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Fundamental Tools in Statistics
Fundamental Tools in Statistics
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1.Multivariate Data Analysis School
2.Cluster Analysis and its...
3.Efficient Design & Analysis of...
4.Introduction to the Design of...
5.Principal Component Analysis and...
6.Preference Mapping in Practice
7.Advanced Experimental Designs
8.Introduction to Generalized...
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Fundamental Tools in Statistics Fundamental Tools in Statistics by Marco Lagimonière

J'ai bien aimé le cours. Cela m'a permis de rafraîchir mes notions de base en statistique qui remontaient à mes années universitaires. Il me reste maintenant à mettre en pratique les connaissances acquises. Merci encore. Vous faites un travail de pro.

 
Fundamental Tools in Statistics Fundamental Tools in Statistics by Ian Chapman

I started with a minimal knowledge of statistics and statistical methods and tests. This course brought me right up to speed with my colleagues. The concepts were clearly explained and there were several examples of each concept. Overall, an enjoyable and informative course.

 
Introduction to the Design of Experiments (DOE) Introduction to the Design of Experiments (DOE) by Roger L. Roy

DOE helped me to understand the different sources of error and variability as well as the tools available to minimize variability - repetition, randomization, blocking and controls. I now have a much better understanding of simple experimental designs and more complex factorial designs - along with the corresponding statistical tools - t-test, and Analysis of Variance (ANOVA) F-test. The breaking down of the components of variability when performing an ANOVA was of great benefit. Moreover, the notion of an interaction between two factors and how to test multiple comparisons were also very useful. The XLSTAT add-in to Microsoft Excel provides powerful and efficient statistical tools that allowed me to obtain faster, more detailed, reliable and accurate results than I have seen with other statistical packages.

 
Fundamental Tools in Statistics Fundamental Tools in Statistics by Roger L. Roy

Excellent overview of the most important concepts in statistics. The box-plot graphical tool was excellent in helping to determine if data can be considered independent, normally distributed samples so that standard statistical analysis can be conducted. The scope of statistical tests, use of the p-value, and how to minimize risks were all clearly explained. Finally, how to interpret confidence intervals and the equivalence between confidence intervals and hypothesis testing helped me to gain more confidence in my analyses.

 
Regression Models for Categorical Data Regression Models for Categorical Data by Luz Mercedes Gonzalez

Excellent cours sur le contexte et les conditions d’utilisation de la régression logistique .Initiation à la régression ordinal et polychotomique .

 
Simple and Multiple Linear Regression Techniques Simple and Multiple Linear Regression Techniques by Luz Mercedes Gonzalez

Excellent cours. Très claire présentation sur la modélisation , l’adéquation du modèle aux données et l’interprétation des résultats.

 
Simple and Multiple Linear Regression Techniques Simple and Multiple Linear Regression Techniques by Sylvie Sanschagrin

Très bon cours, structuré de façon compréhensible et efficace. La formatrice expose clairement les règles à respecter pour effectuer des régressions simples ou multiples. Bons exemples.

 
Regression on Principal Components and PLS Regression Regression on Principal Components and PLS Regression by Marie Ly

Tres bon cours. Bon rappel de la methodologie ACP et tres bonne introduction au PLS. Ca donne envie d'avoir un cours peut etre plus approfondi juste sur la PLS.

 
Regression Models for Categorical Data Regression Models for Categorical Data by Marie Ly

Tres bon cours avec des exemples concrets d'application. Tres bonne introduction a la regression logisitiques.

 
Simple and Multiple Linear Regression Techniques Simple and Multiple Linear Regression Techniques by Marie Ly

excellent cours qui a bien cible mes attentes avec un tres bon resume des avantages et des limites de la regression simple et multiple.


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