Data Processing Technologies in Agroindustry
Objectives
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.
General characterization
Code
11376
Credits
6.0
Responsible teacher
Maria da Graça Azevedo de Brito, Sofia Verónica Trindade Barbosa
Hours
Weekly - 4
Total - 56
Teaching language
Português
Prerequisites
None.
Bibliography
[1] Gotelli, N.J., Ellison, A.M. (2004) A Primer Of Ecological Statistics, Sinauer Associates Inc, 511pp.
[2] McGarigal, K., Cushman, S., Stafford, S. (2000) Multivariate Statistics for Wildlife and Ecology Research, Springer, 283 pp.
[3] Plant, R.E. (2012) Spatial Data Analysis in Ecology and Agriculture Using R, CRC Press.
[4] Burkhart, H.E., Tomé, M. (2012) Modeling Forest Trees and Stands, Springer, 457pp.
[5] Isaaks, E.H., Srivastava, R.M., 1989. Applied Geostatistics. Oxford University Press, 561 pp.
Teaching method
Available soon
Evaluation method
Available soon
Subject matter
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
Programs where the course is taught: