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

Designed to give you the knowledge, practice and tools to analyse and interpret consumer test data and to optimise the sensory properties of food, beverages and personal care products. Gain confidence in analysis of your data using XLSTAT, which method to use when and which outputs to present, using our exercises with written solutions and interpretations.

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


pro Person







Profil des Schulungsleiters

Jean-Paul Maalouf

Jean-Paul Maalouf ist ein statistischer Senior-Berater und arbeitet bei Addinsoft seit 2014. Er hat einen Doktortitel in Biologie und verfügt über umfangreiche Erfahrungen in der Lehre von Statistik, eine Tätigkeit mit der er sich intensiv seit 2012 beschäftigt. Zu seinen Kunden zählen u.a. große französische Forschungsinstitute (INRA, CNRS, INSERM, CIRAD, mehrere Universitäten) sowie Privatunternehmen auf der ganzen Welt. Seine Lehrmethoden konzentrieren sich darauf, statistische Instrumente konzeptionell und nicht mathematisch darzustellen. Statistik ist verständlichlicher für Benutzer geworden, die nicht unbedingt Erfahrung in Mathematik haben. Darüber hinaus müssen statistische Tools schnell und unkompliziert anwendbar sein.