a6-uncertainty.Rmd

---
title: "Uncertainty"
output:
  html_document:
    toc: false
    number_sections: false
params:
  number: 6
  purpose: "The purpose of this assignment is to introduce how we quantify uncertainty around estimated parameters that result from maximizing the log-likelihood function."
---

```{r child = "_knitr_setup.Rmd"}
```

```{r child = "_aheader.Rmd"}
```

# 1. Getting Organized

Download and edit [this template](`r url_template`) when working through this assignment. Notice that this week's template is a .Rmd file.

# 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](https://www.youtube.com/playlist?list=PLzFdSE-D7jUQnbczxdjohRqlBBrrsaFFg) 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](content/slides_uncertainty.pdf) to download the slides in the video as a PDF.

<center>
<iframe width="640" height="360" src="https://www.youtube.com/embed/PmDhvrgB47g" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
</center>

# 3. Reflect, Knit, & Submit

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:

- Write a few sentences in the template you downloaded for this assignment.
- Click the "knit" button to compile your .Rmd file into a html web page.
- Open up the resulting html file in your web browser and see how it looks!
- Create a zip file of everything in your R Project folder and submit the zip file in the "Assignment Submission" page on Blackboard.