Scatterplot

  

Scatterplots typically contain the following elements:

·    An x-axis representing values of a continuous variable. By custom, this is the predictor (independent) variable.

·    A y-axis representing values of a continuous variable. By custom, this is the response (dependent) variable.

·    Symbols plotted at the (x, y) coordinates of your data. Optionally, you can use different colored/shaped symbols to represent different groups on the same plot.

Use a scatterplot to assess the direction, strength, and linearity of the relationship between two variables:

·    Data values tending to rise together indicate a positive correlation.

·    If one data value rises as the other decreases, a negative correlation is indicated.

·    Stronger relationships produce tighter clustering of data points.

·    Outliers will fall farther away from the main group of dots.

You can visually determine if the relationship is linear or curved. If the relationship is curved, then you may need to include higher-order terms to accurately model the curvature (see polynomial regression).

Use scatterplots instead of time series plots when your time-dependent data are not chronologically ordered, or if the data collection intervals are irregular.

Example Output

image\gr_plot.gif

Interpretation

The scatterplot for the BMI and fat data shows a strong positive and linear relationship between the two variables. Body Mass Index (BMI) may be a good predictor of body fat percentage.