Complete the following steps to interpret a scatterplot.

Determine which model relationship best fits your data and assess the strength of the relationship. If a model fits well, you can use the regression equation for that model to describe your data.

To see how well a particular model fits your data, add a fitted regression line. With the graph active, choose . You can hold the pointer over the fitted regression line to see the regression equation.

Determine which model relationship, if any, best fits your data. The following are examples of the types of relationships you can model with a regression fit line.

If your data seem to fit a model, you can explore the relationship using a regression analysis.

Assess how closely the data fit the model to estimate the strength of the relationship between X and Y. When the relationship is strong, the regression equation models the data accurately. If you have a fitted regression line, hold the pointer over it to view the regression equation and the R-squared value. The higher the R-squared value, the more accurately the regression equation models your data.

To quantify the strength of a linear (straight) relationship, use a correlation analysis.

Outliers may indicate unusual conditions in your data. Time-based trends may indicate changing data conditions.

Outliers, which are data values that are far away from other data values, can strongly affect your results.

On a scatterplot, isolated points identify outliers.

Try to identify the cause of any outliers. Correct any data entry or measurement errors. Consider removing data values that are associated with abnormal, one-time events (special causes). Then, repeat the analysis.

If the X variable contains a sequence of time or date values recorded in order, look for time-based trends. To add connect a line to your scatterplot, with the scatterplot active, choose and select Connect Line.

If you collected data in equally-spaced time intervals, you can use a time series plot.