TecnoCampus Foundation
eCampus
University Business
eCampus

What are you looking for?

General information

Subject type: Mandatory

Coordinator: Valeria Bernardo

Trimester: Second term

Credits: 6

Teaching staff: 

Catherine Llaneza Hesse

Academic year: 2026

Teaching course: 2

Languages ​​of instruction

  • Spanish

Competencies / Learning Outcomes

Specific skills

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

Presentation of the subject

From descriptive and inferential statistics, we seek to answer questions such as what is the profitability of education in terms of income, and whether there are differences between sectors? What is the relationship between gender and working hours? Or how the type of merchandise, the distance to the port, and other risks related to the geographical location, determine the price of a transport service?

The course will cover different descriptive statistics techniques and the basic concepts of statistical inference; from data visualization and distribution analysis, to testing statistical hypotheses using confidence intervals and simple and multiple linear regression models. In summary,

You will learn to identify the appropriate statistical tool based on the data to be analyzed, use it, and interpret the results obtained. Data analysis will be done both manually, for a better understanding of the process, and with the use of statistical software, to be able to work with larger databases.

The classroom (physical or virtual) is a safe space, free of sexist, racist, homophobic, transphobic and discriminatory attitudes, either towards students or teachers. We trust that together we can create a safe space where we can make mistakes and learn without having to suffer the prejudices of others.

Contents

Chapter 1: Introduction and basic concepts
What are statistics? What are they used for?
The concepts of population, sample and variable.
Types of variables.

Chapter 2: Unidimensional Statistics
Frequency tables.
Graphical representation of a variable.
Measures of centralization and distribution (dispersion, symmetry and kurtosis).

Chapter 3: Two-dimensional statistics
Association of qualitative variables: graphs, contingency tables.
Association of quantitative variables: graphs, correlation coefficient and linear regression.

Chapter 4: Probability and inference
Probability and probability distributions.
Normal distribution.
Confidence intervals and hypothesis contrasts: mean test and ANOVA.
Hypothesis testing applied to the simple and multiple linear regression model.

Activities and evaluation system

Final exam

 45% (or 60%)

Seminars (Preparation for teamwork)

 25% (5% each)

Team work

 5%
Review tests  15% (or 0%)
Participation and deliveries in class 5%

 

  • A final grade of over 5,0 (out of 10) and an exam grade of over 4,0 (out of 10) are required to pass the course. If you do not meet both requirements, you will have to take the retake exam.
  • The 3 tests can only be taken once and on the dates set. In the event that the 15% that corresponds to the average of the 3 tests has a negative influence on the evaluation of the subject, they will not be included in the evaluation and the exam will be worth 65% instead of 50%. On the other hand, if you do not attend any of the tests, a zero will be assigned (regardless of the reason for the absence).
  • The 50% (or 35%) of the continuous assessment will not be modified in any case.

Any form of academic fraud will be sanctioned in accordance with the center's assessment regulations. If signs of fraud are detected, including the improper use of generative artificial intelligence tools, the subject's teaching staff may call the student for an individual interview with the aim of verifying their authorship.

Bibliography

Basic

Moore, DS, McCabe, GP, & Craig, BA (2012). Introduction to the practice of statistics (7th ed., international ed.). WH Freeman.