Chapter 14 Wrapping up

The analyses commonly used in sensory evaluation (and in other fields that deal with multivariate data) continue to multiply. The R Opus–v2 and the original–cover only a fraction of these analyses, although I am glad to say they are probably the most common. Almost all of the methods we’ve covered here center around eigendecomposition/singular value decomposition. We haven’t touched on methods of explanation and prediction like logistic regression, random-forest modeling, or (the increasingly ubiquitous) neural network.

I wanted to talk about all of these! If you look at my manuscript output, I am an inveterate, methodological dilettante, and learning about how these methods operate, how to run them on my own data, and how to present them in replicable form like the scripts in the R Opus is a big motivating factor of my work. That’s why Bayesian methods intrude into the M/ANOVA chapters, and why I had to delete a rambling and poorly thought out chapter adding a look at Random Forest models into this work.

Everything’s got to stop somewhere. If you feel motivated by what I’ve shown here, or excited to learn how to (for example) fit a Random Forest to predict the consumer liking from the DA data, go do it! There’s no better way of learning data-analysis techniques than getting in, making mistakes, and figuring out how to fix them. I hope it’ll be fun.

Thanks for making it to the end!

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