What does the slope of a regression line indicate?

The slope of a regression line is a crucial aspect of understanding the relationship between the independent and dependent variables in a regression analysis. Specifically, it quantifies how much the dependent variable is expected to change for each one-unit increase in the independent variable. This relationship is foundational to interpreting regression results, as it provides direct insight into the nature of the impact that one variable has on another.

When the slope is positive, it indicates that an increase in the independent variable leads to an increase in the dependent variable, while a negative slope suggests that an increase in the independent variable leads to a decrease in the dependent variable. This clear interpretation is vital for making predictions and understanding the dynamics between the variables involved in the analysis. Therefore, stating that the slope represents the change in the dependent variable for each unit increase in the independent variable accurately captures its significance in regression analyses.

Other options, like total variance or the strength of the relationship between variables, focus on different aspects of data analysis and do not relate to what the slope specifically measures. While correlation can reflect whether variables are positively or negatively related, it is separate from the slope's numeric indication of that relationship's magnitude and direction.