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C207 Data‑Driven Decision Making | WGU OA Prep Quiz 1‑6

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This comprehensive review guide covers all six quizzes for C207 Data‑Driven Decision Making at Western Governors University (WGU). It provides verified questions, answers, and explanations to prepare students for the Objective Assessment (OA). Key areas include: Analytics types: Descriptive, predictive, prescriptive analytics with applications in manufacturing, healthcare, and retail. Data errors & bias: Systematic error, measurement bias, omission, and double‑blind studies to eliminate bias. Levels of measurement: Nominal, ordinal, interval, ratio explained with practical examples. Probability & statistics: Bayes Theorem, multiplication rule, misuse of statistics, representative sampling. Distributions & z‑scores: Normal distribution, variance, standard deviation, z‑score interpretation. Graphical analysis: Histograms, scatterplots, Pareto charts, flowcharts. Hypothesis testing: t‑tests, chi‑square, ANOVA, null hypothesis rejection. Regression & forecasting: Multiple regression, R‑squared, cluster analysis, decision analysis, simulation, seasonality, trend forecasting. Quality tools: Seven Basic Quality Tools, Plan‑Do‑Check‑Act model, quality assurance activities. Performance measurement: KPIs, balanced scorecard, net promoter score, dashboards. Business applications: Cost‑benefit analysis, linear programming, crossover analysis, composite index, cumulative incidence. This guide ensures mastery of data‑driven decision making concepts with applied examples, making it ideal for WGU students preparing for the C207 OA exam. C207 OA prep quiz 1‑6 WGU, WGU C207 data‑driven decision making study guide, descriptive predictive prescriptive analytics examples, systematic error measurement bias omission, levels of measurement nominal ordinal interval ratio, , normal distribution variance z‑score interpretation,

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C207 OA Prep - OA Prep


Data-Driven Decision Making

(Western Governors University)




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, C207 OA Prep- Quiz 1-6 Review
Section 1

1. For companies to attract and retain their best customers, they need a
complete portrait of who they are. To develop this portrait companies turn to
analytics

2. A manufacturer wants to maximize their factory output while specifically
minimizing labor costs. Which type of analytics might they employ to achieve
this goal?
Prescriptive analytics

3. Which type of data error that occurs in measurement is constant within a
data set and is sometimes caused by faulty equipment or bias? Systematic

4. An educator develops a new standardized test to measure math skills of
ninth graders. She has students in her home state of Ohio take the test. If
the test is to be used on a national level, which type of error might be found
in her data? Measurement bias

5. A city government is trying to determine the national origins of its recent
immigrant population. If a survey of the immigrant population is conducted
in English, which type of error might be present in the data? Omission

6. The use of big data is increasingly important to businesses in competitive
markets. Which of the following characteristics is not true of big data?
Can be analyzed with traditional spreadsheets

7. The Davenport-Kim three-stage model consists of framing the problem,
solving the problem, and communicating results. Which two of the following
are part of framing the problem stage? Determine the scope of the
problem, Review of previous findings

8. A healthcare provider is researching blood glucose levels before and after
exercising. Which two elements should be part of any experimental study
such as this?
Treatment procedures, Experimental response

9. Runners cover 26.2 miles in the Olympics marathon. Which level of
measurement is this? Ratio

10. Which level of measurement is the type of cars produced in a Ford
factory? Nominal




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