Due: October 25 by 11:59pm

Weight: This assignment is worth 3% of your final grade.

Purpose: The purpose of this assignment is to introduce how we quantify uncertainty around estimated parameters that result from maximizing the log-likelihood function.

Assessment: This assignment is graded using a check system:

  • ✔+ (110%): Responses shows phenomenal thought and engagement with the course content. I will not assign these often.
  • ✔ (100%): Responses are thoughtful, well-written, and show engagement with the course content. This is the expected level of performance.
  • ✔− (50%): Responses are hastily composed, too short, and/or only cursorily engages with the course content. This grade signals that you need to improve next time. I will hopefully not assign these often.

Notice that this is essentially a pass/fail system. I’m not grading your writing ability and I’m not counting the number of words you write - I’m looking for thoughtful engagement. One or two sentences is not enough. Write at least a paragraph and show me that you did the readings assigned.

Pilot Surveys

In addition to the assignment below, this week you should also help out your fellow classmates by providing feedback on their pilot surveys. For every team other than your own, answer their pilot survey and provide any feedback you have in a row in this spreadsheet. Feedback should be anonymous, constructive, and objective. Note things that didn’t work and / or things that weren’t clear or were confusing.

If you got screened out early in the survey, go back and take it again and pick a response so that you won’t get screened out. Do your best to actually answer the conjoint questions honestly (don’t just click randomly).

Completing all the surveys shouldn’t take more than an hour. In addition to giving everyone very useful feedback, this exercise may also give you new ideas for improving your own survey.

1. Get Organized

Download and edit this template when working through this assignment.

Then unzip the template folder (make sure you unzip it!), then open the .Rproj file to open RStudio. Open the hw7.Rmd file, take notes, and write some example code as you go through the following.

2. Readings

Last week we introduced how we can use maximum likelihood estimation to estimate the unknown parameters of utility models. This week we’ll learn about how to quantify the uncertainty associated with those parameter estimates by watching the third video in our Youtube playlist on choice modeling: Uncertainty

Take notes as you watch the video. Throughout the video, I ask practice questions at several places - you should answer to those questions as part of your reflection. You may submit your answers however you wish, e.g. hand-write them on paper and take a picture and / or type answers in your reflection .Rmd file.

Click here to download the slides in the video as a PDF.

3. Reflect

Reflect on what you’ve learned while going through these readings and exercises. Is there anything that jumped out at you? Anything you found particularly interesting or confusing? Write at least a paragraph in your hw7.Rmd file. Here are some suggestions:

  • Discuss some of the key insights or things you found interesting in the readings or recent class periods.
  • Write about the messiest data you’ve seen.
  • Connect the course content to your own work or project you’re working on.

4. Knit

Click the “knit” button to compile your hw7.Rmd file into a html web page. Then open the hw7.html file in a web browser and proofread your report. Does all of the formatting look correct?

5. Submit

To submit this assignment, create a zip file of all the files in your R project folder for this assignment. Name the zip file hw7-netID.zip, replacing netID with your netID (e.g., hw7-jph.zip). Then copy that zip file into the “submissions” folder in your Box folder created for this class.