ECONOMIC AND BUSINESS DATA ANALYSIS

Integrated educational activity

Course Sheet

Academic Year of enrolment:

2021/2022

Optional/Mandatory:

Compulsory

Degree:

Bachelor's Degree Programme in ECONOMICS AND MANAGEMENT

Location:

SASSARI

Language:

Italian

Moduls:

ECONOMIC AND BUSINESS DATA ANALYSIS - MOD.I

5 credits

SASSARI
ECONOMIC AND BUSINESS DATA ANALYSIS - MOD.II

4 credits

SASSARI

Credits:

Programme Year:

Hours of classroom activity:

Prerequisites:

Basic knowledge of Mathematics
Basic knowledge of Descriptive Statistics and Inferential Analysis

AF code:

A000992

Assessment Methods

Verification of learning:

The exam consists of one written examination. It will take 2 hours and will be carried out in the computer lab. It aims at verifying the knowledge and comprehension of both the theoretical concepts introduced in the course and the practical ability to carry out statistical analysis using the electronic spreadsheet.
The theoretical part consists of 3 multiple-choice questions (only one right answer) without penalty applied for wrong answers (total score is 6). The practical part of examination consists of 4 exercises to solve by the means of the spreadsheet (total score is 24).

- FACE-TO-FACE EXAMINATION
The exam will be carried out in the computer lab
- DISTANCE EXAMINATION
The exam will take place remotely via the Microsoft Teams platform.
It is articulated in the same manner to that prepared for an examination in presence.
The written test will be made available on a single Excel file; this file will be delivered via the moodle platform at the end of the exam.

Teaching period

First semester

Objectives

Provided that statistical analysis based on sample data has an important role in supporting economic and financial decisions, this course has three main learning outcomes: (1) improving knowledge on Inferential Analysis previously acquired in the course of Statistics; (2) providing ability to apply basic statistical tools for the study of quantitative economic and business phenomena; (3) learning how to use the spreadsheet for statistical analysis.
At the end of the course the student will have acquired:
1. KNOWLEDGE AND ABILITY TO UNDERSTAND: knowledge on the main elements of Inferential Analysis, ability to understand the appropriate statistical tools for the analysis of the economic phenomenon in hands, practical ability to use the spreadsheet for the statistical analysis.
2. ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING: ability to recognize and apply the basic tools to carry out the study of the phenomenon, to analyse and interpret the results of the statistical analysis, ability on how to use the spreadsheet for statistical analysis.
3. SELF JUDGEMENT: ability to recognize whether the statistical method applied is used appropriately for the study of the phenomenon.
4. COMMUNICATION SKILLS: ability to present methods and results of a statistical analysis in a precise and rigorous way.
5. LEARNING SKILLS: together with a course in Statistics it encourages in-depth study of the statistical methodologies proposed in the context of advanced specialized courses.

Contents

1. Discrete and continuous Random Variables. The Probability Density Function. The Cumulative Distribution Function. Expectation and Variance of a Random Variables. The Normal Distribution. The Standard Normal Distribution. The Normal Probability Plot. The Uniform Random Variable. The Exponential Random Variable. The Bivariate Probability Distribution Function.
2. A random sample and sampling distribution functions. Distribution of the sample mean. Distribution of the sample variance.
3. Estimators, estimates. Properties of estimators.
4. Confidence Intervals for the mean with Normal probability distribution. Confidence Intervals for the difference of means with Normal probability distribution (indipendent random sample or dependent sample).
5. Statistical Hypotheses for mean. Statistical Hypotheses for the difference of means. Test for the variance with Normal distribution function. Test for equal variances with Normal distribution functions.
6. Classical linear regression model. Correlation analysis. Least squares Method. Inferential analysis on the coefficients of the linear regression model. Errors distribution analysis.
7. Multiple regression model. Ordinary least squares method. Goodness of Fit. Inferential analysis: parameters, least squares estimates and estimators, standard assumptions for the linear regression model, hypothesis tests on the coefficients. Multicollinearity. Specification Analysis.
8. Regression Model with dummy variables.

Recommended Bibliography

P. Newbold, W.L. Carlson, B. Thorne, Statistica, Pearson University.
Other class materials will be provided during the course.

Teaching Methods

Face-to-face lessons (54 hours).
Laboratory sessions (18 hours), aimed at a practical application of the principles and methodologies learned in frontal lessons by means of the spreadsheet.

Contacts/More Information

1) Readiness to offer individual assistance (e.g. explanations during office hours) also in English to incoming students.
2) Availability of supporting material and bibliographic references in English.
3) Readiness to accept examination of incoming students also in English.

Office hours are posted on www.edisea.uniss.it.

Contacts:
Massimo Esposito - email: mesposito@uniss.it -
phone. 079/213039

ECONOMIC AND BUSINESS DATA ANALYSIS

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ECONOMIC AND BUSINESS DATA ANALYSIS