28877 - Laboratory 1

Course Unit Page

Academic Year 2022/2023

Learning outcomes

At the end of the course, the student has the necessary skills and  knowledge to manage and understand experimental data through basic statistical tools.

Course contents

The practical aims of the laboratory deal with the acquisition of the necessary skills for the employment of software commonly used for basic and advanced statistical analyses (MS EXCEL e R). The laboratory also provides the opportunity to get acquainted with common outputs of statistical software that are presented in scientific papers: indices, diagrams, tables.

Unit 1

  • Course presentation and evaluation procedures to obtain the qualification.
  • Introduction to statistical inference, review of descriptive statistics (types of variables, statistical indices, confidence intervals).
  • Database management
  • Hypothesis testing: chi-square test. Practical examples using experimental datasets.

Unit 2

  • Introduction to the software (R and MS EXCEL)
  • Analysis of a typical output generated by statistical software, interpretation and common graphical representations.
  • Data management. Illustration of basic commands in commonly used statistical software (www.r-project.org, Open Office and MS EXCEL).
  • Pivot table

Unit 3

  • Correlation
  • Confidence intervals, practical examples using experimental datasets.

Unit 4

  • Introduction to the normal distribution
  • Variation coefficient, standard error of the mean, indices
  • Hypothesis testing: means test
  • Practical examples of the tests described above using experimental datasets

Unit 5

  • Linear regression: hypothesis, estimation and interpretation of results. The least squares method
  • Use of regression, examples reported in selected scientific papers
  • Dependent and independent variable, precautions when using regression

Unit 6

  • Analysis of variance – one way ANOVA
  • Discussion of elaborations in publications (also in English) - Synthetic indicators generally reported in publications, commonly used diagrams (eg box-plots)

Unit 7

  • Introduction to multivariate statistics: objectives and aims.
  • Practical examples outputs (dendrograms, eigenvalue, etc.) using experimental datasets.
  • End of the course


Power Point presentations, experimental datasets and the scientific publications discussed during the lectures will be provided by the teacher. Video tutorials will also be made available to deepen some specific aspects features of the software.

The materials will be available on ‘Virtuale’

https:// https://virtuale.unibo.it/


  • Statistica per ornitologi e naturalisti; Fowler, Jim; Franco Muzzio ed.
  • Introduzione alla statistica per la biologia; Parker, Reginald Ernest; Edagricole

Teaching methods

The course will be divided into units that include lectures, presentations of case studies and practical examples of statistical analysis on experimental datasets on topics of agricultural interest.

The laboratory part concerning statistical analysis will be supported with material prepared by the teacher (Power Point presentations and experimental datasets) and made available to students. Also, "video tutorials" selected by the teacher relating to the software (R, downloadable free of charge from the website www.r-project.org; MS EXCEL) will be made available.

The laboratory part concerning the analysis of experimental data will be conducted to allow the student to perform the analyzes step-by-step on their laptop. The critical analysis of scientific papers (also in English) will be led by the teacher with the involvement of the student.

The use of laptops is suggested. In consideration of the type of activity and the didactic methods adopted, it is also suggested the prior participation in the training modules 1 and 2 on safety in the study places, in e-learning mode (https://elearning-sicurezza.unibo.it/ )

Assessment methods

The student can pass the course with a successful participation to at least 70% of the lectures. The successful participation is verified by: 1) attendance to the lecture AND 2) passing a brief test at the end of each lecture. The test at the end of each lecture consists of four questions (2 with closed answers and 2 with open answers) on the topics covered during the lecture. To pass the test, the student must correctly answer at least two questions.

Alternatively, for students who do not reach the 70% of succesful participation or who choose not to attend classes, it will be possible to pass the course with a final test which consists of a written test. The final test consists of a questionnaire with open and closed questions concerning the topics covered during the course. During the test, manuals or calculation or multimedia aids are not necessary, and therefore not allowed. In case of need to take the final exam in English, it is necessary to inform the teacher.

Dates, times and locations of the exams are published on the course website.

To register for the exams, the web application AlmaEsami must be used.


Teaching tools

Personal computer and video projector for classroom activities. Students are suggested to use a personal laptop.

Bibliographic material available at the University Library System

Didactic material: the didactic material presented in class, video tutorials, and other material such as datasets and scientific publications is made available to the student in electronic format via ‘Virtuale’. Access to the material is allowed only to students belonging to the mailing list. The mailing list for teacher-student communications is accessible only to students of the course, protected by a password distributed during class or requested to the teacher.

Office hours

See the website of Stefano Targetti