Universidad de Deusto - Bilbao
Area of Study
Taught In English
College level math
Recommended U.S. Semester Credits3
Recommended U.S. Quarter Units6
Hours & Credits
The main goal of the course is to provide students with a set of competences for the understanding and application of statistical concepts and techniques in engineering disciplines. Students will learn to represent data information using tables, graphs and parameters in order to facilitate comprehension and decisions; they will be able to identify situations with random behavior and calculate probability of these phenomena. Besides, they will know, identify and classify random variables from different sources of information. Students will learn to identify and solve problems in which the variable under study follows a known probability distribution. They will elaborate, build up and validate statistical models suitable for real problems. They will make use of estimation and inference for studying the behavior of a population model from a sample of the population under study. Student will study two or more variables sets identifying independence and interdependence situations, and be able to assess the importance of statistics and its proper use in specific engineering problems.
- Represent data information using tables, graphs and parameters in order to facilitate
comprehension and decisions.
- Identify situations with random behaviour and calculate probability of these
- Know, identify and classify random variables from different sources of information.
- Identify and solve problems in which the variable under study follows a known
probability distribution. Elaborate,build up and validate statistical models suitable for
- Use of estimation and inference for studying the behaviour of a population model from
a sample of the population under study.
- Study two or more variables sets identifying independence and interdependence
- Assess the importance of statistics and its proper use in specific engineering problems.
Chapter 1. Descriptive statistics.
Type of data. Graphical and numerical methods for describing quantitative and qualitative data.
Dataset management for descriptive statistics applications.
Chapter 2. Probability calculation.
The concept of probability. Experiments and events. Set theory. Interpretations of probability.
Probability axioms. Study of simple probabilities. Independent events. Conditional probability.
Total Probability theorem. Bayes rule.
Chapter 3. Random variable.
Concept of one-dimensional random variable. Discrete random variables. Continuous random
variables. Uniform distribution. Distribution function. Transformed distribution. Measurements
of position. Measurements of dispersion. Centralization moments. Markov's and Chebyshev's
inequalities. Concept of multidimensional random variable.
Chapter 4. Models for Discrete variables.
Bernouilli distribution. Binomial distribution. Geometric distribution. Negative binomial
distribution. Hypergeometric distribution. Poisson distribution.
Chapter 5. Models for Continuous variables. Uniform distribution. Normal distribution. Central
Chapter 6. Introduction to statistical inference. Sampling theory.
Sample and population. Types of sampling. Statistic concept. Sample distributions. Sample
mean distribution. Corrected sample variance distribution. Statistics and distributions used for
comparison of normal variables.
Chapter 7. Parameters Estimation. Hypothesis testing.
Classical theory of parameters estimation. Point estimation. Confidence interval estimation.
Hypothesis testing. Classification of hypotheses. Parametrical hypothesis testing. Testing
process. Statistics for hypothesis testing: mean, mean difference, mean difference paired
samples, variance, variance quotient, proportion, proportion difference.
Chapter 8. Linear regression and correlation
Linear correlation. The simple linear regression model (SLR). Choice of a regression model.
- Lectures explaining the theoretical aspects
- Resolution of exercises and example problems
- Individual study of lectures material
- Undertaking of proposed exercises and revision
Subject assessment will be done by knowledge tests and also through exercises that must be
handled in during the course.
- Three continuous assessment tests will be done in class time. Each of them will have a value
of 20% of the overall mark. If the student passes them, the corresponding chapters will be
considered as passed, and it is not necessary to repeat the test of that block in the final exam.
- After finishing each unit, an activity or a battery of exercises will be proposed. When the
deadline is finished, the results and indications for auto evaluation will be uploaded to ALUD
(Deusto online platform). All the activities will be valued with a 20% of the final score.
- The final test will have four parts. The fourth one will be compulsory for all students, and will
have a value of 20% of the overall mark. The other three parts will have a value of 60% of the
overall mark, and will be done by those students who have not passed them during continuous
assessment tests done during the course.
Presentations, class notes and exercises statements at ALUD Statistic Course:
Probability and statistics for engineering and the sciences. Jay L. Devore
Introduction to probability and statistics for engineers and scientist. Ross, Sheldon M
Probability & Statistics for Engineers and Scientists. Pearson. R.E. Walpole
Courses and course hours of instruction are subject to change.
Eligibility for courses may be subject to a placement exam and/or pre-requisites.
Credits earned vary according to the policies of the students' home institutions. According to ISA policy and possible visa requirements, students must maintain full-time enrollment status, as determined by their home institutions, for the duration of the program.
Please note that some courses with locals have recommended prerequisite courses. It is the student's responsibility to consult any recommended prerequisites prior to enrolling in their course.