Bayesian Analysis with Excel and R by Conrad Carlberg

Author:Conrad Carlberg [Conrad Carlberg]
Language: eng
Format: epub
Publisher: Addison-Wesley Professional
Published: 2022-11-11T00:00:00+00:00

Setting the Stage

Many variables of interest to scientists and statisticians are distributed as a normal or “bell” curve. (You are also likely to see such curves referred to in the literature as Gaussian.) These curves tend to be symmetric rather than skewed. It is also possible to specify their distributions with two numbers: a measure of central tendency (that is, a mean, median, or mode) and a measure of the spread of the distribution around that central tendency (often by means of a standard deviation or quantile).

One such variable is the cholesterol level in humans. For a variety of reasons, you might find it important and interesting to determine the central tendency and the standard deviation of cholesterol in a sample, with the intent of generalizing your findings to a population. We can use an extension of the techniques discussed in earlier chapters to distill data from that sample into a statement of the central tendency and measure of spread for that variable in the sample itself. We can also describe the population itself, to the degree that the sample is an accurate representation of its population.

Certainly, it’s true that you do not need to use Bayesian techniques to accomplish this sort of analysis. Frequentist techniques of various types have for decades dealt with just this sort of problem. But Bayesian approaches often provide different perspectives than do frequentist approaches, and those differences and approaches often illuminate in ways that frequentist methods don’t.

That said, let’s take up the code.

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