30001 - STATISTICA / STATISTICS
Orario di ricevimento / Student consultation hours
Orario delle lezioni / Class timetable
Calendario esami / Exam timetable
Per la lingua del corso verificare le informazioni sulle classi/
For the instruction language of the course see class group/s below
RAFFAELLA PICCARRETA
Class group/s taught in English
Synchronous Blended: Lezioni erogate in modalità sincrona in aula (max 1 ora per credito online sincrona)
Suggested background knowledge
PREREQUISITES
Mission & Content Summary
MISSION
CONTENT SUMMARY
The course covers the following topics:
- Collection, management and summary of data using frequency distributions, graphical representations and summaries.
- Study of the relationship between two variables.
- Statistical inference and sampling variability.
- Theory of point estimation and confidence intervals.
- Hypothesis testing.
- Simple and multiple regression models
Note that all the descriptive and inferential tools introduced during the course will be applied to data using the statistica software R - and in particular the integrated development environment (IDE) RStudio. Therefore some lessons will be dedicated to the software.
Intended Learning Outcomes (ILO)
KNOWLEDGE AND UNDERSTANDING
- Recognize different types of data.
- Understand the difference between the tools of descriptive and inferential statistics, and identify the most suitable approach for the problem at hand.
- Recognize simple statistical models.
APPLYING KNOWLEDGE AND UNDERSTANDING
- Properly summarize a dataset.
- Estimate, and test hypotheses on, the unknown parameters of a population on the basis of sample data.
- Build simple and multiple regression models to study the relationships between variables of interest.
- Use the R software to address the aformentioned issues.
Teaching methods
- Face-to-face lectures
- Exercises (exercises, database, software etc.)
- Case studies /Incidents (traditional, online)
DETAILS
Beyond traditional classes, the course features hands-on classes, where the statistical software R - and in particular the integrated development environment (IDE) RStudio - is used to apply basic statistical analyses to data. More specifically, during these sessions students will use their laptop to address specific issues, and to interpret the obtained results.
Assessment methods
Continuous assessment | Partial exams | General exam | |
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x | x |
ATTENDING AND NOT ATTENDING STUDENTS
The assessment method, both for attending and not-attending students, consists of 1) two midterm exams or 2) a general exam.
The two midterms consist of theoretical questions, traditional “paper and pencil” derivation exercises (questions based on aggregated data) and on questions concerning the analysis of a data, to be answered using R/Rstudio (installed on each student’s laptop). The maximum grade in each midterm is 31/30.
To pass the exam, a grade higher than or equal to 15 is required in both midterms, and an average of at least 18 points. A final grade equal to 31 is rewarded cum laude.
Important. Students whose grade in the second midterm is lower than 18 can ask to have their final grade not registered, even if they passed the exam. This does not hold for students whose grade in the first midterm is lower than 18, because sitting for the second midterm implies acceptance of the grade in the first midterm.
The general exam is organized as the midterms, and consists of the exact same type of exercises. The maximum grade in the exam is 31/30; The exam is passed with a grade higher than or equal to 18. A final grade equal to 31 is rewarded cum laude.
The exam aims at assessing:
- The ability to identify the proper methodology to solve a given problem.
- The understanding of the logic underlying a certain procedure.
- The ability to compute appropriate statistical measures with both a pocket calculator and a statistical software.
- The ability of suggesting and implementing with R a statistical model, consistent with both the assumptions stated and the data at hand.
- The ability to understand the software output.
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
- P. NEWBOLD, W.L. CARLSON, B. THORNE, Statistics for Business and Economics, Pearson/Prentice Hall, 9th global edition (2019).
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Additional material integrating the textbook, available on the Bboard platform.
- Additional material on R/Studio available on the Bboard platform.