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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Probability & Statistical Inference | 20% | - Correlation vs. causation - Probability rules, distributions, expected value - Hypothesis testing, p-values, confidence intervals |
| Topic 2: Foundations of Data-Driven Decision Making | 20% | - Types of analytics: descriptive, predictive, prescriptive - Role of data in business decisions - Ethics, privacy, and data governance |
| Topic 3: Statistical Concepts & Descriptive Analytics | 25% | - Data visualization: charts, graphs, dashboards - Measures of central tendency, dispersion, distribution - Data types, measurement scales, sampling methods |
| 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% | - Quality & process improvement tools - Decision trees, payoff matrices, expected value - Optimization, sensitivity analysis |
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NEW QUESTION # 56
How is a cost-benefit analysis different in the public and private sectors?
Answer: B
Explanation:
Cost-benefit analysis differs between the public and private sectors primarily because the goals of the two sectors are different. In the public sector, decisions are generally evaluated in terms of the general welfare of the population, including social value, public health, safety, infrastructure, education, and broader community outcomes. In the private sector, cost-benefit analysis is usually more focused on profitability, financial return, efficiency, and shareholder value. While both sectors consider costs and benefits, the definition of "benefit" often changes depending on the mission of the organization. Public-sector benefits may include social improvements that do not generate direct profit, whereas private-sector benefits are often measured through revenue, cost savings, or market performance. The other options are too narrow or incorrect because they suggest one-sided attention to only cost or only benefits. Therefore, the best answer is that the public sector usually focuses on the general welfare of the population, whereas the private sector usually focuses on profits.
NEW QUESTION # 57
The U.S. Postal Service wants to know if local first-class mail is being delivered within two days of postmark.
Which key performance indicator (KPI) should the Postal Service use?
Answer: A
Explanation:
On-time performanceis the most appropriate KPI for measuring whether mail is delivered within a specified timeframe. In data-driven decision making, KPIs must align directly with operational objectives.
The Postal Service's goal is to assess delivery timeliness. On-time performance measures the percentage of mail delivered within the expected service standard, making it a direct and objective indicator.
Customer satisfaction and employee morale provide valuable insights but do not directly measure delivery speed. Incentive performance rate is unrelated to delivery outcomes.
Therefore, the correct answer isC, on-time performance.
NEW QUESTION # 58
Which tool is often referred to as a fishbone chart or diagram?
Answer: C
Explanation:
Thecause-and-effect diagram, also known as thefishbone diagramorIshikawa diagram, is used to identify potential causes of a problem. In data-driven decision making, it helps teams systematically explore root causes rather than focusing on symptoms.
The diagram visually organizes possible causes into categories such as people, processes, materials, machines, and environment. Histograms show distributions, flowcharts depict process steps, and scatter diagrams show relationships between variables.
Therefore, the correct answer isC, cause-and-effect diagram.
NEW QUESTION # 59
How should a marketing consulting firm perform a cluster analysis for a new granola bar?
Answer: D
NEW QUESTION # 60
Which element of an experimental study is described as the procedures applied to each subject?
Answer: B
Explanation:
In an experimental study,treatmentsare defined as the specific procedures or conditions applied to each subject or experimental unit. This is a fundamental concept in experimental design within data-driven decision making and inferential statistics.
Experimental units are the subjects or entities being studied, such as individuals, machines, or products.
Responses are the measured outcomes observed after the treatment is applied. Inputs are factors or variables that may influence the experiment but are not the procedures themselves. Treatments, however, represent the deliberate interventions introduced by the researcher to study their effect on the response variable.
For example, in a pricing experiment, different price levels applied to customers would be considered treatments. In a manufacturing experiment, different machine settings would serve as treatments. By systematically varying treatments, analysts can determine causal relationships between variables.
Data-driven decision making relies on well-designed experiments to support valid conclusions. Clearly defining treatments ensures that the effects of specific actions can be isolated, measured, and analyzed accurately. Therefore, the correct answer isC, as treatments describe the procedures applied to each subject.
NEW QUESTION # 61
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