Then, depending on the curved pattern displayed, and whether or not the origin, is a data point, it will allow you to select the best transformation model to achieve linearity.Īnd once we have transformed the sample data to achieve linearity, we can then find the least-squares regression line and verify our results with a residual plot.įor example, notice how the original data below shows a nonlinear relationship. In contrast, the power model would suggest that we log both the x and y variables.ĭetermining the right model to choose is easiest to determine after looking at a scatterplot of the data. For example, if we choose the logarithmic model, we would take the explanatory variable’s logarithm while keeping the response variable the same. We apply one of the desired transformation models to one or both of the variables. How do we transform the nonlinear quantitative variables to create a linear relationship? In other words, how do we linearize a curved pattern? What are the common types of transformations to achieve linearity? When observation, theory, or experience suggests that the explanatory and response variables follow a familiar model. Okay, so we know that a lack of linearity implies a lack of association, but is there a way to transform one or both of the variables so that a nonlinear relationship can be changed into a linear relationship? This means it can help you obtain further insights into your data that may not be obvious at first. It’s one of several methods that you can use to transform datasets to achieve linearity. Jenn, Founder Calcworkshop ®, 15+ Years Experience (Licensed & Certified Teacher)

Rank transformation xlstat