General information

Subject type: Mandatory

Coordinator: Núria Masferrer Llabinés

Trimester: Second term

Credits: 4

Teaching staff: 

Jose Ignacio Monreal Galán
Albert Prades Colomé 

Teaching languages

  • Catalan
  • Spanish
  • English

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.


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.

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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