## Quiz 05: 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.

5.3: Loop-the-Loops

• Iteration:
• Why are computers good at iteration and humans aren’t?
• What kind of iteration structures exist in R?
• How does vectorization compare to an iteration structure?
• while loops:
• What happens in a while loop if the condition is always evaluating to TRUE?
• How are while loops useful to estimate numerically a solution?
• What issues arise with numeric numbers?
• for loops:
• How do for loops use sequences to process data?
• What are some drawbacks to supplying indices in a for loop?
• Why can you write a for loop as a while loop but not vice versa?
• How does a for loop select the data for a given iteration?
• repeat loops:
• Why is a repeat loop often referred to as a “do while” or running the computation at least once?
• Why does the repeat loop require break to exit it?
• Why can’t you always write a while loop as a repeat loop?
• When should repeat be used over while?

6.1: Randomness

• Random Variables
• What is a sample space? What kinds of sample spaces are available?
• What kinds of randomness exist? Which is the most common type of randomness used? Why?
• How does a seed influence a PRNG?
• What role does modulus play inside a PRNG?
• Sampling
• How does sampling with replacement differ from sampling without replacement?
• What are some scenarios where these sampling techniques would be appropriate?
• What happens if the sample size is small compared to when it is large with respect to the stated frequencies?
• Probability Distributions
• How is a probability distribution related to sampling?
• What are the different kinds of distribution functions available in R?
• What is the relationship between quantile and probability functions?
• Why does the d*() set of functions make little sense in a continuous sample space?
• Caches
• Why is a cache useful for simulations?
• What are some potential drawbacks to using a cache?
• How can we ensure our caches are created correctly?

6.2: Linear Regression

• Matrices
• Why are matrices useful?
• What happens during matrix construction when byrow = TRUE and byrow = FALSE?
• How are variables or observations added to a matrix?
• Why is matrix multiplication defined differently when compared to regular multiplication?
• Linear Relationships
• Why is a linear relationship important?
• What the different viewpoints related to modeling data?
• SLR
• Why do we perform regressions?
• How do we estimate the $$\mathbf{\beta}$$ parameters?
• What are the scalar-form and matrix-form equations for linear regression?
• How does an optimizer work with an objective function?
• MLR
• How are Simple Linear Regression (SLR) and Multiple Linear Regression (MLR) related? Where do they differ?
• What are the analytical solution to Simple & 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?
• How can we coerce factors to an atomic vector?
• Why is stringsAsFactors = TRUE by default?
• What is the meaning of an ordered factor?

7.1: Group Work

• How many stages are there in the group project?
• Is it possible to be fired from a group for bad performance?
• What are the different stages of how groups are formed?
• What are some tools to help faciliate collabortion?

7.2: Bootstrapping

• 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?
• Implementation:
• 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?

7.3: Hypothesis Testing

• Testing Frameworks:
• Why do we use hypothesis testing?
• What is a sampling distribution and how are they used in hypothesis testing?
• What are the four stages of a hypothesis test?
• Ides of Testing:
• How do research questions inform the null hypothesis and alternative hypothesis?
• What are the kinds of assumptions are required to use a hypothesis test?
• Does having more numbers after the decimal place in a test statistic guarantee more accurate results?
• Unpaired (Two-sample) t-Test:
• Is a numeric number more “trustworthy” if there lots of digits after a decimal point?
• How is a test statistic used when computing a p-value?
• Do we have to compute a p-value to make a decision?
• What are some misconceptions about p-values?
• One proportion z-test:
• What happens if a probability value needs to be assessed?
• How does the underlying assumptions between probability and samples differ?

### 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.