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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Decision Models & Prescriptive Analytics | 15% | - Optimization, sensitivity analysis - Quality & process improvement tools - Decision trees, payoff matrices, expected value |
| Topic 2: Probability & Statistical Inference | 20% | - Probability rules, distributions, expected value - Correlation vs. causation - Hypothesis testing, p-values, confidence intervals |
| Topic 3: Statistical Concepts & Descriptive Analytics | 25% | - Data visualization: charts, graphs, dashboards - Data types, measurement scales, sampling methods - Measures of central tendency, dispersion, distribution |
| Topic 4: Foundations of Data-Driven Decision Making | 20% | - Types of analytics: descriptive, predictive, prescriptive - Ethics, privacy, and data governance - Role of data in business decisions |
| Topic 5: Predictive Analytics & Regression | 20% | - Simple & multiple linear regression - Forecasting & trend analysis - Interpreting coefficients, R-squared, significance |
>> Data-Driven-Decision-Making Exam Dumps <<
We present our WGU Data-Driven-Decision-Making real questions in PDF format. It is beneficial for those applicants who are busy in daily routines. The Data-Driven-Decision-Making PDF QUESTIONS contains all the exam questions which will appear in the real test. You can easily get ready for the examination in a short time by just memorizing Data-Driven-Decision-Making Actual Questions.
NEW QUESTION # 89
What is an omission error?
Answer: D
Explanation:
Anomission erroroccurs whencrucial data is missingfrom a dataset, which can significantly compromise the quality of analysis and decision-making. In data-driven decision making, omission errors are a serious concern because missing information can lead to biased results, incorrect interpretations, and flawed conclusions.
Omission errors may arise during data collection, data entry, or data integration processes. For example, failing to record customer demographics, transaction values, or time periods can distort descriptive statistics and weaken predictive models. Unlike inaccuracies, which involve incorrect values, omission errors involve the absence of necessary data altogether.
Outliers represent extreme values and are not omission errors. Similarly, failing to review all data is a process issue rather than a data-quality error definition. Inaccurate data refers to incorrect or erroneous values, not missing ones.
Effective data quality management emphasizes identifying and correcting omission errors through validation rules, completeness checks, and data audits. In data-driven decision making, ensuring that all relevant data is captured is essential for producing reliable insights and supporting sound business decisions. Therefore, the correct answer isD, as an omission error occurs when crucial data is missing.
NEW QUESTION # 90
A county government must increase trust among voters that their tallying machines are accurately calibrated to count their votes. Each department is tasked with creating an online marketing campaign; however, the budget for these campaigns is limited.
How can the county apply data analytic approaches to allocate funds to each department?
Answer: D
Explanation:
Allocating limited resources effectively requires identifying where needs and risks are greatest. In data-driven decision making,measuring voter complaints per departmentprovides a direct, objective indicator of trust issues and communication gaps.
Departments with higher complaint volumes may require greater outreach to restore voter confidence. Using this metric allows funds to be allocated where they will have the greatest impact. Benchmarking turnout rates does not isolate departmental needs, and surveys of controllers or employees introduce subjectivity rather than evidence-based prioritization.
Therefore, the correct answer isA.
NEW QUESTION # 91
The daily sales from a salon are normally distributed with a mean of $1,500 and a standard deviation of $250.
The salon owner notices that sales were $750 on a particular day.
Why should the owner be concerned about sales based on this scenario?
Answer: D
Explanation:
In a normal distribution, most observations fall withinthree standard deviations of the mean. This principle is central to data-driven decision making and statistical process control. Values outside this range are considered highly unusual and may indicate an underlying problem.
Here, the mean is $1,500 and the standard deviation is $250. Three standard deviations below the mean equals
$750 ($1,500 # 3 × $250). Sales of exactly $750 fall at the extreme lower boundary, indicating an unusually low sales day.
Such an outcome signals a potential anomaly that warrants investigation, such as operational issues, staffing shortages, or external disruptions. Values this far from the mean occur very infrequently in normal conditions.
Therefore, the owner should be concerned because sales of $750 areoutside the typical operating range, making optionCthe correct answer.
NEW QUESTION # 92
Two vendors sell a dozen raw materials that a company needs to build its products. Which type of analysis should be used to determine if one of the vendors has a better average price at a given point in time?
Answer: A
Explanation:
To determine whether one vendor has a better average price than another, the company should use data analysis. This is the broad analytical approach that involves collecting pricing information, organizing the values, comparing averages, and possibly applying additional statistical methods to evaluate the difference.
The task is numerical and evidence-based, making data analysis the correct and most complete choice.
Heuristic analysis usually refers to rule-of-thumb decision methods rather than direct statistical comparison.
Background analysis is not the standard term for comparing measurable pricing information. Graphical analysis may help visualize price differences, but graphs alone do not fully describe the method needed to determine average pricing performance. Because the question asks which type of analysis should be used to compare average price, the answer is the one that directly refers to analyzing the numerical data itself.
Therefore, the correct answer is data analysis.
NEW QUESTION # 93
A healthcare organization implements a campaign to improve patient satisfaction during recent stays. The average patient satisfaction before the campaign is M = 4.20, SD = 3.0. Six months after the new campaign, the average patient satisfaction is M = 1.5, SD = 2.0.
Which action should the hospital management team take?
Answer: B
Explanation:
Data-driven decision making requires comparing outcomes before and after an intervention to assess effectiveness. In this scenario, the mean patient satisfaction scoredeclined from 4.20 to 1.5following implementation of the campaign, indicating a substantial decrease in satisfaction.
Despite the reduced standard deviation, the large drop in the mean suggests the campaign did not achieve its intended goal and may have negatively affected patient experience. Continuing or modifying the campaign is not justified without evidence of initial improvement or external factors explaining the decline.
Management decisions must be grounded in outcome data rather than assumptions or unrelated metrics such as patient volume. Ethical and effective use of statistics requires discontinuing interventions that demonstrably worsen outcomes.
Therefore, the correct action is todiscontinue the campaign, making optionAthe correct answer.
NEW QUESTION # 94
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