Statistics & Multivariate Analysis with XLSTAT-Base, Paris 22-24 May 2019

Subscribe to this statistics training course illustrated with XLSTAT-Base that will be held in Paris (FRANCE). 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.

Pre-requisites

Basic experience in using Microsoft Excel

Program

Introduction

  • 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
  • ANCOVA

Machine learning

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

Deploying R procedures in Excel

  • Overviewing the XLSTAT-R code infrastructure

講師プロフィール

Jean-Paul Maalouf

Jean-Paul Maalouf は、2014年からAddinsoftのシニア統計コンサルタントです。彼は生物学のPhD を保持し、2012年から統計学の指導に集中しており、かなりの経験を持ちます。彼のトレーニングの受講者には、フランスの主要な研究機関(INRA, CNRS, INSERM, CIRAD, 複数の大学)および世界中の民間企業が含まれます。 彼の指導法は、数学的というよりも統計ツールの概念的な説明に頼ります。必ずしも数学の経験を持たないユーザー、使用方法を素早く習得したいユーザーにとって、統計がとてもわかりやすくなります。 

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