79196 - Statistics

Course Unit Page


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.

Quality education

Academic Year 2021/2022

Learning outcomes

At the end of the course the students know fundamental concepts of statistics useful for the analysis of simple datasets. In particular, students - are able to apply descriptive tools for data analysis; - are aware of the basic concepts of probability; - know the fundamental of statistical inference, hence can compute relevant summaries from the data and quantify the associated uncertainty. Pre-knowledge of mathematics is required.

Course contents


Types of variables. Statistical units, population and sample.

Data collection and organization. Data matrices and frequency distributions. Graphical representations.

Mean values: mode, median, quartiles, arithmetic mean. Main variability measures and concentration.

Statistical indices.

Introduction to bivariate analysis. The relationship among two variables: association. The relationship among two variables: covariance and linear correlation. The simple linear regression.


PROBABILITY: random events and sample space. Axioms of probability. Conditional probability.

Density and probability functions of a random variable; expected value and variance of a random variable. Bernoulli and Binomial distributions. Normal, standard normal and Student t distributions. Central limit theorem.

STATISTICAL INFERENCE: sample variability of an estimator; the sample mean estimator and its properties.

Point estimation and confidence intervals for the mean and the proportion.

Statistical tests.

Basics of inference in the simple regression model.


Mandatory material:

S. Borra, A. Di Ciaccio. Statistica – metodologie per le scienze economiche e sociali, terza edizione, McGraw-Hill, Milano, any edition.

Teaching material (slides and exercises) will be available on the platform Virtual Learning Environment at: virtuale.unibo.it

Teaching methods

Frontal lectures based mostly on slides. Lectures will be on methods and practicals (tutorials sessions with excersises on blackboard).

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

Assessment methods

The aim of the exam is to assess if the following objectives have been reached:

i) deep knowledge of statistical techniques;

ii) ability to use these techniques for data analysis;

iii) ability to interpret the results and use them for decision making.

PARTIAL EXAMS. It is possible to take two written partial tests: the final score will be the mean of the two single scores, given that both are at least sufficient (>=18).

TOTAL EXAM. It is a written test.

The written exam consists of exercises, multiple-choice items and open-ended items. It is possible to download an sample of written exam in the teaching material. The test duration is from 90 to 120 minutes.

The final mark is expressed on a scale of 30.

The final mark corresponds to the following description of the overall achievement level reached:

< 18: not sufficient (exam failed)

18-23: sufficient

24-25: satisfactory

26-28: good

29-30: very good

30 e lode (30 cum laude): excellent

During the test, it is possible to use a pocket calculator, the statistical tables and a formulary which will be provided in the teaching material.

The examination rules apply for both students attending and not attending classes.

In case online exams will be allowed, the EOL (Esami On Line) platform will be used for the test and Zoom for surveillance.

More information will be given during the lectures and can be find in the Virtuale environment.

Teaching tools

Slides, datasets, examples.

Office hours

See the website of Mariagiulia Matteucci