# Excel Regression Analysis: Decision-Making Insights

11 min read|Last Updated: March 13th, 2024|Categories: excel|
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Regression analysis is a statistical method used to study the magnitude and structure of a relationship between a single dependent variable and one or more independent variables.

Understanding the relationships between variables can enable business owners, executives, and scientists to pinpoint areas for improvement. This can be leveraged to drive strategic planning and decision-making.

## Linear Regression Use Cases

Regression analysis is used in science and business to:

• Explain unknown phenomena. e.g. Why did sales drop last month?
• To decide what to do. e.g. Should we plan a different promotion or go with this one?
• Predict things about the future. e.g. What will sales look like over the next quarter?

Regression analysis can be performed using statistics programs like SPSS, STATA, or Excel, as well as programming languages like Python and R. In this blog, we use Excel to perform linear regression analysis on a real-world dataset.

## Linear Regression Applications

One can find many examples of the usefulness of regression analysis in various fields or domains, such as finance, healthcare, retail, etc. Below are some examples.

• Sales Forecasting:  Regression can be used to predict a salesperson’s total yearly sales from independent variables such as age, education, and years of experience.
• Psychology: Regression can also be used in the field of psychology, for example, to determine individual satisfaction, based on demographic and psychological factors.
• Price Prediction: Regression analysis can be used to predict the price of a house in an area, based on its size number of bedrooms, and so on.
• Income Prediction: Regression can help us predict employment income for independent variables such as hours of work, education, occupation, sex, age, years of experience, and so on.