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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Foundations of Data-Driven Decision Making | 20% | - Ethics, privacy, and data governance - Types of analytics: descriptive, predictive, prescriptive - Role of data in business decisions |
| Topic 2: Statistical Concepts & Descriptive Analytics | 25% | - Data types, measurement scales, sampling methods - Measures of central tendency, dispersion, distribution - Data visualization: charts, graphs, dashboards |
| Topic 3: Probability & Statistical Inference | 20% | - Hypothesis testing, p-values, confidence intervals - Probability rules, distributions, expected value - Correlation vs. causation |
| Topic 4: Decision Models & Prescriptive Analytics | 15% | - Optimization, sensitivity analysis - Decision trees, payoff matrices, expected value - Quality & process improvement tools |
| Topic 5: Predictive Analytics & Regression | 20% | - Interpreting coefficients, R-squared, significance - Simple & multiple linear regression - Forecasting & trend analysis |
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NEW QUESTION # 99
A county government wants to start a cost-effective recycling program. How can the county apply data analytic approaches to attain this goal?
Answer: C
Explanation:
To create a cost-effective recycling program, the county should benchmark similar strategies used by other counties. Benchmarking is a practical data-driven approach that allows decision-makers to compare program designs, costs, participation rates, collection methods, and outcomes across jurisdictions facing similar conditions. By examining how other counties structure successful recycling initiatives, the county can identify efficient practices, realistic cost expectations, and implementation strategies that have already been tested in comparable environments. This approach is especially useful when developing a new program because it reduces trial-and-error and supports evidence-based planning. The other options are less directly related to designing and evaluating a recycling program. Analyzing county contracts or aligning funds with staffing levels may be useful in general budgeting, but they do not specifically address recycling strategy effectiveness. Audit performance research is also not closely tied to recycling program design. Therefore, the most appropriate data analytic approach is benchmarking similar strategies of other counties.
NEW QUESTION # 100
What is an advantage of a balanced scorecard?
Answer: C
Explanation:
A balanced scorecard is valuable because it emphasizes strategy and organizational results. Rather than focusing only on short-term financial outcomes, it connects performance measurement to the organization's broader mission and long-term objectives. It encourages managers to assess performance from multiple perspectives, typically financial, customer, internal process, and learning and growth. This makes it easier to align day-to-day activities with strategic priorities and understand how actions in one area affect results in another. The other options do not describe the true strength of the balanced scorecard. It does not necessarily require little effort to set up, because meaningful implementation often takes planning, metric selection, and alignment across departments. It also does not require minimal data, nor is its purpose simply to increase the amount of data available. Its main benefit is that it helps organizations translate strategy into measurable outcomes and track whether they are achieving the results that matter most. Therefore, the correct answer is that it emphasizes strategy and organizational results.
NEW QUESTION # 101
What results from starting an analysis with flawed data?
Choose 2 answers.
Answer: B,D
Explanation:
Starting an analysis with flawed data significantly undermines the effectiveness of data-driven decision making. One major consequence is thatmore time is spent managing data than analyzing data. Analysts must devote substantial effort to cleaning, validating, and correcting errors before meaningful analysis can occur, delaying insights and increasing costs.
Another critical result is thatmissing data tend to skew the results of the analysis. Incomplete data can distort averages, trends, and statistical relationships, leading to biased conclusions and unreliable decisions.
This is especially problematic in predictive and inferential analytics, where assumptions about data completeness are essential.
Using spreadsheets or placing data in charts does not inherently result from flawed data, nor does it resolve data quality issues. While visualization can help identify errors, it is not a direct outcome of starting with flawed data.
Data-driven decision making emphasizes that poor-quality input leads to poor-quality output. Ensuring data accuracy and completeness before analysis is essential for producing valid insights. Therefore, the correct answers areB and D.
NEW QUESTION # 102
What describes fact-based decision-making according to quality management principles?
Choose 2 answers.
Answer: A,C
Explanation:
Fact-based decision-making is a central quality management principle because it encourages organizations to rely on evidence, measurement, and analysis rather than assumptions or intuition alone. Decisions foster trust in plans when stakeholders can see that choices are grounded in objective information and sound reasoning.
This strengthens accountability and confidence in management actions. Fact-based decisions also help reduce external bias because they rely on verified information rather than opinions, pressure, or unsupported judgment. In contrast, decisions based only on the instincts of experienced leaders do not reflect the core meaning of fact-based management, even though experience may still be valuable. The statement about supplier relationships is not a defining description of fact-based decision-making and does not capture the principle itself. Quality management emphasizes using reliable data to guide planning, improve processes, and support consistent outcomes. Therefore, the two correct answers are that such decisions foster trust in plans and reduce external bias.
NEW QUESTION # 103
A professional services firm is undergoing a business process improvement exercise to improve productivity, staff morale, and client satisfaction while also thinking about the overall long-term financial performance of the company.
Which performance tool would best meet this firm's objectives?
Answer: B
Explanation:
Thebalanced scorecardis the most appropriate performance tool for this scenario because it integrates financial and nonfinancial performance measuresinto a single framework. In data-driven decision making, the balanced scorecard supports a holistic view of organizational performance.
The firm's objectives include productivity (internal processes), staff morale (learning and growth), client satisfaction (customer perspective), and long-term financial performance (financial perspective). The balanced scorecard explicitly incorporates all these dimensions, ensuring alignment between strategic goals and operational execution.
Net promoter score focuses only on customer loyalty, results-based management emphasizes outcomes but lacks multi-perspective integration, and KPI dashboards may display metrics but do not inherently provide strategic balance.
Therefore, the correct answer isC, balanced scorecard.
NEW QUESTION # 104
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