A guide to regression analysis

Regression analysis is a statistical method used by product managers to understand the relationship between various independent variables, such as feature usage or customer demographics, and a dependent variable, which is an outcome of interest like product adoption or user retention. This method helps in identifying factors that drive successful product adoption, improve retention, and segment customers for better targeting. Linear regression is useful for continuous outcomes, while logistic regression is suited for binary outcomes. The blog post emphasizes the importance of clean data and correct variable definition for accurate results, highlighting that tools like Google Sheets and Excel can be used for linear regression, though more sophisticated statistical packages are required for logistic regression. It encourages product managers to utilize regression analysis to predict outcomes and drive business metrics improvement, while cautioning that correlation does not imply causation and that the quality of data is crucial for reliable analysis.