| Code |
14002
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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.
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) Introductory Concepts
a) Fundamental elements of statistics
b) Description of qualitative data
c) Description of quantitative data
i) Graphical methods
ii) Data Reduction: measures of location and dispersion
iii) Representation of Bivariate Data
2) 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) Adjustments to non-linear models
3) Regression and Multiple Correlation
a) Linear model
b) Non-linear models
4) Analysis of Variance
a) Anova with a factor
b) Anova with two factors without repetition
c) Anova with two factors with repetition
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Main Bibliography |
Murteira, B., Ribeiro, C., Silva, J. e Pimenta, C. (2015), Introdução à Estatística – 3ª edição. Escolar Editora.
Maroco, João (2018). Análise Estatística com a utilização do SPSS, 7ª edição, Ed. Report Number.
Guimarães, R. C. e Sarsfield Cabral, J. A. (2010), Estatística – 2ª edição. Editora Verlag Dashofer.
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Teaching Methodologies and Assessment Criteria |
Following Order 2020 / R / 29, the following CEA assessment criteria and admission to the Exam were proposed to students, obtaining their agreement:
1) The practical work, solved in group, will be used for the purpose of approval (=> 50% correctly solved) in CEA and admission to Exam. In this sense, and considering the high risk of contagion, it is my opinion that the global attendance "frequency" scheduled for 06/01 should not be carried out. Students wishing to improve their grade can do it in Exam (in July or September). In Exam, students with practical assignments => 85% correctly solved, will have an added value in the Exam grade.
2) However, if there are students who intend to attend global attendance "frequency"on June 1st, class time, they should explicitly send me an email by 6:00 pm on May 5th, expressing this desire. In this case, the CEA will be the frequency classification. Minimum CEA 6 values.
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Language |
Portuguese. Tutorial support is available in English.
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