Due: Oct 26 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.
Download and edit this template when working through this assignment. Notice that this week’s template is a .Rmd file.
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.
Reflect on what you’ve learned and any questions or points of confusion you have about parameter uncertainty. Is there anything that jumped out at you? Anything you found particularly interesting or confusing? After reflecting, do the following: