BACHELOR OF BUSINESS ADMINISTRATION

Detailed explanation-1: -Multiple regression is a statistical technique that can be used to analyze the relationship between a single dependent variable and several independent variables. The objective of multiple regression analysis is to use the independent variables whose values are known to predict the value of the single dependent value.

Detailed explanation-2: -The simplest form of the regression equation with one dependent and one independent variable is defined by the formula y = c + b*x, where y = estimated dependent variable score, c = constant, b = regression coefficient, and x = score on the independent variable.

Detailed explanation-3: -Answer and Explanation: In regression analysis, a simple linear regression equation assumes only two variables with one independent variable and one dependent variable. However, in multiple regression analysis, the dependent variable is regressed on more than one independent variable.

Detailed explanation-4: -Multiple linear regression (MLR), also known simply as multiple regression, is a statistical technique that uses several explanatory variables to predict the outcome of a response variable. Multiple regression is an extension of linear (OLS) regression that uses just one explanatory variable.

Detailed explanation-5: -Regression analysis is a common statistical method used in finance and investing. Linear regression is one of the most common techniques of regression analysis. Multiple regression is a broader class of regressions that encompasses linear and nonlinear regressions with multiple explanatory variables.