매달, 우리는 1000명 이상의 사람들이 시험 준비를 잘하고 시험을 잘 통과할 수 있도록 도와줍니다.
VPC2Data-Driven Decision Making C207 온라인 연습
최종 업데이트 시간: 2026년03월30일
당신은 온라인 연습 문제를 통해 WGU Data Driven Decision Making 시험지식에 대해 자신이 어떻게 알고 있는지 파악한 후 시험 참가 신청 여부를 결정할 수 있다.
시험을 100% 합격하고 시험 준비 시간을 35% 절약하기를 바라며 Data Driven Decision Making 덤프 (최신 실제 시험 문제)를 사용 선택하여 현재 최신 70개의 시험 문제와 답을 포함하십시오.
정답:
Explanation:
In data-driven decision making, the median is the preferred measure of central tendency when data contain outliers or are skewed. Fundraising donation amounts often exhibit right-skewed distributions, where a small number of very large donations can significantly inflate the mean. Using the mean in such cases may misrepresent what a “typical” donor gives.
The median represents the middle value when donation amounts are ordered from smallest to largest. Because it depends only on position rather than magnitude, it is robust to extreme values. This makes it especially useful for summarizing typical behavior in skewed financial data.
The mean is sensitive to outliers, the z-score measures standardized distance from the mean, and the mode identifies the most frequent value but may not reflect central tendency in continuous donation data. Therefore, the statistic that best represents a typical donation amount is the median, making option C correct.
정답:
Explanation:
The chi-square statistic is used to compare frequencies of categorical, mutually exclusive outcomes.
In data-driven decision making, it is appropriate for analyzing differences between observed counts.
New and used car sales represent mutually exclusive categories, making chi-square the correct choice. Therefore, the correct answer is B.
정답:
Explanation:
Using both mean and median helps identify outliers, such as very high executive salaries that skew the average. A large difference between the two indicates uneven distribution.
Thus, the correct answer is C.
정답:
Explanation:
In a normal distribution, data is symmetrically distributed around the center, causing the mean and median to be approximately equal. This is a fundamental property emphasized in data-driven decision making.
Skewed distributions, such as Pareto, cause the mean and median to differ significantly. Therefore, the correct answer is A.
정답:
Explanation:
A 95% confidence result indicates a statistically significant difference between groups. Since the measured outcome is brand awareness, the correct conclusion is that the advertisement was effective in increasing brand awareness.
Confidence levels do not measure sales, preference, or dislike. Therefore, the correct answer is B.
정답:
Explanation:
When the null hypothesis is accepted in an F-test, it indicates that there is no statistically significant difference between group variances or means, depending on the test design. Acceptance occurs when the test statistic is less than the critical value, meaning the observed variation is within expected limits.
Accepting the null hypothesis implies that no meaningful difference exists between the samples. If the test statistic exceeded the critical value, the null hypothesis would be rejected.
Thus, the correct results are A and D.
정답:
Explanation:
A boxplot is specifically designed to display the distribution of a dataset using quartiles. In data-driven decision making, boxplots visually summarize data through the minimum, first quartile, median, third quartile, and maximum.
Boxplots are useful for identifying spread, central tendency, skewness, and potential outliers. Scatterplots and bivariate charts analyze relationships between variables, while Pareto charts rank categorical data by frequency.
Because quartiles are the defining feature of a boxplot, the correct answer is C.
정답:
Explanation:
The median is the middle value of a dataset when the data is arranged in ascending order. It is a key descriptive statistic used in data-driven decision making because it is resistant to extreme values.
First, sort the data in ascending order:
22, 24, 25, 28, 32, 34, 41, 42, 48, 48, 54
There are 11 values in total, so the median is the 6th value. The 6th value is $34, making it the median.
The median provides insight into the typical transaction size without being influenced by unusually large receipts. Therefore, the correct answer is B.
정답:
Explanation:
The multiplication principle is used to determine the number of possible outcomes when multiple independent choices occur in sequence. In data-driven decision making and probability theory, this rule applies when each event has a fixed number of outcomes and each outcome is independent of the others.
In this scenario, each of the eight voters can independently choose one of three candidates. The total number of possible voting outcomes is calculated by multiplying the number of choices available for each voter. Because the voters act independently and order matters in counting outcomes, the multiplication principle is the correct method.
Conditional probability applies when outcomes depend on prior events, Bayes’ theorem updates probabilities based on new information, and combinations are used when order does not matter. None of these fit the structure of this problem.
