Curricular Unit: | Code: | ||

Statistical Methods and Experimental Planning | 883MEPE | ||

Year: | Level: | Course: | Credits: |

1 | Postgraduate | Ecology and Environmental Health | 6 ects |

Learning Period: | Language of Instruction: | Total Hours: | |

Spring Semester | Portuguese/English | 78 | |

Learning Outcomes of the Curricular Unit: | |||

Objectives: it is intended to teach the most used statistical and experimental planning methods used in the research applications and procedures. To make possible the development of statistic analysis knowledge techniques, presenting pertinent examples and practical situations in order to demonstrate the concepts. Skills: - To characterize (sample or population) data sets and to do it´s correct interpretation (to have critical spirit); - To be able to use inferential statistic tools: confidence intervals and hypothesis tests; To know and to understand the terms; - To autonomously carry through simple tasks as data tabulation, to calculate frequency measures and to be able to collect, analyze and interpret research data; - To evaluate the information contained in a scientific article and to be capable to communicate the research results. | |||

Syllabus: | |||

Statistical basic concepts: Frequency distribution; Test of hypothesis and I and II type of errors. Sampling/experimental design: sampling methods; replication and pseudo-replication; types of studies; how to design an analysis using general principles; Sampling design. Measure comparison: non-parametric (MW, Wilcoxon, KW, Friedman tests) and parametric tests (t-tests and ANOVA); testing the ANOVA assumptions; multifactorial analysis: one, two or higher order ANOVA; fixed/random factors. ANCOVA. Correlation: Pearson/Spearman corr. Coef., Chi-square test. Epidemiologic: OR and RR Intra/inter observer agreement: Cohen Kappa, Weighted Kappa, ICC Sampling size determination: minimum effort way, Rules of Thumb, Power Analysis Curve and response surface analysis: uni and multifactorial linear or nonlinear regression methods; analysis of variance for comparison of different curve and experimental response surface analysis. Logistic regression and Cox regression. | |||

Demonstration of the Syllabus Coherence with the Curricular Unit's Objectives: | |||

Statistical methods and experimental planning are paramount as an aid of scientific research. The selected programmatic contents are those used in exploratory data analysis and of inference of any quantitative research. | |||

Teaching Methodologies (Including Evaluation): | |||

The contents will be transmitted in the classroom, in classes of type theorical-practical where the transmission of theoretical-practical to put into practice the theoretical knowledge through problem solving applied to environment; and tutorial classes. The evaluation is continuous, through application of knowledge in one individual assignment, that intends to be like the material and methods thesis section, written and with an oral presentation for the professor and colleagues | |||

Demonstration of the Coherence between the Teaching Methodologies and the Learning Outcomes: | |||

The methodologies of teaching and learning of this course were programmed to boost application of theoretical concepts learned. It is intended primarily for students to acquire basic knowledge and skills applicable in situations of developing the experimental design of the PhD thesis as well as the data analysis for that experimental design. | |||

Reading: | |||

Box, G.E.P., Hunter, W.G. & Hunter, J.S. Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. Wiley-Interscience, 1978. Kutner, M. H., Nachtsheim, C. J., Neter, J. Applied linear regression models. McGraw-Hill, 4ª edição, 2004. Kutner, Nachtsheim, Neter, Li. Applied linear statistical models, McGraw-Hill Education, 5ª edição, 2013. Maroco, J. Análise Estatística com o SPSS Statistics, Edicões Sílabo, Lda., 6ª edição, 2014. ISBN: 9789899676343 Sokal, R. R. and F. J. Rohlf. Biometry: the principles and practice of statistics in biological research. 3ª edição. W. H. Freeman and Co.: New York. 1995. ISBN: 0-7167-2411-1. Gouveia de Oliveira, A., Bioestatística Descodificada. LIDEL, 2ª Edição. 2014. ISBN: 978-989-752-044-0 |