptive statistics & key metrics 📊 Interpreting Statistical Graphs: Bar charts, error bars & dose-response curves 📊 Hypothesis Testing & Significance Tests: t-Test, ANOVA (including Repeated Measures) 📊 Nonparametric Methods: Mann-Whitney U test, Kruskal-Wallis test, Wilcoxon test, Friedman test 📊 Regression Analysis: Linear & nonlinear models including dose-response curves 📊 Data Validation: Data transformation, residual analysis (homogeneity of variance, normal distribution, lack-of-fit) 📊 Statistical Experimental Design (Design of Experiments, DoE) 📊 Survival Analysis & Models: Kaplan-Meier curves, Cox model & parametric survival models Prerequisites: ✅ No prior knowledge required – ideal for beginners & practitioners. 🕒 Course Duration: 3 days 🔹 Sign up now and apply statistics with confidence!

Course: Introduction to Statistical Analysis with R In this two-day course, you will acquire the necessary skills to perform statistical analysis using R. This course is aimed at beginners who want to learn R and R-Studio and develop practical skills for data analysis. At the beginning of the course, you will receive an introduction to the basics of R and R-Studio. You will learn how to import data from various formats such as Excel, text files, and databases. Additionally, data management with dplyr will be covered so that you can effectively manipulate and analyze data. You will also be introduced to the various help functions in R to help you navigate the R environment efficiently. In the second part of the course, the focus will be on descriptive statistics in R. You will learn how to create and publish summary tables (pivot tables). The powerful ggplot2 graphics package will also be introduced, allowing you to create and export various charts such as bar charts, histograms, scatter plots, and many more. All tools and packages used in the course are free and mostly available as open-source. Course content: 🔹 Introduction to R and R-Studio – Basic operation with R 🔹 Data import – Working with Excel, text files, and databases 🔹 Data management with dplyr – Efficient data processing 🔹 Descriptive statistics with dplyr – Data overview and analysis 🔹 Graphics creation with ggplot2 – Bar charts, histograms, scatter plots, and more Prerequisites: 📌 No prior knowledge required – The course is aimed at beginners. 🕒 Course duration: 2 days 🔹 Sign up now and expand your skills in statistical analysis with R!