ECTS credits ECTS credits: 3
ECTS Hours Rules/Memories Total: 0
Use languages Spanish, Galician, English
Type: Training complements PhD RD99/2011
Departments: Plant Production and Engineering Projects
Areas: Plant Production
Center Higher Polytechnic Engineering School
Call: Annual
Teaching: With teaching
Enrolment: Enrollable
Introduce the student to the basic techniques for the statistical analysis of data in agricultural and forestry research. Make known the tools to decide when to use each technique and verify if the conditions to apply it are met. Present the basic notions for obtaining and interpreting results using statistical tools using SPSS software.
Topic 1. UNIVARIATE ANALYSIS
One-dimensional variables. Exploratory data analysis: missing values, outliers, normality, homoscedasticity. Statistical inference based on a sample: estimation by confidence intervals and by hypothesis testing. Goodness-of-fit tests.
Unit 2. BIVARIATE ANALYSIS I
Statistical Inference based on two samples: parametric and non-parametric methods. Analysis of variance with one factor.
Topic 3. BIVARIATE ANALYSIS II
Qualitative variables: relations. Quantitative variables: correlation, linear and non-linear regression. Logit.
Topic 4. MULTIVARIATE ANALYSIS
Dependency and interdependence techniques for data analysis. Multiple regression. Applications of principal component analysis, correspondence analysis and cluster analysis.
INTERNSHIP PROGRAM
Computer practices: DATA ANALYSIS WITH SPSS.
-SPSS: application programs in agricultural and forestry research.
-Collection and preparation of data.
-Descriptive statistics. Analysis by groups.
-Parametric and non-parametric tests.
-Analysis of categorical variables.
-Regression models.
-Applications of classical multivariate methods.
BROWER, J. E., ZAR, J. H. Y VON ENDE, C. N. (1997): Field and Laboratory Methods for General Ecology, 4ª ed. McGraw-Hill.
CLEWER, A.G.; SCARISBRICK, D.H. (2013): Practical statistics and experimental design for plant and crop science. Wiley.
HENDERSON, P. A. (2003): Practical Methods in Ecology. Blackwell.
LAWSON, J. (2015): Design and Analysis of Experiments with R. CRC Press.
LOGAN, M. (2010): Biostatistical design and analysis using R : a practical guide. Wiley-Blackwell.
MAINDONALD, J.; BRAUN, W. J. (2010): Data Analysis and Graphics Using R. An Example-Based Approach. Cambridge.
MONTGOMERY, C. D. (2005): Diseño y análisis de experimentos. Limusa-Wiley.
MOSQUERA-LOSADA M.R., MCADAM J., RIGUEIRO-RODRÍGUEZ A. (2006): Silvopastoralism and sustainable land management. CABInternational.
RIGUEIRO-RODRÍGUEZ A., MCADAM J., MOSQUERA-LOSADA M.R. (2008): Agroforestry in Europe. Springer.
SMITH, R. L. (1990): Ecology and field biology. Harper Collins Publishers. New York.
TERRADAS, J. (2001): Ecología de la vegetación. Ediciones Omega. Barcelona.
Acquire the ability to:
- Organize, summarize and represent data.
-Choose the appropriate data analysis technique for each case.
-Formulate problems in terms of statistical models.
-Perform with SPSS the calculations required by the proposed methods and interpret the outputs.
-Check the reliability of the starting data and the hypotheses underlying a given technique.
-Interpret the results of statistical analysis.
In all the topics, the content will be presented with a focus on possible applications, presenting the principles of each technique briefly, developing the explanations of the steps to follow through examples. All classes are in the computer room so that students can follow up on all the examples and do the exercises with the computer. The data treatment will be done with the statistical software SPSS.
The students will have a script of practices for each subject in which the resolution of practical cases and the interpretation of some scientific article are proposed in which the statistical techniques studied in class and carried out in practices are used.
This matter will appear among those offered from the USC-Virtual (virtual campus of the USC). Here you will find all the support material for the face-to-face classes (computer presentations, practices...) and the information related to the follow-up of the subject (work calendar, links to web pages, problem data...). Doubts can be consulted and all the resources offered from the USC Virtual Campus can be used. The use of the virtual campus is essential to collect the proposed works and deliver the solutions.
For cases of fraudulent completion of exercises or tests, the provisions of the "Regulations for evaluating the academic performance of students and reviewing grades" will apply.
The evaluation will be done continuously throughout the course through the presentation of proposed activities, attendance to classes will also be valued. Those students who pass these tests for continuous monitoring of the subject will not have to take the final exam.
ECTS: 3
Estimated workload: 76 h.
Total face-to-face/virtual: 24h.
Individual work (estimated): 52 h. (study and preparation of the activities proposed to the students)
María Rosa Mosquera Losada
Coordinador/a- Department
- Plant Production and Engineering Projects
- Area
- Plant Production
- mrosa.mosquera.losada [at] usc.es
- Category
- Professor: University Professor