Teaching plan for the course unit



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General information


Course unit name: Data Analysis

Course unit code: 364552

Academic year: 2021-2022

Coordinator: Vicente Royuela Mora

Department: Department of Econometrics, Statistics and Applied Economics

Credits: 6

Single program: S

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Estimated learning time

Total number of hours 150


Face-to-face and/or online activities



-  Lecture with practical component

Face-to-face and online




-  Problem-solving class

Face-to-face and online



Supervised project


Independent learning






Students should ideally have passed the subject Mathematics before taking this subject.



Competences to be gained during study



CB3 - Ability to gather and interpret relevant data (usually within the field of study) to inform judgements that include reflection on relevant social, scientific or ethical issues.


CG8 - Capacity to communicate in English and/or other foreign languages orally and in writing, comprehension skills, and mastery of specialized language.


CG1 - Commitment to ethical practice (critical and self-critical skills and attitudes that comply with ethical and deontological principles).


CG3 - Capacity for learning and responsibility (capacity for analysis, synthesis, to adopt global perspectove and to apply knowledge in practice).


CG2 - Ability to detect inequalities between people and to design, implement and evaluate policies that facilitate the erradication of discrimination on these grounds on companies and institutions.


CG10 - Capacity to apply ICTs to professional activities.


CE9 - Ability to use quantitative methods to solve real problems in different business areas.

Learning objectives


Referring to knowledge

The course provides students with the knowledge of statistical techniques required to tackle decision-making problems in professional environments. Specifically, students learn to:

— Identify what types of information can be analysed using statistical analysis techniques.

— Identify the appropriate statistical techniques for summarising information from empirical data and facilitating the decision-making process in business contexts.

— Calculate and correctly interpret the results of descriptive analyses, taking into account possible limitations.


Referring to abilities, skills

— Manage data using frequency tables, choose the most appropriate graphical representation in each case, and summarise data using the relevant descriptive statistics.

— Understand basic statistical calculations and the corresponding software (Excel, R).



Teaching blocks


1. What is statistics?

1.1. Aims of statistics

1.2. Descriptive and inferential statistics

1.3. Population and sample

1.4. Data: classification and measurement scales

2. Frequency distributions

2.1. Frequency distribution

2.2. Graphical representations

3. Measures of position

3.1. Average, median and mode

3.2. Properties of the mean

3.3. Measures of location: quantiles

4. Measures of dispersion and shape

4.1. Dispersion: boxplot

4.2. Variance and standard deviation: properties

4.3. Linear transformation: standardised variable

5. Measures of inequality

5.1. Inequality vs. dispersion

5.2. The Lorenz curve and the Gini index

6. Bivariate data analysis

6.1. Joint frequency distributions; Marginal distributions

6.2. Conditional distributions

6.3. Statistical independence

6.4. Association between attributes

7. Association between variables

7.1. Scatter plot

7.2. Linear association: covariance

7.3. Pearson correlation coefficient

7.4. Linear regression

8. Time series analysis and index numbers

8.1. Definition of a time series

8.2. Time series plots

8.3. Index numbers: definition, calculation and interpretation

8.4. Weighted index numbers: Laspeyres and Paasche

8.5. Trend, cyclical, seasonal and irregular components of a time series

8.6. Aggregation schemes: additive vs. multiplication

8.7. Introduction to forecasting methods



Teaching methods and general organization


This subject combines lectures and problem-solving sessions in subgroups, which are organised as follows:

— Theoretical and practical content, which on average accounts for 30 hours. In these sessions students need to achieve the general and specific learning objectives.

— Problem-solving sessions and computer room sessions. These sessions take 15 hours in total. Students work through exercises, usually in the computer room. These sessions are designed to develop the students’ problem-solving skills and agility, in particular by using IT technologies.

The Virtual Campus is intended to facilitate communication between students and the teaching staff. All administrative and academic information for this course unit will be published on the Virtual Campus: the course plan, a schedule of activities, the list of exercises for problem-solving sessions, and a list of additional exercises with model solutions.



Official assessment of learning outcomes


Continuous assessment

Continuous assessment is carried out through:

— A range of assessed activities completed throughout the course, including two classroom tests announced in advance (20%) and practical sessions in class (20%).

— A final examination covering all of the course content, on a date established by the Academic Council (60%). The exam covers the whole course by means of open questions, covering both theoretical and practical aspects by developing applied exercises.

Students who obtain a final examination mark below 4 out of 10 are awarded a grade of “Fail”. Students must obtain a final grade of 5 out of 10 or higher to pass the subject.

Students must attend at least 80% of class activities to be eligible to pass the continuous assessment option.

The repeat assessment exam has the same structure as the single assessment exam.


Examination-based assessment

Students who request single assessment take an examination covering all of the course content (also considering practical issues covered in practical labs with statistical software), on the date established by the Academic Council. The mark obtained in the examination is the final grade awarded for the subject.

To withdraw from continuous assessment, students must opt for single assessment on the day of the final exam.



Reading and study resources

Consulteu la disponibilitat a CERCABIB


GONICK, L., SMITH, W. The cartoon guide to statistics. New York : HarperCollins, 1993

Catāleg UB  Enllaç

LIND, DA., MARCHAL, G., WATHEN, S.A. Statistical techniques in business & economics. 15th ed. New York, NY : McGraw-Hill/Irwin, 2018

Catāleg UB  Enllaç
Versiķ en línia. Castellā (2017)  Enllaç

MOORE, D.S., NOTZ, W.I., FLIGNER, M.A. The basic practice of statistics. 6th ed. New York : W.H. Freeman and Co., 2013

Catāleg UB  Enllaç
Catāleg UB (6a ed., International ed.)  Enllaç

NEWBOLD, P., CARLSON, W., THORNE, B.M. Statistics for business and economics. 8th ed. Harlow, Essex : Pearson Education, 2013

Catāleg UB  Enllaç