## Regression Analysis

An integral part of the Zenoctus Regression Analysis.

### More on Research

The Regression Analysis is a powerful statistical tool, which is used to investigate relationships between variables. The goal in Regression Analysis is to create a mathematical model to predict the value of Y (dependent variable) when we know the value of X (independent variable). The Correlation analysis is often used in conjunction with the Regression Analysis, because it measures the strength of the relationship between the two variables X and Y. There are two basic types:

• Simple Regression Analysis: Applied to estimate the relationship between a dependent variable and a single independent variable; for example, the relationship between sales and customer service.
• Multiple Regression Analysis: Used to estimate the relationship between a dependent variable and two or more independent variables; for example, the relationship between customer satisfaction and customer service and delivery time.

The Multiple Regression Analysis delivers more realistic results than a simple Regression Analysis.

While the correlation analysis provides a single numeric summary of a relation (“the correlation coefficient”), Regression Analysis leads to a prediction equation, which describes the relationship between the variables. When the Rsquare value shows a strong relationship, it can be used to predict values of one variable when other variables have values that are known. Regression Analysis is applied in customer and employee satisfaction studies to answer questions such as:

• How will the overall satisfaction score change if satisfaction with customer service goes up from 5 to 6?
• Which product dimensions or features contribute most to the overall satisfaction to the brand?

Regression Analysis is also applied to predict the future or simulate the outcome when specific actions are taken. Example:

• What happens to the satisfaction value when we improve the delivery time?