Correlation and regression are two important statistical techniques used to study the relationship between variables. Both are widely used in business and computer applications for analysing data, making comparisons, identifying patterns and supporting managerial decision-making. However, they differ in their purpose, interpretation and method of application. Correlation measures the degree and direction of association between two variables. It indicates whether changes in one variable are related to changes in another variable. For example, in business, correlation can be used to examine the relationship between advertising expenditure and sales, employee experience and productivity, or price and demand. The correlation coefficient generally ranges from -1 to +1. A value close to +1 indicates a strong positive relationship, a value close to -1 indicates a strong negative relationship, and a value close to zero indicates little or no linear relationship. Regression, on the other hand, is mainly used to estimate or predict the value of one variable based on the value of another variable or a group of variables. It establishes a mathematical relationship between a dependent variable and one or more independent variables. For example, a business can use regression analysis to predict future sales based on advertising expenditure, market size, income level and other factors. In computer-based business analysis, regression models can be developed using spreadsheet software and statistical applications.