Read the blog post titled "The 13 Steps for Statistical Modeling in any Regression or ANOVA," by Karen Grace-Martin, from the Analysis Factorwebsite (https://www.theanalysisfactor.com/13-steps-regression-anova/). In the post, Grace-Martin states that no matter what statistical model you're running, you need to go through the same steps. The order and specifics of how you do each step will differ depending on the data and the type of model you use. She goes on to emphasize that all of these steps (even pre-modeling) are essential, and if you think of them all as part of the analysis, the modeling process will be faster, easier, and make more sense.

After reading the post, and thinking about all of the different statistical modeling methods we have learned during this course (linear, logistic, cox models), which of Grace-Martin's steps do you think is the most important, and why? Also, what new information did you learn from reading the post? Use examples/points from the blog post (and/or your own research) in your discussion.