Data-Driven-Decision-Making Exam Dumps : VPC2Data-Driven Decision MakingC207

The fundamental difference between cost–benefit evaluation in the public and private sectors lies inhow benefits are defined and measured. In data-driven decision making, private-sector projects primarily focus onrevenue generation and profitability, making optionCthe correct distinction.

Private organizations evaluate benefits using measurable financial outcomes such as revenue, profit margins, and return on investment. These metrics provide clear, quantifiable indicators of success. In contrast, public-sector projects often aim to maximizegeneral public welfare, including social, environmental, and economic benefits that are more difficult to quantify monetarily.

Public-sector benefits may include improved public health, safety, education, or trust in government—outcomes that do not translate directly into revenue. Therefore, while costs are measurable in both sectors, benefits differ substantially in nature.

Options A and B are incorrect because public-sector costs are not minimal and public benefits are often difficult to quantify. Option D incorrectly assigns public welfare to private projects. Thus, the correct answer isC.

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.

Ascatter diagramis used to visually examine therelationship between two quantitative variables. In data-driven decision making, scatter diagrams help analysts assess whether variables move together, whether the relationship is positive, negative, or nonexistent, and whether the relationship appears linear or nonlinear.

Each point on a scatter diagram represents a paired observation of two variables, such as advertising spend and sales revenue or hours studied and test scores. Patterns in the plotted points can suggest correlation, which may later be explored using regression analysis. Scatter diagrams are exploratory tools and do not, by themselves, establish causation.

A prioritization matrix ranks options, frequency differences are examined using bar or Pareto charts, and differences in means are evaluated using hypothesis tests such as t-tests or ANOVA. Therefore, the correct application of a scatter diagram is to demonstraterelationships between variables, making optionBcorrect.