Homework 2

Author

Affiliation

Your Name Here

Old Dominion University

Before You Start

Each section below gives you a situation and some data. Decide what is worth looking into, use what you have learned to look into it, and write it up as a short report for a reader who has not seen the data. Do not use any data that is not already provided to you. At the end, be sure to complete the Process Reflection.

What I have given you:

This week’s tools are the course methods I expect to see in your report.
An outline to use for each report. Note that the outline is only a suggestion, as you may find that the different sections bleed into one another, so you can structure your reports however you’d like (and they need not have the same structure).
Some inspiration for each report. Again, these are merely suggestions, and the best reports will go somewhere we did not think of.

How to use AI: You may (and should!) use AI tools to help you throughout, but what you submit is your responsibility, not the AI’s. Read what it gives you, check it against the course material, and change whatever you would not stand behind. Never submit something you don’t understand, and make sure that what you submit is written in your own voice.

What to submit: Submit the rendered .html file on Canvas, along with the video described at the end. Your submission will be graded with the course rubric.

This week’s tools (Modules 1–4)

Simple regression, with the slope and intercept interpreted in their units. Be sure to consider tools from previous modules, too.

Outline

The data: Describe the data. For example: what is one row? How many rows? Provide some summary measures of the data. What do the important distributions look like? What about some important pairwise relationships? Make note of the choices you made about the data (dropped, recoded, transformed) and why.
Analysis: Provide an analysis of the data which may include tables, figures, and/or models. Say why you include, and what you learn, from each.
Findings and discussion: In plain language, what can you conclude? What can’t you conclude? What do you think about each topic? What else would you want to know or have included in the data?

Tipping

A waiter recorded data (documentation) on the tables they served for a few months: the bill, the tip, the size of the party, and some information about who was at the table and when. For a general audience, write about what these data say about tipping, and what you think about tipping as a way to pay workers.

Some inspiration

A common rule of thumb says to tip 18% of the bill. Do these tables follow it? How would/could you test this?
Some tables tip far more, or far less, than others. Is that noise or a pattern?
According to the data (and/or theory), what would you expect the waiter to earn on a $15 bill? On a $60 bill?
Does the picture look the same for lunch and dinner? What about smokers vs non-smokers?

Commuting

These data (documentation) record the distance (in miles) and time (in minutes) of individual commutes to work in four U.S. cities in 2007. What does this information say about commute patterns in these cities?

Some inspiration

What does the slope say about how fast people travel? Does that seem believable?
Is the relationship the same in each city? If not, what might explain the differences?
Do all of the commutes in the data look like real commutes? Do you think these data are reliable?
How well does a straight line describe the data, and where does it fall short?

Process Reflection

In your own words (Claude Code is not allowed to write this, and it knows that): how did you use Claude on this homework, where did its first attempt fall short, and what did you change or reject, and why?

Video Presentation

Record one continuous screen recording, about ten to twelve minutes for the presentation (no shorter than five, no longer than twenty) plus the two questions below, and upload it to Canvas. Your recording must show your entire screen (including the time) and include your voice. Showing your face is optional.

Part 1: Presentation (20 points). Present your report as if you were briefing a boss or client who has not seen it. Say what question you asked, what data and methods you used, what you found, and what you think it means and does not mean. You should keep your rendered report open while you talk so you can reference items like tables, figures, etc., but you should not read from the report.

Part 2: Questions (10 points). Immediately after, open a new Claude Code session (have it open in the background so you don’t waste time opening it during the recording) and paste the prompt from https://alexcardazzi.github.io/econ311/oral_check_prompt.txt, with your report’s file name filled in. Claude will ask you two questions about your report, as if it were a boss, client, or audience member. Claude may ask why you made a particular choice in your report, so be ready to explain it and to say how a different choice would have changed your results. If a question is unclear, you may ask Claude to reword or clarify it in your own words, once per question. Answer each one out loud, then type “next question” to move on. Keep your report open if you like, but your answers should show that you understand your work, not that you can read it back. Do not edit the prompt, and do not ask Claude to help you answer.

