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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Predictive Analytics & Regression | 20% | - Forecasting & trend analysis - Simple & multiple linear regression - Interpreting coefficients, R-squared, significance |
| Topic 2: Decision Models & Prescriptive Analytics | 15% | - Optimization, sensitivity analysis - Quality & process improvement tools - Decision trees, payoff matrices, expected value |
| Topic 3: Probability & Statistical Inference | 20% | - Correlation vs. causation - Probability rules, distributions, expected value - Hypothesis testing, p-values, confidence intervals |
| Topic 4: Statistical Concepts & Descriptive Analytics | 25% | - Measures of central tendency, dispersion, distribution - Data visualization: charts, graphs, dashboards - Data types, measurement scales, sampling methods |
| Topic 5: Foundations of Data-Driven Decision Making | 20% | - Types of analytics: descriptive, predictive, prescriptive - Ethics, privacy, and data governance - Role of data in business decisions |
>> New WGU Data-Driven-Decision-Making Test Answers <<
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NEW QUESTION # 58
What are two qualities of key performance indicators (KPIs)?
Choose 2 answers.
Answer: C,D
Explanation:
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.
NEW QUESTION # 59
Research data indicate 95% confidence in a study in which subjects who were shown a product advertisement exhibited brand awareness compared to a control group who did not see the advertisement.
What can be concluded from this study?
Answer: D
Explanation:
A 95% confidence result indicates a statistically significant difference between groups. Since the measured outcome isbrand 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 isB.
NEW QUESTION # 60
What is an omission error?
Answer: A
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 # 61
A manager has been assigned to manage a digital marketing analytics team. The manager tasks the team with determining similarities among existing customers in the company's database, such as similarities in products purchased, location, and the average amount spent per order among existing customers.
Which type of activity will help the team accomplish this task?
Answer: B
Explanation:
Data miningis the appropriate activity for identifying patterns, similarities, and relationships within large datasets. In data-driven decision making, data mining techniques such as clustering and association analysis are commonly used to segment customers based on behavior and characteristics.
The task described involves uncovering hidden patterns across multiple variables, which aligns directly with data mining objectives. Linear programming focuses on optimization, regression predicts outcomes, and touchpoint analysis examines customer interactions rather than similarities.
Therefore, the correct answer isA, data mining.
NEW QUESTION # 62
A hospital is restructuring its business and administrative functions.
Which component of service delivery could be analyzed with a data analytics approach to help determine whether the hospital is adequately staffed for each shift?
Answer: A
Explanation:
Patient-to-staff ratiosare a critical analytic measure for determining whether a hospital is adequately staffed for each shift. In data-driven decision making, staffing adequacy is best assessed by examining workload demand relative to available personnel.
Patient-to-staff ratios directly reflect how many patients each staff member is responsible for during a given shift. High ratios may indicate understaffing, increased risk of burnout, and reduced quality of care, while lower ratios suggest more manageable workloads and better patient outcomes.
Staff productivity levels measure efficiency but do not directly capture demand. Education levels reflect qualifications rather than staffing sufficiency. Patient satisfaction is an outcome metric and may be influenced by many factors beyond staffing levels.
By analyzing patient-to-staff ratios across shifts, hospital administrators can identify imbalances, allocate resources more effectively, and improve operational efficiency. Therefore, the correct answer isB.
NEW QUESTION # 63
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