STAT 432

STAT 432

CBTF Exam II Policies

Date and Time

Please see the CBTF scheduler:

CBTF Policies

Please be aware of the CBTF policies:

If you encounter a technology issue during the exam, you must file an incident report with a proctor. Without documentation, we cannot provide accommodation for technology errors encountered during an exam.

Academic Integrity

In short, do not cheat. In addition to the Academic Integrity policies outlined by the CBTF, STAT 432 will hold itself to a higher standard. Any violation will be punished as harshly as possible, which at minimum is a failing grade on the exam.

A non-exhaustive list of academic integrity violations:

You are correct that some of these things may be hard to detect, but in addition to writing the exam in a manner that makes transferring this type of information less useful, we have other methods that you are likely unaware of to detect the above violations.

Be aware that the exam window goes until Friday. However, this does not mean that you may discuss the exam at that time as there may be makeup exams in rare circumstances. Do not discuss any part of the exam until I announce that it is OK to do so.

Content

The exam will focus very heavily of Quiz 07 and Quiz 08. In addition to this quiz material, you are expected to be able to use any modeling technique used for classification that accepts as input formula and data arguments, and the output can be used with the predict() function. With these models and the predict() functions, you are expected to be able to estimate conditional probabilities and make classifications, possibly based on an arbitrary cutoff for binary classification.

Format

You will be given 50 minutes to complete the exam which will be administered through PrairieLearn. You will have access to RStudio with a large set of packages installed. (Those seen in the quizzes.)

R Packages

The following packages are available for use in the CBTF. This list does not imply that you need to know these packages. It is only for your reference.

pkg_list = c(
  'tidyverse',
  'RcppArmadillo',
  'rmarkdown',
  'RSQLite',
  'nycflights13',
  'fueleconomy',
  'babynames',
  'rbenchmark',
  'microbenchmark',
  'maps',
  'maptools',
  'mapproj',
  'mapdata',
  'ggmap',
  'fivethirtyeight',
  'caret',
  'e1071',
  'factoextra',
  'gbm',
  'glmnet',
  'ISLR',
  'kernlab',
  'klaR',
  'mlbench',
  'nnet',
  'pROC',
  'randomForest',
  'rpart',
  'rpart.plot',
  'rsample',
  'kableExtra'
)

Provided Notes

\[\text{Sensitivity} = \frac{\text{TP}}{\text{P}} = \frac{\text{TP}}{\text{TP + FN}}\] \[\text{Specificity} = \frac{\text{TN}}{\text{N}} = \frac{\text{TN}}{\text{TN + FP}}\]

These, and perhaps other similar definitions (related to binary classification) will be provided with any relevant problems. No additional materials will be provided.

FAQ

How many problems are on the exam?

How long will it take to do the exam?

Will the exam be curved?

We’ve seen our grades now, will the exam be curved?

Home