STATISTICS 1

iten
Code
60083
ACADEMIC YEAR
2021/2022
CREDITS
9 credits during the 2nd year of 8697 Business Administration (L-18) GENOVA

9 credits during the 2nd year of 8698 Maritime, Logistics and Transport Economics and Business (L-18) GENOVA

9 credits during the 2nd year of 8699 Economics (L-33) GENOVA

SCIENTIFIC DISCIPLINARY SECTOR
SECS-S/01
LANGUAGE
Italian
TEACHING LOCATION
GENOVA (Business Administration)
semester
2° Semester
sectioning
This unit is divided into 3 sections:
Prerequisites
Prerequisites
You can take the exam for this unit if you passed the following exam(s):
  • Business Administration 8697 (coorte 2020/2021)
  • CALCULUS FOR UNDERGRADUATED STUDENTS. 41138
  • Maritime, Logistics and Transport Economics and Business 8698 (coorte 2020/2021)
  • CALCULUS FOR UNDERGRADUATED STUDENTS. 41138
  • Economics 8699 (coorte 2020/2021)
  • CALCULUS FOR UNDERGRADUATED STUDENTS. 41138
Prerequisites (for future units)
This unit is a prerequisite for:
  • Economics 8699 (coorte 2020/2021)
  • ECONOMETRICS 24615
Teaching materials

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 representation; carry out basic descriptive analyses for one-dimensional phenomena; analyze the relationships between two or more phenomena with particular emphasis on dependence 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.

PREREQUISITES

The course requires knowledge of the basic contents of a course of General Mathematics for Business and 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. Index numbers
  7. Association measures for two variables
  8. Statistical independence
  9. Association between qualitative variables.
  10. Correlation between quantitative variables
  11. 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.

RECOMMENDED READING/BIBLIOGRAPHY

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

TEACHERS AND EXAM BOARD

Office hours: Tuesday 16.30-18.00

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.

Class schedule

STATISTICS 1 B

EXAMS

EXAM DESCRIPTION

The . Because of the COVID 19 pandemics exam rules could change 

 

L'esame consiste in una prova scritta. A seguito della pandemia COVID 19 le modalità d'esame potrebbero cambiare in relazione ai regolamenti di Ateneo. Il regolamento d'esame è pubblicato sulla pagina Aulaweb del corso.

ASSESSMENT METHODS

The written examination consists of:

1) multiple-choice questions of a theoretical nature.

2) theoretical questions with open answers

3) exercises

The questions and exercises 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.

Exam schedule

Date Time Location Type Notes

FURTHER INFORMATION

Attendance

Optional - 6 lecture hours/week in Italian