General information

Subject type: Mandatory

Coordinator: Núria Masferrer Llabinés

Trimester: Second term

Credits: 4

Teaching staff: 

Jose Ignacio Monreal Galán

Teaching languages

Check the schedules of the different groups to know the language of teaching classes. Although the material can be in any of the three languages.


Basic skills
  • B3_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


  • B5_That students have developed those learning skills necessary to undertake further studies with a high degree of autonomy

Specific skills
  • E9_Use mathematical tools and advanced statistical tools for decision making and contrasting various economic assumptions


General competencies
  • G1_Be able to work in a team, actively participating in tasks and negotiating dissenting opinions until reaching consensus positions, thus acquiring the ability to learn together with other team members and create new knowledge

Transversal competences
  • T1_Communicate properly orally and in writing in the official languages ​​of Catalonia

  • T4_Domain the computer tools and their main applications for the ordinary academic and professional activity


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

In the face-to-face sessions, theory sessions and exercises will be combined with practice sessions aimed at applying the concepts worked on. In the practical sessions, data files will be used that will be treated with the appropriate software (mainly Stata, although Excel, R, etc. could be used), with the aim of the student being able to apply the statistical methodology suitable autonomously. Computer use is mandatory in all sessions.

This subject has methodological and digital resources to make it possible to continue it in a non-face-to-face mode if necessary for reasons of force majeure. In this way, the achievement of the same knowledge and skills specified in this teaching plan will be ensured. EIn this case, the Tecnocampus will make available to teachers and students the digital tools necessary to carry out the subject, as well as guides and recommendations that facilitate adaptation to the non-face-to-face mode.

The classroom (physics or virtual) it is a safe, free space of attitudes sexists, racists, homophobic, transphobic i discriminatory, ja be towards the students or towards the faculty. we trust that among all and all we can create a space sure on ens can to err i to learn sense having to suffer prejudice others.


1. Introduction to statistical inference

Concept of sample, population, statistic and parameter.

Population and sample distributions

Binomial, Normal and t-Student distributions.



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 or proportions. The Central Limit Theorem.

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

Point estimate of the population mean, variance, standard deviation, and proportion. The 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 and calculation of sample size to estimate the population mean or 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 variance to one factor and Contingency Tables

Comparison of more than two population means: 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: interpretation of the slope, goodness of fit.

The Ordinary Multiple Linear Regression Model: Previous hypotheses. Inference about the model: joint significance of the model, individual significance of the coefficients. Goodness of fit: the coefficient of determination. Transformations on variables.

Learning activities

In general the structure of the week is as follows:

Classroom activities

Activities outside the classroom

  • In-person theoretical-practical sessions.
  • 7 face-to-face practice seminar sessions with an evaluable final questionnaire.
  • Personal study, carrying out suggested exercises, reviewing notes, consulting didactic material (autonomous).
  • Realization of an evaluable group work.
  • Realization of an evaluable individual work.
  • Realization of review questionnaires in Moodle that can be evaluated.


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 may choose to retake the final exam (60% of the total grade) in the period indicated in the academic calendar, with the condition of obtaining a certain minimum grade out of 10 to be able to accumulate the qualification of the continuous assessment. There is no recovery of the activities carried out in the continuous assessment.


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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