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This category contains training sessions that deal with statistical methods commonly used in life sciences.
    Session Name+   Summary   Length (days) 
 Fundamental Tools in Statistics  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 
 Sample Size and Power Determination  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 
 Regression Models for Life Sciences  Regression Models for Life Sciences  This applied training session in statistics is aimed at all life sciences scientific staff designing experiments and analyzing data. This session covers the regression models used in life sciences: simple linear regression, multiple regression, logistic regression, case of several explanatory variables.   2.0 
 Regression Models for Categorical Data  Regression Models for Categorical Data  This workshop deals with regression models for categorical response variables. A strong emphasis is put on logistic regression.
Fundamental principles underlying binary data modeling is first explored through the inadequacy of linear regression. Then the alternative model logit or logistic regression is presented. Similarities with linear regression are pointed out and tools specific to logistic regression are covered in detail:
  • odds ratio used to quantify the magnitude of effects
  • specificity, sensitivity, ROC curves
  • cross-validation techniques.
    Generalization of the logistic regression model to the case in which the response variable has more than levels is illustrated. 
    		•   1.0 
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