Public University of Navarre



Castellano | Euskara | Academic year: 2019/2020 | Previous academic years:  2016/2017  |  2015/2016 
Bachelor's degree in Agricultural, Food and Rural Environment Engineering at the Universidad Pública de Navarra
Course code: 501201 Subject title: STATISTICS
Credits: 6 Type of subject: Basic Year: 1 Period: 2º S
Department: Estadística, Informática y Matemáticas
Lecturers:
UGARTE MARTINEZ, M. DOLORES (Resp)   [Mentoring ] MONTESINO SAN MARTIN, MANUEL   [Mentoring ]

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Module/Subject matter

Statistics

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Contents

Descriptive Statistics

 

  • Exploring Data

 

Probabilidad

  • Sampling space and events
  • Condicional probability, the law of total probability, Bayes theorem and independent events
  • Probability density function and cummulative density function. Median, mode, percentiles and moments
  • Discrete random variables: Uniform, binomial and Poisson. Continuos random variables: Uniform, exponential, gamma and normal
  • Joint, marginal and conditional distributions of discrete and continuous random variables

 

Inferencial Statistics

 

  • Itroduction to sampling distributions. Parameters and estimators.
  • Chi-square distribution and t-Student
  • Sampling distribution of the mean, of the difference of means, proportion, difference of proportions
  • F Distribution
  • Sampling distribution of the variance and of the ratio of variances
  • Point Estimation and properties. Mean squared error
  • Confidence intervals introduction. Confidence intervals of means, difference of means, proportions, difference of proportions, variances and ratio of variances
  • Hypothesis testing. Introduction. Type I and type II errors. P-value and power of a test.  Hyphotesis testing for means, proportions and variances

 Statistical Modelling

 

  • Introduction to the Analysis of variance and Regression Modelling

 

 

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Descriptors

Descriptive statistics, probability, statistical inference, statistical models

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General proficiencies

  • CB1: Students are able to demonstrate they have acquired knowledge and understanding in a field of study based on the basic foundations gained within their general secondary education together with the support of advanced textbooks and aspects of the latest advances in the field.
  • CB2: Students can apply their knowledge to a job or vocation in a professional manner and have the competences which are generally shown through the elaboration and defence of arguments and problem solving in their field of study.

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Specific proficiencies

  • CG2: Adequate knowledge of the physical problems, technologies, equipment, and water and energy supply systems, the limits imposed by budgetary factors and building regulations, the relationships between installations and/or buildings with farms, agro-food industries and spaces related to gardening and landscaping with their social and environmental surroundings, as well as the need to relate those surroundings from that environment with human needs and environmental protection.
  • CE1: Ability to solve mathematical problems that may arise in engineering. Aptitude for applying knowledge towards: linear algebra, geometry, differential geometry, differential and integral calculus, differential equations and partial derivatives, numerical methods and numerical algorithmic methods, statistics, and optimization.

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Learning outcomes

R1.- To carry out statistical analysis of data to summarize a report in an efficient and accurate way

R2.- To know a statistical package for working with data bases and simulation of random experiments

R3.- To Model problems of random properties using probabilities, conditional probabilities and independent events

R4.- To recognize the main discrete and continuous probability distributions, and the main rules of probabilities

R5.- Utilizar las técnicas inferenciales clásicas: estimación puntual, por intervalos y contrastes de hipótesis.

R6.- To know basic statistical models used in design of experiments,  assessment   and environmental problems

 

RESULTADOS APRENDIZAJE ENAEE:

ENAEE-3: Un conocimiento adecuado de su rama de ingeniería que incluya algún conocimiento a la vanguardia de su campo.
ENAEE-7: La capacidad de elegir y aplicar métodos analíticos y de modelización adecuados.
ENAEE-11: La capacidad de diseñar y realizar experimentos, interpretar los resultados y sacar conclusiones.

 

 

 

 

 

 

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Methodology

Methodology - Activity Attendance  Self-study
A-1 Theoretical clases 30  30
A-2 Computer Labs 15  22
A-3 Debates, group work, etc 4  38
A-4 Monitoring proofs 8  
A-5 Lecture    
A-6 Self-study    
A-7 Exam, evalutaion proofs 3  
A-8 Tutorial    
...    
Total 60 90

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Languages

English

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Evaluation

 

 

Learning outcomes Evaluation Activity Weight (%) Second Chance
 R1-R2-R3-R4-R5-R6  Exam of Theory and Practice with computer  80  Yes
 R1-R2-R3-R4-R5-R6  Monitoring Proof Delivery  20  

 

 

 

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Agenda

Week 1: Descriptive Statistics

Weeks 2-5: Probability 

Weeks 6-14: Inferencial Statistics

Week 15: Analysis of variance and Regression


 

 

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Bibliography

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


Basic

 

Ugarte, M. D., Militino, A. F., Arnholt, A. T. (2016). Probability and Statistics with R.  CRC Press/Chapman and Hall. 2nd Edition

 

 

Supplementary

 

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

Montgomery, D.C. (2002). Probabilidad y Estadística aplicadas a la ingeniería. Limusa-Wiley

 

 

 

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Location

UPNA Aulario

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