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Exam (elaborations)

WGU C207 Data Driven Decision Making Western Governors University 2025–2026 Comprehensive Exam Practice with 350 Questions and Detailed Answers

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This document contains a comprehensive set of 350 practice questions and detailed answers for WGU C207 Data Driven Decision Making, designed for the 2025–2026 exam cycle. It covers key topics such as data analysis, statistical methods, hypothesis testing, regression, and decision-making models. The material is structured to reinforce core concepts, improve analytical skills, and support effective exam preparation.

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WGU C207 EXAM NEWEST 2025/2026 LATEST TESTING
ACTUAL EXAM WITH 350 QUESTIONS AND CORRECT
DETAILED ANSWERS ALREADY GRADED A+ / C207 WGU
ACTUAL EXAM (BRAND NEW) | GUARANTEED PASS



What is the key focus on analysis?
To predict trends using quantitative data.




What are the four levels of measurement?
1. Nominal
2. Ordinal
3. Interval
4. Ratio




Nominal data
is categorical. It has no numerical value. It's not a number.
Examples:
-the types of pizza you sell
-meatball
-veggie
-cheese

,(these are labels, they can't be added or subtracted)




Ordinal data
is ranked, but doesn't have a specific value.
Example:
-the size of pizza (small, medium, large)
(we can't add or subtract these but we can put them in sequential order)
- order (key word)
- no numerical value




Interval data
data is numeric. You can add and subtract it. It has a sequential value. Each
value is equally spaced from the previous value.
Example:
- drink sizes are interval (16 oz, 20 oz, 24 oz) they are equally spaced about (4 oz
spaced)




Ratio data
is numeric. Your sales per day are ratio data.
Example"
- 10 sales of $12.99 a piece = $129.99
(the value has a true value from zero)

,To challenge the validity and reliability of data, ask two things.
Are there any outliers and are there any errors?




Outliers
Don't throw them out. Do them in both ways.
Example:
last week, you were closed for two days for renovations. Sales were a zero for
those two days. That's an outlier. You include the outliers to know how its
effecting your bottom line.




2 categories of errors
1. Random
2. Systematic




Random error
is something that happens just once and will not repeat over time.
Example:
if you are trying to find average delivery times and one delivery was effected by
a four hour Chicago traffic delay, that's random.

, Systematic error
is when your deliveries (example) are slow and it is not by chance.
Example:
A delivery driver has nursed the fuel injector on his car for the past six months.
It breaks down one out of every 20 deliveries he makes. This is Systematic error.
It repeats itself.




Omission error
An error because something is missing.
Example:
A delivery driver didn't clock in or out for his delivery. That data will not be
included in study and it's relevant.
-A data set with an omission error is defined as distorted.




What is used to reduce errors?
a number of quality control tools.
Example:
a survey customers take only allows them to select responses from a list. That
way, they can't type anything in wrong.




Treatment

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