This indicates that the means are significantly different. We can see that these diamonds don’t overlap. The mean diamonds are 95% confidence intervals for the mean strength for each material. JMP adds mean diamonds to the graph and produces several tables of statistical output. To do this, we select Means/Anova/Pooled t from the red triangle. The spread of the data for the two materials is similar, so we run the pooled two-sample t test. To better see the individual observations, we select Display Options from the red triangle, and then Points Jittered. JMP produces a scatterplot of Strength versus Material. One-way analysis is used when you select a continuous response variable and a categorical factor or input variable. The key in the bottom corner of the dialog box tells us that JMP will conduct a one-way analysis. We select Strength as the Y, Response, and Material as the X, Factor. To conduct this test, we select Fit Y by X from the Analyze menu. You want to test the null hypothesis that the mean breaking strength for parts made using the two materials is the same against the alternative that the means are not equal. You’ve measured the breaking strength for 10 randomly selected parts made from each material. Parts are typically made using Material 1, but they can also be made using Material 2. In this scenario, the characteristic of interest is the breaking strength of a part in ksi (kilos per square inch). In this video, you learn how to conduct a two-sample t test using the file Breaking Strength.jmp.
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