Leuven STATistics STATe of the Art Training Initiative

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Leuven STATistics
STATe of the Art
Training Initiative
2014-2015
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Course timetable 2014-2015
DATE
TITLE
PRESENTERS
LEVEL AND
LANGUAGE
September 2014
29, 30 September,
1, 2 October 2014
Flames: Essential Tools for R
Goele Bossaert
Basic (English)
5
October 2014
6, 7, 8 October 2014
Fundamentele statistische
methoden
Marlies Lacante
Basis (Nederlands)
6
14 October 2014
Fundamentele statistische
methoden, toegepast met
SAS Eguide
Martine Beullens
Basis (Nederlands)
8
14 October 2014
Fundamentele statistische
Marlies Lacante
methoden, toegepast met SPSS
Basis (Nederlands)
8
14 October 2014
Fundamental Statistical Resarch Anna Ivanova
Methods, applications with R
Basic (English)
9
13, 14 and 30, 31
October 2013
Optimization and Numerical
Methods in Statistics
Francis Tuerlinckx
Geert Molenberghs
Katrijn Van Deun
Tom Wilderjans
Advanced (English)
7
22, 23, 24 October 2014 Models for Longitudinal
17, 18, 19 November 2014 and Incomplete data
Geert Molenberghs,
Geert Verbeke
Advanced (English)
10
3, 4 November 2014
Advanced programming in R
Jan Wijfels
Intermediate (English)
12
3, 4 November 2014
Regression and Analysis
of Variance
Anna Ivanova
Marlies Lacante
Basic (English)
13
14 November 2014
Regressie- en variantieanalyse, An Carbonez
toegepast met SPSS
Basis (Nederlands)
14
14 November 2014
Regressie- en variantieanalyse, Martine Beullens
toegepast met SAS Eguide
Basis (Nederlands)
14
13, 18 November 2014
Regression and Analysis of
Variance, applications with R
Anna Ivanova
Basic (English)
15
24, 25 November 2014
Uitbreiding bij Regressieen variantieanalyse
An Carbonez
Marlies Lacante
Verdiepend (Nederlands) 16
27, 28 November 2014
Statistical Machine
Learning with R
Jan Wijfels
Advanced (English)
17
November 2014
MORE
ON PAGE
December 2014
10 December 2014
Niet-parametrische statistiek
Marlies Lacante
Basis (Nederlands)
18
February 2015
10, 17, 24 February
3, 10, 17, 31 March
and 28 April 2015
Experimental Design
Peter Goos
Intermediate (English)
19
11, 25 February,
4, 11, 18 March 2015
Chemometrics
Wouter Saeys
Advanced (English)
20
10, 11, 12 March 2015
Cluster analysis, principal
component analysis and
exploratory factor analysis
with SAS, SPSS and R
Anne Marie De Meyer
An Carbonez
Martine Buellens
Intermediate (English)
21
March 2015
April 2015
May 2015
17, 18, 19 March 2015
Fundamental statistical methods Marlies Lacante
Basic (English)
22
19, 20 March 2015
Introduction to the analysis
of contingency tables
An Carbonez
Basic (English)
23
10, 31 March, 28 April
and 5, 12 May 2015
Concepts of multilevel,
longitudinal and mixed models
Geert Verbeke
Advanced (English)
24
23, 24 March 2015
Introduction to correspondence Anne-Marie De Meyer
analysis and multiple
correspondence analysis with
SAS, SPSS and R
Intermediate (English)
25
23 April 2015
Weblecture on Sampling Theory Geert Molenberghs
Intermediate (English)
26
28, 29 April 2015
Logistic Regression Models
with SAS
Anne Marie De Meyer
Intermediate (English)
27
28, 30 April 2015
Logistic Regression Models
with SPSS
Anne Marie De Meyer
Intermediate (English)
27
data to be announced
in 2015
Nonparametic Smoothing
Techniques and Applications
to be announced
Advanced (English)
28
6, 7 May 2015
Poisson regression with SAS
Anne-Marie De Meyer
Intermediate (English)
29
6, 8 May 2015
Poisson regression with SPSS
Anne-Marie De Meyer
Intermediate (English)
29
6, 7, 8 May 2015
Inleiding tot enquêtering
Marlies Lacante
Kristel Hoydonckx
Basis (Nederlands)
30
8, 9, 11, 12 June 2015
Flames: Essential Tools for R
Goele Bossart
Basic (English)
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Preface
I take pleasure and pride in welcoming you to the Leuven STATistics STATe of the Art Training Initiative, a scientific
and educational project of the Leuven Statistics Research Centre (LStat), offering a range of short courses.
Statistics in Leuven is varied and broad based. Statisticians are active throughout the university, in mathematics, computer
science, economy, psychology, education, bio-engineering, engineering, biology, chemistry, medicine, pharmacy, iphysical
education, psychology, social science, linguistics, etc. Many colleagues combine an excellent international scientific
reputation with highly effective teaching skills. At the same time, statistical consulting for internal and external clients
is a wholesome component of LStat’s mission.
It is therefore not surprising that the short course programme has been highly successful and in great demand.
Celebrating this success, we are shifting into higher gear and the time-honoured programme of short courses is
gradually being expanded with further highly relevant topics, many located at the heart of our faculty’s expertise.
Due to increasing demand, some courses are offered more than once per academic year.
A selected set of courses is offered in an open educational concept, in the sense that, for example, also contingents
of students of our highly successful MSc in Statistics partake in them. This ensures stimulating interaction.
Courses take place in one of the university’s campuses, dotted around the beautiful college town of Leuven.
Should your company or institute be looking for a tailor-made training initiative, perhaps on-site, then we will be
delighted to explore options and work towards an individualized proposal.
Professor Marlies Lacante
2013-2015 chair of Lstat
Leuven Statistics Research Centre
Celestijnenlaan 200 B bus 5307
3001 HEVERLEE
+ 32 16 32 22 14
[email protected]
www.lstat.kuleuven.be
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Presenters
Martine Beullens
graduated in Mathematics at KU Leuven. From 1990
onwards she has been working at KU Leuven and the
Federal Police on projects commissioned by the Belgian
government on the development and statistical exploitation
of federal databanks containing judicial or police information.
Currently she is working at KU Leuven at the Teaching
and Learning Vision and Quality department in the Data
management section.
Goele Bossaert
is FLAMES coordinator of the KU Leuven. She obtained
her Phd degree in Educational Sciences in 2012 and a
Master degree in Statistics the following year. She organizes,
together with the other FLAMES coordinators at the four
other Flemish universities, the inter-universitary and local
FLAMES events @ KU Leuven.
An Carbonez
is professor aan het Leuven Statistics Research Centre
(LStat) van de KU Leuven. Ze behaalde haar doctoraat
wiskunde aan de KU Leuven in 1992. Ze is coördinator
van het MSc in Statistics programma van de KU Leuven.
Ze is ook betrokken bij statistische consulting projecten
en het geven van statistische opleidingen binnen bedrijven.
Anne-Marie De Meyer
is Professor at the KU Leuven in the Faculty of Science,
Department of Mathematics. She received her PHD in
Mathematics (Applied Probability) in 1979 and is currently
teaching statistics in the MSc in Statistics and in the
Bachelor of Criminology. Since more than 25 years she
has been involved in the short course program for a
variety of courses in applied statistics and is also active in
the statistical consulting of LStat.
Peter Goos
is a full professor at the Department of Biosystems of the
Faculty of Bioscience Engineering of the University of Leuven
and the Department of Environment, Technology and
Technology Management of the Faculty of Applied Economics
of the University of Antwerp. He has been a guest professor
at the Econometric Institute of the Erasmus School of
Economics (Erasmus University of Rotterdam), the Faculty
of Business and Economics of the University of Leuven,
the Antwerp Management School and the International
School of Management in Saint-Petersburg. Peter Goos
has received the Shewell Award and the Lloyd S. Nelson
Award of the American Society for Quality, the Ziegel Award
and the Statistics in Chemistry Award of the American
Statistical Association, and the Young Statistician Award
of the European Network for Business and Industrial
Statistics. In 2013, Peter Goos was ranked 7th in the top
40 of economists in the Netherlands.
Kristel Hoydonckx
is werkzaam aan de afdeling “Faciliteiten voor onderzoek”
van de KU Leuven en staat daar ondermeer in voor de
enquêteservice.
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Anna Ivanova
at the Harvard School of Public Health (Boston, MA). He is
founding director of the Center for Statistics at Hasselt
University and currently the director of the Interuniversity
Institute for Biostatistics and statistical Bioinformatics,
I-BioStat, a joint initiative of the Hasselt and Leuven
universities.
is a research assistant at L-BioStat of the KU Leuven.
She obtained her Master degree in Statistics from the
KU Leuven in 2004. She carries out statistical consulting
and participates in statistical consulting projects.
Wouter Saeys
Marlies Lacante
is sedert 1974 verbonden aan de onderzoekseenheid
Psychologie van de KU Leuven. Gedurende meer dan
20 jaar was zij betrokken bij het statistiekonderwijs in
de opleiding Psychologie. Momenteel doceert zij binnen
de academische Lerarenopleiding, binnen het Leuven
Statistics Research Centre (Lstat) en binnen de MSc in de
Psychologie. Ze is ook actief in het onderwijsonderzoek,
met focus op survey onderzoek en met speciale aandacht
voor de onderzoeksmethodologie.
Geert Molenberghs
is Professor of Biostatistics at the Universiteit Hasselt and
KU Leuven in Belgium. He received the B.S. degree in
mathematics (1988) and a Ph.D. in biostatistics (1993)
from the Universiteit Antwerpen. Dr Molenberghs published
methodological work on surrogate markers in clinical trials,
categorical data, longitudinal data analysis, and on the
analysis of non-response in clinical and epidemiological
studies. He served as Joint Editor for Applied Statistics
(2001-2004), Co-editor for Biometrics (2007–2009) and
as President of the International Biometric Society (20042005). He currently is Co-editor for Biostatistics (2010–
2013). He was elected Fellow of the American Statistical
Association and received the Guy Medal in Bronze from
the Royal Statistical Society. He has held visiting positions
is assistant Professor at the Department of Biosystems at
the KU Leuven in Belgium. He received his Master degree
in Bioscience Engineering (2002) and a PhD in Bioscience
Engineering (2006) from the KU Leuven. He was a
postdoctoral researcher at the School for Chemical
Engineering and Advanced Materials of the University of
Newcastle upon Tyne (UK) and at the Norwegian Food
Research Institute – Matforsk (Ås, Norway). In 2013,
he received the Young Statistician Award of the European
Network for Business and Industrial Statistics. In general
Wouter’s research deals with light transport modeling and
optical characterization of biological materials, multivariate
data analysis and chemometrics, process monitoring and
control. He is author of 80+ research articles (ISI).
Francis Tuerlinckx
is Professor of Psychology at the KU Leuven in Belgium.
He received the Master degree in psychology (1996) and
a Ph.D. in psychology (2000) from the KU Leuven. He was
a postdoc at the Department of Statistics of Columbia
University (New York). In general, Francis Tuerlinckx’
research deals with the mathematical modeling of various
aspects of human behavior. More specifically, he works
on item response theory, reaction time modeling, and
dynamical systems data analysis for human emotions.
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Presenters
Katrijn Van Deun
is assistant professor in Methodology and Statistics at
Tilburg University and a research fellow of the KU Leuven.
She obtained a Master in psychology, a Master’s degree
in statistics and a PhD in psychology. Her main area of
expertise is scaling, clustering and component analysis
techniques, which she applies in the fields of psychology,
chemometrics and bioinformatics. She has various publications in both methodological and substantive journals
in psychometrics, chemometrics and bioinformatics.
Katrijn is secretary of the Dutch/Flemish Classification society.
Jan Wijffels
is the founder of www.bnosac.be - a consultancy company
specialised in statistical analysis and data mining. He holds
a Master in Commercial Engineering, a MSc in Statistics
and a Master in Artificial Intelligence and has been using
R for 10 years, developing and deploying R-based
solutions for clients in the private sector. He has developed
and co-developed the R packages ffbase, ETLUtils, RMOA
and RMyrrix.
Tom Wilderjans
Geert Verbeke
is Professor in Biostatistics at KU Leuven and Universiteit
Hasselt. He received the B.S. degree in mathematics (1989)
from the KU Leuven, the M.S. in biostatistics (1992) from
Universiteit Hasselt, and earned a Ph.D. in biostatistics
(1995) from the KU Leuven. Geert Verbeke has published
extensively on longitudinal data analyses. He has held
visiting positions at the Gerontology Research Center and
the Johns Hopkins University (Baltimore, MD). Geert Verbeke
is Past President of the Belgian Region of the International
Biometric Society, International Program Chair for the
International Biometric Conference in Montreal (2006),
Board Member of the American Statistical Association.
He is past Joint Editor of the Journal of the Royal Statistical
Society, Series A (2005–2008) and currently editor of
Biometrics (2010– 2013). He is the director of the Leuven
Center for Biostatistics and statistical Bioinformatics
(L-BioStat), and vice-director of the Interuniversity
Institute for Biostatistics and statistical Bioinformatics
(I-BioStat), a joint initiative of the Hasselt and Leuven
universities in Belgium.
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is a post-doctoral researcher at the Fund for Scientific
Research (FWO-Flanders). He obtained a Master’s degree
(2005) and a PhD (2009) in Mathematical Psychology
from the KU Leuven. Tom’s research deals with multivariate data analysis (component analysis, clustering, and
combinations thereof) and model selection.
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Flames: Essential tools for R
Course outline
This course gives an introduction to the use of the statistical
software language R. R is a language for data analysis and
graphics. This introduction course to R is aimed at beginners.
The course covers data handling, graphics, mathematical
functions and some statistical techniques. R is for free and for
more information you can visit the site http://cran.r-project.org/
Target audience
Everybody who is interested in using the R programming
language. You will learn how to write and manage your
R scripts.
Prerequisites
There are no prerequisites.
Presenter
Goele Bossaert
Tourse Material
A .pdf file with the course material will be made available.
Dates
29, 30 September and 1, 2 October 2014 from 9.00 hr
to 12.00 hr
or
8, 9, 11, 12 June 2015 from 9.00 hr to 12.00 hr
Language
English
FLAMES
Flanders Training Network for Methodology and Statistics
(FLAMES) is an inter-university training network rooted in
the five Flemish universities: Free University of Brussels,
Ghent University, Hasselt University, University of Antwerp,
and KU Leuven.
This network aims to support doctoral students and young
empirical researchers in their pursuit of best-in-class training
in methodology and statistics. FLAMES seeks to optimize,
intensify, and extend the methodological and statistical
training currently offered by Flemish universities and bring
existing training to new audiences. It also develops new
training modules for complex and advanced statistical
methods taught by experts, a series of seminars on qualitative
methods, organizes a yearly summer school, and delivers
specialized workshops.
The FLAMES initiative originated in 2013 with the financial
support of the Pact 2020 ('Vlaanderen in Actie', translated:
Flanders in action) and the Flemish Minister of Innovation
Ingrid Lieten.
Flames Price
•
•
•
•
PhDs and postdocs of a Flemish University: free
Other academics: € 120
Non profit/social sector € 200
Private sector € 400
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Fundamentele statistische
methoden
Beschrijving
Deze basiscursus statistiek richt zich op het kiezen van
geschikte statistische methoden en het trekken van de
correcte conclusies uit de verkregen resultaten. Wiskundige
grondslagen van de gebruikte methoden komen in deze
cursus slechts beknopt ter sprake. De nadruk ligt op toepassing in de praktijk. Men krijgt inzicht in het adequaat
gebruik van basis-statistieken: centrummaten, spreidingsmaten, tabellen, box-plots, enz. Daarnaast worden
betrouwbaarheidsintervallen opgesteld en krijgt men de
grondslagen van toetsen van hypothesen.
