OLS (Ordinary Least Squares) in Power BI refers to the implementation of linear regression using the Ordinary Least Squares method. Power BI is a business analytics service by Microsoft that provides interactive visualizations and business intelligence capabilities.

In Power BI, you can perform linear regression using OLS to explore the relationship between two or more variables in your data and make predictions based on that relationship. The process typically involves the following steps:

1. Data import: Load your dataset into Power BI from various sources such as Excel, SQL Server, CSV, etc.

2. Data preparation: Ensure your data is cleaned and properly formatted for analysis. Remove any missing values or outliers that might affect the results.

3. Create a scatter plot: Visualize the relationship between the independent variable(s) and the dependent variable using a scatter plot. This will help you understand the nature of the relationship and whether linear regression is appropriate for your data.

4. Add a trend line: In Power BI, you can add a trend line to the scatter plot by using the “Analytics” pane. The trend line will represent the best-fit line through the data points based on the OLS method.

5. View regression statistics: Power BI provides regression statistics such as R-squared, coefficient values, p-values, etc., to help you understand the quality and significance of the regression model.

6. Make predictions: Once you have a satisfactory regression model, you can use it to make predictions for new data points based on the learned relationship between variables.

It’s worth noting that Power BI doesn’t have built-in native support for OLS as a separate feature; instead, it relies on visualizations and analytic options to perform the analysis. Other dedicated statistical software or programming languages like R or Python might offer more in-depth OLS capabilities and further statistical analysis.