Curricular Unit: | Code: | ||

Applied Statistics | 831ESTA | ||

Year: | Level: | Course: | Credits: |

1 | Undergraduate | Computer Systems Engineering | 7 ects |

Learning Period: | Language of Instruction: | Total Hours: | |

Spring Semester | Portuguese/English | 91 | |

Learning Outcomes of the Curricular Unit: | |||

Randomness and uncertainty are common to many phenomena with which engineering must to deal. In this context, the definition of methods and models which make possible to understand and to infer the behavior of random variables becomes essential. Considering this framework, this curricular unit aims the introduction and development of knowledge and techniques for data collection and analysis that are necessary for modeling random variables. At the end of the unit students stay with specific skills that allow them to describe, analyze and establish conclusions about uni and bi-varied data, calculate probabilities in simple and compound events, understand and use the main theoretical probability distribution models, estimate parameters within a given confidence interval and test hypotheses. | |||

Syllabus: | |||

1 Basic concepts 1.1 Objectives of statistics. 1.2 Population, sample, variables, measurement scales 2 Descriptive Statistics 2.1 Characterization of samples 2.2 Measures of central tendency, partition, dispersion, skewness and kurtosis 2.3 Linear regression. Correlation and determination coefficients 3 Probability Theory 3.1 Random processes, sample spaces and events 3.2 Definitions of probability 3.3 Conditional probability and independent events 3.4. Theorem of Total Probability and Bayes’ Theorem 4. Random variables and probability distributions 4.1 Probability and distribution functions 4.2 Expected value and variance 4.3 Discrete and continuous distributions 5 Confidence interval estimation 5.1 For the mean and the difference between two means 5.2 For a proportion and difference between two proportions 5.3 Sizing samples 6 Hypothesis Tests 6.1 Parametric and nonparametric tests | |||

Demonstration of the Syllabus Coherence with the Curricular Unit's Objectives: | |||

Points 1 and 2 of the syllabus provide concepts and techniques for data collection and analysis and for summarize data, which allow describing, analyzing and establishing simple conclusions about uni and bi-varied data. Points 3 and 4 introduce fundamental concepts concerning probability theory and random variables allowing the calculation of probabilities in simple and compound events and the understanding and application of main theoretical probability distribution models. Points 5 and 6 introduce the essential methods of statistical inference to parameters estimation and hypotheses testing. The contents are in accordance with the objectives of the curricular unit, comprising concepts and techniques involved in data collection, data analysis and inference, necessary to modeling random variables | |||

Teaching Methodologies (Including Evaluation): | |||

The methodology of teaching and learning is expository, interrogative and demonstrative. Drawing on problem solving and oriented study geared to allow understanding and application of fundamental concepts and methods applied in descriptive statistics and statistical inference. The assessment includes: • Three written tests evaluation • Student performance, including attendance, resolution of proposed problems and active participation in classes. | |||

Demonstration of the Coherence between the Teaching Methodologies and the Learning Outcomes: | |||

The proposed methodologies are consistent with the learning objectives of the unit, in a way that they establish a close and continuous coordination between theoretical concepts, practical examples and problem solving. This approach enables the interpretation and practical application of statistical concepts and methods | |||

Reading: | |||

[1] Dekkin, F.M., Karaikamp, C., Lopuhaã, H.P., Meester, L. E. (2010). A Modern Introduction to Probability and Statistics. Understanding Why and How. Springer. [2] Guimarães, R.C. e Cabral, J. A. S. (2010) Estatística. Verlag Dashofer. [3] Montgomery, D.C., Runger, G. C. (2014) Applied Statistics and Probability for Engineers. Wiley. [4] Reis, E.; Melo, P; Andrade, R e Calapez, T. (2015) Estatística Aplicada. Vol. 1 e 2. Edições Sílabo. |