## Quiz 04: 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.

3.1: Derived Variables

• Derived Variables
• What are derived variables?
• How can we create a derived variable?
• What are the different cases of derived variables?
• Comparisons
• What kind of logical values exist?
• How are logical values similar to a reduced set of integers?
• Why is it said that %in% checks for “set” or “collection” membership?
• How do comparisons work with logical operators? Are all these comparisons vectorized?
• Filtering Observations
• What are the different ways to subset data?
• Why does R allow subsetting to work in this manner?
• Logical Operators
• What kids of combining operations are possible?
• Why is it useful that computers perform “short-circuit” evaluation?
• How does the vectorized version of operators differ from the non-vectorized version?
• Why might we want to reduce a logical vector to a single logical value?
• Control Statements
• How does a control structure differ from “flow of control?”
• What kinds of control structures exist? Which control structure do we often use the most?
• How is the vectorized version of if else the same as the unvectorized version?
• What is an alternative way of writing an if-else if-else?
• How does a switch() perform when given a character input vs. integer input?

3.2: Data Oddities

• Data Structures
• What kinds of the data structures exist in R? How are they related to each other?
• What are the 2D data structures? How do they differ from 1D data structures?
• Coercion
• Why and when does coercion occur when working with R data types?
• How is implicit coercion different from explicit coercion?
• Why might we want to explicitly coerce data?
• Missingness and NA
• Describe the different types of missing data.
• What type of missing data can be tested for?
• Why might we want to impute values for missing data instead of removing the observation?
• How do NULL and NA differ from each other? When should one be used over the other?

Note: The following content will likely be tested more indepth on Quiz 04

• SLR and MLR
• Why do we perform regressions?
• How do we estimate the $$\mathbf{\beta}$$ parameters?
• How are Simple Linear Regression (SLR) and Multiple Linear Regression (MLR) related? Where do they differ?
• What is the analytical solution to Multiple Linear Regression?
• Factors
• What are factors? How do factors differ from a character vector?
• Why do we prefer storing data as a factor instead of a character vector when applicable?
• Where in statistical modeling do we find factors being useful?
• Why is stringsAsFactors = TRUE by default?
• What is the meaning of an ordered factor?

### Materials Needed

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

### Policies

• All answers must be reasonably simplified.
• Decimals answers must contain two significant digits.
• Grading will be done as follows:

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.

### DRES

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.

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.

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.”

#### 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.

No.

#### 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.