Analysis and Computational Treatment in the 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;
- Being able to analyse data series according to the principles of parametric and non-parametric statistics;
- Apply data analysis tools in R platform, including importing data, graphical view and the output of reports.
General characterization
Code
13193
Credits
3.0
Responsible teacher
Sofia Verónica Trindade Barbosa
Hours
Weekly - 2
Total - 58
Teaching language
Português
Prerequisites
Available soon
Bibliography
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.
Ares, G. (2013). Mathematical and Statistical Methods in Food Science and Technology. John Wiley & Sons, Ltd. Online ISBN:9781118434635 |DOI:10.1002/9781118434635
Bower, J. A. (2013) Statistical Methods for Food Science: Introductory Procedures for the Food Practitioner. John Wiley & Sons, Ltd. Online ISBN:9781118541593 |DOI:10.1002/9781118541593
[6] Jain, H.K. et al (2022). Textbook of Agricult. Statistics. Publisher: Narendra Publishing House, Rohini, Delhi - 110085ISBN: 9789389996340
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. Parametric and Nonparametric Statistics. Probability distribution laws. Binomial and Poisson laws. Normal distribution law. Tests of hypothesis. Nonparametric tests of hypothesis. Monte Carlo simulation.
Linear and multiple regression. Generalized linear models. Spatio-temporal models. Growth curves.
Multivariate analysis. Methods of factorial analysis. Principal component analysis.
Classification methods. Hierarchical and nonhierarchical (K-means) clustering methods.
Analysis of variance (ANOVA and MANOVA).
Introduction to R platform. Import and export data to R. Objects inside R. Graphical. Aplications for parametris and non-paremtric series. Visualization and presentation of results.