Inhoud van de cursus:
• Beschrijvende grootheden: grafische en numerische
samenvatting van de data
• Verdelingen: Binomiale, Poisson, Normale, T-verdeling
• Steekproefverdeling van het gemiddelde
• Betrouwbaarheidsintervallen
• Hypothese testen omtrent een gemiddelde (één en
twee steekproeven)
• Gepaarde t-test
• Schatten en testen van proporties
Cursusmateriaal
Cursusmateriaal wordt als .pdf file ter beschikking gesteld.
Datum
6, 7, 8 oktober 2014 telkens van 9 u. tot 12 u.
Doelgroep
Iedereen die een opfrissing van fundamentele statistische
technieken wenst.
Taal
Nederlands
Voorkennis
Er wordt geen voorkennis ondersteld.
Lesgever
Prof. Marlies Lacante
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Prijs
• Personeel en studenten KU Leuven en Associatie
KU Leuven: zie: https://icts.kuleuven.be/cursus/
• PhD studenten, niet KU Leuven € 120
• Non profit/sociale sector € 187,50
• Private sector € 450
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Optimization & Numerical
Methods in Statistics
Course outline
Course Materials
Numerical problems are frequently encountered by statisticians. Prominently, the estimation of the parameters of a
statistical model requires the solution of an optimization
problem. In a few simple cases, closed-form solutions exist
but for many probability models the optimal parameter
estimates have to be determined by means of an iterative
algorithm. The goal of this course is threefold. First, we want
to offer the readers an overview of some frequently used
optimization algorithms in (applied) statistics. Second, we want
to provide a framework for understanding the connections
among several optimization algorithms as well as between
optimization and aspects of statistical inference. Third,
although very common, optimization is not the only numerical
problem and therefore some important related topics such
as numerical differentiation and integration will be covered.
A .pdf file with the course material will be made available.
Target audience
The intended target audience includes PhD students and
researchers in a variety of fields, including biostatistics,
psychometrics, educational measurement, public health,
sociology. We aim at readers who apply and possibly develop
statistical models and who wish to learn more about the
basic concepts of numerical techniques, with an emphasis
on optimization problems, and their use in statistics.
Background reading:
• Everitt, B.S. (1987). Introduction to Optimization Methods
and Their Application in Statistics. London: Chapman
& Hall.
• Lange, K. (1999). Numerical Analysis for Statisticians.
New York: Springer.
• Lange, K. (2004). Optimization. New York: Springer.
Dates
13 - 14 and 30 - 31 October 2014: 9.00 hr - 12.30 hr;
13.30 hr - 17.00 hr
Language
English
Price
• Staff and students KU Leuven and Association
KU Leuven: go to: https://icts.kuleuven.be/cursus/
• PhD students, non KU Leuven € 320
• Non profit/social sector € 500
• Private sector € 1200
Prerequisites
Participants should have a basic knowledge of the principles
of statistical inference. This includes some familiarity with
the concept of a likelihood function and likelihood-based
inference for linear, binomial, multinomial, and logistic
regression models. Readers should also have a basic
understanding of matrix algebra. A working knowledge of
the basic elements of univariate calculus is also a prerequisite, including (the concepts of continuity of a function,
derivative and integration).
Presenters
Francis Tuerlinckx, Geert Molenberghs, Katrijn Van Deun,
Tom Wilderjans
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Fundamentele statistische methoden, t
CURSUS 1: FUNDAMENTELE STATISTISCHE METHODEN, TOEGEPAST MET SPSS
Beschrijving
Lesgever
Dit is een inleidende cursus tot het gebruik van SPSS.
Aan de hand van cases wordt geïllustreerd hoe men met
SPSS tot exploratie van gegevens komt. Hierbij wordt de
nodige aandacht besteed aan het interpreteren van de
verkregen output. Hypothesetesten voor onafhankelijke en
gepaarde groepen worden uitgevoerd en besproken. Er is
tijd om zelf te werken met deze software.
Prof. Marlies Lacante
Cursusmateriaal
Cursusmateriaal wordt als .pdf file ter beschikking gesteld.
Datum
Doelgroep
14 oktober 2014, 9 u. - 12 u. en 13 u. - 16 u.
Iedereen die gegevens wenst te exploreren met SPSS.
Taal
Voorkennis
Nederlands
De technieken die aangeleerd werden bij Fundamentele
Statistische Methoden.
CURSUS 2: FUNDAMENTELE STATISTISCHE METHODEN, TOEGEPAST
MET SAS EGUIDE
Beschrijving
Lesgever
Dit is een inleidende cursus tot het gebruik van SAS
Enterprise Guide. Aan de hand van cases wordt geïllustreerd
hoe men met de SAS Eguide tot exploratie van gegevens
komt. Hierbij wordt de nodige aandacht besteed aan het
interpreteren van de verkregen output. Hypothesetesten
voor onafhankelijke en gepaarde groepen worden uitgevoerd
en besproken. Er is tijd om zelf te werken met deze software.
Martine Beullens
Cursusmateriaal
Cursusmateriaal wordt als .pdf file ter beschikking gesteld.
Datum
Doelgroep
Iedereen die gegevens wenst te exploreren met SAS
Eguide.
14 oktober 2014, 9 u. - 12 u. en 13 u. - 16 u.
Taal
Nederlands
Voorkennis
De technieken die aangeleerd werden bij Fundamentele
Statistische Methoden.
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n, toegepast met SAS Eguide, SPSS en R
COURSE 3: FUNDAMENTAL STATISTICAL METHODS, APPLICATIONS WITH R
Course outline
Course Materials
By using cases, one explores data by using R. Attention
is paid to the interpretation of the output. Topics as exploring
data, construction of confidence intervals and hypothesis
testing is covered. This is a hands-on session.
A .pdf file with the course material will be made available.
Target audience
Date
14 October 2014, 9.00 hr - 12.00 hr
and 13.00 hr - 16.00 hr.
Everybody who wants to explore data by using R
Language
Prerequisites
English
Fundamental Statistical Methods (distributions, confidence
intervals, hypothesis testing) and Introduction to R.
Presenter
Anna Ivanova
PRICE COURSE 1, 2 or 3:
• Staff and students KU Leuven and Association
KU Leuven: go to: https://icts.kuleuven.be/cursus/
• PhD students, non KU Leuven € 80
• Non profit/social sector € 125
• Private sector € 300
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Models for Longitudinal and Incomplete D
CONCEPTS, MODELS AND HANDS-ON APPLICATION WITH THE OPTION
TO ANALYSE ONE’S OWN DATA
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Course outline
Target audience
We first present linear mixed models for continuous
hierarchical data. The focus lies on the modeler’s
perspective and on applications. Emphasis will be on
model formulation, parameter estimation, and hypothesis
testing, as well as on the distinction between the randomeffects (hierarchical) model and the implied marginal
model. Apart from classical model building strategies,
many of which have been implemented in standard
statistical software, a number of flexible extensions and
additional tools for model diagnosis will be indicated.
Second, models for non-Gaussian data will be discussed,
with a strong emphasis on generalized estimating
equations (GEE) and the generalized linear mixed model
(GLMM). To usefully introduce this theme, a brief review of
the classical generalized linear modeling framework will be
presented. Similarities and differences with the continuous
case will be discussed. The differences between marginal
models, such as GEE, and random-effects models, such
as the GLMM, will be explained in detail. Third, it is
oftentimes necessary to consider fully non-linear models
for longitudinal data. We will discuss such situations, and
place some emphasis on the non-linear mixed-effects
model. Fourth, non-linear mixed models will be discussed.
Applications in the PK/PD world will be brought to the
front. Fifth, when analyzing hierarchical and longitudinal
data, one is often confronted with missing observations,
i.e., scheduled measurements have not been made, due
to a variety of (known or unknown) reasons. It will be
shown that, if no appropriate measures are taken, missing
data can cause seriously jeopardize results, and interpretation difficulties are bound to occur. Methods to
properly analyze incomplete data, under flexible
assumptions, are presented. Key concepts of sensitivity
analysis are introduced. All developments will be illustrated
with worked examples using the SAS System. However,
the course is conceived such that it will be of benefit to
both SAS users and users of other platforms.
The targeted audience includes methodological and applied
statisticians and researchers in industry, public health organizations, contract research organizations, and academia.
Important: The course will also serve for the MSc in Statistics
students.
