General information

Subject type: Basic

Coordinator: Jesus Ezequiel Martínez Marín

Trimester: Second term

Credits: 6

Teaching staff: 

Alfredo Smilges Gaffe

Teaching languages

  • Catalan
  • Spanish

There may be materials in Spanish, English and Catalan.


Specific skills
  • Select and use quantitative instruments for decision making and contrasting economic hypotheses


Ability to recognize, become familiar with and use statistical techniques when making decisions. All this approach will make compatible the ability to manually calculate the various tools for a small set of data with the ability to use and analyze and interpret the outputs of statistical software. Some questions that will be solved throughout the course with which we will know: How do we know which company pays a higher salary to its employees? Are men's or women's salaries more dispersed? What is the relationship between the reservations of a boat and the occupation that ultimately exists? Can we know the profitability of a year of education? How much does the price of a home in the port area of ​​Barcelona increase by increasing its area by one square meter? Is the above data statistically significant? How can we know if the price of one logistics company is statistically different from that of another?


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.


Learning outcomes

Be able to describe the distribution of a variable at all levels: centralization, dispersion, symmetry and kurtosis (such as the wages paid to its workers by a transport or logistics company)

Identify the strength of the association between variables.

Differentiate between correlation and causality

Quantify the causal relationship between two variables such as employment and reserves

Identify and contrast various statistical hypotheses (means, proportions, independence of attributes, ANOVA, coefficients of a regression model)


Working methodology

In the face-to-face sessions with the whole group, theory sessions will be combined with hand-held exercise resolution sessions and software, using case studies with data from real companies.

In the practical sessions students will work on exercises and databases with the Minitab / SPSS / R program (to be determined). You will need to bring a laptop to the classroom.

There will be a conference by a professional who uses Statistics applied to logistics or maritime business.


Topic 1



  • Basic concepts: population, sample, sampling type, variables, variable types, data and data types

Topic 2

One-dimensional statistics

  • Centralization measures
  • Dispersion measures
  • Symmetry measurements
  • Measures of kurtosis
  • Case 1: transport_rrhh company

Topic 3

Two-dimensional statistics

  • Association of qualitative variables
  • Association of quantitative variables: linear regression
  • Case 2: relationship between sales and advertising


Topic 4


Probability and Inference

  • Probability and probability distributions.
  • The normal distribution
  • Statistical inference
  • Sample distributions
  • Confidence intervals and contrasts of individual hypotheses: mean test, proportions, attribute independence, ANOVA ...
  • Hypothesis contrasts applied to the simple and multiple linear regression model


Learning activities

Exercise Descriptive statistics. Individual (Inside and outside the classroom)

Descriptive statistical test. Individual (Inside and outside the classroom). Virtual

Exercise association between quantitative variables. Individual (Inside and outside the classroom)

Exercise association between quantitative variables. Individual (Inside and outside the classroom)

Association test between quantitative and qualitative variables. (In and out of the classroom). Virtual

Statistical inference exercise. Individual (Inside and outside the classroom)

Statistical inference exercise applied to regression. Individual (Inside and outside the classroom)

Work with computer software. Group (Inside and shape of the classroom)

Exam. Individual

Evaluation system

The quarterly evaluation will take into account the following aspects with the weights indicated:
- Final term exam: 60%
- Group work with database: 30%
- Attendance, participation and behavior in class. Delivery of exercises and proposed practices: 10% 



RAYA, J. (2012): Statistics applied to business and marketing. Prentice Hall

SPIEGELHALTER, D. (2019): The art of statistics: learning by data. Pelican

MOORE, Mc. CABE (2005), Introduction to the practice of Statistics. Freeman


NEWBOLD, PAUL, Carlson, W., Thorne, W. (2007), Statistics for Business and the Economy, 6th edition, Madrid, Prentice Hall