APPLIED STATISTICS FOR BIOLOGY AND BIOTECHNOLOGY

The course is one of the quantitative training activities of the Master degrees in Conservation Science and Technology for Cultural Heritage and in Science and Technology of Bio and Nanomaterials. The aim is to get the student familiar with
the main statistical tools for use in the context of conservation sciences, biology and nanotechnology.
The course provides knowledge of descriptive statistics, probability and inferential statistics, as well as skills in the use of specific programs for data analysis and reporting.
At the end of the course, the student will be able to identify suitable models and methodologies in the context of interest; moreover he will learn to interpret and communicate the obtained results, with the aim of driving appropriate decisions.

1. Knowledge and understanding:
- to know the main tools for graphical representation and summary of a dataset
- to know the basic concepts of probability calculus and distributions for inference
- to know the basic methodologies of statistical inference

2. Ability to apply knowledge and understanding:
- to use specific programs for data analysis and reporting
- to use the appropriate terminology in all the processes of application and communication of the acquired knowledge

3. Ability to judge:
- to apply the acquired knowledge in a specific context, identifying the most appropriate models and methods

4. Communication skills:
- to present in a clear and exhaustive way the results obtained from a statistical analysis, both in written and oral form
- to know how to interact with the other students and with the instructor during the classes and on the virtual forum

5. Learning skills:
- to use and integrate information from notes, books, slides and practical lab sessions
- to assess the achieved knowledge through quizzes, exercises and assignments during the course

Knowledge of basic mathematics at the level of a Bachelor's degree.

The course provides a practical introduction to statistics and experimental design. The aim of the first part of the course is to get the students familiar with the most useful statistical techniques for summarising, representing and finding patterns in datasets. Next, an introduction to elementary probability and distributions is provided. Then, the main methods of multivariate analysis are presented. The last part is devoted to statistical inference methods for testing hypotheses and prediction. Theoretical presentations are always motivated by practical examples and applications to conservation sciences and biotechnology. The use of the statistical package R (http://cran.r-project.org/ ) is also introduced for data analyses.

Review of descriptive statistics: population and samples; types of variables; basic graphical representations and summaries for numerical variables and factors; relationship between two factors and the Chi-squared statistics; relationship between two numerical variables, correlation and regression.
Sampling and experimental design: types of samples, treatments, replications, randomization and blocking.
Probability: sample space, events and probability; independence; discrete and continuous random variables; the most important probability distributions.
Inference: sample distributions; estimation of the mean and the standard deviation of a population; confidence intervals; hypotheses testing and p-values; regression and analysis of variance.

-Teaching material available on Moodle platform.
-Robinson, R. and White, H. (2016) Elementary Statistics with R. Available at http://homerhanumat.github.io/elemStats/

The achievement of the course objectives is assessed through participation in activities and assignments during the course (up to 15 points) and a final oral exam (that integrates the grade on the activities during the course). The use of the software R is part of the program of the course and the main tool for solving the assignments.

Activities and assignments consist of discussions about different topics, solution of quizzes and exercises in Moodle and individual and group work.

The final exam is an oral discussion of the topics studied during the course.

Interactive approach based on lectures, practical lab sessions using R, case studies to be analysed and presented to the class. Use of Moodle platform for discussions and learning assessment. Open-source programs for data analysis and reporting. Moreover, students are encouraged to actively participate in the classes by studying new topics and sharing them with the class.

English

Blended course with 30 hour lectures and 18 hour online activities.

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