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Statistics and Data Analysis

Statistics and Data Analysis

Code	17217
Year	1
Semester	S2
ECTS Credits	5
Workload	T(15H)/TP(30H)
Scientific area	Mathematics
Entry requirements	none
Learning outcomes	On completion of the Statistics and Data Analysis course, the student is expected to be able to: a. Demonstrate the ability to select and justify the most appropriate statistical methods in line with the specific objectives of the study; b. Demonstrate the ability to use the appropriate statistical methods for processing a set of data; c. Demonstrate autonomy and independence in the use of statistical software, particularly SPSS; d. Have the ability to interpret, discuss and communicate clearly and coherently, both in writing and orally, the results of the research carried out; e. Develop the ability to deepen the interpretative analysis of statistical results, articulating them critically to the general context of the research.
Syllabus	1 Statistics and sampling concepts: 1.1 Getting started with SPSS; 1.2 Construction of variables. Types of variables and measurement scales; 1.3 Creating a database; 1.4. Descriptive analysis; 1.5 Sampling theory and sample size determination. 2. Hypothesis testing: One and two samples: 3. Hypothesis testing: More than two samples: 4. Linear regression analysis: 4.1 Correlation; 4.2 Simple linear regression; 4.3 Multiple linear regression; 4.4 Evaluating the quality of model fit. 5. Introduction to logistic regression analysis: 5.1 Introduction to logistic regression. Likelihood ratio; 5.2 The logistic regression model; 5.3 Interpretation of coefficients; 5.4 Variable selection methods; 5.5 Significance and quality of the logistic regression model. 6. Effect size: 6.1 Magnitude of difference between groups and relationship between variables.
Main Bibliography	Cohen, J. (1988). Statistical power analysis for the behavioral sciences, 2sd ed. Hillsdale, NJ: Erlbaum. Guimarães, R.C. & Sarsfield Cabral, J.A. (2010). Estatística, 2ª Ed. Verlag Dashöfer Hosmer, D.W., Sturdivant, X. & Lemeshow, S. (2013). Applied Logistic Regression. Wiley Series in Probability and Statistics, John Wiley & Sons Fritz, C.O., Morris, P.E. & Richler, J.J. (2012). Effect size estimates: current use, calculations, and interpretation. J Exp Psychol: General, 141(2) Marôco, J. (2014). Análise Estatística com o SPSS Statistics. 6ª edição. Edições Sílabo. Montgomery, D.C., Peck, E.A. & Vining, G.G. (2012). Introduction to linear regression analysis. 5th ed. Wiley series in probability and statistics Murteira, B., Pimenta, C., Pimenta, F., Ribeiro, C. & Andrade e Silva, J. (2023). Introdução à Estatística. Escolar Editora Pestana, M.H., & Gageiro, J.N. (2014). Análise de dados para Ciências Sociais: A complementaridade do SPSS. 6ª ed. Lisboa: Edições Sílabo.
Teaching Methodologies and Assessment Criteria	1) Teaching and Learning Assessment (TE): (F1) - A worksheet relating to the contents taught in Chapters 1-3, carried out in a group => 15% of the final grade (3 marks); (F2) - A worksheet on the contents of Chapters 4-6, carried out in groups => 15% of the final mark (3 points); (TI) - A research project of an experimental nature, carried out in a group => 70% of the final mark (14 marks, 8 marks for the written report + 6 marks for the individual oral presentation and discussion of the work carried out). Final grade = Weighted average of assessments If final grade < 6, Fail
Language	Portuguese. Tutorial support is available in English.