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!

Learn in this two-day course how to achieve optimal results with minimal effort using statistical design of experiments (DoE). This method helps you identify statistical relationships and create precise models with as few experiments as possible. What you will learn in this course: ✅ Basics of statistical design of experiments (DoE) – Efficient methods for conducting experiments and analysis ✅ Two-stage experiments – Simple but powerful testing methods for informed decision-making ✅ Block factors & screening designs – Identify the most important influencing factors and analyze interactions ✅ Statistical analysis – Assess the confidence of your results with practical methods ✅ Practical application with Design-Expert® – Create and analyze experimental designs step by step With many practical examples and the software Design-Expert®, you will gain deep insights into efficient experimental methods. 🔹 Sign up now and optimize your experimental design! Course: Introduction to Statistical Design of Experiments (DoE) with Design-Expert® Learn in this two-day course how to conduct efficient and well-founded experiments using statistical design of experiments (DoE). The course offers a hands-on introduction to creating, analyzing, and optimizing experimental designs. Course Content: 📌 Two-stage factorial experimental designs – Create and analyze factorial experiments for well-founded insights 📌 Developing factorial designs – Increase the efficiency and significance of your experiments 📌 Transformations in regression models – Optimize models with adjusted data 📌 Block factors in design and analysis – Structure and refine your experimental designs 📌 Fractional factorial designs – Reduce experimental effort without missing relevant insights 📌 Expanding experimental designs – Systematically add additional experiments 📌 Graphical & statistical analysis – Use charts and statistical metrics to optimally interpret results Prerequisites: 🧠 Basic knowledge of statistics is helpful but not required – statistical fundamentals will be taught in the course. 👨‍🏫 Benefit from expert knowledge and learn how to take your experimental design to the next level with Design-Expert®!

Course: Experimental Design for Mixture Experiments with Design-Expert® In this two-day course, you will learn how to apply statistical experimental design specifically for mixture experiments. Traditional experimental designs are unsuitable for formulation and mixture optimization — instead, mixture designs are needed. This course will provide you with comprehensive knowledge of powerful mixture experimental designs, from creation to graphical analysis and optimization of formulations. You will work with the software Design-Expert® to efficiently create and analyze mixture designs. Why Mixture Experiments? 🔹 Optimizing formulations with multiple components 🔹 Specialized mixture designs for realistic formulations 🔹 Statistical & graphical analysis methods for precise evaluations 🔹 Practical implementation in Design-Expert® with real-world examples Course Content: ✅ Creating & Analyzing Simplex Designs ✅ Selecting suitable mixture designs & models ✅ Generating contour plots in the triangular experimental region ✅ Creating mixture designs with constraints ✅ Optimizing product compositions & formulations ✅ Evaluating design quality & expanding designs ✅ Creating contour diagrams & trace plots for detailed analysis Requirements: 📌 Basic knowledge of experimental design with factorial designs — ideally at the level of the course “Introduction to Experimental Design with Design-Expert®”. 🔹 Sign up now and take your formulation optimization to the next level!

Two-Day Course: Process and Product Optimization with Response Surface Methodology (RSM) In this intensive two-day course, you will learn how to efficiently optimize processes and products using Response Surface Methodology (RSM). This method goes beyond factorial designs and allows for the best possible adjustment of influencing factors to achieve optimal results. Why Response Surface Methodology (RSM)? 🔹 Ideal for complex optimization tasks where factorial designs are insufficient 🔹 Determine optimal factor settings for product and process optimization 🔹 Create models for response surfaces to perform simulations and define process windows 🔹 Use Design-Expert® software to easily and efficiently create RSM designs Course Content: ✅ Expanding factorial designs with center-point runs ✅ Creating RSM experimental designs such as Central Composite Designs (CCD) & Box-Behnken Designs ✅ Selecting appropriate regression models for precise analysis ✅ Determining robust computational conditions to improve process stability ✅ Simultaneously optimizing multiple objectives for comprehensive results ✅ Evaluating design quality to ensure reliable data Requirements: 📌 Basic knowledge of statistics and experimental design with factorial designs is helpful — ideally at the level of the course “Introduction to Experimental Design with Design-Expert®.” 🔹 Sign up now and elevate your optimization skills to the next level!

