Training Sessions in Statistics :: Research and Development

Research and Development

You will find in this category sessions discussing statistical tools geared towards research and development. This includes sessions on design of experiments, from introduction to advanced methods, sample size computations and shelf-life and stability studies. The target audience typically consists of researchers, lab staff and more generally people involved in any discovery process.

Session Name+	Summary	Length (days)
The Analysis of Variance	The focus of this workshop is to present the available tools to compare two groups or more on a continuous or quantitative measurement variable. Starting with the simplest situation, the Student test or t-test, is covered then increasingly complex cases are discussed: the t-test to compare two related samples, the one-way ANOVA, and ANOVA for several factors and interactions. Moreover, multiple comparisons techniques are discussed to locate group differences. Throughout the session, emphasis is put on the conditions of use for each technique, the type of results obtained and their interpretation. A variety of case studies are used to exemplify situations likely to occur in practice, the typical problems and remedial measures.	1.0
Fundamental Tools in Statistics	This hands-on 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 like outlier detection is presented. 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 (alpha and beta), p-values and statistical significance. The use and interpretation of confidence intervals is also discussed. This course can serve as an introductory class or a refresher and provides a solid basis for all other courses.	1.0
Introduction to the Design of Experiments (DOE)	This 2-day hands-on 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.	2.0
Sample Size and Power Determination	Sample size and power determination is a crucial step in setting up efficient R&D studies. It ensures that there are enough study subjects to enable the detection of anticipated effects while determining the minimum sample size required to do so. This course provides participants with practical tools for computing sample sizes to achieve a given level of precision and power in statistical tests.	1.0
Advanced Experimental Designs	This training course covers advanced experimental designs to account for different constraints that experimenters have to deal with, such as: time, resources, material heterogeneity, repeated measures on subjects and randomization constraints. It also deals with how to analyze data and interpret results based on the design selected.	2.0
Screening Designs	This 1-day training reviews how to construct advanced experimental designs to screen for the most influential factors to study in future experiments or studies. These designs require very few runs, thus minimizing the time and cost needed at this preliminary phase.	1.0
Optimization Designs and Response Surface Models	This 2-day course covers the construction of advanced experimental designs specially designed to optimize a process or formulation according to the study objectives and using a minimum number of runs.	2.0
Nonparametric Tests	This training covers the fundamental statistical concepts involved in nonparametric statistical tests. These tests are used whenever the nature of the data requires it, such as rank data, or whenever the assumptions underlying the classical parametric methods, such as t-tests and ANOVA, are not likely to hold.	0.5
Efficient Design & Analysis of Shelf-Life & Stability Studies	This 1-day workshop uses a variety of case studies to present the most important aspects to consider for a sound determination of product shelf life. Starting from the assessment of the differences between shelf-life and stability studies, participants learn for each type of study how to design efficient experiments to determine the failure time of products accurately. The issues discussed include the timepoint selection, how to handle destructive testing, the experiment size and the choice of samples. The workshop also emphasizes the appropriate ways to analyze life data and to adequately interpret and communicate the results obtained. The principle of accelerated shelf-life testing (ASLT) along with the conditions for a successful use are discussed. Throughout the session, FDA and other existing guidelines will be discussed.	1.0

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Multivariate Data Analysis School

26-30 June 2006, Montreal, English For someone studying for a PhD, needing knowledge of ..

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