Universidad Pública de Navarra - Nafarroako Univertsitate Publikoa
Public University of Navarre
| Course code: 240201 | 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 ] | |||||
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.
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
knowledge.
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
| 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 |
Evaluation
| Learning outcome |
Assessment activity |
Weight (%) | It allows test resit |
Minimum 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 | |
Agenda
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
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
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