1.
Open the Analgesics.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select pain and click Y, Response.
4.
Select drug and click X, Factor.
5.
6.
From the red triangle menu, select Analysis of Means Methods > ANOM.
Example of Analysis of Means Chart
For the example in Example of Analysis of Means Chart, the means for drug A and C are statistically different from the overall mean. The drug A mean is lower and the drug C mean is higher. Note the decision limits for the drug types are not the same, due to different sample sizes.
This example uses the Spring Data.jmp sample data table. Four different brands of springs were tested to see what weight is required to extend a spring 0.10 inches. Six springs of each brand were tested. The data was checked for normality, since the ANOMV test is not robust to non-normality. Examine the brands to determine whether the variability is significantly different between brands.
1.
Open the Spring Data.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select Weight and click Y, Response.
4.
Select Brand and click X, Factor.
5.
6.
From the red triangle menu, select Analysis of Means Methods > ANOM for Variances.
Example of Analysis of Means for Variances Chart
From Example of Analysis of Means for Variances Chart, notice that the standard deviation for Brand 2 exceeds the lower decision limit. Therefore, Brand 2 has significantly lower variance than the other brands.
This example uses the Big Class.jmp sample data table. It shows a one-way layout of weight by age, and shows the group comparison using comparison circles that illustrate all possible t-tests.
1.
Open the Big Class.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select weight and click Y, Response.
4.
Select age and click X, Factor.
5.
6.
From the red triangle menu, select Compare Means > Each Pair, Student’s t.
Example of Each Pair, Student’s t Comparison Circles
The means comparison method can be thought of as seeing if the actual difference in the means is greater than the difference that would be significant. This difference is called the LSD (least significant difference). The LSD term is used for Student’s t intervals and in context with intervals for other tests. In the comparison circles graph, the distance between the circles’ centers represent the actual difference. The LSD is what the distance would be if the circles intersected at right angles.
Example of Means Comparisons Report for Each Pair, Student’s t
In Example of Means Comparisons Report for Each Pair, Student’s t, the LSD threshold table shows the difference between the absolute difference in the means and the LSD (least significant difference). If the values are positive, the difference in the two means is larger than the LSD, and the two groups are significantly different.
1.
Open the Big Class.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select weight and click Y, Response.
4.
Select age and click X, Factor.
5.
6.
From the red triangle menu, select Compare Means > All Pairs, Tukey HSD.
Example of All Pairs, Tukey HSD Comparison Circles
Example of Means Comparisons Report for All Pairs, Tukey HSD
In Example of Means Comparisons Report for All Pairs, Tukey HSD, the Tukey-Kramer HSD Threshold matrix shows the actual absolute difference in the means minus the HSD, which is the difference that would be significant. Pairs with a positive value are significantly different. The q* (appearing above the HSD Threshold Matrix table) is the quantile that is used to scale the HSDs. It has a computational role comparable to a Student’s t.
1.
Open the Big Class.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select weight and click Y, Response.
4.
Select age and click X, Factor.
5.
6.
From the red triangle menu, select Compare Means > With Best, Hsu MCB.
Examples of With Best, Hsu MCB Comparison Circles
Example of Means Comparisons Report for With Best, Hsu MCB
1.
Open the Big Class.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select weight and click Y, Response.
4.
Select age and click X, Factor.
5.
6.
From the red triangle menu, select Compare Means > With Control, Dunnett’s.
Alternatively, click on a row to highlight it in the scatterplot before selecting the Compare Means > With Control, Dunnett’s option. The test uses the selected row as the control group.
8.
Example of With Control, Dunnett’s Comparison Circles
Using the comparison circles in Example of With Control, Dunnett’s Comparison Circles, you can conclude that level 17 is the only level that is significantly different from the control level of 12.
1.
Open the Big Class.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select weight and click Y, Response.
4.
Select age and click X, Factor.
5.
Comparison Circles for Four Multiple Comparison Tests
From Comparison Circles for Four Multiple Comparison Tests, notice that for the Student’s t and Hsu methods, age group 15 (the third circle from the top) is significantly different from the control group and appears gray. But, for the Tukey and Dunnett method, age group 15 is not significantly different, and appears red.
1.
Open the Big Class.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select height and click Y, Response.
4.
Select sex and click X, Factor.
5.
Example of the Unequal Variances Report
This example uses the Big Class.jmp sample data table. Examine if the difference in height between males and females is less than 6 inches.
1.
Open the Big Class.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select height and click Y, Response.
4.
Select sex and click X, Factor.
5.
8.
Example of an Equivalence Test
The data in the Drug Toxicity.jmp sample data table shows the toxicity levels for three different formulations of a drug.
1.
Open the Drug Toxicity.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select Toxicity and click Y, Response.
4.
Select Formulation and click X, Factor.
5.
Example of Robust Fit
1.
Open the Typing Data.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select speed and click Y, Response.
4.
Select brand and click X, Factor.
5.
10.
Select the Solve for Power check box.
Example of the Power Details Window
11.
Click Done.
Example of the Power Report
1.
Open the Big Class.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select height and click Y, Response.
4.
Select sex and click X, Factor.
5.
6.
From the red triangle menu, select Normal Quantile Plot > Plot Actual by Quantile.
Example of a Normal Quantile Plot
1.
Open the Analgesics.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select pain and click Y, Response.
4.
Select drug and click X, Factor.
5.
Example of a CDF Plot
1.
Open the Big Class.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select height and click Y, Response.
4.
Select sex and click X, Factor.
5.
6.
From the red triangle menu, select Densities > Compare Densities, Densities > Composition of Densities, and Densities > Proportion of Densities.
Example of the Densities Options
This example uses the Matching.jmp sample data table, which contains data on six animals and the miles that they travel during different seasons.
1.
Open the Matching.jmp sample data table.
2.
Select Analyze > Fit Y by X.
3.
Select miles and click Y, Response.
4.
Select season and click X, Factor.
5.
7.
Select subject as the matching column.
8.
Example of the Matching Column Report
The Matching Fit report shows the season and subject effects with F tests. These are equivalent to the tests that you get with the Fit Model platform if you run two models, one with the interaction term and one without. If there are only two levels, then the F test is equivalent to the paired t-test.
Note: For details about the Fit Model platform, see the Fitting Linear Models book.