By Jose G. Ramirez Ph.D., Brenda S. Ramirez M.S.
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Additional resources for Analyzing and Interpreting Continuous Data Using JMP:: A Step-by-Step Guide
In some cases we can use sound engineering judgment based on past experiences, or use interpretations of data in order to (typically) make good decisions. This is usually the case if the signals in our systems are large, with respect to the noise in the system, or if Chapter 2: Overview of Statistical Concepts and Ideas 25 we can explain them with mechanistic models. That being the case, we still need to ask: how do we understand noise, how can we quantify it, study its effects, and maximize our signal-to-noise ratio?
The same is true of the statistical analysis performed using the data. The quantities derived from the data (statistics) are meaningful only in the context of the data. In other words, for any meaningful analysis, we need to understand how the data is going to be collected, what they represent, and how many we need. 4 Statistical Inference Population of Interest Random Draw Representative Sample Statistical Inference Measured Values We take a random and representative sample from the population in order to make some statistical inferences.
The underlying population must be normally distributed, or close to normally distributed, the data are homogeneous, and the experimental units must be independent from each other. The second type of statistics typographical convention that the reader will encounter is a callout box, like the one below. The information in the callout box is a snippet of a key point that is presented in the main text of the chapter. They are intended to be short and memorable. A p-value is an area under a probability density curve that quantifies the likelihood of observing a test statistic as large as, or larger than, the one obtained from the data.
Analyzing and Interpreting Continuous Data Using JMP:: A Step-by-Step Guide by Jose G. Ramirez Ph.D., Brenda S. Ramirez M.S.