Comments

8 responses to “11 Steps to Data Analysis”

1. 

   elsa

   February 7, 2013

   What is probably missing:
   1) Missing Value Analysis
   2) Outlier management

   Reply
   1. 

      Ryan Swanstrom

      February 7, 2013

      Those would be good additions.

      Thanks for the comment.
      Ryan

      Reply
2. 

   Mark Greenaway

   February 7, 2013

   Does “Challenge the results” include model validation? i.e. are the assumptions of the model met?

   Reply
   1. 

      Ryan Swanstrom

      February 8, 2013

      I would say model validation falls under statistical prediction, but I could also see it being under challenge the results as well. Either way, it is important and needs to occur somewhere.

      Thanks for commenting.
      Ryan

      Reply
3. 

   Harsha Srivatsa

   February 28, 2013

   Ryan,
   How would one correlate the earlier post “Levels of Data Analysis” with this particular post? I guess my question is : Are the steps mentioned above valid for one or more levels? Just trying to get the bigger complete picture

   Reply
   1. 

      Ryan Swanstrom

      February 28, 2013

      Harsha,
      That is a great question. Maybe I will put up a blog post with my thoughts of how the 2 lists fit together.

      Thanks,
      Ryan

      Reply
   2. 

      Ryan Swanstrom

      March 6, 2013

      Realized I haven’t responded to this yet. I don’t think it is worth its own post, so I will just leave my thoughts here. I would say the “Levels of Data Analysis” map into steps 6,7,8, and possibly step 9 above. How does that sound?

      Reply
4. First Steps to Data Analysis in R | Data Science 101

   March 5, 2013

   […] Here are some basic R commands that should useful for obtaining data and looking at data in R. Ideally these commands are useful for steps 4, 5, and 6 of the 11 Steps to Data Analysis. […]

   Reply