28877 - Laboratory 1

Learning outcomes

Upon completion of the course, students will possess the knowledge and skills necessary to utilize commonly used software for data analysis and to interpret experimental datasets using fundamental statistical tools.

Course contents

Course Description
This laboratory provides students with the opportunity to develop proficiency in widely used software tools for data analysis. Emphasis is placed on the interpretation of statistical outputs commonly found in scientific reports and articles, including indices, charts, and tabular data representations.

Prerequisites
Basic understanding of statistics, including concepts such as the mean and probability calculations.

Learning Objectives
By the end of the course, students will be able to:

• Apply commonly used software (e.g., Microsoft Excel and R) to perform basic and advanced statistical analyses;
• Use statistical tools to analyze datasets and produce analytical reports;
• Interpret graphical and tabular statistical outputs in scientific and technical contexts.

Course Structure and Content

Unit 1

• Introduction to the course structure and assessment methods
• Review of descriptive statistics: types of variables, statistical indices, confidence intervals
• Overview of statistical software (R and Microsoft Excel): functions, features, and further resources
• Organization and structuring of databases

Unit 2

• Data formatting in Excel
• Basic Excel functions: SUM, MAX, MIN
• Measures of central tendency: mean, median, mode; standard error of the mean

Unit 3

• Measures of dispersion: variance, standard deviation
• Excel functions: COUNT, COUNTIF, FREQUENCY
• Introduction to basic R functions and R objects

Unit 4

• Logical functions in Excel: IF, AND, OR, nested IF statements
• Data organization and exploration techniques

Unit 5

• Introduction to descriptive and inferential statistics
• Use of Excel’s Data Analysis Tool
• Frequency tables, histograms, box plots
• Integration of tables and charts into reports

Unit 6

• Introduction to Pivot Tables
• Table creation and management
• Use of advanced filters

Unit 7

• Importing data from CSV and text files into Excel; importing into R
• Data visualization techniques
• Graph interpretation and advanced charting elements
• Creating visualizations in R using appropriate packages

Unit 8

• Managing multiple worksheets
• Cross-sheet references and data integration

Unit 9

• Statistical correlation: calculation and interpretation

Unit 10

• Linear regression: assumptions, estimation, and interpretation
• Application of the least squares method

Readings/Bibliography

Materials related to the PowerPoint presentations, experimental datasets, and scientific publications discussed during the lectures. All materials will be made available on the Virtuale platform:

https://virtuale.unibo.it/

Recommended Readings and Supplementary Texts

Statistica per ornitologi e naturalisti;–Jim Fowler; Franco Muzzio Editore

Introduzione alla statistica per la biologia; Reginald Ernest Parker; Edagricole

Analisi statistica con Excel; Diego Giuliani, Maria Michela Dickson; Maggioli Editore, 2015

Teaching methods

The course will be organized into units that include lectures, case studies on data analysis, and practical examples of statistical analysis using experimental datasets related to topics of agronomic interest.

The laboratory sessions focused on setting up statistical analyses will be conducted using materials prepared by the instructor (PowerPoint presentations and experimental datasets), which will be made available to students.

Laboratory sessions involving the analysis of experimental data will be structured to allow students to follow along and perform the analyses step-by-step on their own laptops. Critical analysis of scientific literature will be guided by the instructor with active student participation.

Due to the expected number of participants, access to a computer lab is not feasible. However, given the practical nature of the course, students are strongly encouraged to use their own laptops.

In light of the type of activities and the teaching methods employed, students are also advised to complete the online safety training in advance via the following e-learning platform:
https://elearning-sicurezza.unibo.it/

Assessment methods

Students may obtain course credit through one of the following two options:

Option 1 – Final Examination
Students may pass the course by successfully completing a final individual practical exam, with a maximum duration of 30 minutes.
The exam consists of a task to be completed in Excel, requiring the application of selected functions and analyses covered during the laboratory sessions, as well as the construction of corresponding descriptive outputs (e.g., graphs or tables).
The use of technical manuals, calculators, or multimedia aids is neither required nor permitted during the exam.
Students who wish to take the final exam in English must inform the instructor in advance.

The dates, times, and locations of the examinations are published on the UNIBO website.

Registration for exam sessions must be completed via the AlmaEsami online platform:
https://almaesami.unibo.it/

Option 2 – In-Class Assessment and Simplified Final Exam
Students may alternatively complete two in-class assessments (dates to be agreed upon during the course) and one simplified final exam.
Each in-class assessment will consist of a practical exercise requiring the application of specific functions covered in the course.
The simplified final exam will involve a practical data analysis task and the creation of a corresponding descriptive output (e.g., graph or table).

 

Teaching tools

A personal computer and a video projector will be used during classroom activities.
Students are strongly encouraged to use their own laptops for practical work.

Bibliographic Resources
Relevant literature is available through the University Library System (Sistema Bibliotecario di Ateneo).

Teaching Materials
Teaching materials presented during lectures, as well as additional resources such as datasets and scientific publications, will be made available to students in electronic format via the online platform Virtuale. Access to these materials is restricted to students included in the official course mailing list.

Mailing List
The course mailing list, used for communication between the instructor and students, is accessible only to students enrolled in the course.

Office hours

See the website of Stefano Targetti