Interpret the key results for Scatterplot

Complete the following steps to interpret a scatterplot.

Step 1: Look for a model relationship and assess its strength

Add a regression fit line to the scatterplot to model relationships in your data. (For more information, go to Customize the scatterplot.) If a model fits well, you can use the regression equation for that model to describe your data. Minitab adds a regression table to the output pane that shows the regression equation and the R-squared value (R-sq).

Tip

You can also hold the pointer over the regression fit line to see the regression equation and R-squared value.

Type of relationship

The following are examples of the types of relationships you can model with a regression fit line.

Linear: positive
Linear: negative
Curved: quadratic
Curved: cubic
No relationship

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

Strength of relationship

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. The higher the R-squared value, the more accurately the regression equation models your data.

Weaker relationship
Stronger relationship

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

Step 3: Look for other patterns

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

Outliers

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 errors or measurement errors. Consider removing data values that are associated with abnormal, one-time events (special causes). Then, repeat the analysis.

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