Statistics for Biology
In this curricular unit we intend to explore some of the main methods of statistical modeling covering theory, applications and software. These methods will allow the student to deal with different type of variables (for example, continuous, counts, and presence/absence) and deal with observations indexed in time and/or space, and thus go beyond the standard statistical modelling techniques. Frequently used multivariate statistical methods are also presented.
It is intended that at the end of the course the student had acquired skills and abilities that allowed him to (1) use the generalized linear models as a basic statistical modeling tool, (2) recognize the most appropriate statistical approach according to type and behavior (3) incorporate non-independence cases between observations and (4) consider multivariate analysis. It is also expected that students will learn to use R software as a basic tool for statistical modeling.
Regina Maria Baltazar Bispo
Weekly - 5
Total - 56
Crawley, M. (2012). The R Book. John Wiley & Sons.
Faraway, J. J. (2006) Extending the Linear Model with R. Chapman & Hall / CRC
Johnson, R. and Wichern, D. W. (2007), Applied Multivariate Statistical Analysis, 6th Edition, Prentice Hall, New Jersey
Turkman MAA, Silva GL (2000). Modelos Lineares Generalizados - da teoria à prática. Edições SPE.
The classes will be theoretical-practical (using computer labs). Students will be confronted with practical problems, analyzing data using statistical software R.
It includes the following components:
- 1st mini-test (MT1)- Test, in person, with a weight of 25%. The test will last for 2 hours. The test is rated on a scale of 0 to 20 points (no minimum rating).
- 2nd mini-test (MT2)- Test, in person, with a weight of 25%. The test will last for 2 hours. The test is rated on a scale of 0 to 20 points (no minimum rating).
- Work (T)- Individual work of data analysis. The work will have a weight of 50%. The work is rated on a scale of 0 to 20 values.
Final grade calculation formula (NF): NF = 0.25 x (MT1+ MT2) + 0.5 x T (partial grades rounded to 1 decimal place)
Resource (Improvement)/Special: In-person written test to be held on a single date, within the period provided for in the academic calendar, with a weight of 100%. The exam will last for 3 hours. The exam is rated on a scale from 0 to 20 points.
1. Basic concepts of Statistics. Basic sampling and experimental design techniques. Exploratory data analysis. Statistical inference. Parameter estimation and hypothesis testing.
2. Linear Models (LM) and Generalized Linear Models (GLM)
2.1. Continuous models (classical linear regression, ANOVA, ANCOVA, gamma regression and survival models)
2.2 Discrete models (logistic regression, Poisson regression and negative binomial regression).
2.3 Generalized linear mixed models (GLMM). Fixed vs. random effects (non-independent or grouped data).
3. Introduction to Multivariate Statistics
3.2 Principal Component Analysis
3.3 Cluster Analysis
Programs where the course is taught: