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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Statistical Concepts & Descriptive Analytics | 25% | - Data types, measurement scales, sampling methods - Measures of central tendency, dispersion, distribution - Data visualization: charts, graphs, dashboards |
| Topic 2: Probability & Statistical Inference | 20% | - Correlation vs. causation - Probability rules, distributions, expected value - Hypothesis testing, p-values, confidence intervals |
| Topic 3: Foundations of Data-Driven Decision Making | 20% | - Role of data in business decisions - Ethics, privacy, and data governance - Types of analytics: descriptive, predictive, prescriptive |
| Topic 4: Predictive Analytics & Regression | 20% | - Interpreting coefficients, R-squared, significance - Simple & multiple linear regression - Forecasting & trend analysis |
| Topic 5: Decision Models & Prescriptive Analytics | 15% | - Decision trees, payoff matrices, expected value - Quality & process improvement tools - Optimization, sensitivity analysis |
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NEW QUESTION # 85
A manager has been asked to evaluate the risk of loss for a new business strategy. The manager plots the results of several simulated projections to determine the likelihood of a result being a loss. Which statistic will transform different data sets to the same scale so that the manager can compare the projections?
Answer: C
Explanation:
A z score is used to standardize values from different data sets so they can be compared on the same scale. It expresses how far a value lies from the mean in terms of standard deviations. This makes it especially useful when a manager needs to compare simulated projections that may have different averages and different spreads. By converting the results into z scores, the manager can evaluate relative performance and risk across otherwise non-comparable distributions. Median and mode describe central tendency, but they do not place values on a common standardized scale. Variance measures dispersion, but it does not directly convert or normalize observations for comparison. In risk analysis and simulation-based decision-making, standardization is often necessary when results come from multiple scenarios, models, or assumptions. Z scores provide that standard frame of reference and allow meaningful interpretation of whether a projected loss is unusually high, low, or typical within its own distribution. Therefore, the correct answer is z score because it transforms different data sets to a common comparison scale.
NEW QUESTION # 86
Which element is associated with control charts?
Answer: D
Explanation:
Control charts are a core tool in statistical process control and are used to monitor variation in a process over time. One of their defining features is the inclusion of upper and lower control limits. These limits help determine whether process variation is consistent with common-cause variation or whether unusual, assignable causes may be affecting performance. The chart typically includes a center line representing the process average, along with upper and lower limits that establish the acceptable range of variation under stable conditions. When points fall outside these limits or show nonrandom patterns, the process may require investigation. While hypothesis testing and correlation are important statistical concepts, they are not the primary identifying elements of a control chart. A reliability index may be used in certain engineering or quality contexts, but it is not the standard feature that defines control charts. Therefore, the element most directly associated with control charts is upper and lower limits, since those boundaries are central to evaluating whether a process remains in statistical control over time.
NEW QUESTION # 87
A county government wants to start a cost-effective recycling program. How can the county apply data analytic approaches to attain this goal?
Answer: B
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 # 88
A researcher seeks to pass a bond issue and asks a sample of respondents who have a bachelor's degree if they are voting in favor of the bond because it would be beneficial to the county.
Which type of error does this represent?
Answer: A
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.
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NEW QUESTION # 89
How does Six Sigma relate to a firm's application of a control chart?
Answer: C
Explanation:
Six Sigma is a quality management methodology focused on reducing defects, minimizing variation, and improving process performance through data-driven analysis. Control charts are one of the statistical process control tools commonly used within Six Sigma because they allow firms to continually measure processes and outputs over time. By tracking data points against control limits, organizations can determine whether a process is operating normally or whether unusual variation requires investigation. This ongoing measurement supports the Six Sigma goal of achieving highly consistent performance and identifying sources of error early.
The other options do not accurately describe the relationship. Six Sigma is not equivalent to linear programming, which is an optimization technique. It also does not directly produce a balanced scorecard, which is a broader strategic performance framework. Although supplier and input reliability may matter in quality management, that is not the primary connection between Six Sigma and control charts. Therefore, the best answer is that Six Sigma continually measures processes and outputs through tools such as control charts.
NEW QUESTION # 90
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