Home
Courses
Sports Sciences
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	The curricular unit Statistics and Data Analysis aims to provide students with a solid foundation in statistics, equipping them with the skills necessary for the appropriate application of statistical methods. Upon completion of the curricular unit, students are expected to be able to: a. Demonstrate the ability to select and justify the most appropriate statistical methods in accordance with the specific objectives of a study; b. Apply suitable statistical methods for the analysis of a given dataset; c. Demonstrate autonomy and independence in the use of statistical software, particularly SPSS; d. Interpret, discuss, and clearly communicate, both in written and oral form, the results obtained from statistical analyses; e. Develop the ability to critically interpret statistical results and articulate them within the broader research context.
Syllabus	1. Introduction to Statistics and Sampling Concepts: 1.1. Introduction to SPSS software 1.2. Descriptive Analysis 1.3. Sampling Theory and Sample Size Determination 2. Hypothesis Testing: One and Two Samples: 2.1. Methodology of Hypothesis Testing 2.2. Parametric and Nonparametric Hypothesis Tests 3. Hypothesis Testing: More than Two Samples: 3.1. One-Way ANOVA and Repeated Measures ANOVA 3.2. Kruskal–Wallis Test and Friedman Test 4. Linear Regression Analysis: 4.1. Correlation and Simple Linear Regression 4.2. Multiple Linear Regression 4.3. Assessment of Model Goodness-of-Fit 5. Introduction to Logistic Regression Analysis: 5.1. The Logistic Regression Model and Odds Ratio 5.2. Variable Selection Methods 5.3. Model Significance and Goodness-of-Fit 6. Effect Size: 6.1. Introduction to the Concept of Effect Size 6.2. Magnitude of Differences Between Groups and Relationships Between Variables
Main Bibliography	- Bryman, A., & Duncan, C. (2002). Quantitative Data Analysis with SPSS Release 10 for Windows. A guide for social scientists. New York: Routledge. - Guimarães, R.C. & Sarsfield Cabral, J.A. (2010). Estatística, 2ª Ed. Verlag Dashöfer - 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. - Pereira, A. (2006). SPSS - Guia prático de utilização – Análise de dados para Ciências Sociais e Psicologia. 7ª ed. Edições Sílabo. - Pestana, M.H., & Gageiro, J.N. (2014). Análise de dados para Ciências Sociais: A complementaridade do SPSS. 6ª ed. Lisboa: Edições Sílabo.
Language	Portuguese. Tutorial support is available in English.