Quiz 07: Policies and Information

Location, Date, and Time

Conflicts: There will be no conflict quiz as students are able to choose the time and date of their quiz.

Quiz Content

All quizzes are cumulative. Previous material can reappear on a later quiz.

6.1: Bootstrap

Theory Overview:

  • When should bootstrapping be used?
  • What are the different types of bootstrapping?
  • How does the underlying assumptions change between types?
  • Why does bootstrap rely upon resampling?
  • How can we obtain a quantile from the data?
  • Where is 1-alpha/2 used compared to 1-alpha? Why is one quantity divided by 2 while the other is not?


  • Why does bootstrap rely upon a quantile CI?
  • What is the strategy for implementing each type of bootstrap?
  • Why is it better to allocate a vector of NA values instead of directly specifying a specific data type to hold the bootstrapped statistics?

6.2 Exploratory Data Analysis

Provided with: Data Visualization Cheatsheet


  • What are the different types of EDA?
  • Should you use all the types of EDA for every data set or only use one specific type?
  • How does EDA look at patterns in the data?
  • How should we detect, analyze, and communicate different patterns in the data?
  • How is variation similar to covariation? How are they different?
  • How can you detect if the data is “overfit”?

Graphing Systems in R:

  • What are the three different graphical systems in R?
  • When should each be used in the course of an analysis?
  • Why are the graphical systems similar to a “goldilocks” scenario?

Grammar of Graphics:

  • How does the Grammar of Graphics theory inform the construction of graphs?
  • How are layers specified in ggplot2?
  • Explain the significance of:
    • Aesthetics
    • Geometric Objects
    • Coordinate Systems
    • Facets
  • What are the difference between a local and global aesthetic as it relates to geometries?
  • What are the benefits to using a script to save a graph vs. using a GUI?

Making Graphs:

  • If we have a discrete variable, then what kind of graph should we select?
  • Given a numerical response with a categorical explanatory variable, how should the data be displayed visually?
  • How is a histogram different than a bar plot? Why is this the case?
  • What is the difference between a facet_wrap() and a facet_grid()?


  • Why should a company design a theme for all of their visualizations?

6.3: Tidy data

Provided with: Data Import Cheatsheet

Pipe Operator:

  • What is the pipe operator (%>%) read as?
  • Do all functions have to receive data in their first argument to be used with the pipe operator?

Tidy Data:

  • What is the Anna Karenina Principle? How is it related to tidy data?
  • List the three tenets of tidy data.
  • Describe the five common “messy” forms of data.
  • How does one of these forms relate to frequency tables (e.g. contingency tables)?
  • How are tidy tenets aligned with “long” and “wide” data?
  • Why is a tidy data set powerful in a data analysis?

Tidy Transformation:

  • How do we move from a “wide” data set to a “long” data set and vice versa?
  • How is separate and unite paradigms related to the tidy tenets?

Materials Provided

Students will have access to:

Materials Needed

  • Preferably, a rested mind and non-broken hands that can type.


  • All answers must be reasonably simplified.
  • Decimals answers must contain two significant digits.
  • Grading will be done as follows:
    • A correct answer will receive all points.
    • An incorrect answer will receive proportionally appropriate partial credit.

If you have a technical issue while answering questions or need assistance with opening or starting the quiz, please alert the proctor.

Do not leave the CBTF without filing an issue with the proctor if something goes wrong.


Have a testing accommodation? Please see how the CBTF handles Letters of Accommodation.

The short version: Please bring a copy of the Letter of Accommodation to the CBTF Proctors prior to the test taking place.

Academic Integrity

In short, don’t cheat. Keep your eyes on your own quiz. Do not discuss the quiz with your friends after you have taken it. Any violation will be punished as harshly as possible.

Advice for Studying

The best way to study for a STAT 385 quiz is by writing and reading code. Try to take an idea in STAT 385 and apply it to your own work.

With this being said, there are three other resources that may assist your studies:

  • Topic Outline (Above)
  • Lecture Code
  • Homework

Again, the best way to study is to do programming in some fashion. Whether that be writing code or explaining how code works to someone else.

Consider using resources such as:

  1. RStudio Cloud Primers for interactive practice.
  2. Exercise problems listed in a given section of the readings.

Do not spend time memorizing lecture slides. You will not see any verbatim questions.

Do not try pulling an all-nighter. You can schedule your quiz anytime between a time window. To program efficiently, you need sleep despite the quote:

“Programmers are an organism that turns caffeine into code.”

Frequently Asked Questions

What kind of question types are on the quiz?

There are generally four types of problems:

  • True / False
  • Multiple Selection (e.g. select ALL correct answers from a list)
  • Fill in the blank
  • Writing Code

How many problems are on the quiz?

Only one question with 15012391 subquestions. In all seriousness, do not fixate on a number. There will be a reasonable amount of questions for the time period.

How long will it take to do the quiz?

Depending on your background, the quiz may take:

  • Prior R in-depth experience: 25 minutes
  • Some R experience: 35 minutes
  • No R experience: 50 minutes

Avoid fixating on time. Life will come and go more quickly than you realize. Focus more on the content.

When will the quiz be returned?

As all problems are automatically graded, we should be able to post the quiz results after the examination window closes.

Will the quiz be curved?


We got our grades back, now will the quiz be curved?

No. Curving is only done sparingly at the end of the semester. Individual assignments are not modified.