--- title: "Homework 2" author: "Your Name Here" institute: "Old Dominion University" format: html: theme: united code-tools: true code-fold: true code-summary: "Code" code-copy: hover link-external-newwindow: true tbl-cap-location: top fig-cap-location: bottom self-contained: true editor: source --- ```{r setup, include=FALSE} # INCLUDE THIS AT THE START OF EACH HOMEWORK # DO NOT EDIT THIS knitr::opts_chunk$set(fig.align = 'center') knitr::opts_chunk$set(out.width = '90%') knitr::opts_chunk$set(results = 'hold') knitr::opts_chunk$set(fig.show = 'hold') knitr::opts_chunk$set(dev='svg') knitr::opts_chunk$set(error = TRUE) par(mar = c(4.1, 4.1, 1.1, 4.1)) ``` ## Before You Start Each section below gives you a situation and some data. Decide what is worth looking into, use what you have learned to look into it, and write it up as a short report for a reader who has not seen the data. Do not use any data that is not already provided to you. At the end, be sure to complete the **Process Reflection**. **What I have given you**: - **This week's tools** are the course methods I expect to see in your report. - An **outline** to use for each report. Note that the outline is only a suggestion, as you may find that the different sections bleed into one another, so you can structure your reports however you'd like (and they need not have the same structure). - **Some inspiration** for each report. Again, these are merely suggestions, and the best reports will go somewhere we did not think of. **How to use AI**: You may (and should!) use AI tools to help you throughout, but what you submit is your responsibility, not the AI's. Read what it gives you, check it against the course material, and change whatever you would not stand behind. Never submit something you don't understand, and make sure that what you submit is written in your own voice. **What to submit**: Submit the rendered `.html` file on Canvas, along with the video described at the end. Your submission will be graded with the [course rubric](https://alexcardazzi.github.io/econ311/rubric.html). --- ### This week's tools (Modules 1–4) Simple regression, with the slope and intercept interpreted in their units. Be sure to consider tools from previous modules, too. ### Outline - **The data**: Describe the data. For example: what is one row? How many rows? Provide some summary measures of the data. What do the important distributions look like? What about some important pairwise relationships? Make note of the choices you made about the data (dropped, recoded, transformed) and why. - **Analysis**: Provide an analysis of the data which may include tables, figures, and/or models. Say why you include, and what you learn, from each. - **Findings and discussion**: In plain language, what can you conclude? What can't you conclude? What do you think about each topic? What else would you want to know or have included in the data? ## Tipping A waiter recorded [data](https://vincentarelbundock.github.io/Rdatasets/csv/reshape2/tips.csv) ([documentation](https://vincentarelbundock.github.io/Rdatasets/doc/reshape2/tips.html)) on the tables they served for a few months: the bill, the tip, the size of the party, and some information about who was at the table and when. For a general audience, write about what these data say about tipping, and what you think about tipping as a way to pay workers. ### Some inspiration - A common rule of thumb says to tip 18% of the bill. Do these tables follow it? How would/could you test this? - Some tables tip far more, or far less, than others. Is that noise or a pattern? - According to the data (and/or theory), what would you expect the waiter to earn on a \$15 bill? On a \$60 bill? - Does the picture look the same for lunch and dinner? What about smokers vs non-smokers? ## Commuting These [data](https://vincentarelbundock.github.io/Rdatasets/csv/Stat2Data/MetroCommutes.csv) ([documentation](https://vincentarelbundock.github.io/Rdatasets/doc/Stat2Data/MetroCommutes.html)) record the distance (in miles) and time (in minutes) of individual commutes to work in four U.S. cities in 2007. What does this information say about commute patterns in these cities? ### Some inspiration - What does the slope say about how fast people travel? Does that seem believable? - Is the relationship the same in each city? If not, what might explain the differences? - Do all of the commutes in the data look like real commutes? Do you think these data are reliable? - How well does a straight line describe the data, and where does it fall short? ## Process Reflection In your own words (Claude Code is not allowed to write this, and it knows that): how did you use Claude on this homework, where did its first attempt fall short, and what did you change or reject, and why? ## Video Presentation Record one continuous screen recording, about ten to twelve minutes for the presentation (no shorter than five, no longer than twenty) plus the two questions below, and upload it to Canvas. Your recording must show your entire screen (including the time) and include your voice. Showing your face is optional. **Part 1: Presentation (20 points).** Present your report as if you were briefing a boss or client who has not seen it. Say what question you asked, what data and methods you used, what you found, and what you think it means and does not mean. You should keep your rendered report open while you talk so you can reference items like tables, figures, etc., but you should not read from the report. **Part 2: Questions (10 points).** Immediately after, open a **new** Claude Code session (have it open in the background so you don't waste time opening it during the recording) and paste the prompt from [https://alexcardazzi.github.io/econ311/oral_check_prompt.txt](https://alexcardazzi.github.io/econ311/oral_check_prompt.txt), with your report's file name filled in. Claude will ask you two questions about your report, as if it were a boss, client, or audience member. Claude may ask why you made a particular choice in your report, so be ready to explain it and to say how a different choice would have changed your results. If a question is unclear, you may ask Claude to reword or clarify it in your own words, once per question. Answer each one out loud, then type "next question" to move on. Keep your report open if you like, but your answers should show that you understand your work, not that you can read it back. Do not edit the prompt, and do not ask Claude to help you answer.