Course: Quality by Design (QbD) – Statistical Experimental Design for Pharmaceutical Processes Quality by Design (QbD) is the FDA-recommended method for developing and optimizing pharmaceutical products and processes. The central idea is to define factor spaces within which consistent product quality is ensured. Once a design space is approved by regulatory authorities, the process can be adjusted within these limits without requiring re-approval. This methodology provides new flexibility in the production of pharmaceutical products and medical devices, reducing regulatory hurdles. What You Will Learn in This Course: 📌 Fundamentals of Quality by Design (QbD) & Design of Experiments (DoE) 📌 Statistical Tools: Hypothesis Testing & Regression Analysis 📌 Screening Designs: Efficiently Identifying Key Factors 📌 Response Surface Designs: Defining and Optimizing Design Spaces 📌 Validation of Design Spaces for Regulatory Approval 📌 Practical Implementation with Design-Expert® Software The course will follow a complete QbD cycle using FDA examples and statistically model it with Design-Expert® software. Requirements: 📌 Basic knowledge of Design-Expert® is beneficial. 📌 Knowledge of regression analysis and hypothesis testing is helpful but not required. 🕒 Course Duration: 3 Days 🔹 Sign up now and optimize your pharmaceutical development!

Course: Advanced Time Series Analysis with EViews In this two-day course, you will learn advanced models for time series modeling and their application in econometrics. You will be equipped to understand and apply dynamic models, simultaneous equation models, as well as finance-specific models like ARCH and GARCH. Course Content: 📊 Review of Time Series Analysis – Basic models and techniques of time series analysis in econometrics 📊 AR, MA, ARMA, and ARIMA Models – Construction and application of classical time series models 📊 ARDL Models – Dynamic regression models and their use for analyzing long-term relationships 📊 Simultaneous Equation Models – Representation of interactions between multiple variables in a model 📊 VAR (Vector Autoregressive) and VEC (Vector Error Correction) Models – Multivariate time series analysis to model complex economic relationships 📊 ARCH and GARCH Models – Models for analyzing volatility and time dependence in financial markets Requirements: 📌 Basic knowledge of statistical hypothesis testing and linear regression 📌 Knowledge of using EViews, as taught in the course “Introduction to Econometrics with EViews” 🕒 Course Duration: 2 days 🔹 Sign up now and deepen your knowledge in time series analysis with EViews!

Course: Introduction to Econometrics and Time Series Analysis with EViews This two-day course introduces you to econometrics and time series analysis, showing you how to answer economic questions using statistical methods. Econometrics is a key technology for solving economic problems by combining economic theories with statistical models and datasets. With the methods learned in this course, you will be able to create simulations and forecasts for economic analysis. The focus of the course is on the linear regression model and time series analysis. You will become familiar with the statistical software EViews, which is specifically designed for econometric models and time series analysis and is easy to learn. Course Content: 📊 Introduction to the EViews Software – Basic functions 📊 Answering Econometric Questions Using Statistical Hypothesis Tests 📊 Performing Simple, Multiple, and Nonlinear Regressions 📊 Developing and Analyzing Stationary Time Series Models 📊 Creating Simulations and Forecasts with Econometric Models Requirements: 📌 Basic knowledge of statistics is helpful but not mandatory. 📌 No prior knowledge of EViews is required. 🕒 Course Duration: 2 days 🔹 Sign up now and deepen your knowledge of econometrics and time series analysis with EViews!

Course: Introduction to Programming with EViewsThe learning process begins importing data and progresses to replacing menu commands with programs. Along the way, learners are introduced to the classic elements of a programming language, such as loops and techniques for working with vectors and matrices. You will learn how to utilize the programming language in EViews for conducting data analysis, building models, and making forecasts. This course is ideal for anyone looking to deepen their skills in data handling and econometric analysis with EViews. Course Content: 📊 Getting to Know the EViews Programming Environment – Introduction to the software and programming language 📊 Creating EViews Scripts – Automating data analysis and models 📊 Data Imports and Exports – Working with external datasets and integrating results 📊 Loops and Conditions – Using loops and conditional statements in programming 📊 Error Handling – Tips for error analysis and troubleshooting in programs 📊 Macros and Functions – Creating and using custom functions and macros for modeling 📊 Programming Forecast Models – Building time series and econometric forecast models Requirements: 📌 Basic knowledge of statistics and econometrics 📌 No prior programming knowledge required; this course is designed for beginners in EViews programming 🕒 Course Duration: 2 days 🔹 Sign up now and expand your programming skills in EViews!

Course: Comparative Means with GraphPad Prism – Advanced Course for Experienced Users In this two-day advanced course, you will expand your knowledge of GraphPad Prism and learn the advanced methods for comparing means. The course focuses on the statistical methods implemented in GraphPad Prism such as t-tests, ANOVA, Kruskal-Wallis, normal distribution, and post-tests. You will learn how to apply these methods, interpret them, and use them on your own data. The course provides detailed knowledge on paired and unpaired test methods as well as homogeneity of variance. This course builds on the content from the GraphPad Prism introductory course. It is essential that you are familiar with the software basics, which you can gain by attending the "GraphPad Prism – Introduction" course. Basic statistical knowledge in mean comparison will also be provided. Course contents: 📊 Statistical foundations of mean comparisons 📊 Confident handling of the Prism interface – mean comparison projects (data input and import) 📊 Performing “Column” projects 📊 One Sample T-Test 📊 T-Test and One-Way ANOVA for independent and paired samples 📊 Non-parametric tests for independent and paired samples 📊 Post-tests after mean comparisons 📊 Performing “Grouped” projects 📊 Two-Way ANOVA for independent and paired samples 📊 Post-tests for Two-Way ANOVA 📊 Confident use of graph types and graph design for mean comparisons 📊 Interpretation of statistical metrics and summarizing results in layouts Prerequisites: 📌 This course builds on the "Introduction to Data Analysis with GraphPad Prism" course. 📌 Basic knowledge of operating GraphPad Prism and fundamental statistical knowledge are required. 🕒 Course duration: 2 days 🔹 Sign up now and deepen your skills in mean analysis with GraphPad Prism!

Course: Introduction to GraphPad Prism – Fundamentals of Data Analysis for Beginners In this two-day training, you will gain a solid foundation for working with GraphPad Prism, one of the leading tools for data analysis and visualization in biomedical research. You will learn the complete process of data analysis, from data acquisition to result interpretation. This course is ideal for beginners with no experience in PRISM, as well as for advanced users who want to refresh their knowledge. After the course, you will be able to use the PRISM environment confidently and conduct data analyses independently. You will be able to identify and interpret key metrics of the analysis. Course content: Day 1: Basics of GraphPad Prism 📊 PRISM Interface and Project Types 📊 Data Handling in PRISM: Formats, data entry, and import 📊 Descriptive Statistics: Tables and basic analysis 📊 Creating and Designing Graphs: Different types of charts such as bar charts, scatter plots, box plots, and Kaplan-Meier plots 📊 Graphs for Paired Samples (Before-After, Repeated Measures Graphs) 📊 Visualizing Variability in Graphs: Error bars, standard deviation (SD), confidence intervals (CI) 📊 Exporting Results: Exporting graphs and layouts to PowerPoint and Word Day 2: Statistical Analysis with GraphPad Prism 📊 Mean Comparisons: Performing t-tests, one-way ANOVA, and non-parametric tests 📊 Post-Tests: Selection and execution 📊 XY Projects: Creating dose-response curves, performing regressions, and interpreting the results 📊 Using Post-Tests and Statistical Tools in GraphPad Prism Requirements: No prior knowledge required – the course is ideal for beginners and anyone who needs a refresher. 🕒 Course Duration: 2 days 🔹 Sign up now and improve your analysis skills with GraphPad Prism!

Course: Regression Analysis with GraphPad Prism – Advanced Course for Experienced Users In this 2-day advanced course, you will expand your knowledge of regression analysis with GraphPad Prism. The course focuses on linear and nonlinear regression techniques used in the analysis of biological and medical data. You will learn how to analyze and interpret dose-response curves, enzyme kinetics, and model comparisons using GraphPad Prism. The course provides in-depth knowledge of residual analysis, least squares method, model fitting, and global fitting. Course Content: 📊 Statistical Foundations of Regression – Introduction to linear and nonlinear models 📊 Mastering the Prism Interface – Conducting regression projects (data entry and import) 📊 Linear Regression – Performing and interpreting simple linear regression models 📊 Nonlinear Regression – Applying and fitting nonlinear models in Prism 📊 Data Visualization – Visualizing regression data in GraphPad Prism 📊 Graphs and Charts – Creating and interpreting regression graphs 📊 Statistical Metrics – Interpreting regression statistics and results for your analysis Prerequisites: 📌 Basic knowledge of GraphPad Prism 📌 Statistical knowledge in regression is helpful but not required 🕒 Course Duration: 2 days 🔹 Sign up now and enhance your skills in regression analysis with GraphPad Prism!

Course: Analysis of Variance and Regression – Introduction to Data Analysis Methods In this two-day course, you will learn the two most important methods of data analysis: Analysis of Variance (ANOVA) and Regression. You will gain the necessary knowledge to analyze data with continuous outcome variables, whether through comparing means or modeling relationships between variables. At the beginning of the course, the fundamentals of descriptive statistics, exploratory data analysis, and statistical hypothesis testing will be covered. The focus will then shift to methods for comparing means, starting with the t-test and moving on to one-way and two-way ANOVA. In the second part of the course, regression analysis will be introduced. You will learn how to perform simple linear regression, nonlinear regression, and multiple regression, as well as how to check the assumptions for these models. Course content: 🔎 Exploratory Data Analysis – Initial analysis and visualization of data 🔎 Hypothesis Testing – Selecting and interpreting the right test 🔎 Performing t-Tests – One-sample, paired, and independent t-tests 🔎 Analysis of Variance (ANOVA) – Analyzing and interpreting one-way and two-way ANOVA 🔎 Regression Analysis – Performing and diagnosing simple and multiple regression 🔎 Checking Assumptions – Validating models and reinforcing interpretations Prerequisites: 📌 Basic experience with JMP is required. You can acquire the necessary knowledge in the "Exploratory Data Analysis with JMP" course. 🕒 Course Duration: 2 Days 🔹 Sign up now to deepen your skills in data analysis with analysis of variance and regression!

Course: Classical Experimental Design with JMP In this two-day course, you will learn the classical methods of statistical experimental design. The goal of experimental design is to efficiently answer experimental questions with a minimal number of trials. You will learn how to use factorial and fractional designs to identify relevant factors and determine the best experimental conditions. Course Content: 📊 Fundamentals of statistical experimental design – Concepts and methods for efficiently designing experiments 📊 Randomization, replication, and blocking – Proper application of these techniques in experiments 📊 Full-factorial and fractional designs – Creation and analysis of screening plans to identify relevant factors 📊 Screening designs – Determining the most important factors influencing the results 📊 Response surface designs – Optimizing and visualizing the effect surfaces for modeling complex relationships 📊 Custom experimental designs – Adapting experimental designs to specific requirements and questions Prerequisites: 📌 Knowledge of using JMP 📌 Basic understanding of statistical hypothesis testing, ANOVA, and regression 📌 The necessary content can be acquired in the courses "Exploratory Data Analysis with JMP" and "ANOVA and Regression with JMP" 🕒 Course Duration: 2 days 🔹 Sign up now and learn how to maximize the success of your experiments with statistical experimental design!

Course: Introduction to JMP – Graphical Data Analysis and Exploratory Data Analysis (EDA) In this one-day course, you will be introduced to JMP, the powerful software for graphical data analysis. You will learn how to conduct exploratory data analysis (EDA) and identify patterns in your data. The course will teach you not only how to navigate the JMP interface, with its menus, dialogs, and reports, but also techniques for data management such as importing, exporting, and transposing data. You will also learn how to create and interpret descriptive statistics and present your results visually. Course Content: 📊 Data Import – Importing data from various sources into JMP 📊 Software Navigation – Efficient use of the interface, menus, and reports 📊 Data Management – Working with column and row menus, transposing and organizing data 📊 Descriptive Statistics – Creating and interpreting basic statistical metrics 📊 Graphical Data Analysis – Performing EDA techniques for pattern recognition in data 📊 Result Presentation – Exporting analysis results and creating reports Prerequisites: 📌 No prior knowledge required 🕒 Course Duration: 1 Day 🔹 Register now and begin your journey into the world of data analysis with JMP!

Course: Introduction to JMP Scripting (JSL) – Automating and Customizing Procedures In this two-day course, you will learn how to automate routine procedures, create new procedures, and customize reports using the JMP scripting language (JSL). You will be introduced to JSL to tailor the functionality of JMP to your specific business or research needs. You will learn how to create and save scripts, perform complex data operations, and develop custom analysis and reporting functions. Course Contents: 🔧 Scripting Concepts – Introduction to the JMP scripting language (JSL) and its applications 🔧 Object-Oriented Approach – How to use JSL for complex tasks 🔧 Saving Scripts in the Table Panel – Automating routine procedures 🔧 JSL Building Blocks – Operators, lists, expressions, and basic concepts 🔧 Visual JSL Style – Reading, interpreting, and understanding JSL code 🔧 Functions – Working with For loops, If conditions, and other functions 🔧 Creating Scripts for Data Tables and Platforms – Creating tables, modifying columns, developing scripts for custom platforms and windows 🔧 Dialog and List Fields – Creating user interfaces for data interaction 🔧 Packaging Scripts – Generic arguments and creating reusable scripts Prerequisites: 📌 Experience with JMP (e.g., from the "JMP - Introduction" course) 📌 Programming skills are helpful but not required. 🕒 Course Duration: 2 Days 🔹 Sign up now and enhance your skills in automating and customizing JMP procedures!4o mini

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!