## Public University of Navarre

Academic year: 2023/2024 | Previous academic years:  2022/2023  |  2021/2022  |  2020/2021  |  2019/2020
 Course code: 250201 Subject title: STATISTICS Credits: 6 Type of subject: Basic Year: 1 Period: 2º S Department: Estadística, Informática y Matemáticas Lecturers: SANTAFE RODRIGO, GUZMAN (Resp)   [Mentoring ]

Partes de este texto:

### Module/Subject matter

Module: Basic Training

Subject matter: Mathematics

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### 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

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### 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.

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### Learning outcomes

R1.-To perform descriptive statistical analyses of data to  summarize the information effectively and accurately in writing reports and to understand the relevance of such analyses in different areas of management and knowledge.

R2.-To carry out appropriate statistical procedures according to the nature of the statistical variables in a database.

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

R4.- To model problems in situations of uncertainty by means of events and their probability, conditional probability and independence of events

R5. - To  recognize the main stochastic models, both discrete and continuous, together with general methods of probability that can be adapted 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 in computer science for the 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 and interval estimation.

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### Methodology

 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 continulus evaluation 8 A-5 Lecture A-6 Self-study 75 A-7 Exam, evalutaion tests 4 A-8 Tutorial 4 Total 75 75

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### 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

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### Languages

Spanish, English and Basque

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### Evaluation

Learning
outcome
Assessment
activity
Weight (%) It allows
test resit
Minimum
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

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### Contents

• 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.

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### Agenda

1.-Exploring Data

Displaying Qualitative and Quantitative Data

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

Parameters

Estimators

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

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### Bibliography

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

TEXTBOOK:

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

OTHER BOOKS:

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

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Aulario.

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