Data-Driven-Decision-Making VCE Exam Download

Az-scorestandardizes a value by expressing how many standard deviations it lies from the mean. A fundamental assumption of z-score analysis in data-driven decision making is that the data can be transformed to astandard normal distributionwith amean of zero and a standard deviation of one.

This transformation allows analysts to compare values from different distributions on a common scale and to calculate probabilities using the standard normal table. The formula for a z-score subtracts the mean from the observed value and divides by the standard deviation, resulting in this standardized distribution.

Outliers are not eliminated by default in z-score calculations; instead, z-scores are often used to identify outliers. A standard deviation of 2 is incorrect and would not represent a standardized distribution.

Therefore, the correct answer isA, reflecting the core assumption underlying z-score usage.

In anormal distribution, data is symmetrically distributed around the center, causing the mean and median to be approximately equal. This is a fundamental property emphasized in data-driven decision making.

Skewed distributions, such as Pareto, cause the mean and median to differ significantly. Therefore, the correct answer isA.

Analysis of Variance (ANOVA)is used to compare the means of three or more groups to determine whether statistically significant differences exist. In data-driven decision making, ANOVA is appropriate when evaluating differences across multiple categories.

In this scenario, the analyst is comparing average donor solicitation amounts across three separate events. ANOVA tests whether at least one group mean differs from the others.

Cluster analysis groups data, time series examines trends over time, and logistic regression predicts categorical outcomes. Therefore, the correct answer isD, ANOVA.

To guide budgeting decisions, data-driven decision making emphasizesbenchmarking against comparable organizations. Using theaverage cost per project spent by other similar countiesallows the county to assess whether its planned expenditures are reasonable and competitive.

Benchmarking provides external context that internal historical metrics cannot. While median costs or project counts describe internal performance, they do not indicate whether spending levels are appropriate relative to peers. Comparing average costs across similar counties helps identify inefficiencies, cost-saving opportunities, and realistic budget targets.

Therefore, optionAis the most effective analytic method for cost-based decision-making in this scenario.