Z is for Z-Scores and StandardizingLast April, I wrapped up the A to Z of Statistics with a post about Z-scores. It seems only fitting that I'm wrapping this April A to Z with the same topic. Z-scores are frequently used, sometimes when you don't even realize it. When you take your child to the doctor and they say he's at the x percentile on height, or when you take a standardized test and are told you scored in the y percentile, those values are being derived from Z-scores. You create a Z-score when you subtract the population mean from a value and divide that result by the population standard deviation.Of course, we often will standardize variables in statistics, and the results are similar to Z-scores (though technically not the same if the mean and standard deviation aren't population values). In fact, when I demonstrated the GLM function earlier this month, I skipped a very important step when conducting an analysis with interactions. I should have standardized my continuous predictors first, which means subtracting the variable mean and dividing by the variable standard deviation, creating a new variable with a mean of 0 and a standard deviation of 1 (just like the normal distribution).Let's revisit that GLM analysis. I was predicting verdict (guilty, not guilty) with binomial regression. I did one analysis where I used a handful of attitude items and the participant's guilt rating, and a second analysis where I created interactions between each attitude item and the guilt rating. The purpose was to see if an attitude impacts the threshold - how high the guilt rating needed to be before a participant selected &quot;guilty&quot;. When working with interactions, the individual variables are highly correlated with the interaction variables based on them, so we solve that problem, and make our analysis and output a bit cleaner, by centering our variables and using those centered values to create interactions.I'll go ahead and load my data. Also, since I know I have some missing values, which caused an error when I tried to work with predicted values and residuals yesterday, I'll also go ahead and identify that case/those cases.dissertation