Statistics and Modeling

Abstract

This chapter describes the application of statistical concepts with illustration about statistical models, probability, normal distribution, and analysis of variance (ANOVA). Statistical analysis is an important action process in research that deals with data. It follows well-defined, systematic, and mathematical procedures and rules. Data is information obtained to answer questions related to how much, how many, how long, how fast and how related. Statistics main objective is the analysis of data from generated experiment, but how should this data be collected to address our research questions and what should be our experimental design? Thus, in order to address question of interest clearly and efficiently, we need to organize experiment accurately so that we can have right type and amount of data. This is only possible using experimental design which has been elaborated in this chapter. The designs discussed here are completely randomized design (CRD), randomized complete block design (RCBD), Latin square design, nested and split plot design, strip-plot/split-block design, and split-split plot design. Similarly, factorial experiments have been discussed in detail with description about the interaction. The concept about fractional factorial design, multivariate analysis of variance (MANOVA), and analysis of covariance (ANCOVA) has been presented. Principal component analysis which is the method of multivariate statistics and used to check variation and patterns in a data set was also presented. It is easy way to visualize and explore data. The relationship between one or more variables to generate model which could be used for the prediction analysis has been discussed using concept of regression. Finally, association between two or more variables was presented using correlation. At the end different analytical tools/software were listed which can be used to do different kind of statistical analysis.