# 00914 - Statistics

### Course Unit Page

• Teacher Michele Costa

• Credits 8

• SSD SECS-S/01

• Language Italian

• Campus of Bologna

• Degree Programme First cycle degree programme (L) in Economics, Markets and Institutions (cod. 8038)

### SDGs

This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.    ## Learning outcomes

The course provides an introduction to the main methods and basic tools for quantitative analysis of collective phenomena, with the double aim to develop the ability of interpretation and critical evaluation of statistical information and to make students able to produce and autonomously analyze statistical data.

At the end of the course students:

know the basic elements of descriptive and inferential statistics;

are able to read and to understand articles on magazines and/or specialized journals containing results of quantitative analyses;

are able to criticize syntheses and statistical elaborations from total or sampling surveys;

have the necessary skills to autonomously apply some of the tools proper of the statistical methodology for the description and the quantitative analysis of economic and social phenomena.

## Course contents

1. Descriptive statistics

Absolute, relative and cumulative frequencies. Density and histogram for classified variables. Cumulative distribution functions.

Mode, arithmetic mean, median and quantiles.

Standard deviation and variance. Skewness and kurtosis indexes.

Bivariate descriptive analysis. Statistical association. Covariance and correlation coefficient. Linear dependence and independence. The simple linear regression model.

2. Introduction to probability

Random experiment and space of events. Operations with events. Conditional probability and independence of events.

Law of total probability. Bayes' Theorem. Central limit theorem.

Random variables and probability distribution of discrete random variables and density function of absolutely continuous random variables. Expectation and variance.

Some distributions: binomial, Normal, Chi-Squared, t-Student, Fisher.

3. Inferential statistics

Sample statistics, estimators and their probability distributions. Properties of estimators.

The sample mean. The sample variance.

Point estimate for the mean (in case of known and unknown variance)

Hypothesis tests for the linear model.

Borra S., Di Ciaccio A. (2020). Statistica, metodologie per le scienze economiche e sociali, McGraw-Hill.

Making sense of data through statistics, Dorit Nevo, Legerity press

## Teaching methods

Lectures followed by in-class tutorials.

## Assessment methods

Written exam consisting in exercises, multiple-choice items and open-ended items, aiming at the evaluation of the following educational targets:

- knowledge of the statistical methods taught during the course;

- ability to use these methods in order to analyze and to interpret economic and financial variables.

<18 failed
18-24 sufficient
25-29 good
30 e lode excellent

The final grade of the integrated course is the weighted average of Statistics (weight 2/3) and of the Lab (weight 1/3).

## Teaching tools

Notes, problems and solutions, cases study and exampleson https://virtuale.unibo.it/

## Office hours

See the website of Michele Costa