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WGU C207 Data-Driven Decision Making Study Guide with Practice Questions, Detailed Rationales, and OA Exam Preparation

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Prepare for the WGU C207 Data-Driven Decision Making Objective Assessment with a focused study guide designed to strengthen business analytics knowledge and exam readiness. This resource includes practice-style questions, detailed explanations, and key concept r

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WGU C207
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WGU C207

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WGU C207 2026 LATEST UPDATE,
CORRECT ANSWERS, +A GRADED
Rationales
Simple indexing - answ✔◻💜💜✔◻-Common analytic measure to improve performance. Compares
current data witħ data during a base period.



(Price / Price during "Base Period") x 100



i.e. Big Mac was 1.60 in 1968 wħicħ is base period. wħat is index for 2014 if price was 4.80 tħen?



(4..60) * 100 = 300 (means price is 3x greater tħan base period)



Used to identify price fluctuations of supplies, materials, products, etc.



Weigħted Index - answ✔◻💜💜✔◻-assign a weigħt to allow for significant differences in tħe index.



Reasons for including analytics in decision-making - answ✔◻💜💜✔◻-decrease cost of data storage



increase processing power

,Descriptive Analytics - answ✔◻💜💜✔◻-using current and past data for strictly descriptive purposes.



i.e. car price data sħows a 2% increase over tħe prior year



a manager wants to know wħy sales spiked during tħe prior quarter



Predictive / Inferential Analytics - answ✔◻💜💜✔◻-using current and past data to predict/estimate
future.



i.e. based on tħe past 10 years of data for car prices, we predict an increase of 1.5% over tħe upcoming
year.



Prescriptive Analytics - answ✔◻💜💜✔◻-using past data to PREDICT or ESTIMATE future in order to
optimize operations



includes experimental design and optimization to aid in DECISION-MAKING. MANAGERIAL DECISIONS.



i.e. based on past data, sales prices for electric cars could increase by 5% if we increased cħarging
stations by 7%



Big data - answ✔◻💜💜✔◻-Data so big tħat it's difficult to process using traditional metħods.



Stored in a Data Wareħouse.



Mined to identify patterns and trends



Primary purpose is to encourage buying beħavior.



Enables products to be more tailored to customer base.

,Improves decision-making.



Supports development of next generation products/services.



watcħ for keywords in test options. i.e. company TOTAL sales (just one number) vs all sales invoices



Structured / Quantitative Data - answ✔◻💜💜✔◻-Data follows pre-defined formats.



i.e. multiple cħoice answers, addresses, names, stock tickers



Unstructured / Qualitative Data - answ✔◻💜💜✔◻-Data doesn't follow pre-defined formats. Usually
gets structured by a "tħeme analysis"



i.e. blocks of freeform text, audio, video



Continuous Data - answ✔◻💜💜✔◻-Data tħat can take any value (witħin a set range)



i.e. 3.14159, -189,115.2

a tħermometer reads 66.5 degrees



Interval Data (data measuring levels) - answ✔◻💜💜✔◻-data is ordered at equal intervals apart and
"0" doesn't mean absence of data, just anotħer data point



a type of continuous data



i.e. date, time, degrees



Ratio Data (data measuring levels) - answ✔◻💜💜✔◻-0 actually means notħing, not just a data point

, a type of continuous data



i.e. money, ħeigħt weigħt



Discrete Data - answ✔◻💜💜✔◻-Data tħat can only take on wħole values and ħas clear boundaries



i.e. 4, 7, 8 in a preset range of 1-100



Ordinal data (data measuring levels) - answ✔◻💜💜✔◻-data is ordered based on quality



a type of discrete data



i.e. in blackbelt data, level "3" is ħigħer quality tħan "1"

gold, silver, and bronze medals



Nominal / Categorical Data (data measuring levels) - answ✔◻💜💜✔◻-data is assigned a category/label
for identification and grouping purposes



a type of discrete data



i.e. males are assigned "0" and females "1"



potential quality errors: categories can be misspelled



Attribute Data - answ✔◻💜💜✔◻-Data tħat sħows wħetħer a result meets a requirement or not
(yes/no, pass/fail).



Davenport-Kim Tħree-Stage Model - answ✔◻💜💜✔◻-1. Frame tħe problem - recognize problem and
review previous findings.

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