Even if you’ve never heard the term Generalized Linear Model, you may have run one. It’s a term for a family of models that includes logistic and Poisson regression, among others.
It’s a small leap to generalized linear models, if you already understand linear models. Many, many concepts are the same in both types of models.
But one thing that’s perplexing to many is why generalized linear models have no error term, like linear models do. (more…)
by Danielle Bodicoat
Statistics can tell us a lot about our data, but it’s also important to consider where the underlying data came from when interpreting results, whether they’re our own or somebody else’s.
Not all evidence is created equally, and we should place more trust in some types of evidence than others.
Like any applied skill, mastering statistical analysis requires:
1. building a body of knowledge
2. adeptness of the tools of the trade (aka software package)
3. practice applying the knowledge and using the tools in a realistic, meaningful context.
If you think of other high-level skills you’ve mastered in your life–teaching, survey design, programming, sailing, landscaping, anything–you’ll realize the same three requirements apply.
These three requirements need to be developed over time–over many years to attain mastery. And they need to be developed together. Having more background knowledge improves understanding of how the tools work, and helps the practice go better. Likewise, practice in a real context (not perfect textbook examples) makes the knowledge make more sense, and improves skills with the tools.
I don’t know if this is true of other applied skills, but from what I’ve seen over many years of working with researchers as they master statistical analysis, the journey seems to have 3 stages. Within each stage, developing all 3 requirements–knowledge, tools, and experience–to a level of mastery sets you up well for the next stage. (more…)