30001  STATISTICA / STATISTICS
Dipartimento di Scienze delle Decisioni / Department of Decision Sciences
Per la lingua del corso verificare le informazioni sulle classi/
For the instruction language of the course see class group/s below
PIERO VERONESE
Class group/s taught in English
Lezioni della classe erogate in presenza
Suggested background knowledge
Mission & Content Summary
MISSION
CONTENT SUMMARY
The course covers the following broad areas:
 Collection, management and summary of data using frequency distributions, graphical representations and indexes.
 Study of the relationship between two variables.
 Statistical inference and sampling variability.
 Theory of point estimation and confidence intervals.
 Hypothesis testing.
 Simple regression model and brief introduction to the multiple regression model.
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 statistical models, as regression models, aimed at studying the relationships between variables of interest.
 Use the R software to find the solutions to the aforementioned problems.
Teaching methods
 Facetoface lectures
 Exercises (exercises, database, software etc.)
 Case studies /Incidents (traditional, online)
DETAILS
Beyond the traditional classroom lectures, the teaching method adopts practical sessions using the statistical software R to solve the problems previously illustrated. More specifically, during these sessions students use their pc’s to solve several problems together with the instructor. A realworld dataset is used throughout all the course, thus providing an exhaustive example (with respect to the course contents) of a practical statistical analysis.
Assessment methods
Continuous assessment  Partial exams  General exam  


x  x 
ATTENDING AND NOT ATTENDING STUDENTS
The assessment method, equal for attending and notattending students, considers two alternative ways: 1) three partial exams, 2) a general exam.

Two Partial Exams (PE1,PE2) are traditional written exams (at most 31/30), while in the third one using the R software (PR), the students are asked to conduct a short data analysis session to answer some questions. This last partial exam is worth at most 4 points that are added to the weighted average grade of the remaining two partial exams. Thus the final mark is given by: [ (PE1+PE2)/2]*(27/31) + PR.

A general written exam (at most 31/30). The exam contains explicit questions on the code of the R software, on its working principles and on the interpretation of its output. The Rrelated questions are worth 4 points. A total grade of 31/30 is equivalent to 30/30 cum laude.
Both forms of the exam aim 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 output from the software.
Teaching materials
ATTENDING AND NOT ATTENDING STUDENTS
 P. NEWBOLD, W.L. CARLSON, B. THORNE, Statistics for Business and Economics, Pearson/Prentice Hall, 8th global edition.

Additional material document on Frequency Distributions, available on the Bboard platform.
 Specific material on the use of the R software are available on the Bboard platform since the beginning of the course.