Creascience

Advanced Experimental Designs : This course covers the fundamental concepts in statistics in a non-technical and applied way. This session focuses on the design and the analysis of more complex experimental designs than those seen in the Introduction to the Design of Experiments course. These designs are used to account for different sources of experimental constraints such as material heterogeneity, physical constraints, repeated measures, etc. Their design and analysis will be discussed.

Cluster Analysis and its Applications : Target Audience An applied training session in statistics intended for all the scientific staff collecting large datasets and wishing to graphically summarizing them and identifying groups. Aims It covers, a powerful multivariate technique, that comprises a diverse collection of techniques that can be used to classify objects (e.g. individuals, species, etc). Cluster analysis is a tool of discovery. PrerequisitesThis one-day workshop introduces the important ideas in statistics and data analysis. It assumed that participants have no previous knowledge of statistics or that they have not used it for a long time.

cole d' t sur les analyses multidimensionnelles : Ce cours de 5 jours, offert en fran ais, couvre les aspects pratiques des m thodes multivari es les plus utilis es: analyse en composantes principales - ACP, analyse factorielle, analyse des correspondances, analyse de classification, analyse discriminante, analyse canonique.

Efficient Design & Analysis of Shelf-Life & Stability Studies : Target Audience Attend this session to understand what the key issues are in the efficient set up of shelf-life and stability studies. Discover powerful statistical tools used to predict best before dates accurately. Aims This training session covers the key elements in the design of shelf-life studies as well as the most efficient ways to analyze data. Prerequisites This one-day session introduces the important ideas in statistics and data analysis. It assumes that participants have no previous knowledge of statistics or that they have not used it for a long time.

Fundamental Tools in Statistics : An efficient and effective one-day refresher on the fundamental tools in statistics. This course covers the two main divisions of statistics: descriptive and inferential statistics. First, the course covers descriptive statistics : the different types of variables, variability, distributions and the characterization of distributions through well-known statistics such as the mean and standard deviation as well as other less known statistics such as quartiles. Inferential statistics , also known as statistical testing , is broached next: hypothesis testing, one-sided versus two-sided tests, p-values, risks, significance levels, and confidence intervals.

Introduction a la planification d'exp riences : Cette formation fait le tour des concepts fondamentaux en statistique d'une mani re non-technique et appliqu e. Cette formation couvre les aspects suivants: l'importance de reconna tre le type de variables (r le dans l'exp rience, les valeurs qu'elles prennent),les statistiques descriptives, comment mettre en relation des variables, la statistique inf rentielle (principe, ce qu'est une p-value, les risques inh rents aux tests statistiques, les intervalles de confiance). Ensuite la formation couvre les principes de base en planification d'exp riences: la prise en compte de la variabilit , randomisation, r p tition, blocage et utilisation de t moins. La comparaison de deux more...

Introduction aux tudes de dur e de vie et de stabilit : Les tudes de dur e de vie et de stabilit sont co teuses et longues a r aliser. Il est important de conna tre les l ments-cl dans la mise en place efficace de ces tudes. De plus, les donn es recueillies lors de ces tudes sont souvent censur es et n cessitent par cons quent des outils d'analyse statistique appropri s. Cette formation traite du caract re unique des donn es recueillies dans ces tudes et de l'impact sur les m thodes statistiques de traitement de donn es. Quelles m thodes peuvent tre utilis es et lesquelles ne sont pas recommand es. L'emphase est aussi mise sur l'interpr tation et la mani re de communiquer les r sultats. Des illustrations sont more...

Introduction to the Design of Experiments : This course covers the fundamental concepts in statistics in a non-technical and applied way. This session covers the fundamentals of statistics: the importance of recognizing the type of variables (role in the experiment and the values taken), descriptive statistics, relating variables, inferential statistics (mechanism, what is a p-value, the risks involved in statistical testing, confidence intervals). It also discusses the comparison of two groups using the T-test or Student test. Then the training covers the fundamentals of design of experiments: accounting for variability (randomization, more...

L'analyse de classification et ses applications : Cette formation porte sur une m thode multidimensionnelle puissante, l analyse de classification, qui comprend un ensemble de techniques utilis es pour classifier des objets. Ces "objets" peuvent tre des individus, des pays, des esp ces, des cellules, des g nes, etc. L'analyse de classification est un outil de d couverte, une m thode d'analyse multivari e permettant de former des groupes homog nes d'individus ou d'objets.

L'analyse en composantes principales et ses applications : L analyse en composantes principales , commun ment appel e ACP , est une m thode statistique multidimensionnelle qui permet de synth tiser un ensemble de donn es en identifiant la redondance dans celles-ci. Elle fournit notamment une synth se graphique des r sultats. Cette formation discute des limites des m thodes descriptives traditionnelles pour explorer les jeux de donn es contenant pluusieurs variables et pr sente comment l'ACP: R sume les grands ensembles de donn es Identife les structures et les tendances des donn es Identifie la redondance et les corr lation Produit des graphiques porteur de sens

Les outils essentiels de la statistiques : Cette formation d'une journ e couvre les deux principaux axes sur lesquels s'appuient les m thodes statistiques. Dans un premier temps, la statistique descriptive est abord e. On y traite des diff rents types de variables, du r le des variables, de la variabilit , des distributions et de la caract risation des distributions statistiques a l'aide d'outils connus tels la moyenne et l' cart-type et d'outils moins connus mais plus robustes (moins sensibles aux observations extr mes) tels les quartiles. Ensuite, la statistique inf rentielle - ou plus souvent appel e les tests statistiques - sera abord e: les concepts de test d'hypoth se, de statistique de test, de p-value, de more...

