Teaching plan for the course unit

 

 

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General information

 

Course unit name: Statistics

Course unit code: 364565

Academic year: 2025-2026

Coordinator: Maria Carme Riera I Prunera

Department: Department of Econometrics, Statistics and Applied Economics

Credits: 6

Single program: S

 

 

Estimated learning time

Total number of hours 150

 

Face-to-face and/or online activities

60

 

-  Lecture with practical component

Face-to-face

 

45

 

-  Problem-solving class

Face-to-face

 

15

Supervised project

40

Independent learning

50

 

 

Recommendations

 

Given the content of the subject, it is highly recommended that students have previously passed the subjects Data Analysis and Mathematics.

 

 

Competences / Learning outcomes to be gained during study

 

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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.

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CG5 - Ability to work in a team (capacity to collaborate with others and contribute to a common project, capacity to work in cross-disciplinary and multicultural teams).

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

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

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

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CG10 - Capacity to apply ICTs to professional activities.

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

Learning objectives

 

Referring to knowledge

The course provides an introduction to statistical inference techniques, which aid the decision-making processes in professional business environments. After completing the course, students should be able to:

 

— Describe why sampling is important and explain the difference between descriptive and inferential statistics.

— Distinguish between a point estimate and a confidence interval estimate and create confidence interval estimates for different parameters.

— Identify the estimators’ properties.

— Understand hypothesis-testing methodologies with the aim of verifying the coherence of a previous statement regarding the behaviour of a population based on available sample information.

 

Referring to abilities, skills

— Acquire the capacity to use statistical inference tools for decision-making in theoretical and real situations.

— Apply the knowledge obtained in class to solve real-life problems.

— Acquire the knowledge and understanding of basic statistical calculations and the appropriate tools.

 

 

Teaching blocks

 

1. Probability

1.1. Random experiment and basic grounds of probability

1.2. Conditioned probability and statistical independence

1.3. Bayes’ theorem

2. Distribution models for random variables

2.1. Distribution models for discrete and continuous random variables

2.2. Distributions derived from normal distribution

2.3. Central limit theorem

3. Sampling

3.1. Random sample and sample statistics

3.2. Sampling distributions

4. Point estimation

4.1. Introduction to the estimation process

4.2. Properties of point estimates

4.3. Methods of point estimation

5. Interval estimation

5.1. Definition of confidence intervals

5.2. Confidence intervals for the mean and for the difference between means

5.3. Confidence intervals for the proportion and for the difference between proportions

5.4. Confidence intervals for the variance

5.5. Choosing sample size

6. Hypothesis testing

6.1. Decision rule

6.2. Types of errors; power of a test

6.3. Test hypothesis for the mean and for the difference between means

6.4. Test hypothesis for the variance and for the equality of variances

6.5. Test hypothesis for the proportion and for the difference between proportions

7. Chi-square tests and some non-parametric tests

7.1. Goodness of fit tests

7.2. Association tests

7.3. Runs, ranks and signs tests

 

 

Teaching methods and general organization

 

Given the nature of this subject, meeting the learning objectives requires a significant amount of practical work. The teaching methodology therefore combines theoretical and practical content, with a focus on real-world applications.

The course is delivered through a combination of lectures and problem-solving sessions, organised as follows:

— Two 2-hour lectures per week. The lecturer presents the theoretical and practical material needed for students to meet both the general and specific learning objectives.

— Problem-solving within lectures. After the theoretical explanation, part of the lecture is devoted to working through exercises in small groups. These sessions are designed to develop students’ problem-solving skills.

The Virtual Campus supports communication between students and teaching staff. It hosts all administrative and academic information for the subject: the course plan, activity schedule, exercise lists for problem-solving sessions, and additional exercises with model solutions.

The main activities throughout the course include:

— On-campus learning activities: carried out in the classroom with the lecturer. They comprise both theoretical content and theory with a more practical component.

The total number of hours for these activities is 60. To support these activities, students have access to lecture materials (theoretical and practical) on the Virtual Campus a week before the start of the course.


— Independent learning activities: including problem-solving exercises. At the end of each unit, students must submit a set of 10 exercises applying the theoretical knowledge acquired. The total number of hours for these activities is 50.


— Tutored/directed activities: the discussion of problems, resolving doubts and monitoring students’ independent work. The total number of hours for these activities is 40.

These activities are designed to help students acquire the competences needed to interpret economic, business and sociological information, critically analyse statistical data, and become familiar with the different tools available for doing so.
In addition, independent learning strengthens students’ ICT skills, which are essential for their future professional development.

 

 

Official assessment of learning outcomes

 

To pass the continuous mode of assessment, students must demonstrate that they have acquired the requisite knowledge and competences by meeting the minimum requirements established for both for the compulsory activities and the final examination (see criteria below). The latter can be sat at any of the official examination sessions.

Students may opt for the single mode of assessment up to the last day of class.


Continuous assessment consists of two parts:

— A series of assessed activities throughout the course, including four scheduled in-class tests (30%) and ten regular exercises (10%).

— Team project: students work collaboratively throughout the semester to analyse problems or situations and make final decisions. The project is assessed by both peers and the lecturer. Projects are presented in class and the best receive bonus marks.

Students who obtain a grade lower than 3.5 out of 10 on the continuous assessment are automatically transferred to the single mode of assessment.

— A final examination covering the entire syllabus, to be taken on the date established for that purpose by the Academic Board (60%).

All activities must be completed by the scheduled date and in the designated place. All of the assessed activities completed during the course (continuous assessment activities and final examination) are graded out of 10. Students pass the subject if they achieve a final grade of at least 5 out of 10. However, any student who obtains less than 3.5/10 on the final examination is no longer deemed eligible for continuous assessment and is awarded this mark as their final grade for the subject.

Students must obtain a final grade of 5 out of 10 or higher to pass the subject.

 

 

Examination-based assessment

Students who are unable to meet the requirements for continuous assessment and those who opt for this mode of evaluation are required to sit an examination covering the whole of the syllabus (including practical components issues), on the date established for that purpose by the Academic Board. The mark obtained on this examination constitutes the final grade for the subject.

The written examination assesses both theoretical knowledge and practical applications. Students must demonstrate the knowledge, skills and competences acquired during the course.

To pass the single mode of assessment, students are required to:
a) Submit a written request waiving their right to continuous assessment. The deadline for this request is the day of the final examination.
b) Obtain a minimum mark of 5 out of 10 on the final examination during one of the official examination sessions.

 

Repeat assessment

Students who do not pass either the continuous or single modes of assessment are entitled to a repeat assessment. This takes the form of a final examination covering the contents, skills and competences set out in the course plan.

To pass the repeat assessment exam, students must obtain a minimum mark of 5 out of 10.

In this case, assessment of the subject is based entirely on a single examination worth 100% of the final grade. The exam is to be taken during the official examination session established for that purpose by the Academic Board.

 

 

Reading and study resources

Check availability in Cercabib

Book

GONICK, Larry;  SMITH, Wollcott. The cartoon guide to statistics. New York : HarperCollins, 1993

Catāleg UB  Enllaç

LIND, Douglas A.,  MARCHAL, William G., WATHEN, Samuel A. Statistical techniques in business & economics. 16th ed. New York, NY : McGraw-Hill/Irwin, 2018

Catāleg UB  Enllaç

MOORE, David S., McCABE, George P., CRAIG, Bruce A. Introduction to the practice of statistics. 8th ed. New York, NY : W.H. Freeman, 2014

Catāleg UB  Enllaç

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

Catāleg UB  Enllaç

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

Versiķ en línia (9th ed., 2019)  Enllaç