Course detail

LET5841 - Statistical Tools Applied to Agricultural Entomology

Credit hours

In-class work
per week
per week
15 weeks
120 hours

Carolina Reigada Montoya
Wesley Augusto Conde Godoy

The course aims to introduce basic concepts of statistics commonly applied to experiments in the field of agricultural entomology, enabling students to develop and apply tools more appropriate to each experimental situation, as well as analyze data and interpret results using software R.

1. Descriptive Statistics
1.1. Identification of the biological problem
1.2. Search Planning
1.3. Data collect
1.4. Types of variables
1.5. Frequency distribution and Data representation (sorting of raw data, tables, graphs)
1.6. Measures of position (average, median, fashion, quartiles)
1.7. Dispersion measures (mean deviation, mean squares, variance, standard deviation, coefficient of variation)

2. Introduction to Probability
2.1. Sets, populations and samples
2.2. Experiments, sample space
2.3. Fundamental notions of probability (probability of an event, probability of multiple events, conditional probability)

3. Random Variable
3.1. Discrete Random Variable (Binomial Model, Poisson Model)
3.2. Continuous Random Variable (Normal Model)

4. Classical Hypothesis Testing
4.1. Single Sample (Normality Test, Bootstrap)
4.2. Two samples (Fisher's F test, t-student, Pearson correlation, Chi-square test)
4.3. Non-parametric tests: Mann-Whitney, Wilcoxon, Kruskal-Wallis test and Spearman's coefficient)

5. Experimental Design
5.1. Fully randomized design
5.2. Block design
5.3. Factorial experiments and other types of designs

6. Statistical Models
6.1. Analysis of variance (ANOVA)
6.2. Correlations and Regression
6.3. Analysis of covariance (ANCOVA)
6.4. Multiple Regression
6.5. Linearly Generalized Models (glm)
6.5.1. Count data
6.5.2. Proportion data
6.5.3. Binary data

GALECKI, A.; BURZYKOWSKI, T. 2013. Linear Mixed-Effects Models Using R: A Step-by-Step Approach. Springer- Verlag
GEERT, V.; MOLENBERGHS. 2000. Linear mixed models for longitudinal data. Springer- Verlag.
GEERT, V.; MOLENBERGHS. 2005. Models for Discrete Longitudinal Data. Springer-Verlag
PIMENTEL-GOMES, F. 2000. Curso de Estatística Experimental, 14ºed. Editora F. Pimentel-Gomes.
PINHEIRO, C. J.; BATES, D. M. 2000. Mixed-effects models in S and S-PLUS. New York: Springer-Verlag.