OVERVIEW

The Statistics 1 course aims to equip students with the main tools for the quantitative analysis of economic and social phenomena and lay the necessary foundations to tackle the other quantitative courses of the Course of Studies.

AIMS AND CONTENT

LEARNING OUTCOMES

The main aim of the course is to provide the fundamental tools of descriptive and inferential statistical analysis. The skills that must be acquired at the end of the course concern: mastery of the fundamental concepts of univariate and bivariate descriptive statistics for the collection and synthesis of data; knowledge of the basic elements of probability theory, in order to allow understanding of the fundamental concepts of statistical inference; the acquisition of the main statistical inference techniques (in particular theory of point and interval estimation and hypothesis testing); the mastery of some statistical models for the analysis of statistical relationships (dependence and independence, correlation, regression) from both a descriptive and inferential point of view. The skills acquired also include the ability to read and understand statistical analyses, as well as to produce correct data analyses in simple application contexts.

AIMS AND LEARNING OUTCOMES

Knowledge and understanding: Students will know the main tools for synthesizing information and generalizing what is observed through sample surveys.

Ability to apply knowledge and understanding: Students will be able to reorganize data in univariate and bivariate frequency tables providing adequate graphical representation, carry out basic descriptive analyzes for one-dimensional phenomena, analyze the relationships between two or more phenomena with particular reference to the dependence and linear regression analysis. They will also be able to apply some statistical inference tools to solve simple problems.

Independence of judgement: Students must be able to use the knowledge acquired both on a conceptual and operational level with independent evaluation ability and ability in different application contexts.

Communication skills: Students will acquire the technical language typical of the discipline to communicate clearly and unambiguously with specialist and non-specialist interlocutors.

Learning skills: Students will develop adequate learning skills that allow them to continue to explore the subject independently.

PREREQUISITES

The teaching presupposes knowledge of the basic contents of a course in General Mathematics for economic or business degree courses.

TEACHING METHODS

Lessons and exercises. Since the training objectives concern both theoretical and applicative skills, the lessons in which the methodological aspects of statistics are presented will be alternated with exercises in which numerical problems and examples of simple analyzes on real data are addressed.

Students who have valid certification of physical or learning disabilities  and who wish to discuss possible accommodations or other circumstances regarding lectures, coursework and exams, should speak both with the instructor and with Professor Serena Scotto (scotto@economia.unige.it), the Department’s disability liaison.

SYLLABUS/CONTENT

Part I: Descriptive statistics

1. Introduction to statistics. Descriptive and inferential statistics. Populations and samples.
2. Distributions, frequencies, and cumulative frequencies. Graphs to describe categorical variables, time series, and quantitative variables. Graphs and tables to describe relationships between variables.
3. Location measures: mean, median, mode, percentiles.
4. Variability measures. Box-plot. Measures of  concentration.
5. Measures of relationships between variables.

Part II: Probability

1. Random experiments, outcomes, events.
2. The probability function and its axioms. Rules of probability.
3. Conditional probability and independence. Bivariate probabilities.
4. Bayes’ theorem.
5. Discrete random variables and their properties.
6. Bernoulli, Binomial, Poisson distributions.
7. Continuous random variables and their properties.
8. Uniform and normal distributions.
9. Joint distributions.

Part III: Inference

1. Sampling and sampling distributions.
2. Distribution of the sample mean. The central limit theorem. Distribution of the sample proportion.
3. Point estimation. Estimators and their properties.
4. Confidence intervals. Confidence intervals for the mean and for the proportions.
5. Confidence interval for a proportion
6. Theory of statistical hypothesis testing. Test for a mean, test for a proportion.
7. Comparison of two means.

Part IV: Relationships between variables

1. Correlations and linear regression. The simple linear regression model. Least squares technique least squares coefficient estimators. The explanatory power of a linear regression equation. Inference on the coefficients.
2. Statistical independence and chi-square test.

RECOMMENDED READING/BIBLIOGRAPHY

"Statistica"; Newbold, Carlson, Thorne, Nona edizione, ed. Pearson (2021). Per gli studenti stranieri è possibile fare riferimento al testo originale in lingua inglese.

"Introduzione alla statistica inferenziale - per le scienze economiche e aziendali"; D. De Martini, ed. Esculapio (2024).

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

Lessons begin in the first week of the second semester as per the Department teaching calendar.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The examination consists of a written test. An additional discussion may be requested at the discretion of the examination committee.

ASSESSMENT METHODS

The written exam consists of:

1) multiple choice theoretical questions

2) open-ended theoretical questions

2) exercises 

The questions and exercises are chosen so as to cover, as far as possible, all the topics of the exam programme. The theoretical questions are used to evaluate the student's level of understanding, while the exercises are used to measure the ability to apply the knowledge acquired.