This is the landing page for Applied Statistics II. This page is used for posting of regular announcements and information for students of the class.


Week 15 (5/7 - 5/11) – Dead Week

  • No new content!
  • Monday - open work day on presentations
    • File type doesn’t matter: Powerpoint, Rpres, slidly, beamer, Google slides.
    • Just make sure your graphics render at a decent quality.
  • Wednesday - all presentations have to be done & turned in. Groups randomly drawn to present Wed/Friday
    • Upload final version to our Google Drive folder: Projects/Final Presentations
    • Weekly overview has full details of expectations for presentation.
  • Final review sessions to be done on Mon/Tue of finals week.
    • Attending at least one session will earn credit towards your grade.
    • Fill out this survey to tell me when you are free.

Week 14 (4/30 - 5/6)

  • Poster Session on Monday
  • Upload final poster (as printed) to the Projects/CNS Posters FINAL folder in Google Drive
  • Wed/Friday continue with Missing Data lecture

Week 13 (4/23 - 4/29)

  • FINALLY you get to learn why I harp on you about missing data.
  • Monday: We’ll use the QFT to develop questions about the impacts of missing data, and then talk about how to identify when data are missing.
  • Wednesday we’ll discuss the mechanisms in which data can dissapear, and some common methods to address/eliminate the bias created due to missing data.
  • Friday is an open work day because I want to see a draft of your posters by Friday EOD.
  • Poster Prep Guidance:


  • Yes they must be printed. Use a template provided on either of the guideline pages.
  • Yes this is a serious professional event that can go on your CV.
  • Yes you must dress professionally.
  • Yes your poster will be judged by peers and professionals alike.
  • Yes I must see the poster before printing.
  • Yes at least one person must be at your poster between 12:30 and 2:30 pm.
    • Whomever is putting up the poster between 10 and 11 that morning must check in with me and provide me your group’s presentation schedule. Each person should plan to stand at their poster for at minimum 30 minutes.
    • I will check up on you. Unless previously cleared by me, this should not fall to one person.
  • Upload the PDF or PPT of your poster as printed to the Projects/NSC Posters folder in our Google Drive.

Blackboard gradebook has been updated to include poster session attendance. Final grade breakdown is 500 pts, 24% homework, 20% learning (peer review, QFT), 40% exams, 16% project.

Week 12 (4/16 - 4/22)

  • Spatial data analysis!
  • Making maps is probably the most fun part.
    • Of course there is a bazillion ways & packages to accomplish this task.
  • Want more? Check out courses in our Geography department.

Week 11 (4/9 - 4/15)

  • We’re going through a superficial treatment of longitudinal data.
  • This topic is worthy of it’s own 10 week course and the topic of NUMEROUS texts.
  • This is also SET week - we will be doing ours in person on Friday.
    • Attendance is mandatory and will count towards your grade.
  • Next week is Spatial modeling - so my advice is to get started on the assignment ASAP.
    • I will try to leave time during class for open work this week but cannot guarantee it. I don’t want to rush through the material.

Week 10 (4/2 - 4/8)

  • Abstract submissions for the [College of Natural Science Poster Session] are open until April 16th
    • Submission is a mandatory assignment for this class
  • We’re going to finish talking about the Random Intercept model this week
    • the common methods of fitting these models in R
    • and some common methods of handling covariates in multilevel models.
  • The schedule has been updated to reflect the “surprise” holiday last Friday, and the change in poster session date.
    • The final date has been posted, along with information about the take home portion of the final.

Week 9 (3/26 - 4/1)

  • Exam corrections - Must use error assessment document. A copy can be found [here] and also in the #general channel in Slack.
  • Starting a section on correlated data. QFT on Monday, lecture wed, open work day Friday.
  • Grading status: Gradebook will be fully updated by Tuesday.

Week 8 (3/12 - 3/16)

  • Fast forwarding…
  • Midterm this week! 60% in class, 40% take home.
    • Midterm review on Monday.
    • Class generated using QFT model of question formulation
    • This is worth participation points, so be there and contribute!
    • Midterm materials allowed: 2 pages of notes & a calculator.
  • Take home portion with instructions posted in Slack #assignments channel by EOB (end of business).
  • Project updates - this is worth 20 pts, so make it good. You have 4-5 minutes and 3-4 slides to show me that
    • you know what your data consists of
    • you have gotten it into R and started exploring/visualizing/cleaning
    • you should be starting to do bivariate comparisons at this point
    • you have a clear idea of what model you are working towards
    • and that EVERYONE is contributing. Everyone must share in the presentation

Week 5 (2/19 - 2/25)

  • No new content this week. Working/practicing your model writing and calculating various classification measurements.
  • Wednesday worksheet: Classifying Glass
  • Friday will be an open project work day.

Week 4 (2/12 - 2/18)

  • Solutions to MLR assignment uploaded to Google Drive. I’ll be working my way through the grading this week.
  • This week – Generalized Linear Models. What to do when your outcome isn’t continuous.
  • We’ll talk about 2 cases, binary and count data. Just how to fit and interpret Logistic and Poisson models.
  • New GLM assignment starting, all reviews for Model building are done by Sunday (tonight) EOD.

Week 3 (2/5 - 2/11)

  • Recap of the process of building a model
  • Team check in - Tell me where you are at and what you are working on this week. How can the class help you make progress?
  • Reminders: Regression assignment due Wed, Model building due Friday.

Week 2 (1/29 - 2/4)

  • The main goal this week is to talk about model building. What to look for, how to compare between models or variables, when to stop.
  • Review the course notes and corresponding textbook chapters,and be prepared to answer the questions in the notebook.
  • You’ll also form project teams on Monday. Be sure to have your preferences submitted to me via Slack DM by Sunday EOD.

Week 1 (1/22 - 1/28): Welcome!

  • Welcome to spring semester in statistics with your host, Dr. Robin Donatello.
  • Make sure you acquaint yourself with this website, it will be your guide for the next 16 weeks.
  • We will use several online tools throughout this class, try to get signed up for all of them before Monday.
    • Look under materials and tools links in the navbar for the list.
    • Update your version of R studio for sure. Update R if you’re running < 3.4.
      • Go to [Tools] –> [Global Options] to check the version of R that R Studio is using.
      • Re-install your commonly used packages.(Can be painful. Esp for Rcpp)
  • If you’ve had me in a class before, you know that there will be typos and broken links on this webpage.
      1. breath, and recognize that I’m human too.
      1. good: tell me then email me (this second step is pretty critical unless you witness me fix the code right then.)
      1. better: open up an Issue in Github
      1. best: fork the repo and submit a PR with the fix!