Introduction

In today’s lab you will analyze data from over 1,000 different coffees to explore the relationship between a coffee’s aroma and it’s overall quality. You will also begin working with your team and practicing a collaborative data analysis workflow.

Meet your team!

Click here to see the team assignments for STA 210. This will be your team for labs and the final project.

This will be your team for labs and the final project.

Before you get started on the lab, your TA will walk you through the following:

✅ Icebreaker activity to get to know your teammates.

✅ Come up with a team name. You can’t use the same name as another team, so I encourage you to be creative! Your TA will get your team name by the end of lab.

✅ Fill out the team agreement. This will help you figure out a plan for communication,and working together during labs and outside of lab times. You can find the team agreement in the GitHub repo team-agreement-[github_team_name].

• Have one person from the team clone the repo and start a new RStudio project. This person will type the team’s responses as you discuss the sections of the agreement. No one else in the team should type at this point but should be contributing to the discussion.
• Be sure to push the completed agreement to GitHub. Each team member can refer to the document in this repo or download the PDF of the agreement for future reference. You do not need to submit the agreement on Gradescope.

Getting started

• A repository has already been created for you and your teammates. Everyone in your team has access to the same repo.

• Go to the sta210-fa21 course organization on GitHub.

• You should see a repo with the lab-03 prefix.

• Each person on the team should clone the repository and open a new project in RStudio. Do not make any changes to the .Rmd file until the instructions tell you do to so.

Workflow: Using git and GitHub as a team

Assign each person on your team a number 1 through 4. For teams of three, Team Member 1 can take on the role of Team Member 4.

The following exercises must be done in order. Only one person should type in the .Rmd file and push updates at a time. When it is not your turn to type, you should still share ideas and contribute to the team’s discussion.

library(tidyverse)
library(broom)
library(knitr)
library(ggfortify)

coffee <- read_csv("data/coffee-ratings.csv")

Exercises

Note: Include axis labels and an informative title for all plots. Use the kable function to neatly print tables and regression output. Write all interpretations in the context of the data.

Do the following exercises in order, following each step carefully.

Only one person at a time should type in the .Rmd file and push updates.

If you are working on any portion of the lab virtually, the person working should share their screen and the others should follow along.

1. Visualize the relationship between aroma and the total cup points. What do you observe from the plot? Use the plot the describe the relationship between the two variables.

2. Fit the linear model and neatly display the results using 3 digits.

   • Interpret the slope in the context of the data.

3. Would the members of your group drink a coffee represented by the intercept? Why or why not? Discuss as a group and write the group’s consensus.

4. We will proceed assuming the model conditions hold, so let’s focus on the model diagnostics. We’ll start by examining if there are any points with high leverage in the data.

   • What threshold will you use to determine if there are points with high leverage?
   • Are there any observations with high leverage? If so, how many? Briefly explain, including any output, graphs, etc. you used to determine the response.

5. Next, let’s examine if there are any points with standardized residuals that have large magnitude. Are there any such points in the data? If so, how many? Briefly explain, including any output, graphs, etc. you used to determine the response.

6. Lastly, let’s analyze Cook’s D to determine if there are influential points in the data.

   • Based on Cook’s D, are there any influential points in our data? Briefly explain, including any output, graphs, etc. you used to determine the response.
   • If there are influential points, briefly explain why they are outliers, ie. not in the trend of the rest of the data.
   • If there are influential points, remove those points from the data and refit the model. How do the model coefficients change, if at all?
   • If there are influential points, would you recommend using the model fit with or without these points for inferential conclusions and predictions? Briefly explain why or why not. Additionally, briefly explain potential impacts your choice has on inferential conclusions and/or predictions.

Submission

• Select one team member to upload the team’s PDF submission to Gradescope.
• Be sure to include every team member’s name in the Gradescope submission
• Associate the “Workflow & formatting” graded section with the first page of your PDF, and mark the page associated with each exercise. If any answer spans multiple pages, then mark all pages.

There should only be one submission per team on Gradescope.

Grading (50 pts)

Grading notes:

• The “Workflow & formatting” grade is to assess the reproducible workflow and team work. This includes having at least one meaningful commit by each team member and updating the name and date in the YAML.