MESSAGE
| DATE | 2016-02-29 |
| FROM | From: "soledad.esteban"
|
| SUBJECT | Subject: [Hangout-NYLXS] Course R Without Fear: Applied R for Biologists,
|
From hangout-bounces-at-nylxs.com Mon Feb 29 12:20:26 2016
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Subject: [Hangout-NYLXS] Course R Without Fear: Applied R for Biologists,
Sept 19-23, Barcelona.
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Dear colleagues,
=C2=A0
Registration is open for the workshop "R Without Fear: Applied R for Biolog=
ists =E2=80=93 2ndedition", September 19th-23rd, 2016. =
=C2=A0
Instructor: Dr. Jordi Moya-Lara=C3=B1o (Estaci=C3=B3n Experimental de Zonas=
=C3=81ridas - CSIC, Spain). =
=C2=A0
PLACE: Premises of Sabadell of the Institut Catal=C3=A1 de Paleontologia Mi=
quel Crusafont (Barcelona, Spain).
=C2=A0
WEBPAGE: https://urldefense.proofpoint.com/v2/url?u=3Dhttp-3A__www.transmit=
tingscience.org_courses_stats_applied-2Dr-2Dfor-2Dbio_&d=3DCwIFaQ&c=3DclK7k=
QUTWtAVEOVIgvi0NU5BOUHhpN0H8p7CSfnc_gI&r=3Dx82f3Wlkwtmbr1z8IAt9jA&m=3D3NeQj=
avqZMjbzlgYqdDvR_5KoWdbNU2tSbsz-jnw-ps&s=3DalYcb3OKz-wHV6-IzNJjW7Do0IsgUluz=
620ia7JJ78A&e=3D =
=C2=A0
PROGRAM:
=C2=A0
Introduction to R and its working environment
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Types of variables i=
n R
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Vectors, matrices an=
d data frames
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Self-made functions
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Working with loops a=
nd conditional statements
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 How to merge data ta=
bles
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Search and substitut=
e of data values within tables
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Complex summaries of=
data tables
=C2=A0
Statistical analyses with some theoretical refreshing
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Statistical populati=
ons and samples through example
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Measures of central =
tendency and variability
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Precision, accuracy =
and bias
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Hypothesis testing: =
falsability, Type-I and Type-II errors and statistical power
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Correlation and simp=
le linear regression
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 P-value vs effect ma=
gnitude
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Linear models: resid=
uals, assumptions and interpretation
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Explained and unexpl=
ained variance in a linear model (coefficient of determination)
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Introduction to R gr=
aphics with statistical examples
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 The concept of parti=
al effect: partial regression and correlation
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Additive vs. multipl=
icative effects: disentangling the effect of interactions
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 General and Generali=
zed Linear Models =
=C2=B7=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0=C2=A0 Choosing the =E2=80=
=9Cbest=E2=80=9D models via AIC
=C2=A0
=C2=A0With best regards
Soledad De Esteban-Trivigno
Institut Catal=C3=A0 de Paleontologia=C2=A0Miquel Crusafont=C2=A0(ICP)
Campus de la Universitat Aut=C3=B2noma de Barcelona
Cerdanyola del Vall=C3=A8s (Barcelona). Spain
www.icp.cat=C2=A0=C2=A0
=C2=A0
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