Statistics & Multivariate Analysis with XLSTAT-Base, NYC 12-14 June 2019

Subscribe to this statistics training course illustrated with XLSTAT-Base that will be held in New York. You will learn the basics of descriptive statistics, multivariate data analysis (PCA, CA, AHC), statistical modeling (ANOVA, regression), statistical tests (parametric and nonparametric) as well as machine learning techniques. All of those essential features will be illustrated using the XLSTAT-Base solution. 

Statistics & Multivariate Analysis with XLSTAT-Base, 3-day training

This training goes through most commonly used data analysis methods in a wide variety of fields including research, biostatistics, marketing, sensometrics, finance and industry. The course proposes conceptual and intuitive approaches to descriptive statistics, multivariate data analysis, tests, modeling as well as machine learning. Methods are illustrated with many examples and implementations in XLSTAT-Base, including a thorough interpretation of results. Participants are also given time to practice on real data provided by the trainer. At the end of the training, participants are able to quickly find and implement appropriate statistical methods to answer their own data-related questions, using XLSTAT-Base.


Basic experience in using Microsoft Excel



  • A couple of definitions: individuals, variables, sample, population
  • Making your dataset ready for analysis

Describing data:

  • Quantitative variables: mean, standard deviation, variance, median, quartiles, Histograms, box plots, scatter plots
  • Qualitative variables: frequencies, mode, bar chart, cross tab

Exploring large data sets:

  • Reducing dimensionality: principal component analysis, correspondence analysis
  • Segmenting data: agglomerative hierarchical clustering, k-means

 Hypothesis testing

  • Defining the null hypothesis, the p-value and error risks
  • Parametric tests assumptions
  • Parametric tests vs nonparametric tests
  • One-tailed tests vs two-tailed tests

Modeling data

  • Linear regression
  • One-way ANOVA and multiple comparisons
  • Multi-way ANOVA and interaction effects

Machine learning

  • Supervised vs unsupervised learning
  • Introduction to some supervised machine learning techniques

Deploying R procedures in Excel

  • Overviewing the XLSTAT-R code infrastructure

Perfiles de los capacitadores

Jean-Paul Maalouf

Jean-Paul Maalouf es un consultor estadístico senior que trabaja en Addinsoft desde 2014. Es doctor en biología y tiene una amplia experiencia en la enseñanza de la estadística, una actividad que ha estado practicando intensamente desde 2012. Entre los que se han beneficiado de su actividad formativa se encuentran los principales institutos de investigación franceses (INRA, CNRS, INSERM, CIRAD, varias universidades), así como empresas privadas de todo el mundo. Sus métodos de enseñanza se basan en la explicación de las herramientas estadísticas desde una perspectiva más conceptual que matemática. La estadística llega a ser muy fácil de entender para los usuarios que no necesariamente tienen experiencia en matemáticas y necesitan llegar a ser operativos rápidamente.

Leer mas