📝 HW2

Due date: Tuesday, March 17 at 11:59PM.

⏳ We recommend reading through each problem ASAP so you can accurately estimate the time needed to complete the assignment.

• This is not an assignment to start the night before the due date!

• The assignment covers material all the way up to the lecture before the HW is due, so be sure to start working on problems as soon as you learn the requisite material.

Unless otherwise stated, assignments in STAT C131A are to be done individually.

• See the syllabus for the full course collaboration policy.

Some components of this assignment have not been seen by a previous cohort of STAT C131A students, so there may be some unforeseen hiccups.

• If anything seems confusing or unclear, please post in the HW2 thread on Ed. We are here to support you!

📮 Submission

Submit your assignment via Gradescope. The Gradescope will be live at least a few days before the HW deadline.

• Make sure to tag your answers properly on Gradescope, or else you may be docked points for making the grading process more time-consuming.

Like for HW1, you will submit your screencast feedback in two places: (1) Gradescope and (2) Google Forms.

Google Form for submitting feedback

For coding components, you will produce both (1) a .qmd file with your code and (2) an PDF file containing the code and output.

• On Gradescope, you will submit a single ZIP file containing both the .qmd and PDF files.

• To generate a PDF of your code and output, do not render to PDF. Instead, render your .qmd file as HTML, open the HTML file in a web browser, and then print the HTML as a PDF, making sure that none of your code or output is cut off. You can generate an HTML file in RStudio by pressing Render.

• The rendering process will not work if there are errors in your code, so be sure to leave plenty of time to knit your lab notebooks before the deadline.

• Proofread your PDF to make sure all of your answers and plots are visible. If your PDF file is really long, it is possible that your code is printing out a large dataset or a really long vector. Make sure to comment out any code that prints more information than each question asks you for.

For math problems, prepare a photo of your handwritten answers to each problem, convert the photo to a PDF, and submit the PDF to Gradescope.

• Alternatively, you can use \(\LaTeX\) to typeset your answers within a .qmd file within RStudio, or using another \(\LaTeX\) editor like Overleaf.
• The basics of \(\LaTeX\) are useful to learn if you ever plan to include a mathematical expression in a presentation or document in the future.
• Here’s a nice guide for getting started.
• We can also help with \(\LaTeX\) in office hours or via Ed.

🗣️ Plot presentation feedback (20%)

You will be emailed a screencast ID for another student’s screencast from HW1.

• If you have not received an email with a link by Thursday, February 19th at 11:59pm, please open a private Ed post.

• If you did not submit a screencast for HW1, you will not receive an email with an assigned screencast ID. If you would like to review a screencast for HW2, please open a private Ed post letting us know.

• Public links to screencasts can be found here.

For this exercise, you will write detailed feedback on your assigned screencast.

• Keep in mind that this feedback will be anonymously provided to the student who recorded the screencast.

• Please be supportive but direct and honest with any criticism, and do not forget to point out positive feedback where deserved!

As you write your feedback, you may want to consider the prompts below. However, do not feel limited to just these prompts, and do not feel compelled to address every single prompt.

• What did you enjoy most about the presentation?
• What insights did you find particularly interesting?
• Did the presenter follow the key three tips of describing the x-axis, describing the y-axis, and explaining a plot feature (e.g., a point or line) in context, before diving into the details?
• Could the presenter have done anything to help you understand the plot more easily? Were you confused at any point?
• Did you find the tone of the presentation engaging? Did it sound like the presenter had practiced their presentation, or that they spent time thoughtfully writing a script for the presentation?
• Did the presenter sufficiently describe the contents of the plot?
• Did you have enough background information to understand the plot? Could the presentation have benefited from any more background information?
• Did the presenter describe the key takeaways of the plot? In other words, did the presenter explain why the plot actually matters in a real life context, as opposed to just explain how to read the plot?
• Did the presenter provide any extraneous information or “over-describe” anything? In other words, could the presenter have shortened any parts of the presentation without harming its key takeaways?
• Would any parts of the presentation benefit from more description or detail? Did anything feel rushed?
• Do you have any “nits” about the presentation (i.e., very small changes that could improve the presentation, like a typo or mispronunciation)? If you choose to answer this prompt, it should not take up more than 10% of the text of your entire feedback. Focus your energy on the “big picture” prompts.

Your feedback will be graded based on demonstrated effort and thoughtfulness.

• You should aim to write at least two paragraphs of feedback.

• Your feedback can alternatively be written as an organized, bulleted list equivalent in word count to roughly two paragraphs.

Why complete this problem? Writing detailed feedback on another student’s plot presentation will help you become a better presenter. One of the hardest data science skills to develop is “presentation empathy”, or a sense of how someone who has never seen your work before will interpret your presentations After staring at your own work for hours, it can be hard to see your work with “fresh eyes”. If you are at all surprised by the feedback you receive on your own screencast (or receive feedback with which you disagree!), take that moment as an excellent opportunity to understand how other people can interpret your work differently than you interpret your own work. Remember, it is not the audience’s responsibility to decipher your presentation for its intended interpretation. You need to carefully prepare your presentation so that the intended interpretation is crystal clear!

A-Z Practice with Inference (80%)

Complete questions 6 through 26 of the Midterm (Quiz 1) practice available on the class website.