Bivariate Regressions

Bivariate Regression 1

In the first bivariate regression, the dependent variable is sales while the selected independent variable is Quantity. The reason for selecting sales as the independent variable is to establish the correlation that it has with sales. There was a need to investigate if an increase or decrease in the amount of quantity will have any implications on the amount of sales.

The regression model that was used is that of Regression. Bivariate Regression analysis tests a simple hypothesis mainly between casualty and association. The end goals is to establish if one of the variable is the cause of the changes in the second variable. 

The outcomes of the bivariate regression are as follows

R2 = 0.04

P value = 0.166

Intercept = 16.73

Coefficient = 56.24

The equation for the regression will be Y = 16.73X + 56.24

An interpretation of the equation depict the presence of a positive connection between sales and quantity.

Bivariate Regression 2

In the second bivariate regression, the dependent variable is sales while the chosen independent variable is profit. A key consideration in selecting the independent variable is to establish its relationship with sales. The assumption being investigated is that an increase in profits positively affect the outcomes depicted in sales. 

Regression is the regression model that was used in the second bivariate regression analysis. The approach mainly focuses on prediction. An equation is created that is used to forecast possible output in the future after creating the connection that exists between the variables being investigated.

The results of the second bivariate regression are as follows

R2 = 0.23

P value = 0.0037

Intercept = 193.33

Coefficient = 1.27

The equation will therefore be Y = 193.33X + 1.27

The positive sign shows that an increase in profits will lead to a corresponding increase in the amount of total sales.