| Code |
15360
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| Year |
2
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| Semester |
S2
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| ECTS Credits |
6
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| Workload |
TP(60H)
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| Scientific area |
Mathematics
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Entry requirements |
Curricular Unit Probability and Statistics.
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Mode of delivery |
Presential Classes
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Learning outcomes |
The aim is to develop in students an intuitive understanding of Statistics and statistical reasoning, enabling them, when faced with an unfamiliar problem, to identify the appropriate statistical methods and tools to apply. Students are also expected to acquire practical experience in data analysis using statistical software, namely SPSS. They should be capable of structuring a database for statistical analysis; applying descriptive statistical methods and techniques to characterize a dataset; understanding and applying the main non-parametric tests; specifying, estimating, and interpreting simple and multiple linear regression models; and specifying, estimating, and interpreting analysis of variance models with one or more factors. In general, students should be able to participate in the conduct of statistical studies involving data processing and the interpretation of results.
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Syllabus |
1. Non-Parametric Hypothesis Testing: runs test; Binomial test; Kolmogorov–Smirnov goodness-of-fit test; sign test; Wilcoxon signed-rank test; Mann–Whitney U test; two-sample Kolmogorov–Smirnov test; chi-square test; Fisher’s exact test.
2. Analysis of Variance (ANOVA): one-way ANOVA; two-way ANOVA with and without replication.
3. Simple Regression and Correlation: concepts of regression and covariance; least squares method; simple linear regression model; correlation coefficient; residual variance; coefficient of determination; fitting of non-linear models.
4. Multiple Regression and Correlation: multiple linear regression model; introduction to non-linear models.
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Main Bibliography |
Gonçalves, E., Nogueira, E. e Rosa, A.C. (2016). {\it{Probabilidades e Estatística para Ciências e Tecnologia. Conceitos e exercícios resolvidos}}. Almedina. Cota: M-7.0-00028
Hall, A., Neves, C. e Pereira, A. (2011). Grande Maratona de Estatística no SPSS. Escolar Editora. Cota: EG-4.2-00500
Montgomery, D. e Runger, G. (2011). Applied statistics and probability for engineers, 5ª Edição, John Wiley \& Sons. Cota: MD-14-00531
Ross, S. (2009). Introduction to probability and statistics for engineers and scientists. Amsterdam Elsevier. Cota: F-1.8-01370 (CD)
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Teaching Methodologies and Assessment Criteria |
The assessment of the knowledge and competencies acquired by students during the teaching–learning process is carried out through two written tests (F1 and F2), graded on a scale from 0 to 20, each with a weight of 0.5.
The continuous assessment grade (CEA), expressed on a scale from 0 to 20, is calculated as follows:
CEA = 0.5 F1 + 0.5 F2.
Exemption from the final exam is granted when the CEA is equal to or higher than 9.5, provided that the student obtains a minimum grade of 3 in each written test and achieves attendance of more than 80% of the scheduled classes.
The classification “FREQUENCY” is awarded when the CEA is lower than 9.5 or when the attendance requirements or the minimum grade required in each written test are not met.
The lecturer may require an additional oral examination, duly justified, to validate the assigned grade.
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Language |
Portuguese. Tutorial support is available in English.
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