Question: Scenario (This scenario might be familiar, based on work from previous semesters. The dataset is entirely new, however.) You work for a small private business

Scenario

(This scenario might be familiar, based on work from previous semesters. The dataset is entirely new,

however.)

You work for a small private business college in California, and are trying to understand the factors

behind student success. Colleges (especially private ones) are businesses, and student success is

important to the success of the institution. Successful students tend to share positive views of the

college with other prospective students, tend to return for further training, etc. Moreover, successful

students lead to happy employers, which increases the value of a college's credentials and further

attracts students.

Thus, student success is an important marketing issue, and marketing tools can be used to foster

student success. If we can understand which factors predict student success, we can do things like

better segment the market, and recruit and admit students who are likely to be successful. If we can

identify issues that negatively impact students' success or times when students are at greater risk of

failure, we can take active steps to help them.

You have collected data on past student performance, in the provided file. Each observation represents

one student. The variables in the dataset are:

PROGRAM

DURATION - length of the program, in years

STATUS - Local ('L') or International ('P') (with a small number of other codes, for rare statuses)

GENDER

ESSAYSCORE - score of an essay required as part of the application process

MATHTEST - score on a math test administered as part of the application process

LOGICTEST - score on a logic test administered as part of the application process

TRANSFER_IN - 1 if the student transferred in from another program, 0 if they started directly in

the program

TOTALSEMESTERS - The total number of semesters enrolled to date

PROBATIONTERMS - The number of terms spent on academic probation

MAXGAP - The longest number of terms taken off by the student, when they would normally

have been enrolled

FINISHED - 1 if they have finished their studies, 0 if not (whether or not they graduated

successfully)

AGE - Approximate age of the student, in years, when they started the program

hsMATH1, hsMATH2, hsLANGUAGE - The student's high school grades in two math courses and

the language course required for admission

MATHCERT - 1 if the student completed a math certificate before applying to the program, 0

otherwise

GPA - The student's accumulated GPA, on a 12 point scale

HASGRADUATED - 1 if the student has successfully graduated, 0 otherwise (whether they are

still taking courses, or have terminated their studies)

Your report should include more than the answers to the specific questions; it should also include both

details of your models and how you built them (further outlined below), and your key findings from the

models. Claims you make should be backed up with appropriate justification from your analysis. You

should include only the relevant numbers in the body of the report itself; attach more complete output

from JMP as appendices, as needed.

You should explore each of the key issues and techniques used this term. Your report should detail your

investigation. Your work should show more than simply building a straightforward model or two, like

you would do for an assignment or test question. It is expected that your work will provide evidence

that you thought carefully about the problems and the dataset, and about how the tools examined in

this course can be applied to address them.

Question 4

Use clustering, considering some or all of the following variables: GENDER, ESSAYSCORE, MATHTEST,

LOGICTEST, AGE, hsMATH1, hsMATH2, and hsLANGUAGE, to try to identify segments in the data.

Characterize the segments found.

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