Prerequisites
Throughout the course, it will be assumed that the
participants are familiar with basic statistical modeling
concepts, including linear models (regression and analysis
of variance), as well as generalized linear models (logistic
and Poisson regression) and basic knowledge of mixed
and multilevel models. Moreover, pre-requisite knowledge
should also include general estimation and testing theory
(maximum likelihood, likelihood ratio). When registering for
this course, you have to mention the topics you have
followed before and/or indicate where you became
acquainted with the requested material.
Presenters
Geert Verbeke and Geert Molenberghs
Course Materials
A .pdf file with the course material will be made available.
Background reading:
• Verbeke, G. and Molenberghs, G. (2000) Linear Mixed
Models for Longitudinal Data. New York: Springer.
• Molenberghs, G. and Kenward, M.G. (2007) Missing
Data in Clinical Studies. Chichester: John Wiley & Sons.
• Molenberghs, G. and Verbeke, G. (2005) Models for
Repeated Discrete Data. New York: Springer.
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e Data
Dates
October 22, 2014: 9.00 hr - 12.00 hr
October 23, 2014: 9.00 hr - 12.30 hr; 13.30 hr - 17.00 hr
October 24, 2014: 9.00 hr - 12.30 hr; 13.30 hr - 17.00 hr
November 17, 2014: 9.00 hr - 12.30 hr; 13.30 hr - 17.00 hr
November 18, 2014: 9.00 hr - 12.30 hr; 13.30 hr - 17.00 hr
November 19, 2014: 9.00 hr - 12.00 hr
Language
English
Price
• Staff and students KU Leuven and Association
KU Leuven: go to: https://icts.kuleuven.be/cursus/
• PhD students, non KU Leuven € 400
• Non profit/social sector € 625
• Private sector € 1500
11
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Advanced R programming topics
Course outline
R is the lingua franca of statistical research and data
analysis. But in order to get you up and running with R,
and to get over the steep learning curve, you need to
know how to use it efficiently.
This course is a hands-on course covering the basic toolkit
you need to have in order to use R efficiently for data
analysis tasks.
It is an intermediate course aimed at users who have the
knowledge from the course ‘Essential tools for R’ and who
want to go further to improve and speed up their data
analysis tasks.
The following topics will be covered in detail
• The apply family of functions and basic parallel
programming for these, vectorisation, regular expressions,
string manipulation functions and commonly used
functions from the base package. Useful other packages
for data manipulation.
• Making a basic reproducible report using Sweave and
knitr including tables, graphs and literate programming
• If you want to build your own R package to distribute
your work, you need to understand S3 and S4 methods,
you need the basics of how generics work as well as R
environments, what are namespaces and how are they
useful. This will be covered to help you start up and
build an R package.
• Basic tips on how to organise and develop R code and
test it.
R users interested in getting the fundamentals you need
to know before you can create your own R package.
Business users who want to learn how to get the maximum
out of R by speeding up their code, learn vectorisation,
execute the basics of parallel programming and want to
learn how to build methods and code which is reproducible
in production environments.
Prerequisites
Initial experience in R ranging from a few weeks to several
years.
Course materials
A .pdf file with the course material will be made available.
Presenter
Jan Wijffels
Date
3 and 4 November 2014, 9.00 hr - 12.00 hr
and 13.00 hr - 16.00 hr
Language
English
Target audience
People who have had their initial use of R and want to go
one step further.
This covers people using R for a few months already to
several years. And more specifically users who want to
extend their data manipulation techniques to speed up
their day-to-day data analysis tasks.
Researchers from the university interested in making
reproducible research reports or users who want to use R
as a report generating tool.
12
Price
• Staff and students KU Leuven and Association
KU Leuven: go to: https://icts.kuleuven.be/cursus/
• PhD students, non KU Leuven € 160
• Non profit/social sector € 250
• Private sector € 600
DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 13
Regression and Analysis of variance
Course outline
Prerequisites
Linear statistical models are widely used today in many
applications. Successfully applying these techniques require
a good understanding of the underlying theory and the
practical problems that you may encounter in real-life
situations.
Participants are familiar with basic statistical modeling
concepts (see topics described in Fundamental Statistical
Methods).
Course materials
DAY 1: REGRESSION ANALYSIS
• Correlation
• Simple linear regresssion
Ordinary least squares: estimating parameters, confidence
intervals and tests, diagnostics, prediction.
• Multiple regression
Ordinary least squares: estimating parameters, confidence
intervals and tests, diagnostics, prediction.
Variable selection techniques.
DAY 2: ANALYSIS OF VARIANCE
• One-way Anova
Comparing means
The Anova model: estimating parameters, hypothesis
tests, Anova tabel, F test
Multiple comparisons, Contrasts
• Two-way Anova
The two-way Anova Model
Main effects, interaction effects
Multiple comparisons
Target audience
This course is important for persons involved with
modeling data.
A .pdf file with the course material will be made available.
Presenter
Anna Ivanova, Marlies Lacante
Date
November 3, 4 2014: 9.00 hr - 12.00 hr; 13.00 hr 16.00 hr
Language
English
Price
• Staff and students KU Leuven and Association
KU Leuven: go to: https://icts.kuleuven.be/cursus/
• PhD students, non KU Leuven € 160
• Non profit/social sector € 250
• Private sector € 600
13
DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 14
Regressie- en variantieanalyse, toeg e
CURSUS 1: REGRESSIE- EN VARIANTIEANALYSE, TOEGEPAST MET SPSS
Beschrijving
Lesgever
De technieken die aangeleerd werden bij Regressie- en
variantieanalyse, worden hier toegepast met SPSS. Aan
de hand van cases wordt geïllustreerd hoe men met SPSS
tot het modelleren van gegevens komt. Hierbij wordt de
nodige aandacht besteed aan het interpreteren van de
verkregen output. Er is voldoende tijd om zelf te werken
met deze software.
An Carbonez
Cursusmateriaal
Cursusmateriaal wordt als .pdf file ter beschikking gesteld.
Datum
Doelgroep
14 november 2014 van 9 u. - 12 u. en 13 u. - 16 u.
Iedereen die gegevens wenst te modelleren via SPSS.
Taal
Voorkennis
Nederlands
We veronderstellen een basiskennis van SPSS. Cursisten
dienen eveneens vertrouwd te zijn met de methodiek
aangebracht in Regressie- en variantieanalyse.
CURSUS 2: REGRESSIE- EN VARIANTIEANALYSE, TOEGEPAST MET
SAS EGUIDE
Beschrijving
Lesgever
De technieken die aangeleerd werden bij Regressie- en
variantieanalyse, worden hier toegepast met SAS Eguide.
Aan de hand van cases wordt geïllustreerd hoe men met
de SAS Eguide tot het modelleren van gegevens komt.
Hierbij wordt de nodige aandacht besteed aan het interpreteren van de verkregen output. Er is voldoende tijd om
zelf te werken met deze software.
Martine Beullens
Cursusmateriaal
Cursusmateriaal wordt als .pdf file ter beschikking gesteld.
Datum
Doelgroep
14 november 2014, 9 u. - 12 u. en 13 u. - 16 u.
Iedereen die gegevens wenst te modelleren via SAS Eguide.
Taal
Voorkennis
We veronderstellen een basiskennis van SAS Eguide.
Cursisten dienen eveneens vertrouwd te zijn met de
methodiek aangebracht in Regressie en variantie analyse.
14
Nederlands
DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 15
g epast met SPSS, SAS Eguide en R
COURSE 3: REGRESSION AND ANALYSIS OF VARIANCE: APPLICATIONS WITH R
Course outline
Presenter
The linear models, provided by the course ‘Regression and
Analysis of Variance’, are applied on examples. In this course,
the R package is used. By means of cases, we illustrate
how to model your data in R and how to interpret the
corresponding output. There is a hands-on session to train
you with the functionality of R.
Anna Ivanova
Target audience
Everybody who wants to model data with R.
Course Materials
A .pdf file with the course material will be made available.
Date
13 November 2014, 9.00 hr - 12.00 hr
and 13.00 hr - 16.00 hr.
and 18 November from 9 hr - 12 hr.
Prerequisites
Everybody should be familiar with the techniques covered
in ‘Regression and Analysis of Variance’ and have a basic
knowledge of working with R.
Language
PRICE FOR COURSE 1 AND 2
PRICE FOR COURSE 3
• Staff and students KU Leuven and Association
KU Leuven: go to: https://icts.kuleuven.be/cursus/
• PhD students, non KU Leuven € 80
• Non profit/social sector € 125
• Private sector € 300
• Staff and students KU Leuven and Association
KU Leuven: go to: https://icts.kuleuven.be/cursus/
• PhD students, non KU Leuven € 120
• Non profit/social sector € 187,50
• Private sector € 450
English
15
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Uitbreiding bij Regressieen variantieanalyse
Beschrijving
Voorkennis
De resultaten van een lineaire regressieanalyse zijn sterk
beïnvloedbaar door speciale datapunten. Het detecteren
van uitschieters en invloedrijke waarnemingen wordt in
deze cursus bestudeerd. Daarnaast wordt geïllustreerd
hoe men via robuuste regressie dit probleem kan
opvangen. De praktijk leert ook dat de resultaten van een
lineaire regressie ook sterk beïnvloed worden door
associaties tussen verklarende variabelen. Dit probleem
van multicollineariteit wordt besproken en geïllustreerd.
Verder is er een uitbreiding van variantieanalyse naar
specifieke deelhypothesen en covariantieanalyse. Er wordt
telkens geïllustreerd hoe de analyses met SAS Eguide en
SPSS kunnen uitgevoerd worden.
Cursisten dienen vertrouwd te zijn met de methodiek
aangebracht in ‘Regressie- en variantieanalyse’.
Lesgever
Marlies Lacante, An Carbonez
Cursusmateriaal
Cursusmateriaal wordt als .pdf file ter beschikking gesteld.
Datum
Inhoud van de cursus:
Dag 1: Uitbreiding van regressie
• Speciale datapunten: detectie van uitschieters en invloedrijke waarnemingen
• Inleiding tot robuuste regressie
• Multicollineariteit
24 november 2014 van 9 u. -12 u. en 13 u. -16 u. en
25 november 2014 van 9 u. -12 u.
Taal
Nederlands
Dag 2: (halve dag)
• Covariantie analyse
Prijs
Doelgroep
Deze cursus is bedoeld voor personen die regelmatig
lineaire regressieanalyse wensen te gebruiken.
16
• Personeel en studenten KU Leuven en Associatie
KU Leuven: zie https://icts.kuleuven.be/cursus/
• PhD studenten, niet KU Leuven € 120
• Non profit/sociale sector € 187,50
• Private sector € 450
DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 17
Statistical Machine Learning with R
Course outline
Prerequisites
This course is a hands-on course covering the use
of statistical machine learning methods available in R.
The following basic learning methods will be covered and
used on common datasets.
• classification trees (rpart)
• feed-forward neural networks and multinomial regression
• random forests
• boosting for classification and regression
• bagging for classification and regression
• penalized regression modelling (lasso/ridge regularized
generalized linear models)
• model based recursive partitioning (trees with statistical
models at the nodes)
• training and evaluation will be done through the use of
the caret and ROCR packages
Initial experience in R ranging from a few weeks to several
years (at least understanding of the course 'Essential Tools
for R' is needed). Some practical experience in regression
modelling.
Course materials
A .pdf file with the course material will be made available.
Presenter
Jan Wijffels
Date
The course will cover the techniques from a high-level
viewpoint, useful for day-to-day R users.
Target audience
The course is for R users in industry/academics who are
interested in building predictive models in R which have
some experience with regressions but have less knowledge
of machine learning and techniques of artificial intelligence.
Also persons interested in the statistical learning techniques
itself will find this course usefull.
Or people with a data science background with less
knowledge of R and which are interested in machine
learning in general.
27 and 28 November 2014, 9.00 hr - 12.00 hr
and 13.00 hr - 16.00 hr
Language
English
Price
• Staff and students KU Leuven and Association
KU Leuven: go to: https://icts.kuleuven.be/cursus/
• PhD students, non KU Leuven € 160
• Non profit/social sector € 250
• Private sector € 600
17
DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 18
Niet-parametrische statistiek
Beschrijving
Voorkennis
Deze cursus behandelt een aantal statistische technieken
- analoog aan parametrische statistiek (bv. t-test, variantieanalyse) - waarbij de klassieke onderstellingen uit de
parametrische statistiek niet hoeven gemaakt te worden
(distributievrije technieken), technieken gebaseerd op
‘ordeningen’ of ‘rankings’, alsook technieken specifiek
geschikt voor nominale gegevens.
Cursisten dienen vertrouwd te zijn met de methodiek
aangebracht in ‘Fundamentele Statistische technieken’ en
variantie analyse.
Inhoud van de cursus:
• Chi- kwadraat goodness of fit testen
• Testen mbt verschil tussen twee onafhankelijke
steekproeven
• Testen mbt verschil tussen twee afhankelijke
steekproeven
• Testen mbt verschil tussen meerdere onafhankelijke
steekproeven
• Testen mbt verschil tussen meerdere afhankelijke
steekproeven
• Kengetallen mbt de samenhang tussen variabelen
Lesgevers
Marlies Lacante
Cursusmateriaal
Cursusmateriaal wordt als .pdf file ter beschikking gesteld.
Datum
10 december 2014 van 9 u. tot 12 u.
Taal
Nederlands
Doelgroep
Gebruikers van basis statistische technieken (t-test - variantieanalyse)
Prijs
• Personeel en studenten KU Leuven en Associatie
KU Leuven:zie https://icts.kuleuven.be/cursus/
• PhD studenten, niet KU Leuven € 40
• Non profit/sociale sector € 62,50
• Private sector € 150
18
DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 19
Experimental Design
Course outline
Prerequisites
This course discusses the design of factorial experiments.
Initially, the focus is on completely randomized experimental
designs. Next, the focus shifts to experimental designs
involving a restricted randomization. First, the concept of
blocking is discussed. Next, split-plot and strip-plot designs
are studied.
Prerequisites for the course are knowledge of basic statistics,
regression analysis (least squares, multicollinearity), and matrix
algebra (matrix products, inverse matrices, determinants).
The emphasis in the course is on the optimal design of
experiments. In optimal design of experiments, the experimental design is tailored to the problem at hand (unlike
classical experimental design, where standard designs
from catalogs are chosen).
The course builds on concepts from regression and
analysis of variance, such as fixed and random effects,
power calculations, variance inflation factors, multicollinearity,
confidence intervals, prediction and lack-of-fit tests.
Every topic in the course is introduced and illustrated by
means of a case study from industry. The case studies are
realistic in the sense that they involve quantitative and
qualitative experimental factors, experimenters have to
deal with limited budgets and difficulties to randomize,
and forbidden combinations of factor levels. In each of
the case studies, the goal is to enhance to performance
of a process or a product.
The statistical software package used is JMP.
Presenter
Peter Goos
Course materials
Textbook: Optimal Design of Experiments: A Case Study
Approach (Peter Goos & Bradley Jones)
Dates
February 10, 2015: 9.00 hr - 11.30 hr
February 17, 2015: 9.00 hr - 11.30 hr
February 24, 2015: 9.00 hr - 11.30 hr
March 3, 2015: 9.00 hr - 11.30 hr
March 10, 2015: 9.00 hr - 11.30 hr
March 17, 2015: 9.00 hr - 11.30 hr
March 31, 2015: 9.00 hr - 11.30 hr
April 28, 2015: 9.00 hr - 11.30 hr
Language
English
Target audience
The target audience for the course is master students or
Ph.D. students in statistics, engineers and engineering
students planning to perform experiments. The course is
ideal for Six Sigma black belts, quality managers and
people working in environments where collecting data is
expensive.
Price
• Staff and students KU Leuven and Association
KU Leuven: go to https://icts.kuleuven.be/cursus/
• PhD students, non KU Leuven € 320
• Non profit/social sector € 500
• Private sector € 1200
19
DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 20
Chemometrics
Course outline
Prerequisites
The goal of the course is to teach students how to perform
multivariate sensor calibration. Students will become
familiar with the use of statistical concepts in chemometric
applications. Most attention will be given to the ideas
underlying the different methods and the application of
these methods to realistic examples. Theoretical considerations and equations will be limited to what is needed
to have sufficient insight to properly use the methods.
Most examples will be related to spectroscopy and
analytical chemistry, but the scope is broader. By using a
combination of lectures, computer sessions and take
home assignments the students will really learn how to
apply the chemometric methods. The following aspects of
chemometrics will be handled in this course:
• Classical modelling concepts for quantitative calibration:
Classical Least Squares (CLS), Inverse Least Squares
(ILS), Multivariate Linear Regression (MLR), Principle
Component Regression (PCR) and Partial Least Squares
(PLS).
• Necessary steps for the creation and successful deployment of calibrations; selection of calibration standards
and assessment of the reliability of the models: (Test set
validation vs. Cross-validation, model statistics). Special
attention will be given to the methods for the selection
of the number of principle components or latent variables
in the projection methods.
• Methods for data pre-processing with special attention
for the phenomena of light scattering and instrument
drift and the methods to deal with these phenomena:
derivatives, standard normal variate (SNV), multiplicative
signal correction (MSC) and extended multiplicative signal
correction (EMSC).
• Variable selection in a chemometric context and some
commonly used methods for this.
• Qualitative analysis in a chemometric context: discrimination and classification
• New trends in chemometrics such as functional data
analysis and augmented classical least squares (ACLS).
Knowledge of basic concepts of statistics and linear
algebra is required. Some notions of analytical chemistry,
sensor technology and multivariate statistics are a plus.
Target audience
The intended target audience includes PhD students and
researchers in a variety of fields, including statistics,
chemistry, biosciences and engineering. We aim at readers
who wish to learn more about multivariate calibration of
sensor systems and the use of statistical concepts in
chemometric applications.
20
Presenter
Wouter Saeys
Course Materials
Slides from the lectures
Papers discussed in the lectures
Software manual
Datasets for the take home assignments
Additional material (suggested)
• A user-friendly guide to Multivariate Calibration and
Classification by Naes, Isaksson, Fearn and Davies,
NIR Publications 2004
• Multivariate Calibration by Martens and Naes, 1989
Dates
February 11, 2015: 9.00 hr -12.00 hr
February 25, 2015: 9.00 hr - 12.00 hr
March 4, 2015: 9.00 hr - 12.00 hr
March 11, 2015: 9.00 hr - 12.00 hr
March 18, 2015: 9.00 hr - 12.00 hr
Language
English
Price
• Staff and students KU Leuven and Association
KU Leuven: go to: https://icts.kuleuven.be/cursus/
• PhD students, non KU Leuven € 200
• Non profit/social sector € 312,50
• Private sector € 750
DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 21
Cluster analysis, principal component
analysis and exploratory factor
analysis with SAS, SPSS and R
Course outline
Target audience
Multivariate data consist of observations on two or more
variables for each individual or unit.
Data analysts and scientists involved in analysing multivariate data.
The variables will be generally correlated, and a variety of
techniques are available to analyse these data.
Prerequisites
The objective of cluster analysis is to form groups of
observations such that each group is as homogeneous as
possible with respect to certain characteristics. The groups
are as different as possible.
A practical knowledge of basic statistics will be assumed,
such as standard deviations and correlations.
Presenters
Principal component analysis is one of the popular tools
to summarize quantitative multivariate data.
Anne-Marie De Meyer, An Carbonez and Martine Beullens
During this course, PCA and exploratory factor analysis,
will be introduced and the relation between them examined.
Course Materials
The emphasis of the course will be on the interpretation
of the example data and on the results through the Biplot.
Mathematical details are kept to a minimum.
A.pdf file with course material will be made available
Dates
10, 11 and 12 March 2015 from 9.00 hr - 12.00 hr
For the exercises, participants can choose to use the
statistics package SAS (through Enterprise Guide), SPSS
or R.
Course content:
• Hierarchical cluster analysis
• Nonhierarchical cluster analysis
• Linear combination of variables
• Eigenvalues and eigenvectors
• PCA scores and Factor scores
• What is a loading or the factor pattern?
• Screenplot
• How many components or factors to retain?
• Communalities
• The biplot
• The Varimax rotation
Language
English
Price
• Staff and students KU Leuven: go to
https://icts.kuleuven.be/cursus/
• Staff and students Association KU Leuven
and PhD students, non-KU Leuven € 120
• Non profit/social sector € 187,50
• Private sector € 450
21
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Fundamental Statistical Methods
Course outline
Presenters
This basic course in statistics emphasizes on selecting the
appropriate statistical method and drawing the right
conclusions from the obtained results. Mathematical details
will be kept to a minimum. The emphasis will be on understanding the concepts and on practical applications.
The adequate use of basis statistical summaries (measures
of central tendency, measures of dispersion, box-plots, ...)
will be illustrated. The foundations of confidence intervals
and of testing hypotheses will be dealt with.
Marlies Lacante
Course Materials
A .pdf file with the course material will be made available.
Dates
17, 18, 19 March 2015 from 9.00 hr - 12.00 hr
Course content:
• Descriptive statistics: graphical and numerical summaries
of the data
• Distributions: Binomial, Poisson, Exponential, Normal
and t-distribution
• Distribution of the sample mean
• Confidence intervals
• Hypothesis tests for a population mean (one and two
samples)
• Paired t-test
• Estimating and testing for proportions
Target audience
Anyone who wishes to understand basic statistical
techniques more thoroughly.
Prerequisites
There are no prerequisites
22
Language
English
Price
• Staff and students KU Leuven and Association
KU Leuven: go to: https://icts.kuleuven.be/cursus/
• PhD students, non KU Leuven € 120
• Non profit/social sector € 187,50
• Private sector € 450
DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 23
Introduction to the analysis
of contingency tables
Course outline
Course Materials
In this course, chi-square tests and association measures
are used to identify if there is significant association in
contingency tables and to determine how strong this
association is. By use of examples, it is illustrated that
exact tests are necessary in certain situations. There is
enough time to practice with SAS Eguide, SPSS and R.
A .pdf file with the course material will be made available.
Course content:
• Construction of a contingency table
• Tests for independence: chi square tests
• Association measures
• Analysis of a 2x2 table: relative risk, odds ratio
• Exact test
Dates
19 March 2015 13.00 - 16.00 hr
20 March 2015 9.00 - 12.00 hr and 13.00 - 16.00 hr.
Language
English
Price
Target audience
Everyone who wants to analyse contingency tables.
• Staff and students KU Leuven and Association
KU Leuven: go to: https://icts.kuleuven.be/cursus/
• PhD students, non KU Leuven € 120
• Non profit/social sector € 187,50
• Private sector € 450
Prerequisites
Participants are familiar with basic statistical concepts
(which are e.g. introduced in the course ‘Fundamental
Statistical Methods’).
Presenter
An Carbonez
23
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Concepts of Multilevel,
Longitudinal and Mixed models
Course outline
Course Materials
Starting from ANOVA models with random factor levels,
the concepts of mixed models are introduced and the basics
about inference in random-effects models will be explained.
Afterwards, the mixed ANOVA model is extended to general
linear mixed models for continuous data. Finally, extensions
to models for binary or count data will be briefly discussed.
Omitting all theoretical details, sufficient background will
be given such that practising statisticians can apply mixed
models in a variety of contexts, know how to use up-todate software, and are able to correctly interpret generated
outputs. Many applications, taken from various disciplines,
will be discussed.
A .pdf file with the copies of the transparencies used in
the course will be made available.
Target audience
The targeted audience includes methodological and
applied statisticians and researchers in industry, public
health organizations, contract research organizations and
academia. Important: this course will also serve the MSc
in Statistics students.
Prerequisites
The student knows the basics of statistical inference, and
statistical modeling (regression, Anova and general(ized)
linear models).
