Schulung & Webinare

Interested in the most commonly used statistical methods? This course is for anyone who needs to learn the basics of statistical methods with XLSTAT Basic+.

This course covers the most commonly used data analysis methods in a wide variety of fields, including research, biostatistics, marketing, sensometrics, finance and industry. The methods are illustrated with numerous examples and implemented in XLSTAT Basic+, with an in-depth interpretation of the results. Participants will have time to practice on real data provided by the instructor. At the end of the course, participants should be able to quickly find and implement appropriate statistical methods to answer their own data-related questions using XLSTAT Basic+.

<p class="h2">Main topics covered in this training:</p>

<ul>
    <li>Descriptive statistics</li>
    <li>Exploratory statistics</li>
    <li>Statistical tests</li>
    <li>Statistical modeling&nbsp;</li>
    <li>Machine learning</li>
</ul>

<p class="h2">Required experience:</p>

<p>Trainees must have:</p>

<ul>
    <li>Basic experience using Microsoft Excel</li>
</ul>

<p class="h2">Syllabus:</p>

<h3>Introduction</h3>

<ul>
    <li>A few definitions: individuals, variables, sample, population</li>
    <li>Preparation of a dataset for analysis</li>
</ul>

<h3>Univariate, and bivariate descriptive statistics</h3>

<ul>
    <li>Quantitative variables: mean, standard deviation, variance, median, quartiles, histograms, box plots, scatter plots</li>
    <li>Categorical variables: sorting, mode, bar chart, crosstabs</li>
</ul>

<h3>Multivariate exploratory statistics</h3>

<ul>
    <li>Reducing dimensionality: Principal Component Analysis, Correspondence Analysis</li>
    <li>Clustering data: Hierarchical Ascending Classification, k-means</li>
</ul>

<h3>Statistical tests</h3>

<ul>
    <li>Null hypothesis significance testing &amp; p-values</li>
    <li>Parametric vs. non-parametric tests</li>
    <li>Comparison &amp; association tests</li>
</ul>

<h3>Statistical modeling</h3>

<ul>
    <li>Linear regression&nbsp;</li>
    <li>One-way ANOVA and multiple comparisons&nbsp;</li>
    <li>2-way ANOVA</li>
</ul>

<h3>Machine learning</h3>

<ul>
    <li>Introduction to supervised and unsupervised Machine Learning</li>
</ul>