Basic statistical principles including estimation of population parameters and testing hypotheses. Linear correlation and regression. Analyses of counted and measured data to compare several biological groups including contingency tables and analysis of variance. Data analyses using SAS and R.
Statistical analysis of designed experiments in biological and agricultural research. Topics covered include multiple regression, randomized complete block designs, random and mixed models, split-plot and repeated measures designs, analysis of covariances, power analysis for designed experiments. Data analyses using SAS.
The main focus of the course will be detailed introduction of geostatistical methodology with emphasis on applications in agricultural and environmental research. The following main topics will be discussed: spatial variability and its characterization; mapping agricultural and environmental variables via geostatistical tools; and accounting for spatial variability in analysis of designed experiments.