84555 - Statistics

Learning outcomes

The course provides students with such statistical techniques as graphical tools and summary measures for single and multiple variables, estimation, and hypothesis testing for Gaussian and Binomial populations. At the end of the course students have (a) acquired knowledge of the main statistical techniques for exploratory data analysis and the fundamental concepts of probability and inference from random samples and (b) developed skills to solve elementary probability problems.

Course contents

Course structure:

The topics of the first part of the course (Module 1) are: Univariate and Bivariate Descriptive Statistics (30 hours).

The topics of the second part (Module 2) are: Probability Theory and Statistical Inference (30 hours).

Part 1 – EXPLORATORY DATA ANALYSIS (Module 1)

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 (Module 2)

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

Part 2b - STATISTICAL INFERENCE (Module 2)

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

D.R. Anderson, D.J. Sweeney, T.A. Williams, J.D. Camm, and J.J. Cochran (2015). Statistics for Business and Economics. Cengage Learning.

Teaching methods

Traditional lectures and home assignments.

Assessment methods

Written examination. 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.

The first midterm exam, covering the contents of Module 1, will take place during the spring session (details can be found in Almaesami, please check the subscriptions list on my institutional web page). The second midterm exam, covering the contents of Module 2, will take place at the same time of the first full exam.

For the subsequent sessions, the exam is unique for both Modules and the written examination will comprise exercises on the material covered during the whole course (descriptive statistics, probability theory and statistical inference).

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

Grades:

<18 fail

18-23 pass

24-26 satisfactory

27-28 good

29-30 very good

30 cum laude excellent

Teaching tools

Home assignments:

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

Office hours

See the website of Maroussa Zagoraiou

See the website of Rosamarie Frieri