What is a common output of regression analysis?

In regression analysis, one of the primary outputs is the correlation coefficient between variables. This coefficient quantifies the strength and direction of a linear relationship between the dependent variable and one or more independent variables. A correlation coefficient can range from -1 to +1, where values closer to 1 indicate a strong positive relationship, values closer to -1 indicate a strong negative relationship, and values near 0 suggest little to no linear relationship.

While other choices may describe aspects of data analysis or provide useful information, they do not directly pertain to the outputs specifically derived from regression analysis. For example, identifying the number of groups in a dataset or counting total entries are more related to descriptive statistics rather than regression modeling. Similarly, while graphical representations can help visualize trends, they are not an inherent outcome of regression's statistical calculations. In essence, the correlation coefficient stands out as a key result that reflects the nature of the relationship being analyzed in regression.