Insegnamento a.a. 2020-2021

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.

• 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.

• 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.

• Face-to-face lectures
• Exercises (exercises, database, software etc.)
• Case studies /Incidents (traditional, online)

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 real-world dataset is used throughout all the course, thus providing an exhaustive example (with respect to the course contents) of a practical statistical analysis.

The assessment method, equal for attending and not-attending students, considers two alternative ways: 1) three partial exams, 2) a general exam.

1. 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.

2. 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 R-related 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.

• P. NEWBOLD, W.L. CARLSON, B. THORNE, Statistics for Business and Economics, Pearson/Prentice Hall, 9th 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.