training
R is the standard statistical and graphics freeware
... in research and teaching. It is free, functional and very comprehensive.
R has features for basic statistics and also offers a wide range of multivariate statistical methods such as resampling, through modelling, time series analysis and Bayesian statistics.
R is a reference software for statistical analysis.
Greenwich Statistics conducts training on request. Please feel free to contact us for further information.
The basics
The basics of descriptive statistics
and introduction to the freeware R.
Duration:2 days.
Audience: Anyone who wishes to learn R.
Objectives: To be able to manipulate
data with R. To master the basic concepts
of statistics.
Teaching method: Courses and application exercises on workstation with R.
Program:
Statistics: what is it and what for?
The basics of statistics.
Statistical hypothesis testing and
confidence interval. Background
and introduction to R.
Basic calculations.
Handling vectors, matrices, factors
and data frame.
Create charts and scatter plot with R.
Statistical hypothesis testing on R.
Exploratory analysis
Practise exploratory
statistical analysis.
Duration:2 days.
Audience: Anyone who wishes to learn
about exploratory statistics and its
application in business problem.
Objectives: To increase awareness
for data input and analysis and
capabilities for data profiling.
Teaching method: Courses and application exercises on workstation with R.
Program:
Recall of basic math concept (correlation,
distance, projection, etc.).
Group of individuals through clustering.
Develop typologies by summarizing information through PCA or CA.
Interpreting the outputs of R.
The problem of missing data.
Quantitative analysis
Practise quantitative
analysis and modelling.
Duration:2 days.
Audience: Anyone who wishes to learn
how to apply statistical modelling to
quantitative data.
Objectives: To be able to choose the
method that fits the data, to implement it with R, to interpret the results
and to make forecasts.
Teaching method: Courses and application exercises on workstation with R.
Program:
Presentation of various techniques (simple
linear regression, multiple analysis of covariance, logistic regression, etc.) and their
fields of applications.
Application on two sets of data:
descriptive statistics, choice of one or more
methods, choice of variables, interpretation
of the model and coefficients, graphical
representation.
Implementation is done with R.
