1.1 About me

• Dr. Stefany Coxe

  • Ph.D. in Quantitative Psychology from Arizona State University
  • Biostatistician in Biostatistics Shared Resource
  • Associate Professor in Computational Biomedicine

• Originally from Southern California but I’ve lived in other warm places like Phoenix and Miami

• I love statistics and want you to love them too!

1.2 About your co-instructors

• Yujie Cui, M.S.
  • yujie.cui@cshs.org
• Michael Luu, M.P.H.
  • michael.luu@cshs.org

1.3 This course

• This course covers topics related to applied statistical analysis of experimental studies
  • Using and manipulating datasets, plotting data, probability, estimation and uncertainty, comparing 2 independent or dependent means, comparing 2 independent or dependent proportions, controlling for multiple comparisons

Jeff Edwards, September 2017 CARMA talk:
Becoming methodologically self-reliant is empowering, satisfying, and essential to your success as a researcher.

1.4 Hybrid, flipped course

• We meet in person for 1 hour 30 minutes each week for active learning

  • Work through R code
    
  • Interpret output
  • Q & A

• All other content is delivered asynchronously via web

  • Recorded lectures (before meeting)
    
  • Homework (after meeting)

2.1 Course schedule

• Monday: Materials for the week will be available

• Wednesday by 8pm: Watch lecture video and complete survey

• Thursday at 9am: Meet in person in G-511 Auditorium (PDC)

• Sunday by midnight: Homework assignment due

3.1 Lecture and survey

• Each week, there will be a recorded lecture video

  • Background material to do in-class exercises

  • Watch the video

  • Complete the survey to provide comments and questions

3.2 In-person class time

• Briefly review the lecture and answer any questions

• Code, output, and interpretation

• Wrap up and final questions

3.3 Homework assignments

• Six (6) homework assignments

  • Programming assignments

    • Write code to learn to program in R

  • Data and research question

    • You run the appropriate analyses and write up the results

3.4 Final project

• Pick one of five special topics (see References)

• Write a short “guidebook” on the topic

• More details to come

4.1 Statistical software

• R software
  • OK if you don’t know how to use it (but also great if you do!)
  • Free and open source and works on any platform
    • Rstudio is a graphical IDE (integrated development environment) for R
    • Links in the syllabus to install R and Rstudio

5.1 How can you be successful?

• Come to class prepared and ready to run analyses

  • Videos watched. On time. Software ready. Fed. Caffeinated.

• Communicate with instructors

  • There are more of you than there are us
  • I don’t know what’s going on with each of you

5.2 Where can I find out more about…?

• Check the website. Really.

  • Syllabus, all the course materials, due dates, policies, etc.

  • Many pages of references, organized by topic

• Email me or Yujie or Michael

  • If you can get your question into an email, I can almost always answer it in an email

5.3 What’s next?

Get prepared for our in-person meeting on Thursday

• Watch the Lecture 1 video

  • Complete the survey to provide feedback

• Get your software set up

  • Install R (and Rstudio)

• Come to class ready to do things!