Data Processing Technologies in Agroindustry
At the end of this course the student will have acquired knowledge, skills and abilities to:
- Understand a technical report where the information presented result from a statistical analysis and/or spatial modeling;
- Being able to synthesize information from a qualitative and quantitative dataset, in particular the interpretation and drawing of conclusions;
- Understand the bi-or multivariate relationships between variables of a dataset, and analyze redundancies and gaps of information;
- Distinguish the various sub-populations of a sample, and use the best tools for the generation of sub-sets of data;
- Generate estimated images of a continuous property locally sampled in the study area.
- Apply data analysis tools in R platform, including importing data, graphical view and the output of reports.
Sofia Verónica Trindade Barbosa
Weekly - 4
Total - 56
 Gotelli, N.J., Ellison, A.M. (2004) A Primer Of Ecological Statistics, Sinauer Associates Inc, 511pp.
 McGarigal, K., Cushman, S., Stafford, S. (2000) Multivariate Statistics for Wildlife and Ecology Research, Springer, 283 pp.
 Plant, R.E. (2012) Spatial Data Analysis in Ecology and Agriculture Using R, CRC Press.
 Burkhart, H.E., Tomé, M. (2012) Modeling Forest Trees and Stands, Springer, 457pp.
 Isaaks, E.H., Srivastava, R.M., 1989. Applied Geostatistics. Oxford University Press, 561 pp.
Theoretical and practical sessions of two hours each: i) theoretical lectures with powerpoint ii) practical classes in the computer room. The theoretical explanations are suported with practical examples related with the master course. The classes are based on problem solving, taking as a starting point realistic datasets that reproduce some of the situations that future professionals will work.
The evaluation is preferably of continuous type but alternatively can be made by classical exam. Two written tests for methods (representing 25% + 25% of the final grade), and a report made by groups of two students with a resolution of the practical problems worked in class practices (remaining 50 %) will be developed for the continuous assessment option. Alternatively, students have a final theoretical examination where the grade of the theoretical component tests can be also improved. Frequency is obtained by presence in 2/3 of the practical sessions.
Review of statistical analysis and probability theory. Display data with graphics. Categorical and continuous variables. Auxiliary variables. Univariate and bivariate analysis. Center position, spread, asymmetry and kurtosis. Frequencies. Correlation and similitude. Probability. Uncertainty. Random variables. Probability distribution laws. Binomial and Poisson laws. Normal distribution law. Tests of hypothesis. Monte Carlo simulation. Regression. Generalized linear models. Spatio-temporal models. Growth curves. Multivariate analysis. Principal component analysis. Hierarchical and nonhierarchical (K-means) clustering methods. Analysis of variance (ANOVA). Correspondence analysis. Spatial analysis of continuous variables and modelling. Spatial covariance and variogram. Kriging estimation and posterior validation. Introduction to R platform. Import and export data to R. Objects inside R. Graphical visualization.
Programs where the course is taught: