OVERVIEW

This unit aims to provide fundamental notions of statistics and data analysis. The topics covered include graphical and numerical representations of quantitative and categorical variables
and basic principles of interpretation, modeling and statistical inference.

 

AIMS AND CONTENT

LEARNING OUTCOMES

The purpose of the course is to introduce students to non-deterministic thinking and to tackle topics with non-unique solutions. Therefore, the course is aimed at the study of the main methods of statistical analysis. By the end of the course, the student will have acquired the essential elements to learn how to build a dataset; create graphical representations; analyze data both from a descriptive and analytical point of view; interpret the results.

AIMS AND LEARNING OUTCOMES

Regular attendance and active participation in the proposed training activities, as well as individual study, will enable the student to:

-  Provide examples of applications of statistical techniques and know how to choose the appropriate technique for the context

-  Acquire correct terminology for data analysis

-  Be able to argue and counter-argue in a group with statistical arguments

- Evaluate the presence of possible bias and statistical paradoxes flaws in statistical arguments

- Create useful graphics for representing statistical analyses

PREREQUISITES

None. The course is self-contained

TEACHING METHODS

Frontal lectures in person (unless otherwise advised).

Students who have valid certification of physical or learning disabilities on file with the University and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Federico Scarpa (federico.scarpa@unige.it  ), the Polytechnic School's disability liaison.

SYLLABUS/CONTENT

Definition of data, statistics, statistical unit, statistical variable, categorical variables, quantitative variables, distribution,
exploratory analysis, modalities of a categorical variable, absolute and percentual frequency, pie charts, bar charts, histograms, classes of a histogram, frequency density, analysis of a distribution: model, deviation, form, center, dispertion, outlier, symmetry of a distribution, time-series chart, trend;

arithmetic mean, robustness of a numerical representation, computing the arithmetic mean in particular cases, properties of the mean: (null deviations from the mean, linearity). Median, mode , quartiles , boxplots, definition and computation of variance, standard deviation, and their basic properties;

density curve,  median, "mean", standard deviation in a density curve, normal distribution, "68-95-99.7" rule, standard normal distribution;

explanatory variable, response variable, scatterplot, interpretation of a scatterplot, categorical variables in a scatterplot, correlation index, property of correlation index;

building the least square regression line, limits and cautions for the use of the regression line;

contingency tables, marginal distributions, conditional marginal distribution, Simpson paradox.


 

RECOMMENDED READING/BIBLIOGRAPHY

- David S. Moore, "Introduction to the practice of statistics", seconda edizione, ed. APOGEO.

- slides and materials provided by the professors

TEACHERS AND EXAM BOARD

Exam Board

GABRIELE MOSAICO (President)

FABIO D'AGOSTINO

MATTEO SAVIOZZI

LESSONS

LESSONS START

https://corsi.unige.it/corsi/11429/studenti-orario

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

Written, multiple choice exam

ASSESSMENT METHODS

The exam will verify the knowledge of the concepts and definitions listed in the syllabus

Exam schedule