00914 - Statistics

Learning outcomes

At the end of the course students have the basic tools for analyzing and describing a set of data through graphical representations and numerical indexes. Moreover, the course aims at providing students with the main concepts of probability theory and inference.

Course contents

Course structure:

The first part of the course (i.e. Univariate and Bivariate Descriptive Statistics, 30 hours) will be taught from 17/9-17/10; the second part (i.e. Probability Theory and Statistical Inference, 30 hours) from 5/11-6/12.

Part 1 – EXPLORATORY DATA ANALYSIS

Introduction. The data matrix. Type of variables. Frequency tables. Cumulative frequency distribution. Graphical representations. Summary statistics of location (mean, median, mode) and dispersion. Linear transformations. Two-way tables: joint, marginal and conditional frequencies. Association of two quantitative variables, covariance and correlation. Linear regression.

Part 2a – PROBABILITY

Random events, uncertainty, axioms of Probability, conditional probability and Bayes theorem. Discrete and continuous random variables, Central Limit theorem.

Part 2b - STATISTICAL INFERENCE

Statistical models, population and sampling. Simple random samples and parametric inference. Parameters estimation and confidence intervals. Testing statistical hypotheses: normal and binomial models.

Readings/Bibliography

S. Borra e A. Di Ciaccio (2014) Statistica. Metodologie per le Scienze Economiche e Sociali (III ed.), McGraw-Hill Education.

Teaching methods

Traditional lectures and homework.

 

Assessment methods

Exams:

The assessment is via a written examination.

The first midterm exam, covering the contents of the first 30 hours, will take place at the end of October (details can be found in Almaesami, please check the subscriptions list on my institutional web page). The second midterm, covering the contents of the final 30 hours, will take place at the same time of the first full exam.

In some cases, after the written exam, the lecturer may require an oral exam as a further tool of assessment of the student's preparation.

Grades

<18 fail

18-23 pass

24-26 satisfactory

27-28 good

29-30 very good

30 cum laude excellent

During the exam, students are allowed to use the slides.

 

Teaching tools

Home assignments:

Sets of home assignments will be given during the course, each focusing on one of the main topics: in order, descriptive statistics (univariate and bivariate), probability theory and statistical inference.

Office hours

See the website of Maroussa Zagoraiou