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Regression analysis is a statistical measure for determining the relationship between variables. It includes classifying, analyzing, organizing, and recording variables. There are two types of variables involved that is, dependent variable, and independent variable. Dependent variables are measured, predicted and are expected to be affected by the manipulation of the independent variable (Fox and John 107). A researcher manipulates independent variables to determine their effects on other variables. Regression analysis includes the probability of distribution, which is dependent on the variation of the dependent variable.
Forecasting involves prediction of various measures, regression is used to determine the types of independent variables that are related to dependent variables and analyzes the factors or forms of this relationship (Draper, Norman and Harry 46). There are techniques applied in regression. Ordinary Least Squares and linear regression are some of the regression techniques. They allow the use of unknown numbers or finite numbers in applying regression. These are a parametric regression. Non-parametric regression allows the use of infinite numbers and involves specified functions. They optimize business processes, these aids in enhancement of business productivity and rapid advancement in the world’s economy
Data generating is a basis for which the application of regression analysis is based on according to its relationship with regression method applied. Regression analysis always makes assumptions concerning what data-generating method to use (Draper, Norman, and Harry 116). Their estimation is on a reasonable standard of accuracy. Regression data are for predetermined tasks and involve systematic collection method in a form that suits the regression method applied. Regression analysis is, therefore, a primary tool used today in the business world and even various sectors of the economy and these makes it more advanced.
Draper, Norman R., and Harry Smith. Applied regression analysis. John Wiley & Sons, 2014. Print.
Fox, John. Applied regression analysis and generalized linear models. Sage Publications, 2015. Print.
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