Course code: 252201 |
Subject title: STATISTICS |
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Credits: 6 |
Type of subject: Basic |
Year: 1 |
Period: 2º S |
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Department: Estadística, Informática y Matemáticas |
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Lecturers: |
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FERNANDEZ MILITINO, ANA (Resp) [Mentoring ] | GOICOA MANGADO, TOMÁS [Mentoring ] |

Descriptive statistics, probability, statistical inference.

- 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)

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

**CG3:**Knowledge of basic and tecnological subjects to have the ability to learn new methods and theories, and versatility to adapt to new situations.**CG4:**Problem solving proficiency with personal initiative, decision making, creativity and critical reasoning. Ability to elaborate and communicate knowledge, abilities and skills in industrial engineering.

**CFB1:**Ability to solve mathematical problems in engineering. Ability to apply theoretical knowledge on linear algebra, geometry, differential geometry, calculus, differential equations, numerical methods, algorithms, statistics and optimization.**CFB3:**Basic kowledge on computing, operative systems, data bases and software with engineering applications.

R1. To perform descriptive statistical analysis to databases.

R2. To apply appropriate statistical treatments according to the nature of the statistical variables in databases.

R3. To use a statistical package for statistical analysis of databases and simulation results of random phenomena.

R4. To recognize the main probability distributions, both discrete and continuous, together with methods general of probability.

R5. To use statistical tools to adequately estimate the unknown model parameters raised in engineering by methods of point estimation and intervals.

R6. To learn statistical techniques for facilitating the process of decision making in uncertain environment.

Methodology - Activity | Attendance | Self-study |

A-1 Lectures | 45 | |

A-2 Practical classes | 15 | |

A-3 Self-study | 75 | |

A-4 Exams, tests, and tutorial | 15 | |

Total | 75 | 75 |

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

R5, R6 | Second evaluation (a minimun of 5 over 10 is requiered to pass this part) | 60% | Yes, in final exam |

R1, R2, R3, R4 | Midterm evaluation | 30% | Yes, in final exam |

R1, R2, R3, R4 | Reports of experimental tasks | 10% | Yes, in final exam |

**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

** **

**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