I wrote a tutorial about visualising the statistical uncertainty in statistical models for a conference that took place a couple of months ago, and I’ve just realised that I’ve never advertised this tutorial in this blog. You can find the tutorial here: Visualising statistical uncertainty using model-based graphs.


  1. Why plot models, and why visualise uncertainty?
  2. The principle: An example with simple linear regression
    1. Step 1: Fit the model
    2. Step 2: Compute the conditional means and confidence intervals
    3. Step 3: Plot!
  3. Predictions about individual cases vs. conditional means
  4. More examples
    1. Several continuous predictors
    2. Dealing with categorical predictors
    3. t-tests are models, too
    4. Dealing with interactions
    5. Ordinary logistic regression
    6. Mixed-effects models
    7. Logistic mixed effects models
  5. Caveats
    1. Other things may not be equal
    2. Your model may be misspecified
    3. Other models may yield different pictures

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