By Jose G. Ramirez Ph.D., Brenda S. Ramirez M.S.
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5). 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.
What Sir Ronald Fisher called the “essential safeguard” of randomization leads to selecting units at random from the entire production lot. Randomization forms the physical basis for the validity of statistical tests. Any study can be made more sensitive by increasing the number of units sampled from the population. But how many samples are enough? This is perhaps the most frequently asked question by experimenters. How many samples should we include in our study in order to answer our question with a high degree of confidence?
For example, we must highlight Effective Thickness, which is the name of the response of interest in our JMP table. When these elements come together to provide instructions for creating, for example, a histogram for a response in JMP, it will look like the following: “We use the Analyze > Distribution platform to generate the histogram and the descriptive statistics for the data. ” Similar to statistics notes, JMP notes are scattered throughout the chapters to highlight information that is relevant to using JMP but that might not be obvious.