WGU C207 DATA-DRIVEN DECISION-MAKING FINAL EXAM
2026 MOST TESTED QUESTIONS AND CORRECT VERIFIED
ANSWERS, IN REAL EXAM 100% GUARANTEED PASS
||COMPLETE A+ GUIDE PDF
Define analytics
Turning information into knowledge and developing fact based
strategies to gain a competitive edge.
Descriptive/Diagnostic analytics
Encompasses the set of techniques that describes what has
happened in the past.
Predictive analytics
Consists of techniques that use models constructed from past
data to predict the future or ascertain the impact of one
variable on another.
Prescriptive analytics
Decision models that indicate the best course of action to take.
Three characteristic of big data
1. Structured and unstructured large volumes
2. Analytics is used to discover and communicate meaningful
patterns
3. Analysis of big data requires a system of organization
Davenport-Kim Three-Stage Model*
,A decision-making model developed by Thomas Davenport and
Jinho Kim that consists of three stages:
1. Framing the problem, (why are you trying to do this and what
do we already know)
2. Solving the problem (Choosing the model, collecting the data,
analyzing the model)
3. Communicating results (Communicating and acting on the
results)
Categorical or discrete data variables
Represents types of data that can be divided into groups or
categories.
Example: race, sex, age group, and educational level, happy, etc.
There are two types of categorical data
Nominal and Ordinal
Nominal data*
Identifies, groups, or categories only. Data cannot be arranged
in an ordering scheme. They are used to name or label a series
of values.
Examples, names, pass/fail, gender, race, the color of the eye
Data can not be ordered and makes no sense to calculate means
or standard deviations off this.
Ordinal data*
,Data is placed into some order by some quality. They are
usually non-numeric captions like happiness or discomfort. It is
the ORDER that matters.
They provide good information about the order of values, like a
customer satisfaction survey.
Examples: top ten best cities to live in or top 10 college football
teams.
(ordinal-order matters)
Numerical or continuous variables
They can accept any value and can measure nearly anything.
Height, weight, BP, etc...
Two types of numerical data
Interval and Ratio
Interval data*
It has an order to it and has equal intervals apart.
It gives us the order of values with the ability to quantify the
difference between each one.
Differences between values can be found and meaningful, but
they DO NOT have a true 0. ( no temperature, no true 0 time, or
no true 0 dress size)
These can be added or subtracted but not multiplied
(interval means space in between which is the important thing
to remember).
Ratio data*
Data similar to interval data but does have an absolute 0.
, Ratios are meaningful. (Length, Width, Weight, Distance, age,
income, stock prices, repeat customers...)
Data can be added, subtracted, multiplied, or divided.
Qualitative research
Data not characterized by numbers, usually textual, visual, or
oral responses.
Examples: Hair color, car colors, letter grades, types of coins in
a jar. ANYTHING THAT DOES NOT HAVE A NUMER IN IT!
Quantitative research
Gives you numerical data.
Three elements to a quantitative study design
1. Units, this is the subject or object that is being observed.
(Example: Students are broken up into three different groups)
2. Treatments, the procedures being applied to each subject.
(Examples: which learning is best, lecture, simulation, or no
treatment. Each unit gets a different treatment in this example)
3. Responses, the effect of the experimental design. (What were
the results of each group, who got the b est grades based off the
different treatments.)
Data managements responsibility
Clean and organize data sets, make sure clean good data is
provided.
5 questions asked about data management
2026 MOST TESTED QUESTIONS AND CORRECT VERIFIED
ANSWERS, IN REAL EXAM 100% GUARANTEED PASS
||COMPLETE A+ GUIDE PDF
Define analytics
Turning information into knowledge and developing fact based
strategies to gain a competitive edge.
Descriptive/Diagnostic analytics
Encompasses the set of techniques that describes what has
happened in the past.
Predictive analytics
Consists of techniques that use models constructed from past
data to predict the future or ascertain the impact of one
variable on another.
Prescriptive analytics
Decision models that indicate the best course of action to take.
Three characteristic of big data
1. Structured and unstructured large volumes
2. Analytics is used to discover and communicate meaningful
patterns
3. Analysis of big data requires a system of organization
Davenport-Kim Three-Stage Model*
,A decision-making model developed by Thomas Davenport and
Jinho Kim that consists of three stages:
1. Framing the problem, (why are you trying to do this and what
do we already know)
2. Solving the problem (Choosing the model, collecting the data,
analyzing the model)
3. Communicating results (Communicating and acting on the
results)
Categorical or discrete data variables
Represents types of data that can be divided into groups or
categories.
Example: race, sex, age group, and educational level, happy, etc.
There are two types of categorical data
Nominal and Ordinal
Nominal data*
Identifies, groups, or categories only. Data cannot be arranged
in an ordering scheme. They are used to name or label a series
of values.
Examples, names, pass/fail, gender, race, the color of the eye
Data can not be ordered and makes no sense to calculate means
or standard deviations off this.
Ordinal data*
,Data is placed into some order by some quality. They are
usually non-numeric captions like happiness or discomfort. It is
the ORDER that matters.
They provide good information about the order of values, like a
customer satisfaction survey.
Examples: top ten best cities to live in or top 10 college football
teams.
(ordinal-order matters)
Numerical or continuous variables
They can accept any value and can measure nearly anything.
Height, weight, BP, etc...
Two types of numerical data
Interval and Ratio
Interval data*
It has an order to it and has equal intervals apart.
It gives us the order of values with the ability to quantify the
difference between each one.
Differences between values can be found and meaningful, but
they DO NOT have a true 0. ( no temperature, no true 0 time, or
no true 0 dress size)
These can be added or subtracted but not multiplied
(interval means space in between which is the important thing
to remember).
Ratio data*
Data similar to interval data but does have an absolute 0.
, Ratios are meaningful. (Length, Width, Weight, Distance, age,
income, stock prices, repeat customers...)
Data can be added, subtracted, multiplied, or divided.
Qualitative research
Data not characterized by numbers, usually textual, visual, or
oral responses.
Examples: Hair color, car colors, letter grades, types of coins in
a jar. ANYTHING THAT DOES NOT HAVE A NUMER IN IT!
Quantitative research
Gives you numerical data.
Three elements to a quantitative study design
1. Units, this is the subject or object that is being observed.
(Example: Students are broken up into three different groups)
2. Treatments, the procedures being applied to each subject.
(Examples: which learning is best, lecture, simulation, or no
treatment. Each unit gets a different treatment in this example)
3. Responses, the effect of the experimental design. (What were
the results of each group, who got the b est grades based off the
different treatments.)
Data managements responsibility
Clean and organize data sets, make sure clean good data is
provided.
5 questions asked about data management
