## ECONOMIC AND BUSINESS DATA ANALYSIS

- Basic knowledge of Mathematics

- Basic knowledge of Descriptive Statistics and Inferential Analysis

The exam may be sustained through two modalities:

I) The exam consists of one written examination.

Starting from the summer session (May -July) a student may sustain the complete exam (Modulo I + Modulo II).

The examination will take 1 hour and half and will be carried out in the computer lab. It aims to verify 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. In particular, the theoretical comprehension of the topics introduced in the course will be verified through 3 open-questions (with short answer) and 6 multiple-choice questions (only one right answer) without penalty applied for wrong answers (total score is 16); the practical part of examination consists in 3 exercises to solve by the means of the spreadsheet (total score is 16). Thus the minimal score for passing the exam (18 out of 30) cannot be obtained solving only the practical exercises or only answering to theoretical questions. If the final score is above 30, the student will receive honours.

II) The exam consists of two written examinations.

Starting from the winter session (December – February) a student may sustain the first partial exam concerns topics from the first part of the course (MODULO I). The examination will take 1 hour, consists of both theoretical and a practical part and will be carried out in the computer lab. In particular, it consists in 2 open-questions (with short answer) and 6 multiple-choice questions (only one right answer) without penalty applied for wrong answers (total score is 16); the practical part of examination consists in 2 exercises to solve by the means of the spreadsheet (total score is 16). The first partial test can be taken only in the winter session a.a. 2019/20 (maximum 2 attempts for the partial test).

Starting from the summer session (May – July) a student may sustain the second partial exam concerns topics from the second part of the course (MODULO II). The examination will take 45 minutes, consists of both theoretical and pratical part and will be carried out in the computer lab. In particular, it consists in 1 open-question (with short answer) and 4 multiple-choice questions (only one right answer) without penalty applied for wrong answers (total score is 16); the practical part of examination consists in 1 exercise with simple use of spreadsheet (total score is 16). The second partial test can be taken only in the summer session a.a. 2019/20 (maximum 2 attempts for the partial tests).

To pass the exam, the student is required to get a sufficient score in both examinations separately.

The final score of the exam is the average of the scores in the first and second examinations weighted by the corresponding ECFUs (European University Credits). If the average between the two examinations is more than 30, the student will receive honours.

If the student passes the first examination referred to the first part of the course (MODULO I), but then fails in the second one (MODULO II), then the student will have to repeat all the exam following the first procedure (note that the student has two attempts to pass the second test).

The only exception to this general rule is for Erasmus students who cannot attend to the second partial exam during the summer session as they are abroad. In this case it will be allowed to take the second test in the autumn session (maximum 1 partial test).

Distance exam modality - COVID-19 period: oral exam.

The course is structured into two parts (MODULO I – professor G. Ruiu / MODULO II – professor G. Gonano). 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 common on both parts: (1) improve knowledge on Inferential Analysis previously acquired in the course of Statistics; (2) provide ability to apply basic statistical tools for the study of quantitative economic and business phenomena; (3) learn 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.

1. 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.

8. Non linear regression model. Linear regression model with dummy variables.

9. Multicollinearity. Specification Analysis. Heteroscedasticity.

P. Newbold, W.L. Carlson, B. Thorne, Statistics for Business and Economics, Pearson University, 8th ed., 2013

Chapter 5: 5.1, 5.2, 5.3, 5.5, 5.6

Chapter 6: 6.1, 762, 6.4

Chapter 7: 7.1, 7.2, 7.3

Chapter 8: 8.1, 8.2, 8.4, 8.5

Chapter 9: 9.1, 9.2, 9.3, 9.5

Chapter 10: 10.1, 10.3, 10.4, 10.5

Chapter 11: All

Chapter 13: ALL

Chapter 14: 14.4, 14.5, 14.6

Didattic materials distributed on the on line platform

Frontal lessons (54 hours) and laboratory sessions (18 hours). The latters are aimed for a practical application of the principles and methodologies learned in frontal lessons by means of the spreadsheet.

1) Readiness to offer individual assistance 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:

- Gabriele Ruiu – email: gruiu@uniss.it – tel. 079/213039

- Giovanna Gonano – email: mggonano@uniss.it – tel. 079/213032