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WGU Data-Driven-Decision-Making 덤프는 고객님의WGU Data-Driven-Decision-Making시험패스요망에 제일 가까운 시험대비자료입니다. 많은 자료정리 필요없이 Fast2test에서 제공해드리는 깔끔한WGU Data-Driven-Decision-Making덤프만 있으면 자격증을 절반 취득한것과 같습니다. WGU Data-Driven-Decision-Making 덤프를 다운받아 열공하세요.
| Section | Objectives |
|---|---|
| Hypothesis Testing | - t-tests, chi-square tests, and significance testing - Null and alternative hypotheses |
| Probability and Statistical Inference | - Probability concepts and distributions - Sampling methods and sampling error |
| Regression and Correlation Analysis | - Interpreting correlation and causation - Linear regression modeling |
| Decision Making Models | - Risk and uncertainty in decision-making - Decision trees and expected value analysis |
| Data Fundamentals and Business Analytics | - Descriptive statistics (mean, median, variance, standard deviation) - Data types and data collection methods |
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IT업계에 계속 종사할 의향이 있는 분들께 있어서 국제공인 자격증 몇개를 취득하는건 반드시 해야하는 선택이 아닌가 싶습니다. WGU Data-Driven-Decision-Making 시험은 국제공인 자격증시험의 인기과목으로서 많은 분들이 저희WGU Data-Driven-Decision-Making덤프를 구매하여 시험을 패스하여 자격증 취득에 성공하셨습니다. WGU Data-Driven-Decision-Making 시험의 모든 문제를 커버하고 있는 고품질WGU Data-Driven-Decision-Making덤프를 믿고 자격증 취득에 고고싱~!
질문 # 18
Which two characteristics must a researcher consider concerning data quality when ensuring that an analysis is based on a clean data set?
Choose 2 answers.
정답：A,D
설명：
When evaluating whether a data set is clean enough for analysis, a researcher must focus on data quality dimensions that directly affect validity and usefulness. Two important characteristics are uniqueness and relevance. Data elements must be unique to prevent duplicate records from distorting counts, averages, totals, and trend analyses. Duplicate entries can lead to biased results, especially in customer, transaction, or survey data. Relevance is equally important because even accurate data are not helpful if they do not pertain to the question being studied. A clean data set should support the actual purpose of the analysis rather than merely being complete or large. The statement about age is incorrect because timeliness often matters; outdated data may no longer reflect the current environment. The statement that data cannot contain outliers is also too absolute. Outliers may be valid observations and can sometimes reveal important conditions, anomalies, or data-entry problems that require investigation rather than automatic removal. Thus, the best two characteristics are uniqueness and relevance, because both directly support meaningful, accurate, and decision- ready analysis.
질문 # 19
Why are sample sizes important for ensuring statistical significance?
정답：A
설명：
Sample size is critical for ensuring **statistical significance** because it determines whether results can be confidently generalized to a larger population. In data-driven decision making, larger and appropriately selected samples reduce sampling error and increase the reliability of statistical estimates.
When sample sizes are too small, observed effects may be due to random variation rather than true underlying patterns. Larger samples provide more precise estimates of population parameters and increase the power of hypothesis tests, making it easier to detect meaningful differences or relationships.
While increasing sample size does not eliminate researcher bias, prevent hypothesis misinterpretation, or remove the need for further analysis, it strengthens the validity of conclusions. Statistical significance depends on sample size, effect size, and variability, all of which influence confidence in results.
Therefore, the correct answer is **A**, as adequate sample sizes allow accurate conclusions to be confidently applied to larger populations.
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질문 # 20
Why would a human resources department use both mean and median when doing a salary evaluation of a department?
정답：B
설명：
Using both mean and median helps identifyoutliers, such as very high executive salaries that skew the average. A large difference between the two indicates uneven distribution.
Thus, the correct answer isC.
질문 # 21
What are two qualities of key performance indicators (KPIs)?
Choose 2 answers.
정답：B,C
설명：
Key performance indicators (KPIs) are designed to measure progress toward strategic objectives. In data- driven decision making, effective KPIs arestable, meaningful, and aligned with organizational goalsrather than frequently changing.
One important quality of KPIs is that theycan be used for internal benchmarking, allowing organizations to compare performance across departments, time periods, or projects. This helps identify best practices and performance gaps.
Another defining characteristic is that KPIs often followSMART criteria-Specific, Measurable, Achievable, Relevant, and Time-bound. These criteria ensure KPIs are clearly defined and actionable.
KPIs are not meant to be easily changed, as frequent changes undermine consistency and comparability. They also require ongoing monitoring and maintenance to remain relevant and accurate.
Therefore, the correct answers areB and D.
질문 # 22
How should a marketing consulting firm perform a cluster analysis for a new granola bar?
정답：B
설명：
Cluster analysisis an unsupervised learning technique used to group observations based on similarity. In data- driven decision making, it is commonly used formarket segmentation, allowing firms to identify distinct customer groups with similar preferences or behaviors.
For a new granola bar, cluster analysis helps determine which consumer segments exist, such as health- conscious buyers, convenience-focused consumers, or price-sensitive shoppers. This enables targeted marketing strategies and product positioning.
Understanding reasons for purchase requires survey or causal analysis, not clustering. Competitor benchmarking and trend analysis involve different analytical techniques.
Therefore, the correct answer isB, determining different segments or groups to target.
질문 # 23
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