| Code |
16478
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| Year |
2
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| Semester |
S1
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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 |
None.
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Learning outcomes |
Identify and differentiate between qualitative (categorical) and quantitative (discrete and continuous) data in economic contexts, evaluating their implications in choosing statistical methods. Recognize and apply measurement scales (nominal, ordinal, interval, and ratio) to categorize and analyze variables, interpreting their characteristics and limitations. Organize data, create frequency tables and contingency tables, and develop skills to construct, interpret, and identify patterns. Graphically represent variables and calculate measures of central tendency and dispersion to describe distributions. Calculate and analyze linear relationships between quantitative variables. Define random experiments, apply probabilities and theorems, and understand random variables and their distributions. Apply sampling methods, calculate confidence intervals, formulate and test parametric and non-parametric statistical hypotheses, and assess the assumptions of parametric tests.
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Syllabus |
I. Descriptive Statistics
Data Collection and Organization. Types of Data: Qualitative (Categorical) and Quantitative (Discrete and Continuous). Introduction to software. Frequency tables and graphical representation of univariate and bivariate variables. Linear relationship between two variables: simple and partial correlation.
II. Probability Theory
Random experiments. Events. Axiomatic definition of probability. Properties. Laplace's Law. Conditional probabilities. Independence. Total Probability Theorem and Bayes' Theorem. Random variables. Distribution function. Probability function. Probability density function. Expected value and variance of a random variable. Some common theoretical distributions in economic studies.
III. Statistical Inference
Sampling and Sampling Distribution of the Mean. Confidence Intervals. Introduction to Hypothesis Testing. Some parametric and non-parametric hypothesis tests. Validation of assumptions for parametric tests.
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Main Bibliography |
Agresti, A. Finlay, B. (2009). Statistical Methods for the Social Sciences – 4th edition. Pearson
Guimarães, R. C. e Sarsfield Cabral, J. A. (2010). Estatística – 2ª edição. Editora Verlag Dashofer.
Maroco, João (2018). Análise Estatística com a utilização do SPSS, 7ª edição, Ed. Report Number.
Murteira, B., Ribeiro, C., Silva, J. e Pimenta, C. (2015), Introdução à Estatística – 3ª edição. Escolar Editora.
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Teaching Methodologies and Assessment Criteria |
The teaching methodologies are consistent with the learning outcomes of the curricular unit because the exposition of the syllabus associated with the presentation of practical cases and the resolution of exercises allow the acquisition of the skills.
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Language |
Portuguese. Tutorial support is available in English.
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