Final answer:
Using transformation techniques like logarithmic, exponential, or power transformations can linearize a curved scatterplot, making it possible to apply linear regression to originally nonlinear relationships.
Step-by-step explanation:
When dealing with a scatterplot, the goal is often to find a trend or pattern that best fits the data points. If the points indicate a linear relationship, simple linear regression can be used to model this relationship.
However, if the pattern of the points suggests a curved, or nonlinear relationship, linear regression may not be appropriate. In such cases, applying transformation techniques to the data can help "linearize" the pattern. These techniques, such as taking the logarithm, applying an exponential function, or using a power transformation, re-scale the data in such a way that the resulting pattern can approximate straight line once plotted again. This adjusts the scale or distribution of the data points, making it possible to apply linear regression models to relationships that originally appeared nonlinear. Statisticians must always inspect the scatter plot before deciding on the modeling approach, as the visualization of data provides insights beyond what the correlation coefficient alone could suggest.