Final answer:
To forecast revenue, regression analysis is used to calculate the least-squares regression line from historical data and then predict future sales using this model. For the electronics retailer example, predicted sales are calculated for specific days using the given regression equation.
Step-by-step explanation:
Predicting Sales with Regression Analysis
To forecast revenue for years using regression analysis, we typically follow these steps:
Gather historical sales data for past years.
Plot the data on a scatter plot, where the independent variable (years) is on the x-axis, and the dependent variable (revenue) is on the y-axis.
Determine if there is a significant correlation between the variables.
Calculate the least-squares regression line using the formula â = a + bx, where a is the y-intercept and b is the slope of the line.
Use this regression equation to make forecasts for future years.
For example, the model provided for an electronics retailer is Ŷ = 101.32 + 2.48x, where x is the day and Ŷ represents the sales in thousands. To predict the sales on day 60, plug 60 into the equation: Ŷ = 101.32 + 2.48(60) which calculates to Ŷ = 249.52 thousand dollars.
To predict the sales on day 90, use the same model: Ŷ = 101.32 + 2.48(90), and the predicted sales would be Ŷ = 324.12 thousand dollars.