Acquisition of solid methodological and practical knowledge for the design of experimental tests and sample surveys in agriculture. Acquisition of software usage skills for statistical processing of biological data in agriculture. Acquisition of solid theoretical and practical bases for the presentation, reading and interpretation of the statistical results of experimental tests and sample surveys.
Expected learning outcomes
Students will acquire the ability to manage data and software to process results from experimental tests and sample surveys. They will also acquire the ability to present, read and interpret data from field and laboratory experiments and from sample surveys.
Lesson period: First semester
(In case of multiple editions, please check the period, as it may vary)
Introduction: descriptive and inferential statistics. Samples and populations. Types of variables: qualitative, quantitative. Precision and accuracy.Data visualization: Absolute, relative and cumulative frequency tables. Representing the frequency distributions, diagrams and histograms, percentiles and quantiles, contingency tables. Scatterplot, line chart, maps. Describing Data: Measures of location and dispersion, geometric and arithmetic mean, median, mode, interquartile range, range of variation, deviation, variance, standard deviation, coefficient of variation.Estimate with uncertainty: sampling distribution, standard error and confidence interval. Probability: basic rules, Venn diagrams, probability trees. Sum the odds. Independence and the product rule. Conditional probability. Hypothesis testing: null and alternative hypotheses and statistical significance, P-value. Hypothesis testing and confidence intervals. Error of first and second species. Analysis of proportions: binomial distribution. Estimate of the proportions: confidence interval and standard error of a proportion. Chi-square test and the goodness of fit. Poisson distribution. Contingency tables for the analysis of categorical variables and chi-square test for the analysis of contingency tables. Normal distribution, formula, assumptions and properties. The central limit theorem. Normal approximation for binomial distribution. Inference in a population with a normal distribution: t-distribution, assumptions and properties. t-test for one sample. Comparison between two means, paired comparison between means, comparing the means of two samples. Comparisons between means of multiple groups: analysis of variance. Measurements of relationship between variables: covariance, correlation and linear regression. Least squares. Linear regression. Design of experiment: power, effect size and sampling size. General linear model. Mixed model. Factorial and blocking model applied to agricultural and environmental science.
Prerequisites for admission
Basic knowledge of descriptive statistics. Basic skill in using spreadsheet. The prerequisites and the procedure of examination is the same for the students that will or will not attend the classes. It is really recommended to attend the classes.
The course is organized into frontal classes corresponding to 3 CFUs and practical training for 3 CFUs. The practical training will be carried out in informatic rooms . Each class is organized in both theoretical and practical.
The exam consists of a written and a practical examination followed by an optional interview. The written part consists of 6 questions (1 multiple choice question, 2 open theoretical questions; 2 practical problems; 1 statistical analysis to comment) and a computer session with 1 dataset to analyze with the software used in practical classes. Criteria of evaluations: The exam will assess the ability to organize, summarize and represent the methodologies studied during the course choosing the appropriate experimental methodologies and statistical tests and the ability in using specific technical language and software. The interview is optional and will focus on the results of the written and practical part to complete and clarify mistakes and to improve the final evaluation.