Presenter
Geert Verbeke
24
Dates
March 10, 2015 13.00 hr - 16.00 hr
March 31, 2015 13.00 hr - 16.00 hr
April 28, 2015 13.00 hr - 16.00 hr
May 5, 2015 13.00 hr - 16.00 hr
May 12, 2015 13.00 hr - 16.00 hr
Language
English
Price
• Staff and students KU Leuven and Association
KU Leuven: go to: https://icts.kuleuven.be/cursus/
• PhD students, non KU Leuven € 200
• Non profit/social sector € 312,50
• Private sector € 750
DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 25
Introduction to correspondence
analysis and multiple correspondence
analysis with SAS, SPSS and R
Course outline
Prerequisites
Correspondence analysis (CA) , is an exploratory technique
to simultaneously score the categories and the column
categories in a bivariate contingency table in a lower
dimensional space The objective is also to clarify the
relationship between the row and the column variable.
It is well suited for large contingency tables. It can also be
used for continuous variables as Age, which can be grouped
in different age categories.
A working knowledge of basic statistics (e.g. Pearson
Chi-square Statistic) in contingency tables will be assumed.
In three-way and high dimensional contingency tables, an
introduction to multiple correspondence analysis (MCA) is
presented, also called Principal components for categorical
data. MCA is a method to visualize the joint properties of
more than 2 categorical variables The individual observations
and the categories of the variables can be displayed in the
same plot.
In this course, the focus will be on the data examples,
the interpretation of the results and the Biplot. SAS and/or
SPSS are used for the examples and exercises.
Course content:
• Introduction and short historical overview
• CA
- Revisit Pearson Chi-square statistic
- Inertia and Eigenvalues
- CA row and column coordinates
- CA plots and association between row and
columns
- Quality of the visual presentation
- Illustration in SAS and in SPSS
• MCA
- Super Indicator matrix
- The Burt matrix
- Joint presentation of categories
- Illustration in SAS and in SPSS
Presenter
Anne-Marie De Meyer
Course Materials
A.pdf file with the course material will be made available
Dates
23 and 24 March 2015 from 9.00 hr - 12.00 hr
Language
English
Price
• Staff and students KU Leuven: go to
https://icts.kuleuven.be/cursus/
• Staff and students Association KU Leuven
and PhD students, non-KU Leuven € 80
• Non profit/social sector € 125
• Private sector € 300
Target audience
Data analysts and scientists involved in analysing multivariate categorical data.
25
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Web lecture on Sampling Theory
Course outline
Course Materials
Different methods for selecting a (survey) sample from an
existing population will be considered. Problems arising in
the sampling designs will be discussed. The focus will be
on the concepts rather than on the formulas. Nevertheless,
attention will be paid at the estimation of the population
parameters of interests.
A.pdf file with the course material will be made available
Target audience
Face to face question and answer session 1:
May 5 2015: 18.00-20.00 hr
Everyone with an interest in sampling theory, from an applied
and/or methodological point of view.
Prerequisites
Participants should have an intimate knowledge of basic
concepts of descriptive and inductive statistics.
26
Dates
Face to face lecture: April 23 2015: 16.30 hr - 20.00 hr
The other lectures will be accessible via the web.
Face to face question and answer session 2:
May 12 2015: 18.00-20.00 hr
Language
English
Presenter
Price
Geert Molenberghs
• Staff and students KU Leuven: go to
https://icts.kuleuven.be/cursus/
• Staff and students Association KU Leuven
and PhD students, non-KU Leuven € 200
• Non profit/social sector € 312.5
• Private sector € 750
DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 27
Logistic Regression Models
with SAS and SPSS
Description
Presenter
The focus is on the statistical model with a categorical
outcome or response variable.
Anne-Marie De Meyer
A categorical response variable can be a binary variable,
an ordinal variable or a nominal variable and each type
requires a different model to describe its relationship with
the predictor variables.
Course Materials
We will define, interpret and illustrate the models for each
type of outcome and place the models in the framework
of the Generalized Linear Model.
SAS (through SAS Enterprise Guide) or SPSS are used in
the applications.
Outline
• Introduction
• Binary Logistic Regression
• Multinomial Logistic Regression for nominal outcome
variables
• Proportional Odds Model - Ordinal Logistic Regression
• Logistic regression in the framework of the Generalized
Linear Model
Target audience
Data analysts in all disciplines
A.pdf file with the course material will be made available.
Dates
Logistic regression models with SAS: 28 and 29 April
2015 from 9.00 hr - 12.00 hr
Logistic regression models with SPSS: 28 and 30 April
2015 from 9.00 hr - 12.00 hr
Language
English
Price
• Staff and students KU Leuven: go to
https://icts.kuleuven.be/cursus/
• Staff and students Association KU Leuven
and PhD students, non-KU Leuven € 80
• Non profit/social sector € 125
• Private sector € 300
Prerequisites
‘Fundamental Statistical Methods’ and ‘Introduction to the
analysis of contingency tables’.
Knowledge of the standard regression model
27
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Nonparametric smoothing
techniques and applications
Course outline
Course Materials
Nonparametric smoothing techniques are an important
class of tools for identifying the true signal hidden in noisy
data. These tools are widely used in statistical analysis in
a variety of application areas. This course will provide the
students with a thorough overview of the most important
smoothing techniques (such as kernel smoothing, local
polynomial fitting, spline smoothing, wavelet decomposition,
regularization techniques, …). The course will address
theoretical and computational aspects. We will discuss
how to use these techniques in different settings (e.g. in a
univariate or multivariate regression setting, in case of
incomplete data, …). The course includes illustrations with
data examples and the use of the R software.
A .pdf file with the course material will be made available.
Target audience
PhD students or practitioners/researchers with a good
background knowledge of statistics and statistical inference.
Prerequisites
Participants should have a good background in statistics,
in particular in statistical inference.
Presenter
to be announced
28
Background reading:
• Fan, J. and Gijbels, I. (1996). Local Polynomial Modeling
and Its Applications. Chapman and Hall, New York.
• Hastie, T., Tibshirani, R. and Friedman, J. (2001).
The Elements of Statistical Learning. Springer, New York.
• Simonoff, J.S. (1996). Smoothing Methods in Statistics.
Springer, New York.
Dates
5 days in Spring 2015
Language
English
Price
• Staff and students KU Leuven and Association
KU Leuven: go to: https://icts.kuleuven.be/cursus/
• PhD students, non KU Leuven € 400
• Non profit/social sector € 625
• Private sector € 1500
DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 29
Poisson regression with SAS
or SPSS
Description
Presenter
A Poisson regression model fits a count or the number of
occurrences of an event or the rate of occurrence of an
event as a function of some predictor variables. For example:
the number of occurrences or the rate of a certain disease.
The Poisson model is a special case of the Generalized
Linear model. SAS (through SAS Enterprise Guide) or SPSS
are used in the applications.
Anne-Marie De Meyer
Outline
• Introduction to Poisson regression
• The Poisson model in the framework of the Generalized
Linear model
• Correction for overdispersion
• The negative binomial model
• Poisson regression models for rates
Course Materials
A.pdf file with the course material will be made available.
Dates
Poisson regression with SAS: 6 and 7 May 2015 from
9.00 hr - 12.00 hr
Poisson regression with SPSS: 6 and 8 May 2015 from
9.00 hr - 12.00 hr
Language
Target audience
English
Data analysts in all disciplines
Price
Prerequisites
‘Fundamental Statistical Methods’ and ‘Introduction to the
analysis of contingency tables’.
Knowledge of the standard regression model
• Staff and students KU Leuven: go to
https://icts.kuleuven.be/cursus/
• Staff and students Association KU Leuven
and PhD students, non-KU Leuven € 80
• Non profit/social sector € 125
• Private sector € 300
29
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Inleiding tot enquêtering
Beschrijving
Doelgroep
Via survey onderzoek wil men informatie verzamelen
omtrent mensen, ideeën, opinies, houdingen, plannen,
gezondheid, sociale - educatieve - of familiale achtergrond. Zulk soort onderzoek gebeurt vaak bij sociologische
vraagstellingen, in psychologie, bij marktonderzoek, enz…
Informatie over dergelijke onderwerpen kan men moeilijk
op ‘experimentele wijze’ verzamelen. Daarom moet men
de personen in kwestie bevragen. Dit kan via een interview,
een vragenlijst, een telefonische enquête, enz. ... Dit soort
bevragingen kent een eigen methodologie en eigen onderzoeksregels die moeten gerespecteerd worden. In deze
cursus wordt achtereenvolgens ingegaan op de verschillende
stappen in dit onderzoeksproces.
