# Thesis with multiple regression analysis

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Multiple regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable or sometimes, the outcome, target or criterion variable. The variables we are using to predict the value of the dependent variable are called the independent variables or sometimes, the predictor, explanatory or regressor variables.

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Multiple regression analysis is used to predict the value of a variable dependent using two or more variables independent variables. Multiple regression analysis is an extension of linear regression analysis that uses one predictor to predict the value of a dependent variable. Where Y is the predicted term while X the independent variable. The variable estimated in the model is usually unknown while the independent variables are given.

### Regression analysis

Regression analysis is a quantitative research method which is used when the study involves modelling and analysing several variables, where the relationship includes a dependent variable and one or more independent variables. In simple terms, regression analysis is a quantitative method used to test the nature of relationships between a dependent variable and one or more independent variables. The formulae for regression equation would be.
You would know about linear regression. For the uninitiated, it is the simplest and most largely used statistical model for predictive modeling. Well, mathematicians generally prefer to describe values as either dependent or independent variables. Independent variables are the specific inputs of the experiment and dependent variables are the output.
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