Learning outcomes and their compatibility with the teaching method (knowledge, skills and competencies to be developed by students)
The program structure of this course aims to provide students with the basic knowledge of data analysis, probability and statistical modeling. It is intended to enable students with the following skills:
Propose solutions and decisions based on statistical modeling, data analysis and respective interpretation;
Use of basic research techniques;
Use of the R programming language (open source and free distribution) and some of its packages and plugins that allow performing statistical modeling and data analysis in a friendly and intuitive way;
Lifelong learning of quantitative subjects.
Syllabus
1.Sampling and Sample Distributions: Random Sampling; Random Numbers and Random Variables; Law of Large Numbers. Central Limit Theorem; Sample Statistics; Distribution of Sample Mean 2. Estimation: Estimator and Estimation; Properties of Estimators; Maximum Likelihood Method; Interval Estimation for Mean, Proportion, Difference between Means and Variance. 3. Hypothesis Tests (HT): Null Hypothesis and Alternative Hypothesis; Error Typology; Parametric Tests: HT for Mean, Proportion, Difference Between Means and Variance; Independence Tests (Chi Square & Fisher Exact test); association measures. 4. Linear Regression (LR): Scatter Diagram and Correlation Analysis; Simple LR Model; Least Squares Method. 5. Index numbers: Simple indexes; Aggregate indexes; Deflation of values; Applications.
Demonstration of the syllabus coherence with the curricular unit¿s learning objectives
The programmatic content is in line with the objectives of the course given that the program was designed to address at first the basic concepts of Statistics that support the Data Analysis, passing subsequently by specific techniques of data series analysis. The selected techniques have a close relationship with the objectives, not only of the course unit, but also of the course itself. The techniques will be applied gradually and, whenever possible, will be implemented using free and open source software, in order to minimize the effort of calculation and to lead the focus on the interpretation of results and on the best decision making in order to solve the proposed problem.
Teaching and learning methodologies specific to the curricular unit articulated with the pedagogical model
Continuous assessment or final exam is used. In continuous assessment the class participation component will have a weight of 20% and the remaining 80% will be allocated to the completion of two tests (50% each, minimum score of 8 points in both). Students with a minimum attendance (2/3 of classes) will be admitted to the assessment by frequency, otherwise they will be assessed by exam. The teaching methodology will be based on: (i) exposition of the subject using case studies and computational implementation whenever possible; (ii) resolution of exercises related to each topic of the syllabus; (iii) provision of support material for greater assimilation of the proposed topics; (iv) constant interaction with the students, in order to briefly review the main concepts of the previous class at the beginning of each lesson; (v) encourage the implementation of the methodologies presented in problem solving.
Demonstration of the coherence of teaching and evaluation methodologies between the learning objectives of the curricular unit
It is intended that students acquire skills related to Statistics and Data Analysis. To this end, students must learn to solve problems / exercises on the various topics, using the methodology and technique (s) more appropriate (s), and whenever possible using the R programming language. These problems will be proposed in class. Additionally, whenever possible, students should perform data analysis with emphasis on management and related areas with real data. The assessment is designed to measure the extent to which the skills have in fact been assimilated.
Bibliography (Mandatory resources)
Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J. (2016). Statistics for business & economics. Cengage Learning.
Black, K. (2019). Business statistics: for contemporary decision making. John Wiley & Sons.
Cortinhas, C., & Black, K. (2014). Statistics for business and economics. Wiley Global Education. Crawley, M. J. (2012). The R book (2nd Edition). John Wiley & Sons. Doane, D. P., Seward, L. E., (2014). Estatística aplicada à administração e economia (4ª edição). Artmed Editora Field, A., Miles, J., & Field, Z. (2012). Discovering statistics using R. Sage publications. Kazmier, Leonard J. (2007), Estatística Aplicada à Economia e Administração (4ª edição). McGraw Hill. Pedrosa, A.C. e Gama, S.M. (2018) Introdução Computacional à Probabilidade e Estatística com Excel. Porto Editora. Torgo, L. (2009). A linguagem R-programação para análise de dados. Lisboa: Escolar Editora.
