ISYE6501 INTRODUCTION ANALYTICS
MODELING EXAM SCRIPT 2026 TEST
PAPER QUESTIONS AND SOLUTIONS
GRADED A+
◉ Model Answer: Mathematical approaches to solving analytics
problems
1. Regression for ex. can be called the model of choice.
2. If more detail (descriptive and numerical), the use of, for ex.
Regression, can be called the model.
3. The actual values and output along with 1 and 2 can be referred to as
the model.
◉ Cross Cutting Concepts Answer: Preparing data, measuring the
quality of a model's output, missing data, etc..
◉ Classification Answer: The process of grouping things based on their
similarities
Ex. Classifying consumers by who is likely to pay back a loan and who
is not
◉ Soft Classifier Answer: A soft classifier is used when it is not possible
to completely separate two categories of data points via the classifying
line.
the soft classifier gives as good of a separation as possible rather than a
hard classifier that separates perfectly.
, ◉ Classifier Rules Answer: Must define the best separator.
Misclassifying a data point may be more detrimental than misclassifying
another. the more costly one bad decision is, the more we want to move
away from it.
Its okay to push the classifier in one direction so that it is conservative if
stakes are higher.
◉ Data Point Answer: Every Row, a single observation of information
◉ Attribute/Feature/Covariate/Predictor Answer: Piece of information,
column of data
◉ Responses/Outcomes Answer: Special type of column known as
being the answer for each data point
◉ Structured Data Answer: Can be easily described and stored
◉ Unstructured Data Answer: Usually in form of text, cannot be easily
described or stored
◉ Quantitative Data Answer: Most numerical data, numbers have a
meaning
◉ Non Quantitative Data Answer: Descriptive data, Categorical data,
any data without numerical meaning ex. zip codes
MODELING EXAM SCRIPT 2026 TEST
PAPER QUESTIONS AND SOLUTIONS
GRADED A+
◉ Model Answer: Mathematical approaches to solving analytics
problems
1. Regression for ex. can be called the model of choice.
2. If more detail (descriptive and numerical), the use of, for ex.
Regression, can be called the model.
3. The actual values and output along with 1 and 2 can be referred to as
the model.
◉ Cross Cutting Concepts Answer: Preparing data, measuring the
quality of a model's output, missing data, etc..
◉ Classification Answer: The process of grouping things based on their
similarities
Ex. Classifying consumers by who is likely to pay back a loan and who
is not
◉ Soft Classifier Answer: A soft classifier is used when it is not possible
to completely separate two categories of data points via the classifying
line.
the soft classifier gives as good of a separation as possible rather than a
hard classifier that separates perfectly.
, ◉ Classifier Rules Answer: Must define the best separator.
Misclassifying a data point may be more detrimental than misclassifying
another. the more costly one bad decision is, the more we want to move
away from it.
Its okay to push the classifier in one direction so that it is conservative if
stakes are higher.
◉ Data Point Answer: Every Row, a single observation of information
◉ Attribute/Feature/Covariate/Predictor Answer: Piece of information,
column of data
◉ Responses/Outcomes Answer: Special type of column known as
being the answer for each data point
◉ Structured Data Answer: Can be easily described and stored
◉ Unstructured Data Answer: Usually in form of text, cannot be easily
described or stored
◉ Quantitative Data Answer: Most numerical data, numbers have a
meaning
◉ Non Quantitative Data Answer: Descriptive data, Categorical data,
any data without numerical meaning ex. zip codes
