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Data Files for the R Tutorial
Internet Sites on R
Place and time
Place: Faculté des Hautes Etudes Commerciales, Université de
Lausanne.
Date: May 2–4, 2012
Lectures and exercises: 13.00 – 17.30 hours in room 6, CEI (Internef building, level 0)
Students
The course is offered to students of the Faculté des Hautes Etudes Commerciales (HEC), Université de Lausanne.
Objectives
This three-days short course gives an introduction to the use of R, a software environment for statistical computing and graphics. The basics of R are taught so as to get students started with their own applied statistical problems. The course combines theoretical and practical work: after theoretical sessions with ample illustrations, the students are invited to make specific exercises and apply statistical and graphical R functions to their own data sets. The tutorial and exercises are intended to take away any potential hesitation to use the R program, and to try and convince students of its wide-spread practical utility.
In general, it will take some efforts to go through first stages of unfamiliarity and programming discomfort perhaps, but in the end it certainly pays off to be in full control of statistical analysis and graphical display of results, and to diverge from unthoughtful mouse-clicking practices to the benefit of research quality.
Prerequisites
Working knowledge of basic statistics, regression analysis, or the general linear model.
Practical recommendations
Students are encouraged to use their own data sets for analysis with R software, requiring a clear research problem formulation to start with. It is also recommended that they bring their own laptops; if they don't have one, they could use UNIL computers.
Preliminary outline
1. Introduction to R
– R language essentials
– R programming, functions
– Data input and output
– Missing data
2. Probability distributions and random sampling
– Discrete and continuous distributions
– Random numbers, random sampling
– Monte Carlo experimentation, bootstrap procedures
3. Descriptive statistics and graphical data display
– Graphical display of frequency distributions
– Summary statistics for single-sample and grouped data
– Robust statistics using Wilcox's functions
– Descriptives for tables
4. Null hypothesis significance testing
– Student's t- and other parametric tests
– Nonparametric tests
– Association and correlation
– Power calculations and sample size determination
5. The linear model
– Linear regression analysis
– Analysis of variance
– Analysis of covariance
– Logistic regression
– Inspection of residuals and checking assumptions
6. Structural equation modeling (optional)
– The lavaan package
Recommended literature
There will be six theoretical lectures of two hours with illustrations, followed by two hours of supervised practical work. An accompynying document of the R tutorial – with exercises – will be made available before the course starts. This R Tutorial for Applied Statistics can be obtained at the Reproduction Service of the Faculty by the end of April. The book shown at the top of this page is from the following list of references.
Software
In the lectures and during practical work the R software will be used, an open source environment for statistical computing. For a general introduction we refer to The R Project for Statistical Computing, providing further guidance and references.
Evaluation and exam
The course does not impose an exam. The students' evaluation of the course is informal.
Questions and
remarks
Students should feel free to contact the lecturer by e-mail, a.boomsma@rug.nl, or otherwise.