Complete the following steps to interpret a matrix plot.

Look for model relationships between pairs of variables. 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.

The following examples show different 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.