Therefore, the correct answer is A, multiplication principle.
정답:
Explanation:
This scenario requires the use of conditional probability, which applies when the likelihood of one event depends on the occurrence of another event. In data-driven decision making, conditional probability is used to model dependent events within processes, workflows, and project timelines.
In this case, the enterprise analytics team’s ability to move into production is dependent on the infrastructure team completing the migration. Because one event cannot occur unless another event has already occurred, the probability of completing the project on time must account for this dependency.
The multiplication principle applies to independent events, Bayes’ theorem updates probabilities based on new information, and combinations are used for counting outcomes, not dependency analysis. Conditional probability explicitly captures the relationship between dependent tasks.
Project risk analysis and scheduling often rely on conditional probability to assess completion likelihood when tasks are sequentially linked. Therefore, the correct answer is C, conditional probability.
정답:
Explanation:
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.
정답:
Explanation:
This scenario represents selection bias, which occurs when a sample is not representative of the population being studied. In data-driven decision making, valid conclusions depend on collecting data from a sample that accurately reflects the broader population.
By surveying only respondents with a bachelor’s degree, the researcher systematically excludes other segments of the population who may have different opinions about the bond issue. Educational attainment may influence voting behavior, making the sample biased toward a particular viewpoint. As a result, the findings cannot be generalized to the entire voting population.
While the wording of the question may be persuasive, the primary statistical error is the non-random and restricted selection of respondents. Response bias relates to how participants answer questions, whereas this issue arises before responses are even collected. Faulty operationalization and confusion of causality are not applicable here.
Data-driven decision making stresses ethical sampling practices to avoid misleading conclusions.
Therefore, the correct answer is D, selection bias.
정답:
Explanation:
An appropriate management use of statistics is understanding the demographics of customers, which supports informed, proactive decision-making. Data-driven decision making emphasizes using statistical analysis to explore patterns, characteristics, and trends that help organizations better understand their customers and markets.
Analyzing customer demographics such as age, income, location, and preferences allows managers to segment markets, tailor products, improve services, and allocate resources effectively. This use of statistics is descriptive and diagnostic in nature and directly supports strategic planning.
Ordering products for an entire region based on data from a single store is inappropriate due to lack of representativeness. Justifying decisions after implementation reflects misuse of statistics, as analytics should inform decisions beforehand, not rationalize them after the fact. Implementing changes solely based on survey response rate ignores the content and validity of responses.
Ethical data-driven decision making requires that statistics be used responsibly, transparently, and with appropriate context. Therefore, the correct answer is A, as understanding customer demographics represents a proper and effective use of statistics by management.
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정답:
Explanation:
A quasi-experimental study is commonly referred to as an observational study in data-driven decision making. Unlike true experiments, quasi-experimental studies do not involve random assignment of subjects to treatment and control groups. Instead, researchers observe outcomes in naturally occurring groups and attempt to draw conclusions about relationships between variables.
In observational studies, the researcher does not control the assignment of treatments. As a result, these studies are more susceptible to bias and confounding variables than randomized experiments. However, they are often necessary when controlled experimentation is impractical, unethical, or too costly. For example, studying the impact of policy changes or economic conditions typically relies on observational data.
Blind studies are a form of experimental design used to reduce bias, hypothesis testing is a statistical process rather than a study type, and content validity refers to measurement quality. None of these represent quasi-experimental designs.
In data-driven decision making, observational (quasi-experimental) studies are valuable for identifying associations and generating insights, but analysts must be cautious not to infer causality without proper controls. Therefore, the correct answer is A.
정답: D
Explanation:
To make a valid comparison in data-driven decision making, samples must be comparable and drawn from the same population, differing only in the factor being evaluated. In this case, the goal is to compare patient satisfaction between online telemedicine visits and in-person visits at Family Practice A.
Using online Family Practice A telemedicine visits and in-person Family Practice A visits ensures that both samples come from the same organization, patient base, and survey methodology. This controls for external factors such as practice standards, demographics, and survey design, allowing differences in ratings to be attributed to the visit type rather than unrelated variables.
Comparing total visits to only one visit type introduces imbalance. Including other family practices introduces external variation and invalidates the comparison. Data-driven decision making stresses consistency and relevance in sample selection to ensure accurate conclusions.
Therefore, the correct answer is D, as it uses comparable samples that isolate the variable of interest.