have the same mean BMI, and before choosing a test we’ll compare the variances of the two populations. We’ll test the hypothesis that adult women and men in the U.S. A lean person has lower BMI than an overweight person. BMI is a measure of body fat as a percentage of total weight. Let’s shift back to the NHANES data, and consider the Body Mass Index (BMI) for the adult men and women in the samples. The Fit Y by X platform in JMP includes a command that facilitates the judgment. Both in the t-tests and in techniques we’ll see later, we may need to decide whether two or more groups have equal or unequal variances before we choose a statistical procedure. In our earlier discussion of the t-test we noted that there are two versions of the test, corresponding to the conditions in which the samples share the same variance or have different variances. (please note that results may vary depending on your version of SAS software) The following excerpt is from SAS Press author Robert Carver and his book 'Practical Data Analysis with JMP' Copyright © 2010, SAS Institute Inc., Cary, North Carolina, USA. To read a free chapter and reviews of the book, visit Robert's author page. I'm using JMP v13 (and it was this way in previous versions as well).If you're using JMP, please read on. This week's tip is from Professor Robert Carver and his popular book Practical Data Analysis with JMP. I do not have a copy of the referenced text, and the FDA macros mentioned are decades old now.Ĭan anyone enlighten me on why the pooled MSE is not appropriate in this case (but it is in other two models considered: Common Intercept/Common Slope, Different Intercepts/Common Slope)? (2007), Statistical Design and Analysis of Stability Studies. In JMP help, they reference: Chow, Shein-Chung. I also believe this method was implemented to maintain consistency with results from a SAS macro called STAB that was written by FDA researchers.' JMP's response was: 'Our development team chose to match Chow’s codes. My question is this (I posed this question to JMP as well) for Model 1 (as defined above) why not use the pooled MSE? Prediction intervals are computed for each batch using individual mean squared errors, and the interval that crosses the specification limit first is used to estimate the expiration date.”

“When Model 1 (different slopes and different intercepts) is used for estimating the expiration date, the MSE (mean squared error) is not pooled across batches. Good day: I have a question about the stability analysis platform in JMP.

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