Model Critique

For this lab, you’ll be working with a group of other classmates, and each group will be assigned to critique a lab from a previous week.

Your group will have three goals:

  1. Create an explicit business scenario which might leverage the data (and methods) used in the lab.
  2. Critique the models (or analyses) present in the lab based on this scenario.
  3. Devise a list of ethical and epistemological concerns that might pertain to this lab in the context of your business scenario.

Goal 1: Business Scenario

First, create your own context for the lab. This should be a business use-case such as “a real estate firm aims to present housing trends (and recommendations) for their clients in Ames, Iowa”.

You do not need to solve the problem, you only need to define it.

Your scenario should include the following:

  • Customer or Audience: who exactly will use your results?
  • Problem Statement: identify a business need or a possible customer request. This should be actionable, in that it should call for an action taken.
    • E.g., the statement “we need to analyze sales data” is not a good problem statement, but “the company needs to know if they should stop selling product A” is better.
  • Scope: What variables from the data (in the lab) can address the issue presented in your problem statement? What analyses would you use? You’ll need to define any assumptions you feel need to be made before you move forward.
    • If you feel the content in the lab cannot sufficiently address the issue, try to devise a more applicable problem statement.
  • Objective: Define your success criteria. In other words, suppose you started working on this problem in earnest; how will you know when you are done? For example, you might want to “identify the factors that most influence <some variable>.”
    • Note: words like “identify”, “maximize”, “determine”, etc. could be useful here. Feel free to find the right action verbs that work for you!

Goal 2: Model Critique

Since this is a class, and not a workplace, we need to be careful not to present too much information to you all at once. For this reason, our labs are often not as analytically rigorous or thorough as they might be in practice … So here, your goal is to:

Present a list of at least 3 (improved) analyses you would recommend for your business scenario. Each proposed analysis should be accompanied by a “proof of concept” R implementation. (As usual, execute R code blocks here in the RMarkdown file.)

In the lab your group has been assigned, consider issues with models, statistical improvements, interpretations, analyses, visualizations, etc. Use this notebook as a sandbox for trying out different code, and investigating the data from a different perspective. Take notes on all the issues you see, and propose your solutions (even if you might need to request more data or resources to accomplish those solutions).

You’ll want to consider the following:

  • Analytical issues, such as the current model assumptions.
  • Issues with the data itself.
  • Statistical improvements; what do we know now that we didn’t know (or at least didn’t use) then? Are there other methods that would be appropriate?
  • Are there better visualizations which could have been used?

Feel free to use the reading for the week associated with your assigned lab to help refresh your memory on the concepts presented.

Goal 3: Ethical and Epistemological Concerns

Review the materials from the Week 5 lesson on Ethics and Epistemology. This includes lecture slides, the lecture video, or the reading. You should also consider doing supplementary research on the topic at hand (e.g., news outlets, historical articles, etc.). Some issues you might want to consider include:

  • Overcoming biases (existing or potential).
  • Possible risks or societal implications.
  • Crucial issues which might not be measurable.
  • Who would be affected by this project, and how does that affect your critique?

Example

For example, in Week 10-11, we used the year built, square footage, elevation, and the number of bedrooms to determine the price of an apartment. A few questions you might ask are:

  • Is this a “good” selection of variables? What could we be missing, or are there potential biases inherent in the groups of apartments here?
  • Nowhere in the lab do we investigate the assumptions of a linear model. Is the relationship between the response (i.e., \(\log(\text{price})\)) and each of these variables linear? Are the error terms evenly distributed?
  • Is it possible that our conclusions are more appropriate for some group(s) of the data and not others?
  • What if assumptions are not met? What could happen to this model if it were deployed on a platform like Zillow?
  • Consider different evaluation metrics between models. What is a practical use for these values?

Share your model critique in this notebook as your data dive submission for the week. Make sure to include your own R code which executes suggested routines.