Outliers in regression analysis can greatly affect the results by distorting the slope and intercept of the regression line. They can reduce the accuracy of predictions, inflate error terms, and impact measures like R². Outliers may also hide or exaggerate real trends in the data. It’s important to detect them using tools like residual plots or Cook’s distance to ensure reliable analysis.