In this chapter, we will explore the basics of performing linear regression analysis using Microsoft Excel. Linear regression is a fundamental statistical method that allows us to understand the relationship between two or more variables.

By the end of this chapter, you'll learn how to use Excel to model and predict outcomes based on historical data, an essential skill in many fields such as economics, business, and science.

Task

In this practical exercise, you will apply the linear regression techniques learned in this chapter to a real dataset.

Note

Use the dataset from the previous chapter.

The goal is to predict Monthly Sales based on the Advertising Budget and Monthly Visitors.

• Begin by opening your Excel workbook that contains the columns Monthly Sales and Advertising Budget among others.
• Navigate to the Data tab in Excel and select Data Analysis.
• From the list of analysis tools, choose Regression and proceed.
• In the Regression dialog box, designate Monthly Sales as the Dependent Variable (Y Range) and Advertising Budget and Monthly Visitors as the Independent Variable (X Range).
• If your selected data includes headers, check the Labels box. Specify where you want the analysis results to appear on your worksheet by selecting the Output Range.
• Check Residuals to include an error analysis in your output.
• Click OK to run the Regression Analysis. Review the results, paying particular attention to the R-squared value to evaluate the model's fit, and assess the coefficients to gauge the impact of the Advertising Budget and the Monthly Visitors on Monthly Sales.
• Utilize the coefficients derived from your regression analysis to craft a formula that predicts sales based on varying advertising budgets.
• Experiment with this formula by altering the values of the Advertising Budget and the Monthly Visitors to observe the changes in predicted Monthly Sales.