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 - Available soon
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
The teaching model adopted is kind of theoretical and practical: i) theoretical and practical lectures with multimedia support; ii) practical lectures of analysis and processing of data in R in computer lab; development of applications of real case studies.
Evaluation method
The evaluation is preferably of the continuous type, but, alternatively, can be made by a classical final examination. The continuous evaluation consists of two written tests for the algorithms and methods (representing 25% + 25% of the final grade) and a report made by groups of two students were application of a real case study is developed (50% remaining). Alternatively, and only for the theoretical component, students can opt for the final exam, where they can also improve the grade of the tests. The frequency is obtained by attending 2/3 of the practical classes.
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.