ISYE 6501 - MIDTERM 2 CERTIFICATION
EVALUATION EXAM Q&A: 2026 STUDY GUIDE
100% CORRECT
◍ What are Outliers? Answer: Data points that are noticeably
different from the rest of the data, often caused by errors or anomalies
in data entry or measurement.
◍ What are Patterns in Missing Data? Answer: The idea that some
data points might be more likely to be missing due to the way they are
collected, observed, or recorded.
◍ What is Bias in Missing Data? Answer: When missing data is more
likely to occur for certain values, creating a biased dataset (e.g.,
higher-income individuals more likely to omit income data).
◍ What is Data Cleaning? Answer: The process of handling missing
data, outliers, and correcting errors in data to make it suitable for
analysis.
◍ What is Missing Data Bias? Answer: When the missing data is not
random, but influenced by specific factors or characteristics, such as
income levels or car speed.
, ◍ What are the Types of Missing Data? Answer: The different
reasons or methods behind why data might be missing, including
technical errors, data collection issues, or intentional omissions.
◍ What are the Challenges with Missing Data? Answer: The
difficulty in making accurate conclusions from a dataset when data is
missing, as it can impact the validity of models and analysis.
◍ What is the Difference Between Random and Non-Random
Missing Data? Answer: Data that is missing randomly does not
depend on other variables in the dataset, while non-random missing
data is influenced by certain factors (e.g., higher incomes leading to
missing income data).
◍ What are Data Availability Issues? Answer: Instances where data
cannot be collected or recorded due to factors like equipment failure,
human error, or intentional omission.
◍ What are Methods for Dealing with Missing Data? Answer:
Methods used to handle missing data, including imputation, deletion,
or statistical modeling, depending on the nature and extent of the
missing data.
◍ What are Three Ways to Handle Missing Data? Answer: The three
options are: 1) Throw away data points with missing data, 2) Use
categorical variables to indicate missing data, or 3) Estimate the
missing value through imputation.
EVALUATION EXAM Q&A: 2026 STUDY GUIDE
100% CORRECT
◍ What are Outliers? Answer: Data points that are noticeably
different from the rest of the data, often caused by errors or anomalies
in data entry or measurement.
◍ What are Patterns in Missing Data? Answer: The idea that some
data points might be more likely to be missing due to the way they are
collected, observed, or recorded.
◍ What is Bias in Missing Data? Answer: When missing data is more
likely to occur for certain values, creating a biased dataset (e.g.,
higher-income individuals more likely to omit income data).
◍ What is Data Cleaning? Answer: The process of handling missing
data, outliers, and correcting errors in data to make it suitable for
analysis.
◍ What is Missing Data Bias? Answer: When the missing data is not
random, but influenced by specific factors or characteristics, such as
income levels or car speed.
, ◍ What are the Types of Missing Data? Answer: The different
reasons or methods behind why data might be missing, including
technical errors, data collection issues, or intentional omissions.
◍ What are the Challenges with Missing Data? Answer: The
difficulty in making accurate conclusions from a dataset when data is
missing, as it can impact the validity of models and analysis.
◍ What is the Difference Between Random and Non-Random
Missing Data? Answer: Data that is missing randomly does not
depend on other variables in the dataset, while non-random missing
data is influenced by certain factors (e.g., higher incomes leading to
missing income data).
◍ What are Data Availability Issues? Answer: Instances where data
cannot be collected or recorded due to factors like equipment failure,
human error, or intentional omission.
◍ What are Methods for Dealing with Missing Data? Answer:
Methods used to handle missing data, including imputation, deletion,
or statistical modeling, depending on the nature and extent of the
missing data.
◍ What are Three Ways to Handle Missing Data? Answer: The three
options are: 1) Throw away data points with missing data, 2) Use
categorical variables to indicate missing data, or 3) Estimate the
missing value through imputation.
