00914 - Statistics

Learning outcomes

At the end of the course, the student knows the basic topics of statistics from both a methodological and an applied perspective. In particular, the student is able to:

• summarize information in statistical distributions and graphs;
• choose and compute the measures of central tendency and variability that are most appropriate for specific empirical problems;
• measure the strength of the relationship between variables, even when they are of different types;
• construct covariance, correlation, and dissimilarity matrices
• use classical dimensionality-reduction methods
• address classification problems

Course contents

• Introduction to the course. Statistical surveys. Types of variables. Measurement scales of variables. Data matrices.
• Frequency distributions and their graphical representations. The empirical distribution function. Cumulative frequencies. Statistical ratios. Simple and complex index numbers.
• Measures of location: means and their properties. Measures of variability and mutability and their properties. Concentration. Notes on skewness and kurtosis. Boxplots.
• Bivariate frequency distributions. Marginal and conditional distributions. Conditional mean and variance. Decomposition of deviance. Study of independence and association. Dependence in mean.
• Scatterplots. Covariance and linear correlation. Linear regression. Notes on local regression.
• The multidimensional data matrix and the corresponding structures for multivariate analysis (mean vector, covariance and correlation matrices).
• Cluster analysis: similarity and dissimilarity measures, distance measures, and agglomerative hierarchical methods.
  
• Principal component analysis.
  

Readings/Bibliography

Required materials:

Piccolo D. (2004) Statistica per le Decisioni, Il Mulino, PART I.

Additional teaching material (slides, exercises, etc.) prepared by the instructor is available on Virtuale

Teaching methods

• Frontal lectures.
• Exercises sessions.
• Laboratory sessions in which real data will be analyzed using Excel and R.

Attending the lessons is not mandatory, but it is strongly recommended.

Given the type of activities and teaching methods used, attendance at this training activity requires all students to first complete modules 1 and 2 of the e-learning training course on safety in the workplace.

Assessment methods

• The purpose of the exam is to assess whether the following learning objectives have been achieved:
  - an in-depth knowledge of techniques for summarizing statistical data and for analyzing relationships between two or more variables;
  - the ability to critically analyze sets of univariate and multivariate data.
• The exam can be taken in two ways:
  Partial exams: two partial exams are scheduled, one at the end of each teaching period, each lasting two hours. Students who take both partial exams may accept the grade resulting from the average of the scores obtained in the two tests.
  Total exam: during regular exam sessions, a single exam covering the entire program is offered, lasting two and a half hours.
• The exam is written and consists of a set of quizzes on the theoretical part and some practical exercises to be completed using the software R + RStudio + Excel.
• The oral exam is generally optional. However, in some cases it is mandatory at the discretion of the instructor. To be admitted to the oral exam, the written exam must first be passed.
• During the written examination, students are allowed to consult only handwritten notes that they have prepared themselves. The notes may contain formulas, explanations, R code, and any other material deemed useful for the examination. The notes must be limited to one double-sided A4 sheet for each midterm exam, or two double-sided A4 sheets for the comprehensive final exam. No other materials may be consulted.
• Assessment criteria: grades will be awarded according to the following scale:
  <18 insufficient
  18–23 sufficient
  24–26 fair
  27–28 good
  29–30 very good
  30 with honours excellent
• With regard to assessment, the use of AI is prohibited. Any use of AI constitutes a violation of academic integrity.
• Students with specific learning disorders (SLD) or temporary or permanent disabilities are encouraged to contact the University's dedicated support office in good time. The office will propose any appropriate accommodations for eligible students. These accommodations must be submitted to the course instructor for approval at least 15 days in advance. The instructor will assess their appropriateness, taking into account the intended learning outcomes of the course.
• Registration for the exam is mandatory, and all students must register through the AlmaEsami platform in accordance with the general rules established by the University.

Teaching tools

Slides, datasets, examples.

Office hours

See the website of Luca Scrucca