By Jose G. Ramirez Ph.D., Brenda S. Ramirez M.S.

In response to real-world purposes, interpreting and examining non-stop facts utilizing JMP: A step by step consultant, by means of Jose Ramirez, Ph.D., and Brenda S. Ramirez, M.S., combines statistical directions with a strong and well known software program platform to resolve universal difficulties in engineering and technological know-how. within the many case stories supplied, the authors truly manage the matter, clarify how the knowledge have been accumulated, express the research utilizing JMP, interpret the output in a trouble-free approach, after which draw conclusions and make techniques. This step by step layout permits clients new to stats or JMP to profit as they move, however the booklet may also be worthwhile to these with a few familiarity with statistics and JMP. The ebook incorporates a foreword written by means of Professor Douglas C. Montgomery.

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28 Analyzing and Interpreting Continuous Data Using JMP: A Step-by-Step Guide 3. statistical inference based on a sample drawn from an unknown universe, and the statistics calculated from the sample. 3). 4. checking for homogeneity to make sure that we can reasonably assume that our data came from a single universe rather than multiple ones. This is important because the other three ways to use statistics depend on this assumption to be of any practical value. 2 Measurement Scales, Modeling Types, and Roles Although when Lord Kelvin lectured at the Institution of Civil Engineers in 1883 he was referring to numerical quantities, his statement about being able to quantify phenomena is still relevant: “In physical science the first essential step in the direction of learning any subject is to find principles of numerical reckoning and practicable methods for measuring some quality connected with it.

Chapter 2: Overview of Statistical Concepts and Ideas 43 • Experimental Unit (EU): This is the smallest unit that is affected by the “treatment,” or factors, we want to study. The output from the JMP sample size calculations will dictate how many experimental units we will need in our study. Note, this is not necessarily the same as the number of measurements that we will have at the end of our study. If we decide, for example, to measure each experimental unit more than once, then we will end up with more readings than we have experimental units.

In other words, the sample size, n, and the sampling scheme for how to select the n samples defines the sampling plan for our study. There are many ways of selecting random samples from populations. Below, we discuss three popular sampling schemes that are commonly used in engineering and scientific studies: the simple random sample, the stratified simple random sample, and the cluster random sample. • Simple Random Sample: For this sampling scheme, every unit in the population has an equal chance of being selected for the study.