Quantitative Methods and Statistics for the Social Sciences
A.Y. 2020/2021
Learning objectives
The course aims to provide students with the essential mathematical tools which are necessary to properly use quantitative methods and with the basic logical and mathematical tools for data collection, grouping and interpretation.
Expected learning outcomes
At the end of the course the student will know how to properly use the essential mathematical tools which are necessary to use quantitative methods. He/she will also be able to properly use the basic logical and mathematical tools for data collection, grouping and interpretation.
Lesson period: Second trimester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second trimester
Teaching methods
Mathematics (I trim): teaching will be in presence.
Lessons will be registered in order to allow all students to attend classes either in synchronous and asynchronous modes.
The procedure to attend in classroom will be communicated through the Ariel website.
In the event that the health situation makes it necessary, the lectures of Statistics (II trim.) will be held on the Microsoft Teams platform and will be streamed synchronously based on the timetable of the second term. All the lectures will be also recorded and made available on Ariel.
Teaching material:
The program and teaching material will not change.
Examination and evaluation criteria:
The exam is written and in classroom. It consists of 2, 45 minutes sessions (that can be taken separately) one for each module.
The exams online are carried out using the platform exam.net, following the rules illustrated at the University web page. The classroom and online exams will have the same structure and, whenever possible, will be held simultaneously.
Mathematics (I trim): teaching will be in presence.
Lessons will be registered in order to allow all students to attend classes either in synchronous and asynchronous modes.
The procedure to attend in classroom will be communicated through the Ariel website.
In the event that the health situation makes it necessary, the lectures of Statistics (II trim.) will be held on the Microsoft Teams platform and will be streamed synchronously based on the timetable of the second term. All the lectures will be also recorded and made available on Ariel.
Teaching material:
The program and teaching material will not change.
Examination and evaluation criteria:
The exam is written and in classroom. It consists of 2, 45 minutes sessions (that can be taken separately) one for each module.
The exams online are carried out using the platform exam.net, following the rules illustrated at the University web page. The classroom and online exams will have the same structure and, whenever possible, will be held simultaneously.
Prerequisites for admission
No specific prerequisites are required.
Assessment methods and Criteria
The exam consists of two written papers (one for each module), with theoretical questions as well as exercises. Students are allowed to sit to one module and complete the exam in a different session.
Modulo Matematica
Course syllabus
Review of basic concepts of geometry and algebra.
Real functions: definitions, odd and even functions, elementary functions,
bounded function, upper and lower bounds, compound and inverse functions, maxima and minima, monotone functions, concavity and convexity.
Limits, definition and properties, difference quotient, derivative, differentiation rules, higher order derivatives, extrema, concavity and convexity of functions.
Optimisation: partial derivatives, uncontrained and introduction to constrained optimisation.
Real functions: definitions, odd and even functions, elementary functions,
bounded function, upper and lower bounds, compound and inverse functions, maxima and minima, monotone functions, concavity and convexity.
Limits, definition and properties, difference quotient, derivative, differentiation rules, higher order derivatives, extrema, concavity and convexity of functions.
Optimisation: partial derivatives, uncontrained and introduction to constrained optimisation.
Teaching methods
The teaching method consists in traditional face to face learning.
Teaching Resources
Essential Mathematics for Economic Analysis, fourth edition
(Knut Sydsaeter & Peter Hammond with Arne Strom), ed. Pearson
(Knut Sydsaeter & Peter Hammond with Arne Strom), ed. Pearson
Modulo Statistica
Course syllabus
Elementary probability theory.
Types of data and frequency distributions
Graphical representation.
Location indices.
Scale indices.
Measures of dependence/correlation.
Random variables (Bernoulli, binomial, Gaussian).
Confidence intervals and tests on the mean/proportions.
Simple linear regression model.
Types of data and frequency distributions
Graphical representation.
Location indices.
Scale indices.
Measures of dependence/correlation.
Random variables (Bernoulli, binomial, Gaussian).
Confidence intervals and tests on the mean/proportions.
Simple linear regression model.
Teaching methods
The teaching method consists in traditional face to face learning.
Teaching Resources
S. Iacus, Statistica. McGraw-Hill Education
Modules or teaching units
Modulo Matematica
SECS-S/06 - MATHEMATICAL METHODS OF ECONOMICS, FINANCE AND ACTUARIAL SCIENCES - University credits: 6
Lessons: 40 hours
Professor:
Lupi Veronica
Modulo Statistica
SECS-S/01 - STATISTICS - University credits: 6
Lessons: 40 hours
Professor:
Leorato Samantha
Professor(s)
Reception:
Next office hours: Thursday 17.04 online only (send an email to request an appointment); Thursday 24.04 from 9:30 to 12:30, online only (send an email to request an appointment); Tuesday 29.04 from 10:30 to 12, Wednesday 30.04 from 14 to 15:30
Room 32 third floor