Overview of Class Design

This class will have a combination of asynchronous lectures and synchronous discussion and R simulations which will be pre-recorded. In the synchronous session, we will tentatively meet every Friday at 1:30 pm to do a quick overview of lectures, work through R and review lab assignments in real time. There will be weekly lab assignments. Students will partner up and work on a data analysis project throughout the semester. Methods covered in this course include exploratory data analysis, correlation and bivariate analysis, linear regression, panel data regression, and probit/logistic regression. Collective and reflective learning will be the key cornerstone for excelling in this class!

Special Note about Learning in a Pandemic

I am committed to ensuring your successful learning in this class. Please talk to me and ask questions during or after class time. I commit to being thoughtful and empathetic towards each of you as you navigate the current circumstances and your personal situations. I will also be open, fair, professional, and passionate in teaching this class. In turn, I ask that you exercise the same consideration and compassion towards your classmates, TAs and GAs, UMass Amherst staff, and your professors. We are all moving through this differently, but together.

Course Values

• Empathy: Care and respect for each other and be empathetic and compassionate.
• Openness: Be proactive to ask questions. Be willing to learn by trial and error.
• Integrity: Take responsibilities for your own and team work. Be fair and trustworthy.
• Professionalism: Have high standards and solid preparation for the class and the team.
• Perseverance and passion: Actively engage and participate to strive for your own learning goals.

Required Software

Installing R & R Studio

You will need to install R (the free and open source statistical computing language used in this course) and R Studio (the graphical user interface for R) in advance before the first class.

• Install R software
• Install R Studio Desktop

There are infinitely many new packages available for us to use. It is highly recommended to use the free RStudio, an interface for writing R documents and working with data. For this class, you will have access to RStudio Cloud where you can use R online anywhere throughout the semester.

You will use R Studio Desktop (on your computer) to work on your assignment, you can use R script (.R file) to explore, summarize and analyze your data throughout the course. More instructions will follow in the first few labs.

Alternatively, if you are already familiar with it, you can also use R Markdown (.Rmd file) to “knit” your assignment quiz submissions in the .pdf format.