General information

Subject type: Mandatory

Coordinator: Alex Araujo Batlle

Trimester: Third term

Credits: 4

Teaching staff: 

Marta Martínez Egea


Basic skills
  • CB1. That students have demonstrated knowledge and understanding in a field of study that is based on general secondary education, and is usually found at a level that, while supported by advanced textbooks, also includes some aspects. involving knowledge from the forefront of their field of study.

  • CB3. That students have the ability to gather and interpret relevant data (usually within their area of ​​study) to make judgments that include reflection on relevant social, scientific, or ethical issues.

  • CB5. That students have developed those learning skills necessary to undertake further studies with a high degree of autonomy.

Specific skills
  • CE9. Use mathematical tools and advanced statistical tools for decision making.

General competencies
  • CG1. Be able to work in a team, actively participate in tasks and negotiate in the face of dissenting opinions until reaching consensus positions, thus acquiring the ability to learn together with other team members and create new knowledge.

  • CG2. Be able to innovate by developing an open attitude towards change and be willing to re-evaluate old mental models that limit thinking.

Transversal competences
  • CT1. Communicate properly orally and in writing in the two official languages ​​of Catalonia.

  • CT2. Show willingness to learn about new cultures, experiment with new methodologies and encourage international exchange.

  • CT3. Show willingness to learn about new cultures, experiment with new methodologies and encourage international exchange.

  • CT4. Master computer tools and their main applications for ordinary academic and professional activity.

  • CT5. Develop tasks applying the acquired knowledge with flexibility and creativity and adapting them to new contexts and situations.


The subject "Statistical Inference for Business Management" is a continuation of the subject "Fundamentals of Statistics and Data Analysis", which students have previously taken. The subject wants to establish in the student a solid theoretical knowledge on the matter, as well as affect the capacity of his practical application in the study of the real world, especially in the economic field.

In particular, in this subject the basic concepts of statistical inference will be addressed, starting with the sample distributions of mean and proportion, univariate data modeling, confidence intervals and hypothesis contrasts. In addition, the most elementary comparison contrasts lead to the study of single and multiple linear regression.

It is therefore an instrumental subject that provides statistical tools that are used in different contexts. In addition, the role of computers in facilitating the study of databases should be highlighted.

Learning outcomes

  • Master the mathematical and statistical aspects of economic and / or business problems for decision making.
  • Understand and apply the basic concepts of probability and statistical inference, basic statistical calculations and the computer tools that facilitate them. 

Working methodology

Theoretical sessions

MD1.Master class: Expository class sessions based on the teacher's explanation attended by all students enrolled in the subject

MD3. Presentations: Multimedia formats that support face-to-face classes.

Autonomous learning

MD4. Video capsules: Resource in video format, which includes contents or demonstrations of the thematic axes of the subjects. These capsules are integrated into the structure of the subject and serve students to review as many times as necessary the ideas or proposals that the teacher needs to highlight from their classes.

MD9. Solving exercises and problems: Non-contact activity dedicated to the resolution of practical exercises based on the data provided by the teacher

MD11. Non-contact tutorials: for which the student will have telematic resources such as e-mail and ESCSET intranet resources

The face-to-face sessions will combine theory and exercise sessions with practice sessions aimed at the application of the concepts worked on. In the practical sessions, data files will be used that will be treated with the appropriate software (Excel, R, Gretl spreadsheet, etc.), with the aim that the student can apply the appropriate statistical methodology autonomously. You will be notified in which class sessions the use of a computer is mandatory.

This subject has methodological and digital resources to make possible its continuity in non-contact mode in the case of being necessary for reasons related to the Covid-19. In this way, the achievement of the same knowledge and skills that are specified in this teaching plan will be ensured.

The Tecnocampus will make available to teachers and students the digital tools needed to carry out the course, as well as guides and recommendations that facilitate adaptation to the non-contact mode.


1. Introduction to statistical inference

Concept of sample, population, statistic and parameter.

Population and sample distributions

Binomial and Normal distributions.

The t-Student distribution.



2. Punctual estimation of population parameters. Confidence intervals of population parameters. The sample size

Distribution of the sample mean, the sample proportion and the sum or difference of sample means.

The Central Limit Theorem.

Estimator concept: Robustness, bias and efficiency of an estimator.

Punctual estimation of the population average.

Punctual estimation of the variance and population standard deviation.

Punctual estimation of the population proportion.

Standard error.

Estimation by interval. Confidence level. Estimation error.

Confidence intervals of population mean, population proportion, difference in population averages, and difference in population proportions

Relationship between sample size and estimation error.

Calculation of sample size to estimate the mean or population proportion.


3. Contrast of statistical hypotheses

Concepts of null hypothesis and alternative hypothesis. Significance level, Type I error (alpha), Type II error (beta). P-value. Critical value. Zero hypothesis rejection zone.

Contrast of the population average.

Contrast of the population proportion.

Contrast the difference in population means for independent samples.

Contrast the difference in population proportions for independent samples


4. Design of experiments: Analysis of the variance to a factor iTing Contingency tables

Comparison of more than two population averages.

Analysis of variance (ANOVA).

Fisher-Snedecor F distribution.

Attribute independence test.

The Ji-Square distribution.


5. Introduction to the analysis of Linear Regression Models (single and multiple)

The Simple Linear Regression Model: Preliminary hypotheses, inferences on the slope and the ordinate at the origin.

Confidence intervals for coefficients, expected values, and new observations.

Analysis of variations. The coefficient of determination.

Introduction to the Multiple Linear Regression Model. Preliminary hypotheses. Individual meaning of the coefficients. Joint meaning of the model. The goodness of fit.

Learning activities

In general the structure of the week is as follows:

Classroom activities

Activities outside the classroom

  • 1 face-to-face theoretical-practical biweekly session.
  • 1 face-to-face bi-weekly internship seminar session with a final evaluable questionnaire.
  • 1 asynchronous non-contact biweekly theoretical-practical session.
  • 1 biweekly streaming practice seminar session with a final evaluable questionnaire.
  • Personal study, carrying out suggested exercises, reviewing notes, consulting didactic material (autonomous).
  • Carrying out a group work.
  • Carrying out individual work.
  • Completion of review questionnaires in Moodle.


Evaluation system

40% of the grade of the subject will correspond to the continuous evaluation during the course, from the participation and presentation of works.

60% of the grade of the subject will correspond to an exam at the end of the term, where the student will have to obtain a certain minimum grade out of 10 to be able to accumulate the grade of the continuous assessment.

To pass the subject it is necessary that the weighted average mark is greater than or equal to 5.

If the student does not pass the course, he / she will be able to opt for a recovery of the final exam (60% of the total mark) in the period indicated in the academic calendar, with the condition of obtaining a minimum of 5 points out of 10 for to be able to accumulate the qualification of the continuous evaluation. There is no recovery of the activities carried out in the continuous evaluation.


Note on online seminars and questionnaires


Individual work + group work


Final exam


A student who has not applied for the first call CANNOT apply for recovery.



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