Determination of Single Factor Fixed Effect Experiments in CRD with Multiple Linear Regression Analysis

Abstract

Regression analysis and ANOVA (Analysis of Variance) are two methods in the statistical theory to analyze the behavior of one variable compared to another. Usually statisticians deal with regression models and analysis of variance models as separate subjects, especially when refined in the initial levels. In fact, can be deal with analysis of variance model as a special case of the multiple regression models. Therefore, any model of analysis of variance can be solved by using multiple regression analysis and access to same results of matching to analysis of variance. The regression analysis is widely used in predicting and forecasting. It is also used to establish relationships in experimental data. Dummy variables are variables that take the values of only 0 or 1. They may be explanatory or outcome variables; however, the focus of this article is explanatory or independent variable construction and usage. We do not recommend using multiple regression method instead of the analysis of variance method, but it can be shown that however the multiple regression analysis method contain more than one method of analysis operations with respect to analysis of variance, but the use is more useful than in some designs especially in experimental data. Interpretation of the relationship between the analysis of variance of single factor fixed effect balanced CRD where  all  eplications  are equal and multiple regression analysis model by using dummy variables, and a more  detailed introduction of a teaching techniques is presented.

Keywords: Fixed Model, Analysis of Variance, Complete Randomized Design, Dummy Variables, Multiple Regression Analysis.