Use to test for a statistical differences in comparing three or more population means.
One-Way Analysis of Variance
- From an open JMP® data table, select Analyze > Fit Y by X.
- Click on a continuous variable from Select Columns, and Click Y, Response (continuous variables have blue triangles).
- Click on a categorical variable and click X, Factor (categorical variables have red or green bars). Click OK.
The Oneway Analysis output window will display. - Click on the red triangle, and select Means/Anova.
Some of the additions to the report include:
- Mean diamonds (95% Confidence Intervals) added to the graph.
- The Summary of Fit.
- The Analysis of Variance (Anova) table.
- Means for Oneway Anova, containing summary statistics and confidence intervals for each mean (based on the pooled estimate of the standard error).
• The null hypothesis is that there are no differences between the population means (i.e., all means are equal).
• Prob > F is the p-value for the whole model test. Since the Prob > F is less than 0.05, reject the null hypothesis of equal means. Conclude that there are differences between at least two of the means.
• To determine which means are different, a post hoc multiple comparison technique can be used.
Notes: The default confidence level is 95% (i.e., significant level of 0.05.) Select Set α Level under the red triangle to change.
Analysis can also be made assuming unequal variances. Select Unequal Variances under the red triangle to perform analysis.
Multiple Comparison Procedures
From the Oneway Analysis output window (shown above), click on the red triangle, select Compare Means, and select one of the five methods.
Each Pair, Student’s t has been selected. This produces comparison circles (shown), along with statistical output (not shown). Click on a circle for a mean to test for paired differences.
• The selected mean will have a bold, red circle and variable label.
• Means that are not significantly different from the selected mean will have unbolded, red circles and variable labels.
• Means that are significantly different from the selected mean will have gray circles and gray italicized variable labels.
In this example, the mean for big is significantly different from the mean for small, but is not significantly different from the mean for medium.
Visit Basic Analysis > Oneway Analysis in JMP Help to learn more.