
Course unit
APPLIED STATISTICS FOR EVOLUTIONARY BIOLOGY
SCP8084940, A.A. 2019/20
Information concerning the students who enrolled in A.Y. 2019/20
ECTS: details
Type 
ScientificDisciplinary Sector 
Credits allocated 
Educational activities in elective or integrative disciplines 
SECSS/01 
Statistics 
2.0 
Core courses 
SECSS/02 
Statistics for Experimental and Technological Research 
2.0 
Core courses 
BIO/18 
Genetics 
2.0 
Course unit organization
Period 
First semester 
Year 
1st Year 
Teaching method 
frontal 
Type of hours 
Credits 
Teaching hours 
Hours of Individual study 
Shifts 
Practice 
2.0 
32 
18.0 
No turn 
Lecture 
4.0 
32 
68.0 
No turn 
Prerequisites:

The approach adopted will be informal and mathematical notation will be kept to a minimum. The only real prerequisite being elementary algebra. Basic statistical knowledge is nonetheless recommended (i.e. from a course in elementary statistics). 
Target skills and knowledge:

 Ability to perform widely used statistical anlyses and to adequately interpret the results;
 Capacity to critically understand the main statistical methods employed in the biological literature. 
Examination methods:

Written exam. 
Assessment criteria:

Assessment will be based on the comprehension of the principle concepts and on the capacity to apply them autonomously. 
Course unit contents:

 Basic ideas. From the research problem to the probabilistic model. Sampling, observational and experimental studies. Satistical tests: hypotheses, interpretation of pvalues, error types, power. The problem of tests/multiple comparisons. Confidence intervals.
 Elementary methods. Inference on a proportion, and comparison of two proportions. Student's single and two sample 't' for paired data. Inference in large samples. Non parametric methods: Wilcoxon (one and two samples) and KruskallWallis tests. Correlation coefficient.
 Introduction to T, an open source environment for statistical computing and graphics. Beginning R programming. Examples of analysis of biological data with R.
 Advanced methods. One and twoway analysis of variance. Regression: linear and logistic models. Exploration of multivariate data: principle components and group analysis.
 Introduction to R, an open source environment for statistical computing and graphics. Beginning R programming. Examples of analysis of biological data using R. 
Planned learning activities and teaching methods:

The course emphasizes the ideas upon which the methods presented are based, and the interpretation of results and not the mathematical formulation or the calculation techniques. Numerous realworld examples, of biological and environmental relevance, will be used to justify and illustrate the various methods and models. An appreciable number of lessons will be organized in the computer laboratory using R (http://www.rproject.org). 
Additional notes about suggested reading:

 Slides of the lessons, as well as any other useful material will be made available on line.
 Relevant reference textbooks will be suggested during the starting lessons, based on the students' previous training. 
Textbooks (and optional supplementary readings) 

M. C. Whitlock, D. Schluter, Analisi statistica dei dati biologici. : Zanichelli, 2010.

B. Shahbaba, Biostatistics with R. An Introduction to Statistics Through Biological Data.. : Springer, 2012.


