Statistics Training

The following statistics courses are offered:

 

Statistics in Practice

Statistics doesn't really have a good reputation. Too mathematical, strange logic or obscure recipes sum it up for most people. Yet, many decisions are taken based on data, and that's what modern statistics is all about: understanding data and coming to meaningful conclusions. In this training, the focus is on graphical analysis and "common sense" statistics. The amount of mathematics is strongly reduced as compared to a typical stats course. What is not reduced is the emphasis on applications of statistics in every day's practice in business and industry.

Course objective
As a result of this course, you'll develop a real "feel" for statistics. You'll be able to choose an appropriate technique for the most common types of problems, and interpret the results correctly.

Prior knowledge
No prior knowledge is required.

Course contents

Module 1

  • Graphical exploration of data: scatter plot, histogram, dot plot, box plot, normal probability plot
    • Descriptive statistics: mean, median, variance, IQR, correlation coefficient
  • Collecting data: representative sampling, paired comparisons

Module 2

  • Dealing with random variables: distributions of random variables

Module 3

  • Confidence intervals: how large is it really?
  • Hypothesis testing: statistical significance versus practical relevance
  • Sample size calculations: how many data do I need?

Module 4

  • Comparing groups: 1-way ANOVA
  • Relating two variables: Linear Regression
  • Relating two categorical variables: contingency tables

Some cases & applications:

  • detecting a change in a process
  • judging the difference between two products or systems
  • calculating the number of data needed to detect a certain improvement
  • investigating the effect of different types of a constituent on the product properties
  • investigating the effect of a process parameter on a characteristic


Course set-up
This is a three-day course with hands-on computer exercises. Course fee is 1.200 Euro, exclusive VAT.  To apply, download the application form here, and mail it back to info@statsquare.com.


Design of Experiments – A step-by-step approach

 

Whatever you want to investigate or optimize, the optimal way of doing it is called Design of Experiments (DOE), a.k.a. Experimental Design, Statistical Design of Experiments (SDE) or Multi-Variable Testing (MVT).

Course objective

As a result of this course, you'll be able to analyze a problem and develop a real "feel" for statistics. You'll be able to choose an appropriate technique for the most common types of problems and interpret the results correctly.


Prior knowledge
For Module I, no prior knowledge is assumed. For modules II and III a basic knowledge of statistics - as treated in the course Statistics in Practice - is recommended.

Course contents

Module I: Doing the right experiments

  • One Variable At a Time versus Experimental Design
  • Synergies and Antagonistic effects: the concept of interacting variables
  • Replication, 2-level blocking variables and randomisation
  • Exploration: Full Factorial, Fractional Factorial, Minimum-Run designs
  • Optimization: Response-Surface-Model (RSM) designs

Module II: Analyzing designed experiments

  • Statistical analysis of the results
  • Graphical validation: residual analysis
  • Bringing the message: effects graphs, 3D and contour plots

Module III: Stepping up

  • Multi-response optimisation
  • Categorical variables: classical designs and their analysis
  • Multi-level blocking and random effects
  • Robustness investigation (minimising the influence of nuisance variables): Taguchi approach and alternatives


Course set-up
The course consists of three one-day modules with hands-on computer exercises. Module I is required for Modules II and III.  The fee amounts to 400 Euro per module, exclusive VAT.  To apply, download the application form here, and mail it back to info@statsquare.com.

 

Multivariate analysis / Datamining

Massive amounts of data are collected and often stowed away in databases without further analysis. Or only simple graphical analyses are performed, that may be insufficient to bring the valuable information to the surface. If there are a large number of factors at play, a multivariate datamining approach is the only alternative. This course is guaranteed to bring you some fascinating eye-openers and mind-boggling insights

Course objective

This course will open your mind to multivariate thinking and introduce you to a class of more advanced methods.


Prior knowledge
Although prior knowledge is strictly speaking not required, most participants will have previously attended statistics or DOE courses ar have some experience in analyzing data.

Course contents

Module I: PCA and clustering (unsupervised methods)

  • Exploratory multivariate analysis
    • Visualisation of large datasets
    • Principal Component Analysis (PCA)
  • Cluster analysis: searching for groups of similar samples

Module II: Quantitative analysis: in search of cause-effect relations

  • Partial Least Squares (PLS)
  • Validation of regression models
  • Interpretation of regression models
  • Feasibility study: does a quantitative analysis make sense?

Module III: Classification (supervised pattern recognition): predicting class membership

  • Linear Discriminant Analysis (LDA)
  • Soft Independent Modeling of Class Analogy (SIMCA)
  • Partial Least Squares Discriminant Analysis (PLS-DA)
  • Validation of classifiers
  • Interpretation of classification models


Course set-up
The course consists of three one-day modules with hands-on computer exercises. Module I is required for Modules II and III.

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