What effect does an outlier have on the value of correlation in regression analysis?

In regression analysis, the presence of outliers can significantly affect the correlation between the variables being studied. An outlier is a data point that is significantly different from the rest of the data. This unusual observation can leverage its influence in determining the correlation coefficient, which measures the strength and direction of the linear relationship between two variables.

While it might be tempting to assume that outliers always increase the correlation, the reality is that the effect of an outlier depends on its position relative to the overall data trend. An outlier that is positioned in a way that aligns with the current trend can increase correlation, while one that deviates drastically from the trend can decrease it. Therefore, the correct option acknowledges this variability by stating that outliers may increase or decrease the correlation value.

Understanding the dual potential of outliers helps clarify how they can either strengthen or weaken perceived relationships between variables, making option D the appropriate choice in this context.