| 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 |
It is expected the development of student’s statistical reasoning, which will allow him/her to decide the proper statistical method to apply for data analysis purpose. It is also intended that the student acquires practice in data analysis using statistical programs, namely SPSS..
The student must be able to structure a database for statistical analysis; use methods and techniques of descriptive statistics to characterize the data set; specify, estimate and interpret the model of simple linear regression and multiple linear regression, as well as test the model's assumptions and proceed to the diagnostic analysis; specify, estimate and interpret an analysis of variance with one or more factors.
In general, the student should be able to participate in carrying out statistical studies involving statistical treatment of data and interpretation of results.
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Syllabus |
1) Non-parametric hypothesis tests
a) Tests for a single sample: The runs test for randomness; Binomial test; Kolmogorov-Smirnov goodness of fit test
b) Tests for one sample or two paired samples: Sign Test; Wilcoxon Test (Signed Ranks)
c) Tests for two independent samples: Mann-Whitney U-test; Kolmogorov test
d) Tests for nominal data: Chi-square test; Fisher's exact test
2) Analysis of Variance
a) Anova with one factor
b) Anova with two factors without repetition
c) Anova with two factors with repetition
3) Simple Regression and Correlation
a) Introduction to the concept of regression and covariance
b) Least Squares Method
c) Linear regression
d) Correlation coefficient, residual variance and determination coefficient
e) Fitting non-linear models
4) Multiple Regression and Correlation
a) Linear model
b) 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 classes are theoretical-practical, in which the student will be guided to the resolution of exercises involving statistical inference techniques, using SPSS whenever necessary.
The assessment of knowledge and skills acquired by students during the teaching-learning process is done by means of two tests (F_1 and F_2, rated from 0 to 20) with a weighting of 0.5 each.
The final teaching-learning classification (CEA,,expressed in 0 to 20 values) is calculated as follows
CEA=0,5F_1+0,5F_2,
Final exam exemption is granted when the CEA is equal to or higher than 9.5 points, requiring a minimum score of 3 points in each test. The grade of ``FREQUENCY'' is awarded.when CEA is less than 9,5 points.
Grades above 18 points are defended with an oral exam.
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Language |
Portuguese. Tutorial support is available in English.
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