Public University of Navarre

CastellanoEuskara | Academic year: 2023/2024 | Previous academic years:  2022/2023 
Bachelor's degree in Computer Science at the Universidad Pública de Navarra
Course code: 240201 Subject title: STATISTICS
Credits: 6 Type of subject: Basic Year: 1 Period: 2º S
Department: Estadística, Informática y Matemáticas
SANTAFE RODRIGO, GUZMAN (Resp)   [Mentoring ]

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Module/Subject matter



General proficiencies

  • G8 Knowledge of basic and tecnological subjects to have the ability to learn new methods and theories, and versatility to adapt to new situations
  • G9 Problem solving proficiency with personal initiative, decision making, creativity and critical reasoning. Ability to elaborate and communicate knowledge, abilities and skills in computer engineering
  • T1 Analysis and synthesis ability
  • T3 Oral and written communication
  • T4 Problem solving
  • T8 Self-learning


Specific proficiencies

  • FB1 Ability to solve mathematical problems in engineering. Ability to apply theoretical knowledge on linear algebra, differential and integral calculus, numerical methods, numerical algorithms, statistics and optimization
  • FB3 Ability to understand and master the basic concepts of discrete matemathics, logic, algorithmics and computational complexity, and their applications to problem solving in engineering.


Learning outcomes

R1.- To perform descriptive statistical analysis of data sets in order to summarize effective and precise information when drafting
reports and statements and understand these analyses as an important application of databases in different fields of management and

R2.- To apply appropriate statistical treatments according to the nature of the statistical variables included in a database.

R3.- To use a statistical package for statistical data base processing and simulation of random phenomena.

R4.- To model problems in environment of uncertainty by assigning probability event, the calculation of the conditional probability and the use of independent events.

R5. - To recognize the main stochastic models, both discrete and continuous, together with general methods of probability that can adapt to new models not specifically listed.

R6.- To model stochastic relationships between variables.

R7.- To implement and understand the basis and scope of probability applications incomputer science for teh analysis of computational complexity, the methods of random
number generation, simulation techniques, coding methods in the transmission of information, the Internet topology, treatment of transmission errors, the evolution of certain
data structures and operation of communication networks.

R8.- Using statistical tools to adequately estimate the unknown parameters of statistical models posed in engineering by methods of point estimation and interval.

R9.- Learning statistical techniques to facilitate the process of decision making in uncertain environment: hypothesis testing



Methodology - Activity Attendance  Self-study
A-1 Theoretical clases 44  
A-2 Computer Labs 14  
A-3 Debates, group work, etc 1  
A-4 Monitoring proofs 8  
A-5 Lecture    
A-6 Self-study    75
A-7 Exam, evalutaion proofs 4  
A-8 Tutorial 4  
Total 75 75


Relationship between formative activities and proficiencies/learning outcomes

Proficiency Activities
G8 A-1, A-2, A-4, A-6, A-8
G9 A-1, A-2, A-4, A-6, A-8
FB1 A-1, A-2, A-4, A-6, A-7, A-8
FB3 A-1, A-2, A-4, A-6, A-8
T1 A-1, A-2, A-3, A-4, A-6, A-8
T3  A-1, A-2, A-3,  A-4, A-7
T4  A-2, A-4, A-7, A-8
T8 A-2, A-4, A-6







Weight (%) It allows
test resit
required grade
R4,R5,R6 Midterm evaluation 30 Yes  
R1,R2,R3,R4,R5,R6,R7,R8,R9 Second exam 60 Yes 5 out of 10
R1,R2,R3,R4,R5,R6,R7,R8,R9 Monitoring exercises 10 Yes  








  • Descriptive Statistics (Chap. 2)
  • General Probability and Random Variables (Chap. 3)
  • Univariate probability distribution (Chap. 4)
  • Sampling and sampling distribution (Chap. 6)
  • Point estimation and confidence intervals (Chap. 7-8)
  • Hypothesis testing (Chap. 9)
  • Introduction to modeling in statistics: anova (Chapter 11)

Descriptive statistics and statistical software -R- will be dealt with through the whole course.




 1.-Exploring Data

Displaying Qualitative and Quantitative Data

Measures of Location and Spread

Bivariate and Multivariate data


 2.- General Probability and Random Variables

Counting Techniques (a review)

Axiomatic Probability

Discrete and Continuous Random Variables


3.- Univariate probability distribution

Discrete and Continuous Univariate Probability Distributions


6 Sampling and Sampling Distributions




Sampling Distribution of the Sample Mean, Sample Variance and Sample Proportion

Sampling Distributions Associated with the Normal Distribution

7.- Point Estimation and Confidence Intervals

Properties of Point Estimators

Confidence Intervals


8.-Hypothesis Testing


Type I and Type II Errors

Power Function

Uniformly Most Powerful Test

p-Value or Critical Level

Tests of Significance


9. Introduction to modeling in statistics





Access the bibliography that your professor has requested from the Library.



Ugarte, M. D., Militino, A. F., Arnholt, A. T. (2016). Probability and Statistics with R. Second Edition. CRC Press/Chapman and Hall.




Devore, J. (2005) Applied statistics for engineers and scientists. Thomson