12.2 Mean Variance Portfolio: Important concepts.11.2 Volatility Modelling & Forecasting using GARCH.9.1 Fixed and Random effects using the plm package.7.1 Investment \(\beta\) using R (Single Index Model).5.3.2 Layered graphics using \(\mathtt\).4.3.1 Example-Descriptive Statistics of Stock Returns.4.2 Data Transformation from Wide to Long (or vice versa).4.1.3 Sub setting and Logical Data Selection.2.3.4 Reading from Data Files from other Statistical Systems.
1.6 Task Views in R-Introduction & Installation.1.2 Installing R and RStudio on Windows.The rice research scientists should take advantage of these new opportunities adequately in adoption of the new highly potential advanced technologies while planning experimental designs, data collection, analysis and interpretation of their research data sets. Some of these advanced tools can be well applied in different branches of rice research, including crop improvement, crop production, crop protection, social sciences as well as agricultural engineering. The most recent technologies such as micro-array analysis, though cost effective, provide estimates of gene expressions for thousands of genes simultaneously and need attention by the molecular biologists. The probable applications of recent advanced tools of linear and non-linear mixed models like the linear mixed model, generalized linear model, and generalized linear mixed models have been presented. The highly informative means of displaying a range of numerical data through construction of box and whisker plots has been suggested. Common mathematical tools such as monomolecular, exponential, logistic, Gompertz and linked differential equations take an important place in growth curve analysis of disease epidemics. Disease forecasting methods by simulation models for plant diseases have a great potentiality in practical disease control strategies. The advanced statistical tools, such as non-parametric analysis of disease association, meta-analysis, Bayesian analysis, and decision theory, take an important place in analysis of disease dynamics. These tools include multivariate analysis of disease dynamics involving principal component analysis, cluster analysis, factor analysis, pattern analysis, discriminant analysis, multivariate analysis of variance, correspondence analysis, canonical correlation analysis, redundancy analysis, genetic diversity analysis, and stability analysis, which involve in joint regression, additive main effects and multiplicative interactions, and genotype-by-environment interaction biplot analysis. There has been a significant advancement in the application of statistical tools in plant pathology during the past four decades.
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