Statistics & Multivariate analysis with XLSTAT, Virtual class: Online, March 9-11 2022 (French)
Attend this course directly over the internet without having to travel. 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 Basic+ solution.
Statistics & Multivariate Analysis with XLSTAT Basic+, 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 Basic+, 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 Basic+.
Basic experience in using Microsoft Excel
- A couple of definitions: individuals, variables, sample, population
- Making your dataset ready for analysis
- 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
- 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
- Linear regression
- One-way ANOVA and multiple comparisons
- Multi-way ANOVA and interaction effects
- Supervised vs unsupervised learning
- Introduction to some supervised machine learning techniques
Senior statistics consultant
Jean-Paul Maalouf is a senior statistics consultant working at Addinsoft since 2014. He holds a PhD in biology and has a substantial experience in teaching statistics, an activity he has been intensively practicing since 2012. His training beneficiaries include major French research institutes (INRA, CNRS, INSERM, CIRAD, several universities) as well as private companies around the world. His teaching methods rely on explaining statistical tools conceptually rather than mathematically. Statistics become very easy to understand for the users who do not necessarily have experience in mathematics and need to become operational very quickly.