Regression analysis is all about data and numbers. In an organization, big or small, numbers and data are very important to analyze its strength, weakness, risks, or opportunities.
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Regression analysis is a type of statistical model. It is a statistical process that estimates the relationship between the dependent variable (also called the outcome variable) and the independent variable. There can be one or more independent variables (also called predictors, features, or covariates). Regression analysis is significant in all kinds of businesses. It allows the managers to make informed and effective decisions regarding the business. It also ensures the smooth functioning of the business.
To analyze and estimate,the researcher chooses a model and then use it for analyzing and estimating. The following are the components of the regression model:
It is understood that regression analysis is all about numbers and calculations. For interpreting the meaningful statistics using regression and measure real-world relationships, researchers rely on some basic assumptions. These assumptions are:
The graphical representation or linear approach to model the relationship between the dependent variable and the independent variable is known as linear regression. The estimation methods of linear regression are a least-square estimation and related techniques, maximum-likelihood estimation, and related techniques, etc.
When the observational data is modelled by a function that is nonlinear and depends on independent variables is known as nonlinear regression.
The importance of regression analysis can be explored with the following points:
It helps scholars to get answers for some of the important questions such as:
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