BACHELOR OF BUSINESS ADMINISTRATION

Detailed explanation-1: -The difference between the observed value of y and the value of y predicted by the estimated regression equation is called a residual. The least squares method chooses the parameter estimates such that the sum of the squared residuals is minimized.

Detailed explanation-2: -A residual is the difference between an observed value of the response variable and the value predicted by the regression line.

Detailed explanation-3: -Regression Analysis The difference between the observed value of y and the predicted value of y is the error, or residual.

Detailed explanation-4: -Technically, regression analysis estimates the relationship between the expected value (mean) of the independent variable [E(y)] and the dependent variable. In simple linear regression, the predicted value of the dependent variable is given by the equation = b0 + b1x.

Detailed explanation-5: -In regression analysis, a residual is defined as the difference between the dependent variable’s actual and predicted value. The correct option is option a). Reason: Graphically, the error is the gap between the fitted regression line and the observed data point in the data set.