Bivariate regressions
Bivariate regression analysis is an excellent tool to help you answer questions about a business. When you use bivariate analysis, you can discover whether there is a strong correlation between a dependent and an independent variable. As a business consultant, you will probably want to test a hypothesis for cause and effect when you use a scatterplot and a line of best fit, which will show you the strength of the correlation.
In this scenario, you will continue to work as a business consultant trainee with the superstore client. The superstore would like to know which key attributes have an impact on its sales revenue and the number of orders. Your vice president would like you to perform two bivariate regressions to analyze the data. Remember that the superstore is interested in whether specific trends are identified that can help grow its business through improved operations and sales. Then you will write a report for your vice president of operations in which you describe the regression models and the key attributes you chose to analyze. Additionally, you will explain why you chose to analyze those key attributes.
Prompt
Your task is to create two bivariate regressions using Excel. You will also write a short report that describes the regression model you used and why you chose to analyze your selected independent variables.
Perform two bivariate regressions on the data using the Superstore Excel Workbook to complete this step. This workbook contains your work from previous modules. Both bivariate regressions should analyze Sales with the independent variables of your choice.
Create one bivariate regression that is placed within the Bivariate_Regression_1 worksheet
Create one bivariate regression that is placed within the Bivariate_Regression_2 worksheet.
Explain the results of the bivariate regressions. For each bivariate regression performed, address the following:
Why did you choose your selected independent variable?
Explain the regression model used.
Include the key regression output values that include: R2, p value, intercept, and coefficients.
Explain the regression equation performed.
supply have a direct relationship which states that an increase in the price will result in increased supply. The law of supply states that the supply will be affected only by the price, and the other elements are kept constant and the increase in price will increase the demand (Arnold, 2008). Therefore, it can be stated that an increase in price at £40 will result in an increase in demand, and this can be evaluated from the graph above which shows that when the price becomes £40, the supply becomes 70m units per year.
Income Effect
Normal goods are the goods, whose demand shows a direct relationship with the income as such goods are the goods that the customers demand when the income increases, and the affordability also increases. The goods whose demand falls when the income of the individual decreases, and the demand increases when the income is increased are known as normal goods (Gans, King and Mankiw, 2011). Therefore, assuming SmartWatch as a normal good, then the demand for such will increase, when the income of the individual increases. This effect is also known as income effect which states that when a person is earning more, then his affordability and capability is also increased, which provides him more opportunities to purchase quality products. Therefore, the demand for SmartWatch will increase when the income will increase, while the other elements or factors remain constant.
Question 2
Price Elasticity of Demand
The price elasticity of demand shows the relationship between the changes in the quantity relative to the price of a commodity. This is calculated by dividing the percentage in quantity demanded with the percentage change in price (Goodwin et al., 2018).