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## OVERVIEW

The course “Statistics 1” aims to provide students with the main tools for the quantitative analysis of economic and social phenomena, and to supply the basic statistical knowledge needed to face the other quantitative courses of the Degree Programme.

## AIMS AND CONTENT

### LEARNING OUTCOMES

Introductory Statistics. In the course the main topics of descriptive statistics, probability and inferential statistics are discussed.

### AIMS AND LEARNING OUTCOMES

Knowledge and understanding: Students will know the main tools used to summarize the information and for the generalization of the information 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 representations; carry out basic descriptive analyses for univariate phenomena; analyze the relationships between two or more phenomena with particular emphasis on association and linear regression analysis. They will also be able to apply some statistical inference tools to solve simple problems.

Making judgements: Students must be able to use the acquired knowledge both on a theoretical and operational level with autonomous assessment skills, in various applicative contexts.

Communication skills: Students will acquire the technical language typical of the discipline to communicate clearly and without ambiguity with both statisticians and non-statisticians.

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

Connections of the course with other courses in the programme: The topics introduced in this course will allow the students to properly understand numerical data within other disciplines and will put the bases to study and analyze quantitative methods in other quantitative courses.

### PREREQUISITES

The course requires knowledge of the basic skills of a course of General Mathematics for Business and/or Economics.

### TEACHING METHODS

Traditional lessons and exercises and through AulaWeb. Since the training objectives concern both theoretical and applicative skills, there will be both lessons focused on the methodological aspects of statistics and lessons based on exercises in which numerical problems are faced and examples of simple analyzes on real data are discussed.

Due to the Covid19 pandemic, students will be informed via email and AulaWeb messages on the actual method of the lessons (in person or remotely).

### SYLLABUS/CONTENT

Part I: Elements of descriptive statistics

1. Introduction to data collection
2. Distribution of a variable and its representation: Frequency distributions and diagrams
3. Location indices: means, median, mode, percentiles
4. Scale indices
5. Measures of  concentration
6. Association measures for two variables
7. Statistical independence
8. Association between qualitative variables.
9. Correlation between quantitative variables
10. Simple linear regression

Part II: Probability

1. Events and events sigma-field
2. Kolmogorov axioms
3. Conditional probability and independence
4. Bayes theorem
5. Discrete and continuous random variables:
1. Bernoulli and Binomial r.v.
2. Poisson r.v.
3. Normal r.v.
6. The Central Limit Theorem

Part III: Introduction to statistical inference

1. Sampling and sampling distributions
2. Point estimation
3. Estimators and their properties
4. Interval estimation
5. Confidence interval for a mean (variance unknown)
6. Confidence interval for a proportion
7. Theory of statistical
8. Test for a mean, test for a proportion.
9. Comparison of two means.

Foreign students are asked to contact the teachers to agree on the text book in English language.

## TEACHERS AND EXAM BOARD

### Exam Board

MARTA NAI RUSCONE

LUCA PERSICO

FABIO RAPALLO

## LESSONS

### TEACHING METHODS

Traditional lessons and exercises and through AulaWeb. Since the training objectives concern both theoretical and applicative skills, there will be both lessons focused on the methodological aspects of statistics and lessons based on exercises in which numerical problems are faced and examples of simple analyzes on real data are discussed.

Due to the Covid19 pandemic, students will be informed via email and AulaWeb messages on the actual method of the lessons (in person or remotely).

### LESSONS START

Classes will start in the first week of the second semester according to the calendar of the Department of Economics.

STATISTICS 1 C

## EXAMS

### EXAM DESCRIPTION

The examination consists of a written test which includes:

1) multiple-choice questions of a theoretical nature.

2) theoretical questions with open answers

3) exercises

The examination regulations are published on the course's Aulaweb page.

As a result of the COVID 19 pandemic, the examination procedures may change in relation to the University regulations.

### ASSESSMENT METHODS

The questions and exercises in the written exam are chosen to cover, as far as possible, all the topics of the program. The theoretical questions are used to assess the student's level of understanding, while the exercises are used to measure the ability to apply the acquired knowledge. The details on the exam preparation and the required level of detail for all the topics will be illustrated during the lectures.

### Exam schedule

Date Time Location Type Notes
15/12/2021 09:30 GENOVA Scritto
17/01/2022 09:30 GENOVA Scritto
07/02/2022 09:30 GENOVA Scritto
04/05/2022 14:00 GENOVA Orale Appello straordinario riservato esclusivamente ai laureandi a.a. 2020/21
06/06/2022 09:30 GENOVA Scritto
20/06/2022 09:30 GENOVA Scritto
11/07/2022 09:30 GENOVA Scritto