BACHELOR OF BUSINESS ADMINISTRATION

Detailed explanation-1: -Linear regression analysis is used to predict the value of a variable based on the value of another variable. The variable you want to predict is called the dependent variable. The variable you are using to predict the other variable’s value is called the independent variable.

Detailed explanation-2: -Regression allows researchers to predict or explain the variation in one variable based on another variable. Definitions: ❖ The variable that researchers are trying to explain or predict is called the response variable. It is also sometimes called the dependent variable because it depends on another variable.

Detailed explanation-3: -You can use regression equations to make predictions. Regression equations are a crucial part of the statistical output after you fit a model. The coefficients in the equation define the relationship between each independent variable and the dependent variable.

Detailed explanation-4: -When there is a single continuous dependent variable and a single independent variable, the analysis is called a simple linear regression analysis . This analysis assumes that there is a linear association between the two variables.

Detailed explanation-5: -If y represents the dependent variable and x the independent variable, this relationship is described as the regression of y on x. The relationship can be represented by a simple equation called the regression equation.