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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Statistical Concepts & Descriptive Analytics | 25% | - Data visualization: charts, graphs, dashboards - Measures of central tendency, dispersion, distribution - Data types, measurement scales, sampling methods |
| Topic 2: Probability & Statistical Inference | 20% | - Probability rules, distributions, expected value - Hypothesis testing, p-values, confidence intervals - Correlation vs. causation |
| Topic 3: Foundations of Data-Driven Decision Making | 20% | - Role of data in business decisions - Types of analytics: descriptive, predictive, prescriptive - Ethics, privacy, and data governance |
| Topic 4: Predictive Analytics & Regression | 20% | - Forecasting & trend analysis - Interpreting coefficients, R-squared, significance - Simple & multiple linear regression |
| Topic 5: Decision Models & Prescriptive Analytics | 15% | - Quality & process improvement tools - Decision trees, payoff matrices, expected value - Optimization, sensitivity analysis |
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NEW QUESTION # 10
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 # 11
Why would a human resources department use both mean and median when doing a salary evaluation of a department?
Answer: B
Explanation:
Using both mean and median helps identifyoutliers, such as very high executive salaries that skew the average. A large difference between the two indicates uneven distribution.
Thus, the correct answer isC.
NEW QUESTION # 12
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: D
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 # 13
What is a disadvantage of a key performance indicator (KPI)?
Answer: D
Explanation:
A key performance indicator is useful because it provides a measurable way to track progress toward a goal, but one disadvantage is that it often focuses only on quantitative measures. This can be limiting because not every important aspect of organizational performance is easily captured in numerical form. For example, employee morale, trust, innovation culture, and relationship quality may be highly important but difficult to represent fully through a KPI. When managers rely too heavily on numerical indicators alone, they may overlook meaningful qualitative context or encourage behavior aimed at improving the metric rather than improving the underlying process. The other choices are less accurate. A KPI does not only indicate statistically significant change, and it does not inherently make data-driven measurement difficult. It can be used for both short-term and long-term objectives depending on design. The real weakness is that a KPI may oversimplify performance by emphasizing what can be counted rather than everything that matters. Therefore, the correct answer is that it only accounts for quantitative measures.
NEW QUESTION # 14
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 # 15
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