Life, Reliability and Survival Models : This one-day workshop offers an overview of the modeling techniques used for life data (time-to-event) and their specific features, the interpretation of results, strengths and weaknesses and also discusses their practical realization. Several applications in the social sciences, engineering and life sciences will be presented. Upon completion of this training course, participants will have learned: The motivation and the principle underlying statistical tools to analyze life data To be able to use tools used to compare survival curves To know the scope and limitations of the various approaches available

Logistic Regression : Logistic regression is a statistical tool used to assess the effect of several explanatory variables on a categorical response variable, that is a variable that can only take on a limited set of values. This situation is encountered often in several fields of applications. Here are a few examples of questions that can be adressed using logistic regression: Finance : What is the impact of the characteristics and habits of consumers on their capacity to reimburse a loan? Medicine : Does a given treatment allow patients to recover from a given disease? Biology: Can physical characteristics more...

Logistic Regression : Logistic regression is a statistical tool used to assess the effect of several explanatory variables on a categorical response variable, that is a variable that can only take on a limited set of values. This session covers logistic regression , a technique used whenever the response variable is binary or categorical, that is, when it can only take on a limited number of values. An example of a categorical variable is the severity of a disease: not severe, mildly severe, moderately severe, very severe. An example of a binary variable is the survival of patients who received particular treatments: yes or no. The training starts with a refresher on multiple linear regression a statistical more...

Multivariate Data Analysis Summer School : This 5-day course, offered in English, focuses on the practical aspects of the most widely used multivariate methods: Principal Component Analysis (PCA), Factor Analysis, Correspondence Analysis, Cluster Analysis, Discriminant Analysis and Canonical Analysis.

Nonlinear Regression : This half-day workshop offers an overview of nonlinear regression. This special type of regression technique is commonly used to model growth curves and to establish relationships between dose and responses. We discuss the strengths and weaknesses of the techniques, their practical implementation and the interpretation of results produced from the use of these techniques. Upon completion of this course, participants will understand the underlying assumptions of the technique and will be able to: Explain the context of use of nonlinear linear regression Construct nonlinear regression models Assess the goodness-of-fit of the model to the data Identify common issues in nonlinear regression, diagnose problems and fix them Interpret statistical software output

Principal Component Analysis and its Applications : Principal Component Analysis (PCA) is a multivariate method which can identify redundancy or correlation among a set of measurements or variables for the purpose of data reduction. This powerful exploratory tool provides insightful graphical summaries with ability to include additional information as well. This training course discusses the limitations of traditional descriptive tools for exploring datasets with several variables and presents how PCA can: Summarize large sets of data Identify structure, trends in the data Identify redundancy, correlation in the data Produce insightful graphical displays of the results

Regression on Principal Components and PLS Regression : This one-day workshop covers two advanced modeling methods used to handle common problems encountered in relating a variable to a set of explanatory variables, the interpretation of results, strengths and weaknesses and practical realization. Upon completion of this course, participants will be able to: Know the context of use for regression on principal components and partial least squares regression Understand what the underlying assumptions of the techniques are Construct the regression models Assess the goodness-of-fit of the models to the data Identify common issues in these regression models, diagnose problems and fix them Interpret statistical software output

Screening and Optimization Designs : Target Audience This applied training session in statistics is aimed at all scientific staff collecting data and making decisions based on them. Aims It covers the design of efficient experiments requiring few runs for screening and optimization purposes. Prerequisites This three-day course deals with the construction of advanced designs for screening and optimization. It assumes that attendees are familiar with the construction of factorial designs and a working knowledge of the analysis of variance. Course Format Different teaching tools are used: lectures, group discussions and interpretation hands-on exercices on more...

Simple and Multiple Linear Regression Techniques : This one-day workshop offers an overview of simple and multiple linear regression techniques, starting with the interpretation of results, strengths and weaknesses and also discusses their practical realization. Upon completion of this course, participants will be able to: Explain the context of use of simple and multiple linear regression Understand what the underlying assumptions of the techniques are Construct simple/multiple regression models Assess the goodness-of-fit of the model to the data Identify common issues in regression, diagnose problems and fix them Interpret statistical software output

Workshop on Regression Techniques : This half-day workshop provides participants with the opportunity to put into practice the methods seen during the different seminars on regression analysis and to apply them on their own data sets using their own statistical software with the support of experienced instructors here to assist them. Upon completion of this workshop, participants will be able to: Validate their data before running a regression analysis Format their data according to the specifications of their software to fit a regression Determine the most appropriate regression technique to solve a given problem Fit a regression with their software Produce insightful diagnostics and graphical summaries to assess more...

Multivariate Techniques for Sensory and Consumer Studies : This 3-day session is targeted towards sensory and consumer scientists interested in an applied workshop on multivariate data analysis of sensory and consumer test data with a strong emphasis on graphical summary representations. During the first two days, classical multivariate techniques including Principal Component Analysis (PCA), Factor Analysis, Correspondence Analysis, Cluster more...