Gebruikers van vragenlijstonderzoek
Tevens zal een half dagdeel besteed worden aan de enquêteservice aan de KU Leuven, die gebaseerd is op de opensource software “Limesurvey”. Deze software laat gebruikers
toe om snel zeer krachtige online enquêtes te ontwikkelen.
Cursusmateriaal
Inhoud van de cursus:
• analyse van de onderzoeksvraag: wat wil men te
weten komen?
• verzamelen van de gevraagde informatie
• welke regels moet men in acht nemen bij het formuleren
van de vragen? (invloed van de vraagstelling op het
antwoord, betrouwbaarheid en validiteit)
• methoden van steekproeftrekkingen
• verwerken van de gegevens
• rapportering
• hoe werkt de enquêteservice van de KU Leuven
• algemene instellingen voor de enquête
• beschikbare vraagtypes en hun mogelijkheden
• werken met tokens
• uitnodigen van de respondenten en opvolgen van de
responses
• exporteren van de resultaten naar statistische pakketten
30
Voorkennis
Cursisten dienen vertrouwd te zijn met de methodiek aangebracht in ‘Fundamentele Statistische technieken’ en de
cursus ‘Regressie- en variantie analyse’.
Lesgevers
Marlies Lacante en Kristel Hoydonckx
Cursusmateriaal wordt als .pdf file ter beschikking gesteld.
Datum
6 en 7 mei 2015 telkens van 9 u. - 12 u.,
8 mei 2015 van 9 u. -12 u. en 13 u. - 16 u.
Taal
Nederlands
Prijs
• Personeel en studenten KU Leuven en Associatie
KU Leuven: zie https://icts.kuleuven.be/cursus/
• PhD studenten, niet KU Leuven € 160
• Non profit/sociale sector € 250
• Private sector € 600
DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 31
Statistical consulting Service
Consulting was our historical embryo and remains a core business.
The statistical consulting service center acts as the main pivot to determine the ideal combination between the customer
and the most appropriate university entity.
We recommend that you contact us in an early stage of your project and write a short description of your problem and
send it to [email protected].
The LStat Statistical consulting Service covers
• Statistical support for researchers within the university. The LStat provides statistical help for university research
groups and for the central administration of the university. We help you with advice and we offer support with the
design of your study and with the statistical analysis of your data whether elementary or sophisticated. The first
hour of first-line consulting is provided free of charge.
• Statistical service and execution of projects for government and industry, in service or in partnership.
The LStat uses the administrative help of Leuven Research and Development (LRD) in the negotiation of the contracts
with industries and the private sector.
We have experience with major financial companies, international institutions, manufacturers, medical organizations,
marketing companies, FMCG as well as small and medium-sized enterprises.
Our solutions range from basic regression, multivariate techniques, analysis of variance, mixed models, data mining,
process control, to risk theory, categorical data analysis, longitudinal data analysis and tailor-made simulations and
calculations.
If you have any questions, do not hesitate to contact us at: [email protected] and take a look at our website:
http://lstat.kuleuven.be/consulting/
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DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 32
Practical Matters
• REGISTRATION COSTS
The indicated prices correspond to training for 1 person.
There are several fee categories:
- Students and staff from KU Leuven and Association KU Leuven
- PhD students from other universities € 80 / full day
- non-profit sector, social sector € 125 / full day
- private sector € 300 / full day
Prices include all course material. If the course takes a whole day and the course takes place at the Arenberg
campus in Heverlee a sandwich lunch is included as well.
Payments have to be settled before the start of the course.
• CONFIRMATION
You will receive a confirmation upon receipt of your application form. This confirmation gives information
on how to make the payment and on the course venue. Please contact us in case you do not
receive a confirmation letter.
• DISCOUNT
When you subscribe for several courses, you can get a discount of 10% if the total number of full training
days equals or exceeds 5 days per person and a discount of 20% is attributed if you follow courses for
at least 10 full days.
• CANCELLATION
- If you are unable to attend a course for which you have registered, you can let a colleague replace you.
- Full cancellation for a specific course always has to be done in writing. Administrative costs for cancellation
are set at € 20 when the cancellation is carried out more than 2 weeks before the course takes place.
After that, the full course fee will be charged.
INFORMATION
For other questions on registration and extra information contact:
tel. + 32 16 32 22 14
[email protected]
www.lstat.kuleuven.be
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DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 33
F a c u lty o f S c ie nce
Registration form Short courses in Statistics 2014-2015
Post or e mail this form to: LStat, Celestijnenlaan 200B, 3001 HEVERLEE, Belgium or [email protected]
or use the registration form at www.lstat.kuleuven.be
Staff and students of (Association) KU Leuven should register online: https://icts.kuleuven.be/cursus/
Applicants details:
Mr. / Mrs. / Ms.
Family name ____________________________________________ First name ______________________________
Company/Institute __________________________________________________
Street ________________________________________________________________________________ Number ______________________
P.O. Box ____________ Postcode ______________ City ____________________________________ Country ______________________
E mail address ____________________________________________________
Fee category:
n PhD students, non- KU Leuven
n Non profit/social sector
n Private sector
Price per full day: € 80
Price per full day: € 125
Price per full day: € 300
Indicate the courses that you wish to attend:
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
n
Flames: Essential Tools for R
Fundamentele statistische methoden
Fundamentele statistische methoden, toegepast met SAS Eguide
Fundamentele statistische methoden, toegepast met SPSS
Fundamental Statistical Methods, applications with R
Optimization and Numerical Methods in Statistics
Models for Longitudinal and Incomplete data
Advanced programming in R
Regression and Analysis of Variance
Regressie- en variantieanalyse toegepast met SPSS
Regressie- en variantieanalyse toegepast met SAS Eguide
Regression and Analysis of Variance, applications with R
Uitbreiding bij Regressie- en variantieanalyse
Statistical Machine Learning with R
Niet-parametrische statistiek
Experimental Design
n Chemometrics
n Fundamental statistical methods
n Cluster analysis, principal component analysis and exploratory factor
analysis with SAS, SPSS and R
n Introduction to the analysis of contingency tables.
n Concepts of multilevel, longitudinal and mixed models
n Introduction to correspondence analysis and multiple correspondence
analysis with SAS, SPSS and R
n Weblecture on Sampling Theory
n Logistic Regression Models with SAS
n Logistic Regression Models with SPSS
n Nonparametic Smoothing Techniques and Applications
n Poisson regression with SAS
n Poisson regression with SPSS
n Inleiding tot enquêtering
n Flames: Essential Tools for R
29, 30 September, 1, 2, October 2014
6, 7, 8 October 2014
14 October 2014
14 October 2014
14 October 2014
13,14 and 30,31 October 2014
22, 23, 24 October 2014, 17, 18, 19 November 2014
3, 4 November 2014
3, 4 November 2014
14 November 2014
14 November 2014
13, 18 November 2014
24, 25 November 2014
27, 28 November 2014
10 December 2014
10, 17, 24 February and 3, 10, 17, 31 March
and 28 April
11, 25 February, 4, 11, 18 March 2015
17, 18, 19 March 2015
10, 11, 12 March 2015
19, 20 March 2015
10, 31 March, 28 April, and 5, 12 May 2015
23, 24 March 2015
23 April 2015
28, 29 April 2015
28, 30 April 2015
data to be announced in 2015
6, 7 May 2015
6, 8 May 2015
6, 7, 8 May 2015
8, 9, 11, 12 June 2015
DOC_BRO_LSTAT_2014-2015_DOC_BRO_LSTAT_2014-2015 28/08/14 08:45 Pagina 34
v.u.: Prof. M. Lacante, Leuven Statistics Research Centre, Celestijnenlaan 200 B, 3001 HEVERLEE, België
LEUVEN STATISTICS
RESEARCH CENTRE
Celestijnenlaan 200 B
3001 HEVERLEE, België
tel. + 32 16 32 22 14
[email protected]
www.lstat.kuleuven.be

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