Computer Technology and Statistics Knowledge
A.Y. 2024/2025
Learning objectives
To acquire the knowledge of descriptive and inferential statistics crucial for data management and the description of complex systems, as well as for the evaluation of agricultural trials. To acquire the computer skills necessary for the management of data matrices through spreadsheets, use of formulas and application of statistical tests. To acquire the computer skills necessary for the synoptic and graphical representation of data. Use of search engines for systematic analysis of bibliographic sources. To acquire knowledge of the fundamentals of multivariate statistical analysis (chemometrics).
Expected learning outcomes
To describe the analysed systems using the main statistical indicators. To analyse the data by means of the main statistical tests for the evaluation and objective comparison of the data. To use application software for management, statistical processing, data storage and their graphic representation. To acquire skills to use search engines for systematic analysis of bibliographic sources. To acquire basics skills to perform a multivariate data analysis.
Lesson period: First semester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
First semester
Course syllabus
Introduction to statistics
- Definition of descriptive and inferential statistics
- Concepts of: population, sample, variable
- Qualitative (nominal and ordinal) and quantitative (discrete and continuous) statistical variables
Bibliographic research
- Use of search engines for systematic analysis of bibliographic sources
- Analysis and management of the identified bibliography
Data collection and management
- Structures and general characteristics of the main spreadsheets and presentation programs
- How to select and apply spreadsheets (MS Excel) and presentation programs (MS PowerPoint)
- Data entry in Excel and calculation of derived variables
- Basic functions of spreadsheets; how to import and export data
- Application of the main functions (MS Excel)
- Criteria for choosing graphic representations
Sample analysis
- Minimum / maximum; average / fashion / median; differences between means
- Frequency; percentages
- Standard deviation
- Distribution and quartiles
- Correlation
- Regression
- Identification of outliers
- Student t test
- Chi square test
- Test F, variance analysis
- Probability; normal distribution
- Multivariate data analysis, basic concepts. Principal Component Analysis (PCA).
- Definition of descriptive and inferential statistics
- Concepts of: population, sample, variable
- Qualitative (nominal and ordinal) and quantitative (discrete and continuous) statistical variables
Bibliographic research
- Use of search engines for systematic analysis of bibliographic sources
- Analysis and management of the identified bibliography
Data collection and management
- Structures and general characteristics of the main spreadsheets and presentation programs
- How to select and apply spreadsheets (MS Excel) and presentation programs (MS PowerPoint)
- Data entry in Excel and calculation of derived variables
- Basic functions of spreadsheets; how to import and export data
- Application of the main functions (MS Excel)
- Criteria for choosing graphic representations
Sample analysis
- Minimum / maximum; average / fashion / median; differences between means
- Frequency; percentages
- Standard deviation
- Distribution and quartiles
- Correlation
- Regression
- Identification of outliers
- Student t test
- Chi square test
- Test F, variance analysis
- Probability; normal distribution
- Multivariate data analysis, basic concepts. Principal Component Analysis (PCA).
Prerequisites for admission
The course requires good basic knowledge of mathematics.
Teaching methods
The course includes classroom lectures for theory and practical applications using data processing software (Microsoft Excel) and presentation (Microsoft PowerPoint).
Teaching Resources
Slides and lecture notes.
Book: Introduzione alla Statistica, di M. K. Pelosi e T. M. Sandifer, ed. McGraw-Hill, 2009
Book: Introduzione alla Statistica, di M. K. Pelosi e T. M. Sandifer, ed. McGraw-Hill, 2009
Assessment methods and Criteria
The exam consists of a 60-minute written test with 10 closed questions and one open question. For each correct answer, the following score will be assigned: 2,5 points for closed-ended questions; 1 to 5 for the open-ended question. The sum of the scores will provide the candidate's total score. Final grade: approved (grade ≥ 18/30) or not approved (grade <18/30).
Students with SLD or disability certifications are kindly requested to contact the teacher at least 15 days before the date of the exam session to agree on individual exam requirements. In the email please make sure to add in cc the competent offices: [email protected] (for students with SLD) o [email protected] (for students with disability).
Students with SLD or disability certifications are kindly requested to contact the teacher at least 15 days before the date of the exam session to agree on individual exam requirements. In the email please make sure to add in cc the competent offices: [email protected] (for students with SLD) o [email protected] (for students with disability).
Educational website(s)
Professor(s)
Reception:
by appointment only
Department of Agricultural and Environmental Sciences - via Celoria 2, Milano