A training session specially designed to be offered online.
This workshop offers an introduction to the fundamental principles and concepts in statistics.
The first part covers classical and more recent exploratory data analysis (EDA) techniques to describe data with numerical and graphical tools. The various uses of these methods such as outlier detection is discussed.
The second part addresses, with the help of real-life examples, the principles underlying statistical testing and decision-making in the presence of uncertainty. It covers risks involved, effect size, p-values as well as statistical significance and practical relevance. The use and interpretation of confidence intervals is also discussed.
An excellent introductory module and a solid basis for all other courses.
Become more confident when discussing clinical results with leading healthcare providers. This course covers key biostatistical concepts required to review and interpret findings published in the biomedical literature. The exact course curriculum is tailored to the therapy area of interest. Selected scientific publications are reviewed, discussed and criticised.
Variation is present in every experiment. Learn about DoE techniques to control variation, and to maximise data quality.
This workshop presents classical techniques to design efficient experiments as well as the tools to analyze their results. The principles of sample size calculations, strategies to remove undesirable sources of variability like the use of blocks and controls, as well as the most commonly used experimental designs are discussed. The statistical analysis of designed experiments is progressively introduced, starting with the t-test method used to compare two groups. Then, the analysis of variance technique (ANOVA) is extensively covered from simple one-factor experiments to more advanced multi-factor situations where the interaction between factors needs to be considered. Multiple comparisons techniques used to locate differences are also presented.
Learn about advanced experimental designs to account for experimental various types of constraints such as time, available resources, material heterogeneity, randomization restrictions when certain factors are more difficult or costly to change than others, different sizes of experimental units as well as repeated measures. In this course, the construction of advanced designs and their statistical analysis is covered with the help of real case studies.
The linear regression is a method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data. Building a regression model with stats packages has become straightforward. However, interpreting the software output and building a good model are no simple tasks.
Learn about statistical modeling with a focus on linear models. What is a model? Estimating and interpreting model coefficients. Dealing with continuous and categorical predictors and interactions. Evaluating model performance: explanatory vs. predictive. Common pitfalls and best practices.
